Patent Publication Number: US-2020277658-A1

Title: Methods and systems for microbiome characterization, monitoring and treatment

Description:
CROSS-REFERENCE 
     This application is a continuation of U.S. application Ser. No. 14/437,133, filed Apr. 20, 2015, now U.S. Pat. No. 10,633,714, which is a National Stage entry of PCT Application No. PCT/US2014/047491, filed on Jul. 21, 2014, which claims the benefit of U.S. Provisional Application No. 61/856,711 filed on Jul. 21, 2013, each of which is incorporated herein by reference in its entirety. 
    
    
     SEQUENCES 
     The instant application contains a Sequence Listing which has been submitted electronically in ASCII format and is hereby incorporated by reference in its entirety. Said ASCII copy, created on Jul. 21, 2014, is named 46790-701.601_SL.txt and is 171,171 bytes in size 
     BACKGROUND OF THE DISCLOSURE 
     A typical healthy individual&#39;s body is inhabited with trillions of microbes across various body sites called microbiomes. Some examples of microbiome sites include skin, intestinal, stomach, gut, oral, conjunctival, and vaginal. To better understand the role of these microbiomes and how they affect physiology and disease state we can analyze what microbes comprise a microbiome and how they correlate or affect the health status and clinical response of an individual. 
     For example, the human gut microbiome is known to play a key role in many health conditions, including obesity, gastrointestinal health, nutrient absorption, and drug metabolism among others. Owing to such discoveries, the NIH has invested $150 million in the analysis of the Human Microbiome Project over the next 5-years for analyzing the microbial composition of various human body sites. 
     Despite this awareness of the interrelation between microbiomes and health, the complexity of the microbiome, as well as difficulties in categorizing and characterizing the constituents of the microbiome have made understanding these relationships challenging. Consequently, these challenges have presented hurdles in the development of diagnostic and therapeutic applications. 
     Metagenomic approaches to understanding the microbiome stand to help further illuminate the roles of the microbiomes and have only recently been enabled by “next-generation” sequencing technologies. While the information uncovered by these studies will become increasingly valuable to those interested in targeting the microbiome for therapeutic interventions and consumer products, transforming this large amount of data into meaningful data that can be used to develop diagnostics and therapeutics presents a significant hurdle. Two apparent bottlenecks in harnessing the power of the microbiome, is the cost of undertaking these analyses and the intrinsic complexity of metagenomic analysis mentioned above. 
     The current gold standard in the field for taxonomic classification of bacterial species is through the DNA sequencing of the 16S ribosomal RNA (rRNA) subunit. The 16S rRNA subunit was chosen as an “ideal” target for classification because it is universally present in all bacteria and it contains nine variable regions which can be used to distinguish taxonomies. However, focusing solely on the 16S rRNA subunit presents its own technical challenges owing to the fact that some bacteria share the same variable regions resulting in misclassification. 
     Furthermore, current “second-generation” sequencing technologies being used to sequence the 16S rRNA subunit have read lengths which often yield incomplete coverage of theses variable regions. For example, sequencing by 454 gives average read lengths of 500 bp and Illumina&#39;s MiSeq and HiSeq platforms give average read lengths of 100-450 bp. With these read lengths, bacterial classification often suffer from issues of accuracy, especially in a complex metagenomic sample such as a microbiome sample. 
     The present disclosure provides solutions to these limitations by providing methods, systems, compositions, and kits that yield more accurate information and hence more accurate classification of a microbiome. Such information allows for multiplex and efficiency advantages over the current technology and the development of consumer products such diagnostic tests, therapeutics and probiotic therapies. 
     SUMMARY OF THE INVENTION 
     The present disclosure provides a method of classifying a microbe, comprising: obtaining a nucleic acid sequence of a 16S or 23 S ribosomal subunit contained within a single read length of a first microbe; and comparing said nucleic acid sequence of a first microbe to a reference; and identifying the first microbe at the strain level or sub-strain level based on the comparing. 
     The present disclosure provides a method of profiling a microbiome in a subject, comprising: obtaining nucleic acids sequences of a 16S ribosomal subunit from at least one microbe in a biological sample obtained from the subject; analyzing said at least one microbe within said biological sample based upon the nucleic acids sequences obtained; and determining a profile of the microbiome based on said analyzing. In some embodiments, determining a profile of the microbiome in said subject can be based on 50 or fewer microbes, 55 or fewer microbes, 60 or fewer microbes, 65 or fewer microbes, 70 or fewer microbes, 75 or fewer microbes, 80 or fewer microbes, 85 or fewer microbes, 90 or fewer microbes, 100 or fewer microbes, 200 or fewer microbes, 300 or fewer microbes, 400 or fewer microbe, 500 or fewer microbes, 600 or fewer microbes, 700 or fewer microbes, or 800 or fewer microbes. In some embodiments determining a profile of the microbiome in said subject has an accuracy greater than 70% based on the measurements. In some embodiments, the method can further comprise obtaining nucleic acids sequences of from at least one microbe in a biological sample taken at least two different points of time. In some embodiments, analyzing uses long read sequencing platforms. 
     The present disclosure provides a method of profiling a microbiome in a subject, comprising: obtaining nucleic acids sequences of a 16S and 23S ribosomal subunit from at least one microbe in a biological sample obtained from said subject; analyzing said at least one microbe within said biological sample based upon the sequences obtained; and determining a profile of the microbiome in said subject based on said analyzing. In some embodiments, determining a profile of the microbiome in said subject can be based on 50 or fewer microbes, 55 or fewer microbes, 60 or fewer microbes, 65 or fewer microbes, 70 or fewer microbes, 75 or fewer microbes, 80 or fewer microbes, 85 or fewer microbes, 90 or fewer microbes, 100 or fewer microbes, 200 or fewer microbes, 300 or fewer microbes, 400 or fewer microbe, 500 or fewer microbes, 600 or fewer microbes, 700 or fewer microbes, or 800 or fewer microbes. In some embodiments, determining a profile of the microbiome in said subject has an accuracy greater than 70% based on the measurements. In some embodiments, the method can further comprising obtaining nucleic acids sequences of from at least one microbe in a biological sample taken at least two different points of time. In some embodiments, analyzing uses long read sequencing platforms. 
     The present disclosure provides a method of determining metabolic pathways that are indicative of a health status in a subject, comprising: obtaining RNA sequences from a biological sample from a subject, such that the entire transcript is contained within a single read length; analyzing said transcripts by a sequencing method; comparing the sequenced transcripts to a reference; and determining the metabolic pathways that are indicative of a health status. In some embodiments, analyzing uses long read sequencing platforms. 
     The present disclosure provides a method of treating a disease in a subject, comprising: measuring a microbiome profile in a biological sample obtained from the subject, wherein the microbiome profile comprises at least one microbe; detecting a presence or absence of the disease in the subject based upon said measuring; and treating the disease in the subject based upon said detecting. 
     The present disclosure provides a method, comprising: obtaining data comprising a measurement of a microbiome panel in a biological sample obtained from a subject, wherein said microbiome panel comprises at least two microbes; generating a microbiome profile of said microbiome panel based upon the measurement data; comparing said microbiome profile of said microbiome panel to a reference profile; and determining a likelihood of a disease status in said subject based said comparing. 
     The present disclosure provides a method, comprising: obtaining data comprising a measurement of a microbiome panel in a biological sample obtained from a subject, wherein said microbiome panel comprises at least two microbes; generating a microbiome profile of said microbiome panel based upon the measurement data; comparing said microbiome profile of said microbiome panel to a threshold level of a reference; and determining a likelihood of a disease status in said subject based on said comparing of at least one threshold level of a reference of said microbiome panel. 
     The present disclosure provides a method of diagnosing a subject a disease in a subject, comprising: measuring a microbe panel in a biological sample obtained from the subject, wherein the microbiome panel comprises at least one microbe; detecting a presence or absence of a disease state in said subject based upon said measuring; and; recommending to the subject at least one microbial-based therapeutic or cosmetic for treatment of said disease based on the detecting. In some embodiments, detecting a presence or absence of said disease state with a sensitivity that is greater than 70%. In some embodiments, detecting a presence or absence said disease state with a sensitivity and specificity that is greater than 70%. In some embodiments, comprising detecting a presence or absence said disease state with a sensitivity that is greater than 75%, 80%, 85%, 90%, or 95%. In some embodiments, detecting a presence or absence said disease state with a sensitivity and specificity that is greater than 75%, 80%, 85%, 90%, or 95%. In some embodiments, the panel of microbes comprise 2 or fewer microbes, 3 or fewer microbes, 4 or fewer microbes, 5 or fewer microbes, 6 or fewer microbes, 7 or fewer microbes, 8 or fewer microbes, 9 or fewer microbes, 10 or fewer microbes, 11 or fewer microbes, no more than 12 microbes, 13 or fewer microbes, 14 or fewer microbes, 15 or fewer microbes, 16 or fewer microbes, 18 or fewer microbes, 19 or fewer microbes, 20 or fewer microbes, 25 or fewer microbes, 30 or fewer microbes, 35 or fewer microbes, or 40 or fewer microbes. 
     The present disclosure provides a computer system for determining a microbiome profile in a subject, the computer system comprising: a memory unit for receiving data comprising measurement of a microbe panel from a biological sample of the subject, wherein the microbe panel comprises at least one marker of a microbe; computer-executable instructions for analyzing the measurement data according to a method of any of the preceding claims; and computer-executable instructions for determining a presence or absence of at least disease in the subject based upon said analyzing. In some embodiments, the computer system further comprises computer-executable instructions to generate a report of the presence or absence of the at least one disease in the subject. In some embodiments, computer system can further comprises a user interface configured to communicate or display said report to a user. 
     The present disclosure provides a computer readable medium comprising: computer-executable instructions for analyzing data comprising measurement of a microbiome profile from a biological sample obtained from a subject, wherein the microbiome profile comprises at least one marker selected from at least one microbe; and computer-executable instructions for determining a presence or absence of at least disease in the subject based upon the analyzing. 
     The present disclosure provides a kit, comprising: one or more compositions for use in measuring a microbiome profile in a biological sample obtained from a subject, wherein the microbiome profile comprises at least one marker to at least one microbe; and instructions for performing a method of any of the preceding claims. In some embodiments, a kit can further comprises a computer readable medium. 
     Any of the methods provided herein can include embodiments in wherein the disease is inflammatory bowel disease (IBD), preterm labor, obesity, diabetic foot ulcers, bacteremia, acne, infantile colic, type II diabetes,  C. difficile , irritable bowel syndrome (IBS), asthma, autism, psoriasis, allergies, cardiovascular disease, cancer depression, cystic fibrosis, multiple sclerosis, urinary tract infection, radiation enteropathy, drug metabolism, chronic fatigue, type I diabetes, halitosis, and tooth decay. 
     Any of the methods provided herein can include embodiments wherein the biological sample is taken from a microbiome is selected from the group skin microbiome, umbilical microbiome, vaginal microbiome, conjunctival microbiome, intestinal microbiome, stomach microbiome, gut microbiome, oral microbiome, nasal microbiome, gastrointestinal tract microbiome, urogenital tract microbiome, or a combination thereof. 
     Any of the methods provided herein can include embodiments wherein the microbiome panel comprises a microbial marker of the 16S or 23S ribosomal subunit, or wherein the microbiome panel comprises a microbial marker to the entire 16S or 23S ribosomal subunit transcript, or wherein the microbiome panel comprises a microbial marker of the entire 16S or 23S ribosomal subunit transcript and the intergenic region between said 16S or 23S ribosomal subunit transcript, or wherein the microbiome panel comprises a microbial marker of at least one variable region of the 16S or 23S ribosomal subunit. 
     Any of the methods provided herein can include embodiments wherein the measuring comprises detecting or measuring a level of a fragment, antigen, or binding partner of the 16S or 23S ribosomal subunit, or wherein the measuring comprises detecting or measuring a level of a fragment, antigen, or binding partner of a 16S or 23S ribosomal subunit variable region. 
     Any of the methods provided herein can include embodiments wherein the subject is a human subject, or wherein the subject is asymptomatic for a disease, or wherein the subject is presenting with at least one clinical symptom for said disease. 
     Any of the methods provided herein can include embodiments wherein measuring comprises use of at least one of: an immunoassay, flow cytometry assay, biochip assay, microarray assay, and sequencing assay. Any of the methods provided herein can include embodiments wherein measuring comprises detecting or measuring a level of a microbe. Any of the methods provided herein can include embodiments wherein measuring comprises detecting or measuring a level of a marker on a microbe. 
     Any of the methods provided herein can include embodiments wherein the disease state determined is a poor clinical outcome, a good clinical outcome, a high risk of disease, a low risk of disease, a complete response, a partial response, a stable disease, a non-response, or a recommended treatments for disease management. 
     INCORPORATION BY REFERENCE 
     All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The novel features of the disclosure are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present disclosure will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the disclosure are utilized, and the accompanying drawings of which: 
         FIG. 1  depicts an exemplary computer system for implementing a method described herein. This includes a continually enlarging database of full rRNA operons as the methods described herein allow this to be expanded in a cost-effective manner that hasn&#39;t been previously available. 
         FIGS. 2A and 2B .  FIG. 2A  depicts the hypervariable regions of the 16s operon.  FIG. 2B  depicts simulation results in which the microbe classification accuracy (Y-axis) is plotted as a function of different 16S ribosomal amplicon target sizes. Bacterial sequences from the Ribosomal Database Project were used, with different sequencing platform error rates (X-axis). Utilizing the full 16S amplicon (top line) enables the highest classification accuracy for the practically realized error rates depicted (&lt;5%). 
         FIG. 3  depicts changes in an individual&#39;s gut microbiome profile to changes in diet. In this example, the change in weight (dashed line) is a lagging indicator, when compared to the change in the microbiome structure. Subject-specific diet recommendation can be based on quantitative microbiome signatures. 
         FIGS. 4A and 4B  depict that different genera have different locations of variation that would allow one to distinguish strains. This means that utilizing methods in which only a portion of 16S is observed would intrinsically limit the strain level classification accuracy. The boxplots on the bottom have an X-axis that corresponds to the method utilized (CBT corresponds to the full length 16S amplicon), and a Y-axis that corresponds to the percentage strain classification accuracy achieved.  FIG. 4A  depicts strain level resolution data with a method described herein for the  Nitrosomonas  genus, in which the per strain divergence is concentrated in the V3, 4, and 5 regions of 16S  FIG. 4B  depicts strain level resolution data with a method described herein for the  Staphylococcus  genus, in which the per strain divergence is concentrated in the V8, and 9 regions of 16S. 
         FIGS. 5A and 5B  depict a study in which subjects (that include cases and controls for a specific indications as provided herein) apply a microbial therapeutic/cosmetic containing a consortia of strains.  FIG. 5A  depicts how the methods of this disclosure can be used to quantify the successful application of the microbial therapeutic/cosmetic over time. The addition of a non-commensal strain to the consortia, aids in distinguishing applied strains from the background variation of individual microbiomes for that site (e.g. skin, gut, mouth, etc.).  FIG. 5B  depicts how the methods in the present disclosure can be used to discover stabilizing commensal strains. Those strains that when present, are correlated to the longevity of the applied consortia, would become candidates for expanding the initial consortia to produce formulations with increased stability and efficacy. 
         FIGS. 6A and 6B .  FIG. 6A  depicts the resolution of a microbiome using current standard methods that resolve down to the genus level of a microbe comprising a microbiome  FIG. 6B  depicts the resolution of a microbiome using a method described herein that resolves down to the strain level of a microbe comprising a microbiome. 
         FIGS. 7A and 7B .  FIG. 7A  depicts a prophetic example in which usage of the standard approaches currently being used for genus level resolution. The results indicate that the standard approaches do not separate disease cases from healthy controls because the variation at the strain level is masked  FIG. 7B  depicts a prophetic example in which the disease cases are well separated from healthy controls using the methods provided herein. 
         FIG. 8 . depicts a top ten list of microbial strains responsible for the distinguishing of healthy control and disease subjects depicted in Example 5,  FIG. 7B . This information can be used for diagnostics and therapeutics for this indication. 
     
    
    
     DETAILED DESCRIPTION OF THE DISCLOSURE 
     I. Definitions 
     As used in the specification and claims, the singular forms “a”, “an” and “the” include plural references unless the context clearly dictates otherwise. For example, the term “a sample” includes a plurality of samples, including mixtures thereof. 
     The term “microbes”, “microorganisms” as used herein, refers to any single-celled organisms, bacteria, archaea, protozoa, and unicellular fungi. 
     The term “microbiome”, as used herein, refers to the ecological community of commensal, symbiotic, or pathogenic microorganisms that inhabit a body space on a subject. 
     The terms “determining”, “measuring”, “evaluating”, “assessing,” “assaying,” and “analyzing” can be used interchangeably herein to refer to any form of measurement, and include determining if an element is present or not. (e.g., detection). These terms can include both quantitative and/or qualitative determinations. Assessing may be relative or absolute. These terms can include use of the algorithms and databases described herein. “Detecting the presence of” can include determining the amount of something present, as well as determining whether it is present or absent. The term “genome assembly algorithm” as used herein, refers to any method capable of aligning short reads with reference sequences under conditions that a complete sequence of the genome may be determined. 
     The term “genome” as used herein, refers to the entirety of an organism&#39;s hereditary information that is encoded in its primary DNA sequence. The genome includes both the genes and the non-coding sequences. For example, the genome may represent a microbial genome or a mammalian genome. 
     “Nucleic acid sequence” and “nucleotide sequence” as used herein refer to an oligonucleotide or polynucleotide, and fragments or portions thereof, and to DNA or RNA of genomic or synthetic origin which may be single- or double-stranded, and represent the sense or antisense strand. 
     The terms “homology” and “homologous” as used herein in reference to nucleotide sequences refer to a degree of complementarity with other nucleotide sequences. There may be partial homology or complete homology (i.e., identity). A nucleotide sequence which is partially complementary, i.e., “substantially homologous,” to a nucleic acid sequence is one that at least partially inhibits a completely complementary sequence from hybridizing to a target nucleic acid sequence. 
     The term “sequencing” as used herein refers to sequencing methods for determining the order of the nucleotide bases—adenine, guanine, cytosine, and thymine—in a nucleic acid molecule (e.g., a DNA or RNA nucleic acid molecule. 
     The term “biochip” or “array” can refer to a solid substrate having a generally planar surface to which an adsorbent is attached. A surface of the biochip can comprise a plurality of addressable locations, each of which location may have the adsorbent bound there. Biochips can be adapted to engage a probe interface, and therefore, function as probes. Protein biochips are adapted for the capture of polypeptides and can be comprise surfaces having chromatographic or biospecific adsorbents attached thereto at addressable locations. Microarray chips are generally used for DNA and RNA gene expression detection. 
     The term “barcode” as used herein, refers to any unique, non-naturally occurring, nucleic acid sequence that may be used to identify the originating genome of a nucleic acid fragment. 
     The terms “subject,” “individual” or “patient” are used interchangeably herein. A “subject” can be a biological entity containing expressed genetic materials. The biological entity can be a plant, animal, or microbe, including, e.g., bacteria, bacterial plasmids, viruses, fungi, and protozoa. The subject can be tissues, cells and their progeny of a biological entity obtained in vivo or cultured in vitro. The subject can be a mammal. The mammal can be a human. The subject may be diagnosed or suspected of being at high risk for a disease. In some cases, the subject is not necessarily diagnosed or suspected of being at high risk for the disease. 
     The terms “treatment” or “treating” are used interchangeably herein. These terms can refer to an approach for obtaining beneficial or desired results including but not limited to a therapeutic benefit and/or a prophylactic benefit. A therapeutic benefit can mean eradication or amelioration of the underlying disorder being treated. Also, a therapeutic benefit can be achieved with the eradication or amelioration of one or more of the physiological symptoms associated with the underlying disorder such that an improvement is observed in the subject, notwithstanding that the subject may still be afflicted with the underlying disorder. A prophylactic effect includes delaying, preventing, or eliminating the appearance of a disease or condition, delaying or eliminating the onset of symptoms of a disease or condition, slowing, halting, or reversing the progression of a disease or condition, or any combination thereof. For prophylactic benefit, a subject at risk of developing a particular disease, or to a subject reporting one or more of the physiological symptoms of a disease may undergo treatment, even though a diagnosis of this disease may not have been made. 
     Overview 
     The present disclosure is generally directed to the identification, classification or quantification of at least one microbiome comprising comprehensive analysis of at least one of the 16S and 23S ribosomal RNA (rRNA) subunits or intergenic regions. 
     The present disclosure also provides for the determination of a microbiome profile of a subject. The methods, compositions, systems and kit can generate a cohort-generalized microbiome profile or a subject-specific microbiome profile. The microbial profile can have an accuracy of 70% or greater based on measurement of 15 or fewer microbes in the biological sample. Such profiling method can have at least an accuracy greater than 70% based on measurement of no more than 2 microbes, 3 or fewer microbes, 4 or fewer microbes, 5 or fewer microbes, 6 or fewer microbes, 7 or fewer microbes, 8 or fewer microbes, 9 or fewer microbes, 10 or fewer microbes, 11 or fewer microbes, no more than 12 microbes, 13 or fewer microbes, 14 or fewer microbes, 15 or fewer microbes, 16 or fewer microbes, 18 or fewer microbes, 19 or fewer microbes, 20 or fewer microbes, 25 or fewer microbes, 30 or fewer microbes, 35 or fewer microbes, 40 or fewer microbes, 45 or fewer microbes, 50 or fewer microbes, 55 or fewer microbes, 60 or fewer microbes, 65 or fewer microbes, 70 or fewer microbes, 75 or fewer microbes, 80 or fewer microbes, 85 or fewer microbes, 90 or fewer microbes, or 100 or fewer microbes, 200 or fewer microbes, 300 or fewer microbes, 400 or fewer microbes, 500 or fewer microbes, 600 or fewer microbes, 700 or fewer microbes, or 800 or fewer microbes. 
     Such microbiome profile can be used in part or solely to calculate a quantitative score. A quantitative score can also use the microbiome profile in combination with one or more clinical factors such as age group, ethnicity, sexual habits, hygiene habits, product use, dietary regimen, weight, gender, medical history, risk factors, or family history. 
     In some embodiments, a quantitative score can indicate an increased or decreased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. 
     In some applications, a decrease or increase in one or more microbes&#39; threshold values in a subject&#39;s microbiome profile indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. In some applications, a decrease or increase in one or more microbes&#39; threshold values in a subject&#39;s microbiome indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. 
     Provided herein are microbial-based compositions that can be administered as therapeutic or as a cosmetic to a subject. Also provided herein are various formulations of the microbial-based compositions. One or more, or combinations of microbes or formulations provided herein can be used to develop appropriate compostions for treating a subject suffering from a condition. Any of the methods, compositions, kits, and systems described herein can be used to generate a therapeutic/cosmetics consumer product composition. 
     Any of the methods, compositions, kits, and systems described herein can be used to determine or predict disease status of a subject. Disease status can include such information as a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. 
     The methods, compositions, systems and kits described herein provide a diagnostic assay for a disease, as described herein, based on detection or measurement of one or more microbes in a biological sample or a microbiome profile obtained from a subject. In some applications, the biological sample is collected from a healthy subject who wants to determine one or more of their microbiome profiles. In some applications, the biological sample collected from a subject with a disease who wants to determine one or more of their microbiome profiles. Indications that can be used with the methods, compositions, systems and kits as described herein include, but are not limited to IBD, preterm labor, obesity, diabetic foot ulcers, bacteremia, acne, infantile colic, type 2 diabetes,  C. difficile , IBS, asthma, autism, psoriasis, allergies, cardiovascular disease, cancer, depression, cystic fibrosis, multiple sclerosis, urinary tract infection, radiation enteropathy, drug metabolism, chronic fatigue, type 1 diabetes, halitosis, and tooth decay. 
     A biological sample can be any sample type from any microbiome on the body of a subject. Some examples of microbiomes that can be used with the present disclosure include the skin microbiome, umbilical microbiome, vaginal microbiome, conjunctival microbiome, intestinal microbiome, stomach microbiome, gut microbiome and oral microbiome, nasal microbiome, gastrointestinal tract microbiome, and the urogenital tract microbiome. Depending on the application the biological sample can be whole blood, serum, plasma, mucosa, saliva, cheek swab, urine, stool, cells, tissue, bodily fluid or a combination thereof. 
     The diagnostic assays or methods provided herein can have at least one of a sensitivity of greater than 70% and specificity of greater than 70% of a disease status, prediction of disease response or outcome. Such diagnostic methods can have at least one of a sensitivity of 70% or greater and specificity of greater than 70% based on measurement of 15 or fewer microbes in the biological sample. Such diagnostic assays or method can have at least one of a sensitivity greater than 70% and specificity greater than 70% based on measurement of no more than 2 microbes, 3 or fewer microbes, 4 or fewer microbes, 5 or fewer microbes, 6 or fewer microbes, 7 or fewer microbes, 8 or fewer microbes, 9 or fewer microbes, 10 or fewer microbes, 11 or fewer microbes, no more than 12 microbes, 13 or fewer microbes, 14 or fewer microbes, 15 or fewer microbes, 16 or fewer microbes, 18 or fewer microbes, 19 or fewer microbes, 20 or fewer microbes, 25 or fewer microbes, 30 or fewer microbes, 35 or fewer microbes, 40 or fewer microbes, 45 or fewer microbes, 50 or fewer microbes, 55 or fewer microbes, 60 or fewer microbes, 65 or fewer microbes, 70 or fewer microbes, 75 or fewer microbes, 80 or fewer microbes, 85 or fewer microbes, 90 or fewer microbes, or 100 or fewer microbes. 
     The methods, compositions, systems and kits described herein can be used to generate a report. In some applications, a report can include information such as the degree of likelihood (increase or decrease) of one or more of health status of a disease state: presence or absence of a disease state, a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. 
     An exemplary method can comprise at least one of the following steps: obtaining a biological sample from a subject; measuring a panel of microbes in the biological sample of the subject; determining a disease status upon the measuring; and generating a report that provides information of disease status upon the results of the determining. 
     An exemplary method of the present disclosure can comprise at least one of the following steps: obtaining a biological sample from a subject, measuring a panel of microbes in the biological sample of the subject, determining a presence or absence at least one microbe in a subject&#39;s microbiome upon the measuring; generating a report that provides information on the absence or quantity of at least one microbe in a subject&#39;s microbiome profile upon determining, and optionally providing information from a cohort that has been determined to have a similar microbiome profile as the subject-specific microbiome profile. 
     The methods, compositions, systems and kits described herein also provide quality control assay for a manufactured microbial-based therapeutic/cosmetic. For example, the methods, compositions, systems and kits described herein can be used to develop companion diagnostic test to determine if a manufactured microbial-based therapeutic/cosmetic has maintained its genetic integrity during the manufacturing or storage process. 
     II. Methods 
     A. Microbiome Profiling 
     The present disclosure provides for methods for measuring at least one microbe in biological sample from at least one microbiome from a subject and determining a microbiome profile. A microbiome profile can be assessed using any suitable detection means that can measure or quantify one or more microbes (bacteria, fungi, viruses and archaea) that comprise a microbiome. 
     In general, the present disclosure employs long read length sequencing processes and systems to measure the full 16S or the 23S ribosomal subunits, their intergenic regions and optionally other genetic elements with discriminative power in a particular microbe in order to identify informative microbiome profiles. 
     In some applications, the microbial profile of a subject is in part determine using additional clinical information such as the subject&#39;s age, weight, gender, medical history, risk factors, family history or any other clinically relevant information. 
     In some applications, a subject&#39;s microbiome profile can comprise a single microbiome. For example, a subject&#39;s microbiome profile can comprise of at least one biological sample from only the subject&#39;s intestinal microbiome. For example, a subject&#39;s microbiome profile can comprise of at least one biological sample from only the subject&#39;s stomach microbiome. For example, a subject&#39;s microbiome profile can comprise of at least one biological sample from only the subject&#39;s gut microbiome. For example, a subject&#39;s microbiome profile can comprise of at least one biological sample from only the subject&#39;s oral microbiome. 
     In some applications, a subject&#39;s microbiome profile can comprise of at least one biological sample from more than one microbiome. For example, a subject&#39;s microbiome profile can comprise of at least one biological sample from the subject&#39;s skin microbiome and umbilical microbiome. In another example, a subject&#39;s microbiome profile can comprise of at least one biological sample from the subject&#39;s intestinal microbiome, stomach microbiome, gut microbiome and oral microbiome. In another example, a subject&#39;s microbiome profile can comprise of at least one biological sample from the subject&#39;s intestinal microbiome and at least one biological sample from stomach microbiome. In another example, a subject&#39;s microbiome profile can comprise of at least one biological sample from the subject&#39;s gut microbiome and at least one biological sample from oral microbiome. In some applications, a subject&#39;s microbiome profile can comprise of at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 microbiomes. 
     In some applications, a subject&#39;s microbiome profile can comprise of one microbe. In some applications, a subject&#39;s microbiome profile can comprise of 2 microbes, 3 or fewer microbes, 4 or fewer microbes, 5 or fewer microbes, 6 or fewer microbes, 7 or fewer microbes, 8 or fewer microbes, 9 or fewer microbes, 10 or fewer microbes, 11 or fewer microbes, no more than 12 microbes, 13 or fewer microbes, 14 or fewer microbes, 15 or fewer microbes, 16 or fewer microbes, 18 or fewer microbes, 19 or fewer microbes, 20 or fewer microbes, 25 or fewer microbes, 30 or fewer microbes, 35 or fewer microbes, 40 or fewer microbes, 45 or fewer microbes, 50 or fewer microbes, 55 or fewer microbes, 60 or fewer microbes, 65 or fewer microbes, 70 or fewer microbes, 75 or fewer microbes, 80 or fewer microbes, 85 or fewer microbes, 90 or fewer microbes, 100 or fewer microbes, 200 or fewer microbes, 300 or fewer microbes, 400 or fewer microbe, 500 or fewer microbes, 600 or fewer microbes, 700 or fewer microbes, or 800 or fewer microbes. 
     In some applications the entire genome to the microbe will be analyzed to determine a subject&#39;s microbiome profile. In other applications, the variable regions of the microbe&#39;s genome will be analyzed to determine a subject&#39;s microbiome profile. For example genetic variation in the genome can include restriction fragment length polymorphisms, single nucleotide polymorphisms, insertions, deletions, indels (insertions-deletions), microsatellite repeats, minisatellite repeats, short tandem repeats, transposable elements, randomly amplified polymorphic DNA, amplification fragment length polymorphism or a combination thereof. 
     In some applications, the entire genomic region of the 16S or 23S ribosomal subunit to the microbe will be analyzed to determine a subject&#39;s microbiome profile. In some applications, the variable regions of the 16S or 23S ribosomal subunit to the microbe will be analyzed to determine a subject&#39;s microbiome profile. 
     The methods, compositions, systems provided herein can generate a cohort-generalized microbiome profile or a subject-specific microbiome profile. A cohort-generalized microbiome profile can be determined upon the measurement data from more than one subject&#39;s microbiome from a particular group. For example, a cohort, can be subjects from a particular age group, region of the world, ethnicity, religious group, sexual habits, hygiene habits, product use, dietary regimen, weight, gender, medical history, risk factors, family history, or combinations thereof. A subject-specific microbiome profile can be determined from one or more microbiomes from a subject. 
     An exemplary method can comprise at least one of the following steps: obtaining a biological sample from a subject; measuring at least one microbe in the biological sample of the subject; detecting or measuring the presence or absence of at least one microbe upon measuring; and generating a report that provides details the presence, absence, or quantity of at least one microbe in a subject&#39;s microbiome. 
     An exemplary method can comprise at least one of the following steps: obtaining a biological sample from a subject; measuring a panel of microbes in the biological sample of the subject; detecting a presence or absence of the panel of microbes upon measuring; determining the subject&#39;s microbiome profile; and generating a report that provides details about the determined microbiome profile or a similar microbiome profile. 
     The present disclosure provides diagnostic assays for predicting a disease status of a subject or likelihood of a subject&#39;s response to a therapeutic. The diagnostic assay can use the presence of one or more microbes to calculate a quantitative score that can be used to predict disease status or likelihood of response to a therapeutic in a subject. In some applications, the diagnostic assay can use the presence of one or more microbes and one or more characteristics, such as, e.g., age, weight, gender, medical history, risk factors, family history, or a combination thereof to calculate a quantitative score that can be used to predict disease status or likelihood of response to a therapeutic in a subject. 
     In some applications, a decrease in one or more microbes&#39; threshold values in a subject&#39;s microbiome profile indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. In some embodiments, a decrease in the quantitative score indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. 
     In some applications, a decrease in one or more microbes&#39; threshold values in a subject&#39;s microbiome profile indicates a decreased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. In some embodiments, a decrease in the quantitative score indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. 
     In some applications, an increase in one or more microbes&#39; threshold values in a subject&#39;s microbiome profile indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. In some applications, a decrease in one or more microbes&#39; threshold values indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. 
     In some applications, an increase in one or more microbes&#39; threshold values in a subject&#39;s microbiome profile indicates a decreased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. In some applications, a decrease in one or more microbes&#39; threshold values indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. 
     In some applications, a similar microbiome profile to a reference profile indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. In some applications, a dissimilar microbiome profile to a reference profile indicates one or more of: an increased likelihood of a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. 
     In some applications, a similar microbiome profile to a reference profile indicates a decreased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. In some applications, a dissimilar microbiome profile to a reference profile indicates one or more of: an increased likelihood of a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. 
     B. Samples 
     Biological samples can be collected from a subject who wants information on one or more of their microbiomes. Any sample type from any microbiome on the body of a subject can be used with the methods, systems, and kits of the present disclosure. Examples of microbiomes that can be used with the present disclosure include but are not limited to skin microbiome, umbilical microbiome, vaginal microbiome, conjunctival microbiome, intestinal microbiome, stomach microbiome, gut microbiome and oral microbiome, nasal microbiome, gastrointestinal tract microbiome, and urogenital tract microbiome. 
     Depending on the application the selection of a biological sample may be tailored to that specific application. In any of the methods provided herein the biological sample can be whole blood, serum, plasma, mucosa, saliva, cheek swab, urine, stool, cells, tissue, lymph fluid, CNS fluid, and lesion exudates or a combination thereof. 
     C. Sample Preparation 
     Biological samples used with the methods, composition, systems, and kits provide hererin may be processed using any means known in the art or otherwise described herein in order to enable measurement of one or more microbes. Sample preparation can comprise any one of the following steps or combination of steps: 1) a sterile swab is first dipped into a tube containing sterile 1×PBS to wet. 2) The swab is swiped across the area of interest 10-20 times with enough vigor that the tissue is slightly pink/red colored afterwards. 3) The swab is gently dipped into 300 uL of lysis buffer (described herein) in a sterile 1.5 mL tube. 4) The swab is left in the microcentrifuge tube for shipping to a laboratory to be further analyzed as provided herein. The samples obtained can be shipped overnight at room temperature. 
     Shipping bacterial cells in buffers have inherent biases associated with them—some strains are able to continue propagating on the very few nutrients that come along with sample collection while other strains will undergo apoptosis in the absence of a very specific environment. As a result, samples shipped in this fashion often have an initial profiling/population bias associated with cellular integrity. 
     Current approaches strongly enrich for intact cells by first centrifuging the collected sample. The resulting pellet, formed from the intact cells within the sample, is then the precursor for all of the downstream steps. In contrast, the present methods provided herein includes a purification step to concentrate any DNA present in the supernatant (e.g. from already lysed cells). This DNA is then combined with the DNA extracted from the standard pellet preparation, and this combination now forms the more complete precursor to the downstream steps. 
     In the present methods provided herein, microbiome samples can be immediately put into the specific lysis buffer cocktail described below, rather than standard buffers are stable for 3 days at room temperature, allowing enough time for samples that need to be shipped. These samples can then be processed as usual and DNA will remain intact for use in long readlength sequencing and microbiome profiling. The Lysis buffer contains: 20 mM Tris, pH8.0, 20 mM EDTA, 1% SDS, 0.5% Tween, 1% Triton X-100, and 400 ug/mL proteinase K. 
     The subsequent extraction of DNA from human microbiome samples includes several steps, each of which contribute to sample integrity and artifactual chimeric molecule production. For the amplicon approach these steps can include: PCR, sample quantification (e.g. Qubit, nanodrop, bioanalyzer, etc.), Blue Pippin size selection, 0.5× Ampure purification, sample quantification, DNA end repair, 0.5× Ampure purification, blunt end adaptor ligation, exo-nuclease treatment, two 0.5× Ampure purifications, and final Blue Pippen size selection. Depending on the sample one or more of these steps maybe removed to improve the fidelity of the microbiome profiling. This is determined empirically in a feed-back loop with the profiling, by using known mock communities as tests, or by introducing known quantities of non-commensal strains into the sample. 
     In some applications, the method does not use an amplification step. Examples of such methods include those methods that use sequencing by Whole Genome Shotgun (WGS) sequencing. These approaches can provide a benefit by removing amplification bias, which is known to skew microbial distributions. In addition, such approaches also allows for de novo discovery of pertinent elements, for example such as bacterial plasmids, fungi and viruses. 
     The practice of the methods of the present disclosure can employ conventional techniques of immunology, biochemistry, chemistry, molecular biology, microbiology, cell biology, genomics and recombinant DNA, which are within the skill of the art. See, e.g., Sambrook, Fritsch and Maniatis, MOLECULAR CLONING: A LABORATORY MANUAL, 4th edition (2012); CURRENT PROTOCOLS IN MOLECULAR BIOLOGY (F. M. Ausubel, et al. eds., (1987)); the series METHODS IN ENZYMOLOGY (Academic Press, Inc.): PCR 2: A PRACTICAL APPROACH (M. J. MacPherson, B. D. Hames and G. R. Taylor eds. (1995)), CULTURE OF ANIMAL CELLS: A MANUAL OF BASIC TECHNIQUE AND SPECIALIZED APPLICATIONS, 6th Edition (R. I. Freshney, ed. (2010), and Lange, et. al., Molecular Systems Biology Vol. 4:Article 222 (2008), which are hereby incorporated by reference. For example, preparation of a biological sample may comprise, e.g., extraction or isolation of intracellular material from a cell or tissue such as the extraction of nucleic acids, protein, or other macromolecules. Sample preparation which can be used with the methods of disclosure include but are not limited to, centrifugation, affinity chromatography, magnetic separation, immunoassay, nucleic acid assay, receptor-based assay, cytometric assay, colorimetric assay, enzymatic assay, electrophoretic assay, electrochemical assay, spectroscopic assay, chromatographic assay, microscopic assay, topographic assay, calorimetric assay, radioisotope assay, protein synthesis assay, histological assay, culture assay, and combinations thereof. 
     Accessing the nucleic acids and macromolecules from the intercellular space of the sample may generally be performed by either physical, chemical methods, or a combination of both. 
     The nucleic acids used with the methods described herein can be isolated from any biological samples using shearing methods which preserve the integrity and continuity of genomic DNA. 
     Nucleic acids sample that can be used with the present disclosure include all types of DNA and RNA. The length of nucleic acids can be about 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10,000, 20,000, 30,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000, 100,000, 200,000, 300,000, 400,000, 500,000, 600,000, 700,000, 800,000, 900,000, 1,000,000, 2,000,000, 3,000,000, 4,000,000, 5,000,000, 6,000,000, 7,000,000, 8,000,000, 9,000,000, or 10,000,000, nucleotides or base pairs in length. 
     Variations found in the nucleic acids “variable regions” may provide a means for distinguishing genomes or strains. Examples of such variation in the nucleic acids include but are not limited to, polymorphisms such as: restriction fragment length polymorphisms, single nucleotide polymorphisms, insertions, deletions, indels (insertions-deletions), microsatellite repeats, minisatellite repeats, short tandem repeats, transposable elements, randomly amplified polymorphic DNA, and amplification fragment length polymorphism. 
     D. Detection 
     Profiling of a microbiome can be conducted by various means known in the art, some of which are provided herein. In some applications, microbiome profiling can comprise one or more detection means. 
     In some applications samples will be measured using sequencing methods. In certain application the PCR primers, provided herein, will contain unique barcode identifiers. In some applications the addition of the barcode identifiers allows for multiplexing of multiple samples (e.g. biological samples or microbiome samples) using the computers systems and executable-code provide herein. In applications where single molecule sequencing is used, computers systems and executable-code that can derive base modification detection for each sample, as provided herein, can be useful. 
     Examples sequencing technologies that can be used with the present methods, systems and kits of the disclosure for achieving long read lengths include but are not limited to the SMRT® sequencing systems from Pacific Biosciences. In particular, the SMRT systems produce average read lengths in excess of 5000 bases in length, based upon single molecule, real time observation of nucleic acid replication (See, e.g., U.S. Pat. Nos. 7,056,661, 7,056,676, 7,052,847 and 7,033,764, and Chin C S, et. al., Nonhybrid finished microbial genome assemblies from long read SMRT sequencing data. Nat Methods. 2013 June; 10(6): 563-9) 
     Other sequencing systems and approaches that can be used with the present disclosure include but are not limited to long read length Sanger sequencing, long read ensemble sequencing approaches, e.g., Illumina/Moleculo sequencing and potentially, other single molecule sequencing approaches, such as Nanopore sequencing technologies. 
     In applications where long read sequencing is used with the methods of the present disclosure, long read sequencing can include sequencing that provides a contiguous sequence read of longer than 500 bases, preferably, longer than 800 bases, more preferably, greater than 1000 bases, and in most preferred aspects, longer than 1500 bases. Long read sequencing can also include sequencing that provides a contiguous sequence read of longer than 1500 bases, preferably, longer than 2000 bases, more preferably, greater than 3000 bases, and longer than 4500 bases. 
     In particularly preferred aspects, single molecule, real time sequencing approaches are preferred, such as the Pacific Biosciences SMRT technology. In particular, by providing both extremely long read lengths and single molecule resolution, these systems provide advantages for classification accuracy as well as the potential for extremely high multiplex, e.g., with different individual molecules providing different opportunities to classify different constituents within a microbiome sample. 
     Because each single molecule read spans the entire 16S and/or 23S region, assembly is not required. Therefore, barcoding and multiplexing can be employed to reduce the cost per strain/sample. 
     Suitable barcodes that can be used with the invention include but are not limited to SEQ ID NOS 1-16 and SEQ ID NOS 660-742. 
     The present disclosure takes advantage of the long read length, single molecule sequencing system in order to classify microbial organisms in a mixed population. The resulting “microbiome profile” will take advantage of the outputs, which contain the entire 16S and 23S regions, to achieve unparalleled classification accuracy at a lower cost. 
     Additionally the present invention includes a non-amplification mode in which a whole genome shotgun (WGS) sample is used to profile the microbiome. This removes amplification bias from the profiling which is known to be problematic. Reads from any part of the meta-genome can thus be used as the strain identifier. This allows for de novo discovery of pertinent elements such as bacterial plasmids, fungi and viruses. This also expands the possible level of variation to be observed since the requirement of known constant regions is removed. By comparing the results of the amplicon approach with WGS, biases in the amplicon approach can be corrected and a sample efficient, amplicon version, can be created for specific applications. This also allows the usage of base modification signal since the sample preparation does not include an amplification step. 
     In addition to read length and multiplex benefits, single molecule real time sequencing is also useful for obtaining information on base modifications present in a given organism&#39;s genome. In particular, by using long read length technologies which are sensitive to base modifications, the disclosure can take advantage of this additional or 5 th  base data to add classification specificity to sample analyses and classification. Because DNA modifications are known to affect gene expression, it is clear that microbes, such as bacteria with the same genome but different base modifications should be considered different strains with distinct activities/host interactions. 
     In addition to applying the read length and multiplex advantages towards improving phylogenetic classification using 16S, 23S, or other genomic locations, these advantages can also be used towards a fuller delineation of the true expression profile of the microbiome. Thus, full length transcript sequencing will yield a more complete picture of what metabolic pathways are most relevant for improving host health. 
     Microbiome profiling can comprise usage of a nucleic acid microarray. The microbiome can be measured in either fresh or fixed sample using microarray technology. In this method, polynucleotide sequences of interest (including cDNAs, RNA, mRNA, ect. and oligonucleotides) can be plated, or arrayed, on a microchip substrate. The arrayed sequences can be then hybridized with specific probes with complementarity to the oligonucleotides on the substrate. The arrayed sequences can also be PCR amplified inserts of nucleic acid clones can be applied to a substrate in a dense array. 
     In some applications, there can be greater than 100, 500, 1,000, 2000, 3,000 4,000, 5,000, 6,000, 7,000, 8,000, 9,000 or 10,000 nucleotide sequences can be applied to the substrate. The microarrayed genes, variable regions, intergenic regions or other regions of interest are immobilized on the microchip at greater than 100, 500, 1,000, 2000, 3,000 4,000, 5,000, 6,000, 7,000, 8,000, 9,000 or 10,000 elements each, can be suitable for hybridization under stringent conditions. 
     Fluorescently labeled probes may be generated through incorporation of fluorescent nucleotides. Labeled probes can then be applied to the chip hybridize with specificity to each spot of the array. After stringent washing to remove non-specifically bound probes, the microarray chip can be scanned by a device such as, confocal laser microscopy or by another detection method, such as a CCD camera. Quantitation of hybridization of each arrayed element allows for assessment of corresponding abundance. With dual color fluorescence, separately labeled probes generated from more than one source of nucleic acids can be hybridized to the array. The relative abundance of the nucleic acids sources corresponding to each specified gene can be thus determined. Analysis of the microarray readout can be performed by commercially available equipment. 
     Microbiome profiling can further comprise of use of a biochip. Biochips can be used to screen a large number of macromolecules. Biochips can be designed with immobilized nucleic acid molecules, full-length proteins, antibodies, affibodies (small molecules engineered to mimic monoclonal antibodies), aptamers (nucleic acid-based ligands) or chemical compounds. A chip could be designed to detect multiple macromolecule types on one chip. For example, a chip could be designed to detect nucleic acid molecules, proteins and metabolites on one chip. The biochip can be used to and designed to simultaneously analyze a panel microbes in a single sample. 
     In some applications, microbiome profiling can comprise use of a protein microarray. Protein microarray can be a particular type of biochip which can be used with the present disclosure. The chip can comprise a support surface such as a glass slide, nitrocellulose membrane, bead, or microtitre plate, to which an array of capture proteins can be bound in an arrayed format onto a solid surface. Protein array detection methods can give a high signal and a low background. Detection probe molecules, typically labeled with a fluorescent dye, can be added to the array. Any reaction between the probe and the immobilized protein can result in emission of a detectable signal. Such protein microarrays can be rapid, automated, and offer high sensitivity of protein markers known to be located on a microbe read-outs for diagnostic tests. 
     In some applications, microbiome profiling can comprise use of an analytical protein microarrays can be constructed using a library of antibodies, aptamers or affibodies. The array can be probed with a complex protein solution from a biological sample that function by capturing protein molecules they specifically bind to. Analysis of the resulting binding reactions using various detection systems can provide information about expression levels of particular proteins in the sample as well as measurements of binding affinities and specificities. This type of protein microarray can be especially useful in comparing protein expression in different samples. Functional protein microarrays can be constructed by immobilizing large numbers of purified full-length functional proteins or protein domains and can be used to identify protein-protein, protein-DNA, protein-RNA, protein-phospholipid, and protein-small molecule interactions, to assay enzymatic activity and to detect antibodies and demonstrate their specificity. These protein microarray biochips can be used to study the biochemical activities of the entire proteome in a sample. 
     In some applications, microbiome profiling can comprise use of reverse phase protein microarray (RPA). Reverse phase protein microarray can be constructed from tissue and cell lysates that can be arrayed onto the microarray and probed with antibodies against the target protein of interest. These antibodies can be detected with chemiluminescent, fluorescent or colorimetric assays. In addition to the protein in the lysate, reference control peptides can be printed on the slides to allow for protein quantification. 
     In some applications, microbiome profiling can further comprise use of a digital PCR device or droplet digital PCR device. Droplet digital PCR can be used to partition molecules such as DNA, RNA or protein in a biological sample to a compartment and identifying and measure molecules in that compartment. Interrogation of each droplet can yield counts and measurements of molecules present in the biological sample. 
     E. Primers and Probes 
     The analysis of the 16S ribosomal RNA gene is one approach that can be used to understand microbial diversity. Another approach that can be applied is the analysis of the 23S ribosomal RNA gene. The accuracy of these analyses depends strongly on the choice of primers. 
     Primers can be prepared by a variety of methods including, but not limited to, cloning of appropriate sequences and direct chemical synthesis using methods well known in the art (Narang et al., Methods Enzymol. 68:90 (1979); Brown et al., Methods Enzymol. 68:109 (1979)). Primers can also be obtained from commercial sources such as Integrated DNA Technologies, Operon Technologies, Amersham Pharmacia Biotech, Sigma, and Life Technologies. In addition, computer programs can also be used to design primers, including but not limited to Array Designer Software (Arrayit Inc.), Oligonucleotide Probe Sequence Design Software for Genetic Analysis (Olympus Optical Co.), NetPrimer, and DNAsis from Hitachi Software Engineering. 
     Primers that can be used analyze the 16S ribosomal RNA gene include but are not limited to SEQ ID NOS 17-24. 
     Primers that can be used analyze the 16S ribosomal RNA gene include but are not limited to SEQ ID NOS 17-24 and 646-656. 
     Primers that can be used analyze the 23S ribosomal RNA gene include but are not limited to SEQ ID NOS 25-67 and 657-659. 
     Microbial diversity can be further described by approaches analyzing the intergenic region between 16S ribosomal RNA and 23S ribosomal RNA. Primers that can be used to analyze the intergenic region between 16S ribosomal RNA and 23S ribosomal RNA include but are not limited to SEQ ID NOS 270-364 (forward intergenic primers) and 551-645 (reverse intergenic primers). 
     Primers that can be designed to specifically amplify and identified variable regions in the 16S ribosomal RNA and 23S ribosomal RNA include but are not limited to SEQ ID NOS 87-180 (forward 16S primers), 181-269 (forward 23S primers), 365-461 (reverse 16S primers) and 462-550 (reverse 23S primers). Primers can be designed to specifically amplify any identified variable regions in a microbe or similar distinguishing genetic element. 
     Primers or probes described herein can also include polynucleotides having at least 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% homology to any of the nucleic acid sequences described herein. 
     Primers or probes described herein can also include polypeptide having at least 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% homology to any of the nucleic acid sequences described herein. 
     In some applications, it may be useful to incorporate restriction sites into primer or probe sequence depending on the particular application. Examples of restriction enzymes that can be used with the methods of the present discloure include but are not limited to: AatII, Acc65I, AccI, AciI, AclI, AcuI, AfeI, AflII, AflIII, AgeI, AhdI, AleI, AluI, AlwI, AlwNI, ApaI, ApaLI, ApeKI, ApoI, AscI, AseI, AsiSI, AvaI, AvaII, AvrlI, BaeGI, BaeI, BamHI, BanI, BanII, BbsI, BbvCI, BbvI, BccI, BceAI, BcgI, BciVI, BclI, BfaI, BfuAI, BfuCI, BglI, BglII, BlpI, BmgBI, BmrI, BmtI, BpmI, Bpu10I, BpuEI, BsaAI, BsaBI, BsaHI, BsaI, BsalI, BsaWI, BsaXI, BseRI, BseYI, BsgI, BsiEI, BsiHKAI, BsiWI, BslI, BsmAI, BsmBI, BsmFI, BsmI, BsoBI, Bsp1286I, BspCNI, BspDI, BspEI, BspHI, BspMI, BspQI, BsrBI, BsrDI, BsrFI, BsrGI, BsrI, BssHII, BssKI, BssSI, BstAPI, BstBI, BstEII, BstNI, BstUI, BstXI, BstYI, BstZ17I, Bsu36I, BtgI, BtgZI, BtsCI, BtsI, Cac8I, ClaI, CspCI, CviAII, CviKI-1, CviQI, DdeI, DpnI, DpnlI, DraI, DraIII, DrdI, EaeI, EagI, EarI, EciI, Eco53kI, EcoNI, EcoO109I, EcoP15I, EcoRI, EcoRV, FatI, FauI, Fnu4HI, FokI, FseI, FspI, HaelI, HaeIII, HgaI, HhaI, HincII, HindIII, HinfI, HinPlI, HpaI, HpaII, HphI, Hpyl66II, Hpy188I, Hpy188III, Hpy99I, HpyAV, HpyCH4III, HpyCH4IV, HpyCH4V, KasI, KpnI, MboI, MboII, MfeI, MluI, MlyI, MmeI, MnlI, MscI, MseI, MslI, MspAlI, MspI, MwoI, NaeI, NacI, Nb.BbvCI, Nb.BsmI, Nb.BsrDI, Nb.BtsI, NciI, NcoI, NdeI, NgoMIV, NheI, NlaIII, NlaIV, NmeAIII, NotI, NruI, NsiI, NspI, Nt.AlwI, Nt.BbvCI, Nt.BsmAI, Nt.BspQI, Nt.BstNBI, Nt.CviPII, Pad, PaeR7I, PciI, PflFI, PflMI, PhoI, PleI, PmeI, PmlI, PpuMI, PshAI, PsiI, PspGI, PspOMI, PspXI, PstI, PvuI, PvuII, RsaI, RsrII, SacI, SacII, SalI, SapI, Sau3AI, Sau96I, SbfI, ScaI, ScrFI, SexAI, SfaNI, SfcI, SfiI, SfoI, SgrAI, SmaI, SmlI, SnaBI, SpeI, SphI, SspI, StuI, StyD4I, StyI, SwaI, T, TaqaI, TfiI, TliI, TseI, Tsp45I, Tsp509I, TspMI, TspRI, Tth111I, XbaI, XcmI, XhoI, XmaI, XmnI, and ZraI. 
     In some applications, it may be useful to incorporate a barcode into primer or probe sequence depending on the particular application. Examples of barcodes that can be used with the invention are provided herein. Barcoding of the biological sample can be used to facilitate multiplexing assays as provided herein. Barcoding of the biological sample can be used in methods described herein that use sequencing as the detection means. 
     F. Algorithm-Based Methods 
     The present disclosure provides for machine learning algorithms for building a diagnostic microbiome profile of a subject. Depending on the application a diagnostic microbiome profile can a generate score from a microbiome profile, can be a comparison to a reference microbiome profile, can be the level of a microbiome profile above a defined threshold or a combination thereof. 
     The present disclosure includes sequencing methods that utilize higher error rate, single molecule platforms. These systems are asynchronous in nature, thus providing the long readlengths necessary to properly classify the complex strains that make up the microbiome. 
     In the case of amplicon sequencing (16S, 23S, and other marker genes) the raw data produced from this platform is first filtered for proper primer orientation, pairing, and completeness. The resulting molecules are then filtered based on quality (with quality thresholds of greater than 0.95, 0.99, 0.999, etc. being possible). These molecules then form the basis set of reads to be used to establish the de novo clusters, and can directly be compared to the known reference databases. Molecules that only partially match to the known reference database are appended to a novel hit database. Stringent read length thresholds of &gt;1,000 bases prevent spurious reads from mistakenly making it into the novel hit database. An empirically (using mock community or non-commensal introduced strain) determined error weighting is used in the clustering to minimize the effect of platform specific sequencing artifacts affecting the clustering. 
     In the case of WGS, empirically determined (using mock community or non-commensal introduced strain) cutoff read lengths and accuracies are used and those are matched to a maximum sensitivity threshold. The resulting molecules are then filtered based on quality (with quality thresholds of greater than 0.95, 0.99, 0.999, etc. being possible). These molecules then form the basis set of reads to be used to establish the de novo clusters, and can directly be compared to the known reference databases. Molecules that only partially match to the known reference database are appended to a novel hit database. Stringent read length thresholds of &gt;1,000 bases prevent spurious reads from mistakenly making it into the novel hit database. An empirically (using mock community or non-commensal introduced strain) determined error weighting is used in the clustering to minimize the effect of platform specific sequencing artifacts affecting the clustering. 
     Examples of machine learning algorithms that can be used include, but are not limited to: elastic networks, random forests, support vector machines, and logistic regression. The algorithms provided herein can aid in selection of important microbes and transform the underlying measurements into a score or probability relating to, for example, disease risk, disease likelihood, presence or absence of disease, treatment response, and/or classification of disease status. 
     Any of the methods, kits, and systems described herein can utilize a diagnostic assay for predicting a disease status of a subject or likelihood of a subject&#39;s response to a therapeutic. The diagnostic assay can use the presence of one or more microbes to calculate a quantitative score that can be used to predict disease status or likelihood of response to a therapeutic in a subject. The diagnostic assay can use the presence of one or more microbes and one or more characteristics, such as, e.g., age, weight, gender, medical history, risk factors, family history to calculate a quantitative score that can be used to predict disease status or likelihood of response to a therapeutic in a subject. 
     In some applications, an increase in a score in the diagnostic assay indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. In some embodiments, a decrease in the quantitative score indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. 
     In some applications, a decrease in a score in the diagnostic assay indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. In some embodiments, a decrease in the quantitative score indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. 
     In some applications, a similar microbiome profile to a reference profile in the diagnostic assay indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. In some applications, a dissimilar microbiome profile to a reference profile indicates one or more of: an increased likelihood of a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. 
     In some applications, an increase in one or more microbes&#39; threshold values in the diagnostic assay indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. In some applications, a decrease in one or more microbes threshold values indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. 
     In some applications, a decrease in one or more microbes&#39; threshold values in the diagnostic assay indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. In some applications, a decrease in one or more microbes threshold values indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. 
     The present disclosure provides methods of treatments. Provided herein are methods for generalized-treatment recommendations for a subject based on their microbiome profiling and methods for subject-specific treatment recommendation. Methods for treatments can comprise one of the following steps: determining a first ratio of a level of a subject-specific microbiome profile to a level of a second microbiome profile in a biological sample obtained from at least one subject; detecting a presence or absence of a disease in the subject based upon the determining; and recommending to the subject at least one generalized or subject-specific treatment to ameliorate disease symptoms. 
     Any dignostic microbiome profile, a subject-specific microbiome profile, or a therapeutic/cosmetic described herein can include one or more, but are not limited to the following microbes:  Abiotrophia, Abiotrophia defectiva, Abiotrophia, Acetanaerobacterium, Acetanaerobacterium elongatum, Acetanaerobacterium, Acetivibrio, Acetivibrio bacterium, Acetivibrio, Acetobacterium, Acetobacterium, Acetobacterium woodii, Acholeplasma, Acholeplasma, Acidaminococcus, Acidaminococcus fermentans, Acidaminococcus, Acidianus, Acidianus brierleyi, Acidianus, Acidovorax, Acidovorax, Acinetobacter, Acinetobacter guillouiae, Acinetobacter junii, Acinetobacter, Actinobacillus, Actinobacillus  M1933/96/1 , Actinomyces, Actinomyces  ICM34 , Actinomyces  ICM41 , Actinomyces  ICM54 , Actinomyces lingnae, Actinomyces odontolyticus, Actinomyces oral, Actinomyces  ph3 , Actinomyces, Adlercreutzia, Adlercreutzia equolifaciens, Adlercreutzia intestinal, Adlercreutzia, Aerococcus, Aerococcus, Aeromonas, Aeromonas  165C,  Aeromonas hydrophila, Aeromonas  RC50 , Aeromonas, Aeropyrum, Aeropyrum pernix, Aeropyrum, Aggregatibacter, Aggregatibacter, Agreia, Agreia bicolorata, Agreia, Agromonas, Agromonas  C S30 , Akkermansia, Akkermansia muciniphila, Akkermansia, Alistipes, Alistipes  ANH,  Alistipes  AP11 , Alistipes  bacterium,  Alistipes  CCUG,  Alistipes  DJF_B185 , Alistipes  DSM,  Alistipes  EBA6-25c12 , Alistipes finegoldii, Alistipes indistinctus, Alistipes  JC136 , Alistipes  NMLO5A004 , Alistipes onderdonkii, Alistipes putredinis, Alistipes  RMA,  Alistipes senegalensis, Alistipes shahii, Alistipes Smarlab, Alistipes, Alkalibaculum, Alkalibaculum, Alkaliflexus, Alkaliflexus, Allisonella, Allisonella histaminiformans, Allisonella, Alloscardovia, Alloscardovia omnicolens, Anaerofilum, Anaerofilum, Anaerofustis, Anaerofustis stercorihominis, Anaerofustis, Anaeroplasma, Anaeroplasma, Anaerostipes, Anaerostipes  08964 , Anaerostipes  1y-2 , Anaerostipes  494a,  Anaerostipes  5_1_63FAA,  Anaerostipes  AIP,  Anaerostipes  bacterium,  Anaerostipes butyraticus, Anaerostipes caccae, Anaerostipes hadrum, Anaerostipes Anaerostipes indolis, Anaerostipes, Anaerotruncus, Anaerotruncus colihominis, Anaerotruncus  NML,  Anaerotruncus, Aquincola, Aquincola, Arcobacter, Arcobacter, Arthrobacter, Arthrobacter  FV1-1 , Asaccharobacter, Asaccharobacter celatus, Asaccharobacter, Asteroleplasma, Asteroleplasma, Atopobacter, Atopobacter phocae, Atopobium, Atopobium parvulum, Atopobium rimae, Atopobium, Bacteriovorax, Bacteriovorax, Bacteroides, Bacteroides  31SF18 , Bacteroides  326-8 , Bacteroides  35AE31 , Bacteroides  35AE37 , Bacteroides  35BE34 , Bacteroides  4072 , Bacteroides  7853 , Bacteroides acidifaciens, Bacteroides  AP1 , Bacteroides  AR20 , Bacteroides  AR29 , Bacteroides  B2 , Bacteroides  bacterium,  Bacteroides barnesiae, Bacteroides  BLBE-6 , Bacteroides  BV-1 , Bacteroides  caccae,  Bacteroides  CannelCatfish9 , Bacteroides cellulosilyticus, Bacteroides chinchillae, Bacteroides  CIP103040 , Bacteroides  clams,  Bacteroides coprocola, Bacteroides coprophilus, Bacteroides  D8 , Bacteroides  DJF_B097 , Bacteroides  dnLKV2 , Bacteroides  dnLKV7 , Bacteroides  dnLKV9 , Bacteroides  dorei,  Bacteroides  EBA5-17 , Bacteroides eggerthii, Bacteroides  enrichment,  Bacteroides  F-4 , Bacteroides faecichinchillae, Bacteroides faecis, Bacteroides fecal, Bacteroides finegoldii, Bacteroides fragilis, Bacteroides gallinamm, Bacteroides helcogenes, Bacteroides  ic1292 , Bacteroides intestinalis, Bacteroides massiliensis, Bacteroides mpnisolate, Bacteroides  NB-8 , Bacteroides  new,  Bacteroides  nlaezlc13 , Bacteroides  nlaezlc158 , Bacteroides  nlaezlc159 , Bacteroides  nlaezlc161 , Bacteroides  nlaezlc163 , Bacteroides  nlaezlc167 , Bacteroides  nlaezlc172 , Bacteroides  nlaezlc18 , Bacteroides  nlaezlc182 , Bacteroides  nlaezlc190 , Bacteroides  nlaezlc198 , Bacteroides  nlaezlc204 , Bacteroides  nlaezlc205 , Bacteroides  nlaezlc206 , Bacteroides  nlaezlc207 , Bacteroides  nlaezlc211 , Bacteroides  nlaezlc218 , Bacteroides  nlaezlc257 , Bacteroides  nlaezlc260 , Bacteroides  nlaezlc261 , Bacteroides  nlaezlc263 , Bacteroides  nlaezlc308 , Bacteroides  nlaezlc315 , Bacteroides  nlaezlc322 , Bacteroides  nlaezlc324 , Bacteroides  nlaezlc331 , Bacteroides  nlaezlc339 , Bacteroides  nlaezlc36 , Bacteroides  nlaezlc367 , Bacteroides  nlaezlc375 , Bacteroides  nlaezlc376 , Bacteroides  nlaezlc380 , Bacteroides  nlaezlc391 , Bacteroides  nlaezlc459 , Bacteroides  nlaezlc484 , Bacteroides  nlaezlc501 , Bacteroides  nlaezlc504 , Bacteroides  nlaezlc515 , Bacteroides  nlaezlc519 , Bacteroides  nlaezlc532 , Bacteroides  nlaezlc557 , Bacteroides  nlaezlc57 , Bacteroides  nlaezlc574 , Bacteroides  nlaezlc592 , Bacteroides  nlaezlg105 , Bacteroides  nlaezlg117 , Bacteroides  nlaezlg127 , Bacteroides  nlaezlg136 , Bacteroides  nlaezlg143 , Bacteroides  nlaezlg157 , Bacteroides  nlaezlg167 , Bacteroides  nlaezlg171 , Bacteroides  nlaezlg187 , Bacteroides  nlaezlg194 , Bacteroides  nlaezlg195 , Bacteroides  nlaezlg199 , Bacteroides  nlaezlg209 , Bacteroides  nlaezlg212 , Bacteroides  nlaezlg213 , Bacteroides  nlaezlg218 , Bacteroides  nlaezlg221 , Bacteroides  nlaezlg228 , Bacteroides  nlaezlg234 , Bacteroides  nlaezlg237 , Bacteroides  nlaezlg24 , Bacteroides  nlaezlg245 , Bacteroides  nlaezlg257 , Bacteroides  nlaezlg27 , Bacteroides  nlaezlg285 , Bacteroides  nlaezlg288 , Bacteroides  nlaezlg295 , Bacteroides  nlaezlg296 , Bacteroides  nlaezlg303 , Bacteroides  nlaezlg310 , Bacteroides  nlaezlg312 , Bacteroides  nlaezlg327 , Bacteroides  nlaezlg329 , Bacteroides  nlaezlg336 , Bacteroides  nlaezlg338 , Bacteroides  nlaezlg347 , Bacteroides  nlaezlg356 , Bacteroides  nlaezlg373 , Bacteroides  nlaezlg376 , Bacteroides  nlaezlg380 , Bacteroides  nlaezlg382 , Bacteroides  nlaezlg385 , Bacteroides  nlaezlg4 , Bacteroides  nlaezlg422 , Bacteroides  nlaezlg437 , Bacteroides  nlaezlg454 , Bacteroides  nlaezlg455 , Bacteroides  nlaezlg456 , Bacteroides  nlaezlg458 , Bacteroides  nlaezlg459 , Bacteroides  nlaezlg46 , Bacteroides  nlaezlg461 , Bacteroides  nlaezlg475 , Bacteroides  nlaezlg481 , Bacteroides  nlaezlg484 , Bacteroides  nlaezlg5 , Bacteroides  nlaezlg502 , Bacteroides  nlaezlg515 , Bacteroides  nlaezlg518 , Bacteroides  nlaezlg521 , Bacteroides  nlaezlg54 , Bacteroides  nlaezlg6 , Bacteroides  nlaezlg8 , Bacteroides  nlaezlg80 , Bacteroides  nlaezlg98 , Bacteroides  nlaezlh120 , Bacteroides  nlaezlh15 , Bacteroides  nlaezlh162 , Bacteroides  nlaezlh17 , Bacteroides  nlaezlh174 , Bacteroides  nlaezlh18 , Bacteroides  nlaezlh188 , Bacteroides  nlaezlh192 , Bacteroides  nlaezlh194 , Bacteroides  nlaezlh195 , Bacteroides  nlaezlh207 , Bacteroides  nlaezlh22 , Bacteroides  nlaezlh250 , Bacteroides  nlaezlh251 , Bacteroides  nlaezlh28 , Bacteroides  nlaezlh313 , Bacteroides  nlaezlh319 , Bacteroides  nlaezlh321 , Bacteroides  nlaezlh328 , Bacteroides  nlaezlh334 , Bacteroides  nlaezlh390 , Bacteroides  nlaezlh391 , Bacteroides  nlaezlh414 , Bacteroides  nlaezlh416 , Bacteroides  nlaezlh419 , Bacteroides  nlaezlh429 , Bacteroides  nlaezlh439 , Bacteroides  nlaezlh444 , Bacteroides  nlaezlh45 , Bacteroides  nlaezlh46 , Bacteroides  nlaezlh462 , Bacteroides  nlaezlh463 , Bacteroides  nlaezlh465 , Bacteroides  nlaezlh468 , Bacteroides  nlaezlh471 , Bacteroides  nlaezlh472 , Bacteroides  nlaezlh474 , Bacteroides  nlaezlh479 , Bacteroides  nlaezlh482 , Bacteroides  nlaezlh49 , Bacteroides  nlaezlh493 , Bacteroides  nlaezlh496 , Bacteroides  nlaezlh497 , Bacteroides  nlaezlh499 , Bacteroides  nlaezlh50 , Bacteroides  nlaezlh531 , Bacteroides  nlaezlh535 , Bacteroides  nlaezlh8 , Bacteroides  nlaezlp104 , Bacteroides  nlaezlp105 , Bacteroides  nlaezlp108 , Bacteroides  nlaezlp132 , Bacteroides  nlaezlp133 , Bacteroides  nlaezlp151 , Bacteroides  nlaezlp157 , Bacteroides  nlaezlp166 , Bacteroides  nlaezlp167 , Bacteroides  nlaezlp171 , Bacteroides  nlaezlp178 , Bacteroides  nlaezlp187 , Bacteroides  nlaezlp191 , Bacteroides  nlaezlp196 , Bacteroides  nlaezlp208 , Bacteroides  nlaezlp213 , Bacteroides  nlaezlp228 , Bacteroides  nlaezlp233 , Bacteroides  nlaezlp267 , Bacteroides  nlaezlp278 , Bacteroides  nlaezlp282 , Bacteroides  nlaezlp286 , Bacteroides  nlaezlp295 , Bacteroides  nlaezlp299 , Bacteroides  nlaezlp301 , Bacteroides  nlaezlp302 , Bacteroides  nlaezlp304 , Bacteroides  nlaezlp317 , Bacteroides  nlaezlp319 , Bacteroides  nlaezlp32 , Bacteroides  nlaezlp332 , Bacteroides  nlaezlp349 , Bacteroides  nlaezlp35 , Bacteroides  nlaezlp356 , Bacteroides  nlaezlp370 , Bacteroides  nlaezlp371 , Bacteroides  nlaezlp376 , Bacteroides  nlaezlp395 , Bacteroides  nlaezlp402 , Bacteroides  nlaezlp403 , Bacteroides  nlaezlp409 , Bacteroides  nlaezlp412 , Bacteroides  nlaezlp436 , Bacteroides  nlaezlp438 , Bacteroides  nlaezlp440 , Bacteroides  nlaezlp447 , Bacteroides  nlaezlp448 , Bacteroides  nlaezlp451 , Bacteroides  nlaezlp476 , Bacteroides  nlaezlp478 , Bacteroides  nlaezlp483 , Bacteroides  nlaezlp489 , Bacteroides  nlaezlp493 , Bacteroides  nlaezlp557 , Bacteroides  nlaezlp559 , Bacteroides  nlaezlp564 , Bacteroides  nlaezlp565 , Bacteroides  nlaezlp572 , Bacteroides  nlaezlp573 , Bacteroides  nlaezlp576 , Bacteroides  nlaezlp591 , Bacteroides  nlaezlp592 , Bacteroides  nlaezlp631 , Bacteroides  nlaezlp633 , Bacteroides  nlaezlp696 , Bacteroides  nlaezlp7 , Bacteroides  nlaezlp720 , Bacteroides  nlaezlp730 , Bacteroides  nlaezlp736 , Bacteroides  nlaezlp737 , Bacteroides  nlaezlp754 , Bacteroides  nlaezlp759 , Bacteroides  nlaezlp774 , Bacteroides  nlaezlp828 , Bacteroides  nlaezlp854 , Bacteroides  nlaezlp860 , Bacteroides  nlaezlp886 , Bacteroides  nlaezlp887 , Bacteroides  nlaezlp900 , Bacteroides  nlaezlp909 , Bacteroides  nlaezlp913 , Bacteroides  nlaezlp916 , Bacteroides  nlaezlp920 , Bacteroides  nlaezlp96 , Bacteroides nordii, Bacteroides oleiciplenus, Bacteroides ovatus, Bacteroides paurosaccharolyticus, Bacteroides plebeius, Bacteroides  R6 , Bacteroides rodentium, Bacteroides  S-17 , Bacteroides  S-18 , Bacteroides salyersiae, Bacteroides  SLC1-38 , Bacteroides  Smarlab,  Bacteroides  &#39;Smarlab,  Bacteroides stercorirosoris, Bacteroides stercoris, Bacteroides  str,  Bacteroides thetaiotaomicron, Bacteroides  TP-5 , Bacteroides, Bacteroides uniformis, Bacteroides vulgatus, Bacteroides  WA1 , Bacteroides  WH2 , Bacteroides  WH302 , Bacteroides  WH305 , Bacteroides  XB12B,  Bacteroides  XB44A,  Bacteroides  XO77B42 , Bacteroides xylanisolvens, Barnesiella, Barnesiella intestinihominis, Barnesiella  NSB1 , Barnesiella, Barnesiella viscericola, Bavariicoccus, Bavariicoccus, Bdellovibrio, Bdellovibrio  oral,  Bergeriella, Bergeriella, Bifidobacterium, Bifidobacterium  103 , Bifidobacterium  108 , Bifidobacterium  113 , Bifidobacterium  120 , Bifidobacterium  138 , Bifidobacterium  33 , Bifidobacterium  Acbbto5 , Bifidobacterium adolescentis, Bifidobacterium  Amsbbt12 , Bifidobacterium angulatum, Bifidobacterium animalis, Bifidobacterium bacterium, Bifidobacterium bifidum, Bifidobacterium  Bisn6 , Bifidobacterium  Bma6 , Bifidobacterium breve, Bifidobacterium catenulatum, Bifidobacterium choerinum, Bifidobacterium coryneforme, Bifidobacterium dentium, Bifidobacterium  DJF WC44 , Bifidobacterium  F-10 , Bifidobacterium  F-11 , Bifidobacterium  group,  Bifidobacterium  h12 , Bifidobacterium  HMLN1 , Bifidobacterium  HMLN12 , Bifidobacterium  HMLN5 , Bifidobacterium  iarfr2341d,  Bifidobacterium  iarfr642d48 , Bifidobacterium  ic1332 , Bifidobacterium indicum, Bifidobacterium kashiwanohense, Bifidobacterium  LISLUCIII-2 , Bifidobacterium longum, Bifidobacterium  M45 , Bifidobacterium merycicum, Bifidobacterium minimum, Bifidobacterium  MSXSB,  Bifidobacterium  oral,  Bifidobacterium  PG12A,  Bifidobacterium  PL1 , Bifidobacterium pseudocatenulatum, Bifidobacterium pseudolongum, Bifidobacterium pullorum, Bifidobacterium ruminantium, Bifidobacterium  S-10 , Bifidobacterium saeculare, Bifidobacterium saguini, Bifidobacterium scardovii, Bifidobacterium simiae, Bifidobacterium  SLPYG-1 , Bifidobacterium stellenboschense, Bifidobacterium stercoris, Bifidobacterium  TM-7 , Bifidobacterium  Trm9 , Bifidobacterium, Bilophila, Bilophila  nlaezlh528 , Bilophila, Bilophila wadsworthia, Blautia, Blautia bacterium, Blautia  CE2 , Blautia  CE6 , Blautia coccoides, Blautia  DJF VR52 , Blautia  DJF VR67 , Blautia  DJF VR70k1 , Blautia formate, Blautia glucerasea, Blautia hansenii, Blautia  ic1272 , Blautia  IES,  Blautia  K-1 , Blautia luti, Blautia  M-1 , Blautia  mpnisolate,  Blautia  nlaezlc25 , Blautia  nlaezlc259 , Blautia  nlaezlc51 , Blautia  nlaezlc520 , Blautia  nlaezlc542 , Blautia  nlaezlc544 , Blautia  nlaezlh27 , Blautia  nlaezlh316 , Blautia  nlaezlh317 , Blautia obeum, Blautia producta, Blautia productus, Blautia schinkii, Blautia  Ser5 , Blautia  Ser8 , Blautia, Blautia  WAL,  Blautia wexlerae, Blautia  YHC-4 , Brenneria, Brenneria, Brevibacterium, Brevibacterium, Brochothrix, Brochothrix thermosphacta, Buttiauxella, Buttiauxella  57916 , Buttiauxella gaviniae, Butyricicoccus, Butyricicoccus bacterium, Butyricicoccus, Butyricimonas, Butyricimonas  180-3 , Butyricimonas  214-4 , Butyricimonas  bacterium,  Butyricimonas  GD2 , Butyricimonas synergistica, Butyricimonas, Butyricimonas virosa, Butyrivibrio, Butyrivibrio fibrisolvens, Butyrivibrio hungatei, Butyrivibrio, Caldimicrobium, Caldimicrobium, Caldisericum, Caldisericum, Campylobacter, Campylobacter coli, Campylobacter hominis, Campylobacter, Capnocytophaga, Capnocytophaga, Carnobacterium, Carnobacterium alterfunditum, Carnobacterium, Caryophanon, Caryophanon, Catenibacterium, Catenibacterium mitsuokai, Catenibacterium, Catonella, Catonella, Caulobacter, Caulobacter, Cellulophaga, Cellulophaga, Cellulosilyticum, Cellulosilyticum, Cetobacterium, Cetobacterium, Chelatococcus, Chelatococcus, Chlorobium, Chlorobium, Chryseobacterium, Chryseobacterium  A1005 , Chryseobacterium  KJ9C8 , Chryseobacterium, Citrobacter, Citrobacter  1 , Citrobacter agglomerans, Citrobacter amalonaticus, Citrobacter ascorbata, Citrobacter bacterium, Citrobacter  BinzhouCLT,  Citrobacter braakii, Citrobacter  enrichment,  Citrobacter  F24 , Citrobacter  F96 , Citrobacter farmeri, Citrobacter freundii, Citrobacter gillenii, Citrobacter  HBKC SR1 , Citrobacter  HD4.9 , Citrobacter hormaechei, Citrobacter  191-3 , Citrobacter  ka55 , Citrobacter lapagei, Citrobacter  LAR-1 , Citrobacter ludwigii, Citrobacter  MEB5 , Citrobacter  MS36 , Citrobacter murliniae, Citrobacter  nlaezlc269 , Citrobacter  P014 , Citrobacter  P042bN,  Citrobacter  P046a,  Citrobacter  P073 , Citrobacter  SR3 , Citrobacter  T1 , Citrobacter  tnt4 , Citrobacter  tnt5 , Citrobacter  trout,  Citrobacter  TSA-1 , Citrobacter, Citrobacter werkmanii, Cloacibacillus, Cloacibacillus  adv66 , Cloacibacillus  nlaezlp702 , Cloacibacillus  NML05A017 , Cloacibacillus, Cloacibacterium, Cloacibacterium, Collinsella, Collinsella  A-1 , Collinsella aerofaciens, Collinsella  AUH-Julong21 , Collinsella bacterium, Collinsella  CCUG,  Collinsella, Comamonas, Comamonas straminea, Comamonas testosteroni, Conexibacter, Conexibacter, Coprobacillus, Coprobacillus bacterium, Coprobacillus cateniformis, Coprobacillus  TM-40 , Coprobacillus, Coprococcus, Coprococcus  14505 , Coprococcus  bacterium,  Coprococcus catus, Coprococcus comes, Coprococcus eutactus, Coprococcus nexile, Coprococcus, Coraliomargarita, Coraliomargarita fucoidanolyticus, Coraliomargarita marisflavi, Coraliomargarita, Corynebacterium, Corynebacterium amycolatum, Corynebacterium durum, Coxiella, Coxiella, Cronobacter, Cronobacter dublinensis, Cronobacter sakazakii, Cronobacter turicensis, Cryptobacterium, Cryptobacterium curtum, Cupriavidus, Cupriavidus eutropha, Dechloromonas, Dechloromonas  HZ,  Desulfobacterium, Desulfobacterium, Desulfobulbus, Desulfobulbus, Desulfopila, Desulfopila  La4.1 , Desulfovibrio, Desulfovibrio  D4 , Desulfovibrio desulfuricans, Desulfovibrio  DSM12803 , Desulfovibrio  enrichment,  Desulfovibrio fairfieldensis, Desulfovibrio  LNB1 , Desulfovibrio piger, Desulfovibrio, Dialister, Dialister  E2_20 , Dialister  GBA27 , Dialister invisus, Dialister  oral,  Dialister succinatiphilus, Dialister, Dorea, Dorea  auhjulong64 , Dorea  bacterium,  Dorea formicigenerans, Dorea longicatena, Dorea mpnisolate, Dorea, Dysgonomonas, Dysgonomonas gadei, Dysgonomonas, Edwardsiella, Edwardsiella tarda, Eggerthella, Eggerthella  E1 , Eggerthella lenta, Eggerthella  MLGO43 , Eggerthella  MVA1 , Eggerthella  S6-C1 , Eggerthella  SDG-2 , Eggerthella sinensis, Eggerthella  str,  Eggerthella, Enhydrobacter, Enhydrobacter, Enterobacter, Enterobacter  1050 , Enterobacter  1122 , Enterobacter  77000 , Enterobacter  82353 , Enterobacter  9C,  Enterobacter  ASC,  Enterobacter adecarboxylata, Enterobacter aerogenes, Enterobacter agglomerans, Enterobacter  AJAR-A2 , Enterobacter amnigenus, Enterobacter asburiae, Enterobacter  B1(2012),  Enterobacter  B363 , Enterobacter  B509 , Enterobacter  bacterium,  Enterobacter  Badong3 , Enterobacter  BEC441 , Enterobacter  C8 , Enterobacter cancerogenus, Enterobacter cloacae, Enterobacter  CO,  Enterobacter  core2 , Enterobacter cowanii, Enterobacter  dc6 , Enterobacter  DRSBII,  Enterobacter  enrichment,  Enterobacter  FL13-2-1 , Enterobacter  GIST-NKst10 , Enterobacter  GIST-NKst9 , Enterobacter  GJ1-11 , Enterobacter  gx-148 , Enterobacter hormaechei, Enterobacter  I-Bh20-21 , Enterobacter  ICB113 , Enterobacter kobei, Enterobacter  KW14 , Enterobacter 112 , Enterobacter ludwigii, Enterobacter  M10_1B,  Enterobacter  M1R3 , Enterobacter  marine,  Enterobacter  NCCP-167 , Enterobacter  of,  Enterobacter oryzae, Enterobacter oxytoca, Enterobacter  P101 , Enterobacter  S11 , Enterobacter  SEL2 , Enterobacter  SPh,  Enterobacter  SSASP5 , Enterobacter terrigena, Enterobacter  TNT3 , Enterobacter  TP2MC,  Enterobacter  T S4 , Enterobacter  TSSAS2-48 , Enterobacter, Enterobacter  ZYXCA1 , Enterococcus, Enterococcus  020824/02-A,  Enterococcus  1275 b, Enterococcus  16C,  Enterococcus  48 , Enterococcus  6114 , Enterococcus  ABRIINW-H61 , Enterococcus asini, Enterococcus avium, Enterococcus azikeevi, Enterococcus bacterium, Enterococcus  BBDP57 , Enterococcus  BPH34 , Enterococcus  Bt,  Enterococcus canis, Enterococcus casseliflavus, Enterococcus  CmNA2 , Enterococcus  Da-20 , Enterococcus devriesei, Enterococcus  di spar,  Enterococcus  DJF 030 , Enterococcus  DMB4 , Enterococcus durans, Enterococcus enrichment, Enterococcus  F81 , Enterococcus faecalis, Enterococcus faecium, Enterococcus  fcc9 , Enterococcus fecal, Enterococcus flavescens, Enterococcus fluvialis, Enterococcus  FR-3 , Enterococcus  FUA3374 , Enterococcus gallinarum, Enterococcus  GHAPRB1 , Enterococcus  GSC-2 , Enterococcus  GYPB01,  Enterococcus hermanniensis, Enterococcus hirae, Enterococcus lactis, Enterococcus malodoratus, Enterococcus manure, Enterococcus marine, Enterococcus  MNC1,  Enterococcus moraviensis, Enterococcus  MS2 , Enterococcus mundtii, Enterococcus  NAB15 , Enterococcus  NBRC,  Enterococcus  nlaezlc434 , Enterococcus  nlaezlg106 , Enterococcus  nlaezlg87 , Enterococcus  nlaezlh339 , Enterococcus  nlaezlh375 , Enterococcus  nlaezlh381 , Enterococcus  nlaezlh383 , Enterococcus  nlaezlh405 , Enterococcus  nlaezlp116 , Enterococcus  nlaezlp148 , Enterococcus  nlaezlp401 , Enterococcus  nlaezlp650 , Enterococcus pseudoavium, Enterococcus  R-25205 , Enterococcus raffinosus, Enterococcus rottae, Enterococcus  RU07 , Enterococcus saccharolyticus, Enterococcus saccharominimus, Enterococcus sanguinicola, Enterococcus  SCA16 , Enterococcus  SCA2 , Enterococcus  SE138 , Enterococcus  SF-1 , Enterococcus sulfureus, Enterococcus  SV6 , Enterococcus  te1a,  Enterococcus  te32a,  Enterococcus  te42a,  Enterococcus  te45r,  Enterococcus  te49a,  Enterococcus  te51a,  Enterococcus  te58r,  Enterococcus  te59r,  Enterococcus  te61r,  Enterococcus  te93r,  Enterococcus  te95a,  Enterococcus, Enterorhabdus, Enterorhabdus caecimuris, Enterorhabdus, Erwinia, Erwinia agglomerans, Erwinia enterica, Erwinia rhapontici, Erwinia tasmaniensis, Erwinia , Erysipelotrichaceae_incertae_sedis, Erysipelotrichaceae_incertae_sedis aff, Erysipelotrichaceae_incertae_sedis bacterium, Erysipelotrichaceae_incertae_sedis biforme, Erysipelotrichaceae_incertae_sedis C-1, Erysipelotrichaceae_incertae_sedis cylindroides, Erysipelotrichaceae_incertae_sedis GK12, Erysipelotrichaceae_incertae_sedis  innocuum , Erysipelotrichaceae_incertae_sedis nlaezlc332, Erysipelotrichaceae_incertae_sedis nlaezlc340, Erysipelotrichaceae_incertae_sedis nlaezlg420, Erysipelotrichaceae_incertae_sedis nlaezlg425, Erysipelotrichaceae_incertae_sedis nlaezlg440, Erysipelotrichaceae_incertae_sedis nlaezlg463, Erysipelotrichaceae_incertae_sedis nlaezlh340, Erysipelotrichaceae_incertae_sedis nlaezlh354, Erysipelotrichaceae_incertae_sedis nlaezlh379, Erysipelotrichaceae_incertae_sedis nlaezlh380, Erysipelotrichaceae_incertae_sedis nlaezlh385, Erysipelotrichaceae_incertae_sedis nlaezlh410, Erysipelotrichaceae_incertae_sedis  tortuosum , Erysipelotrichaceae_incertae_sedis,  Escherichia/Shigella, Escherichia/Shigella  29(2010),  Escherichia/Shigella  4091 , Escherichia/Shigella  4104 , Escherichia/Shigella  8gw18 , Escherichia/Shigella  A94 , Escherichia/Shigella albertii, Escherichia/Shigella  B-1012 , Escherichia/Shigella  B4 , Escherichia/Shigella  bacterium,  Escherichia/Shigella  BBDP15 , Escherichia/Shigella  BBDP80 , Escherichia/Shigella boydii, Escherichia/Shigella carotovorum, Escherichia/Shigella  CERAR,  Escherichia/Shigella coli, Escherichia/Shigella  DBC-1 , Escherichia/Shigella  dc262011 , Escherichia/Shigella dysenteriae, Escherichia/Shigella  enrichment,  Escherichia/Shigella escherichia, Escherichia/Shigella fecal, Escherichia/Shigella fergusonii, Escherichia/Shigella flexneri, Escherichia/Shigella  GDR05 , Escherichia/Shigella  GDR07 , Escherichia/Shigella  H7 , Escherichia/Shigella  marine,  Escherichia/Shigella  ML2-46 , Escherichia/Shigella  mpnisolate,  Escherichia/Shigella  NA,  Escherichia/Shigella  nlaezlg330 , Escherichia/Shigella  nlaezlg400 , Escherichia/Shigella  nlaezlg441 , Escherichia/Shigella  nlaezlg506 , Escherichia/Shigella  nlaezlh204 , Escherichia/Shigella  nlaezlh208 , Escherichia/Shigella  nlaezlh209 , Escherichia/Shigella  nlaezlh213 , Escherichia/Shigella  nlaezlh214 , Escherichia/Shigella  nlaezlh4 , Escherichia/Shigella  nlaezlh435 , Escherichia/Shigella  nlaezlh81 , Escherichia/Shigella  nlaezlp126 , Escherichia/Shigella  nlaezlp198 , Escherichia/Shigella  nlaezlp21 , Escherichia/Shigella  nlaezlp235 , Escherichia/Shigella  nlaezlp237 , Escherichia/Shigella  nlaezlp239 , Escherichia/Shigella  nlaezlp25 , Escherichia/Shigella  nlaezlp252 , Escherichia/Shigella  nlaezlp275 , Escherichia/Shigella  nlaezlp280 , Escherichia/Shigella  nlaezlp51 , Escherichia/Shigella  nlaezlp53 , Escherichia/Shigella  nlaezlp669 , Escherichia/Shigella  nlaezlp676 , Escherichia/Shigella  nlaezlp717 , Escherichia/Shigella  nlaezlp731 , Escherichia/Shigella  nlaezlp826 , Escherichia/Shigella  nlaezlp877 , Escherichia/Shigella  nlaezlp884 , Escherichia/Shigella  NMU-ST2 , Escherichia/Shigella  oc182011 , Escherichia/Shigella  of,  Escherichia/Shigella  proteobacterium,  Escherichia/Shigella  Q1 , Escherichia/Shigella  sakazakii,  Escherichia/Shigella  SF6 , Escherichia/Shigella  sm1719 , Escherichia/Shigella  SOD-7317 , Escherichia/Shigella sonnei, Escherichia/Shigella  SW86 , Escherichia/Shigella, Escherichia/Shigella vulneris, Ethanoligenens, Ethanoligenens harbinense, Ethanoligenens, Eubacterium, Eubacterium  ARC-2 , Eubacterium callanderi, Eubacterium  E-1 , Eubacterium  G3(2011),  Eubacterium infirmum, Eubacterium limosum, Eubacterium methylotrophicum, Eubacterium  nlaezlp439 , Eubacterium  nlaezlp457 , Eubacterium  nlaezlp458 , Eubacterium  nlaezlp469 , Eubacterium  nlaezlp474 , Eubacterium  oral,  Eubacterium saphenum, Eubacterium sulci, Eubacterium, Eubacterium  WAL,  Euglenida, Euglenida longa, Faecalibacterium, Faecalibacterium  bacterium,  Faecalibacterium canine, Faecalibacterium  DJF VR20 , Faecalibacterium  ic1379 , Faecalibacterium prausnitzii, Faecalibacterium, Filibacter, Filibacter globispora, Flavobacterium, Flavobacterium  SSL03 , Flavobacterium, Flavonifractor, Flavonifractor  AUH-JLC235 , Flavonifractor  enrichment,  Flavonifractor  nlaezlc354 , Flavonifractor orbiscindens, Flavonifractor plautii, Flavonifractor, Francisella, Francisella piscicida, Fusobacterium, Fusobacterium nucleatum, Fusobacterium, Gardnerella, Gardnerella, Gardnerella vaginalis, Gemmiger, Gemmiger  DJF VR33k2 , Gemmiger formicilis, Gemmiger, Geobacter, Geobacter, Gordonibacter, Gordonibacter  bacterium,  Gordonibacter intestinal, Gordonibacter pamelaeae, Gordonibacter , Gp2, Gp2, Gp21, Gp21, Gp4, Gp4, Gp6, Gp6 , Granulicatella, Granulicatella adiacens, Granulicatella  enrichment,  Granulicatella  oral,  Granulicatella paraadiacens, Granulicatella, Haemophilus, Haemophilus, Hafnia, Hafnia  3-12(2010),  Hafnia alvei, Hafnia  CC16 , Hafnia proteus, Hafnia, Haliea, Haliea, Hallella, Hallella seregens, Hallella, Herbaspirillum, Herbaspirillum  022S4-11 , Herbaspirillum seropedicae, Hespellia, Hespellia porcina, Hespellia stercorisuis, Hespellia, Holdemania, Holdemania AP 2 , Holdemania filiformis, Holdemania, Howardella, Howardella, Howardella ureilytica, Hydrogenoanaerobacterium, Hydrogenoanaerobacterium saccharovorans, Hydrogenophaga, Hydrogenophaga bacterium, Ilumatobacter, Ilumatobacter, Janthinobacterium, Janthinobacterium  C30An7 , Janthinobacterium, Jeotgalicoccus, Jeotgalicoccus, Klebsiella, Klebsiella aerogenes, Klebsiella bacterium, Klebsiella  E1L1 , Klebsiella  EB2-THQ,  Klebsiella  enrichment,  Klebsiella  F83 , Klebsiella  G1-6 , Klebsiella  gg160e,  Klebsiella granulomatis, Klebsiella  HaNA20 , Klebsiella  HF2 , Klebsiella  ii_3_chl_1 , Klebsiella  KALAICIBA17 , Klebsiella kpu, Klebsiella  M3 , Klebsiella  MB45 , Klebsiella milletis, Klebsiella  NCCP-138 , Klebsiella  ok1_1_9_S16 , Klebsiella  ok1_1_9_S54 , Klebsiella planticola, Klebsiella pneumoniae, Klebsiella poinarii, Klebsiella  PSB26 , Klebsiella  RS,  Klebsiella  Se14 , Klebsiella  SRC DSD12 , Klebsiella  td153s,  Klebsiella  TG-1 , Klebsiella  TPS5 , Klebsiella, Klebsiella variicola, Klebsiella  WB-2 , Klebsiella  Y9 , Klebsiella  zlmy,  Kluyvera, Kluyvera  An5-1 , Kluyvera cryocrescens, Kluyvera, Kocuria, Kocuria  2216.35.31, Kurthia, Kurthia,  Lachnobacterium, Lachnobacterium  C12b,  Lachnobacterium ,  0 , Lachnospiracea_incertae_sedis bacterium, Lachnospiracea_incertae_sedis  contortum , Lachnospiracea_incertae_sedis Eg2, Lachnospiracea_incertae_sedis eligens, Lachnospiracea_incertae_sedis ethanolgignens, Lachnospiracea_incertae_sedis galacturonicus, Lachnospiracea_incertae_sedis gnavus, Lachnospiracea_incertae_sedis  hallii , Lachnospiracea_incertae_sedis hydrogenotrophica, Lachnospiracea_incertae_sedis ID5, Lachnospiracea_incertae_sedis intestinal, Lachnospiracea_incertae_sedis mpnisolate, Lachnospiracea_incertae_sedis pectinoschiza, Lachnospiracea_incertae_sedis ramulus, Lachnospiracea_incertae_sedis rectale, Lachnospiracea_incertae_sedis RLB1, Lachnospiracea_incertae_sedis rumen, Lachnospiracea_incertae_sedis SY8519, Lachnospiracea_incertae_sedis torques, Lachnospiracea_incertae_sedis, Lachnospiracea_incertae_sedis uniforme, Lachnospiracea_incertae_sedis ventriosum, Lachnospiracea_incertae_sedis xylanophilum, Lachnospiracea_incertae_sedis ye62 , Lactobacillus, Lactobacillus  5-1-2 , Lactobacillus  66 c, Lactobacillus acidophilus, Lactobacillus arizonensis, Lactobacillus  B5406 , Lactobacillus brevis, Lactobacillus casei, Lactobacillus crispatus, Lactobacillus curvatus, Lactobacillus delbrueckii, Lactobacillus fermentum, Lactobacillus gasseri, Lactobacillus helveticus, Lactobacillus hominis, Lactobacillus  ID9203 , Lactobacillus  IDSAc,  Lactobacillus  intestinal,  Lactobacillus johnsonii, Lactobacillus lactis, Lactobacillus manihotivorans, Lactobacillus mucosae, Lactobacillus  NA,  Lactobacillus oris, Lactobacillus  P23 , Lactobacillus  P8 , Lactobacillus paracasei, Lactobacillus paraplantarum, Lactobacillus pentosus, Lactobacillus plantarum, Lactobacillus pontis, Lactobacillus rennanqilfy 10 , Lactobacillus rennanqilfy 14 , Lactobacillus rennanqily 19 , Lactobacillus reuteri, Lactobacillus rhamnosus, Lactobacillus salivarius, Lactobacillus sanfranciscensis, Lactobacillus suntoryeus, Lactobacillus  T3R1C1 , Lactobacillus, Lactobacillus vaginalis, Lactobacillus zeae, Lactococcus, Lactococcus  56 , Lactococcus  CR-317S,  Lactococcus  CW-1 , Lactococcus  D8 , Lactococcus  Da-18 , Lactococcus  DAP39 , Lactococcus delbrueckii, Lactococcus  F116 , Lactococcus fujiensis, Lactococcus  G22 , Lactococcus  garvieae,  Lactococcus lactis, Lactococcus manure, Lactococcus  RTS,  Lactococcus  SXVIII1(2011),  Lactococcus  TP2MJ,  Lactococcus  TP2ML,  Lactococcus  TP2MN,  Lactococcus  U5-1 , Lactococcus, Lactonifactor, Lactonifactor bacterium, Lactonifactor longoviformis, Lactonifactor  nlaezlc533 , Lactonifactor, Leclercia, Leclercia, Lentisphaera, Lentisphaera, Leuconostoc, Leuconostoc carnosum, Leuconostoc citreum, Leuconostoc  garlicum,  Leuconostoc gasicomitatum, Leuconostoc gelidum, Leuconostoc inhae, Leuconostoc lactis, Leuconostoc  MEBE2 , Leuconostoc mesenteroides, Leuconostoc pseudomesenteroides, Leuconostoc, Limnobacter, Limnobacter  spf3 , Luteolibacter, Luteolibacter  bacterium,  Lutispora, Lutispora, Marinifilum, Marinifilum, Marinobacter, Marinobacter arcticus, Mariprofundus, Mariprofundus, Marvinbryantia, Marvinbryantia, Megamonas, Megamonas, Megasphaera, Megasphaera, Melissococcus, Melissococcus faecalis, Methanobacterium, Methanobacterium subterraneum, Methanobrevibacter, Methanobrevibacter arboriphilus, Methanobrevibacter millerae, Methanobrevibacter olleyae, Methanobrevibacter oralis, Methanobrevibacter  SM9 , Methanobrevibacter smithii, Methanobrevibacter, Methanosphaera, Methanosphaera stadtmanae, Methanosphaera, Methylobacterium, Methylobacterium adhaesivum, Methylobacterium bacterium, Methylobacterium  iEII3 , Methylobacterium  MP3 , Methylobacterium oryzae, Methylobacterium  PB132 , Methylobacterium  PB20 , Methylobacterium  PB280 , Methylobacterium  PDD-23b-14 , Methylobacterium radiotolerans, Methylobacterium  SKJH-1 , Methylobacterium, Mitsuokella, Mitsuokella jalaludinii, Mitsuokella, Morganella, Morganella morganii, Morganella, Moritella, Moritella  2D2 , Moryella, Moryella indoligenes, Moryella naviforme, Moryella, Mycobacterium, Mycobacterium tuberculosis, Mycobacterium, Negativicoccus, Negativicoccus, Nitrosomonas, Nitrosomonas eutropha, Novosphingobium, Novosphingobium, Odoribacter, Odoribacter laneus, Odoribacter splanchnicus, Odoribacter, Olsenella, Olsenella  1832 , Olsenella  F0206 , Olsenella, Orbus, Orbus gilliamella, Oribacterium, Oribacterium, Oscillibacter, Oscillibacter bacterium, Oscillibacter enrichment, Oscillibacter, Owenweeksia, Owenweeksia, Oxalobacter, Oxalobacter formigenes, Oxalobacter, Paludibacter, Paludibacter, Pantoea, Pantoea agglomerans, Pantoea eucalypti, Pantoea, Papillibacter, Papillibacter cinnamivorans, Papillibacter, Parabacteroides, Parabacteroides  ASF519 , Parabacteroides  CR-34 , Parabacteroides distasonis, Parabacteroides  DJF_B084 , Parabacteroides  DJF_B086 , Parabacteroides  dnLKV8 , Parabacteroides  enrichment,  Parabacteroides fecal, Parabacteroides goldsteinii, Parabacteroides gordonii, Parabacteroides johnsonii, Parabacteroides merdae, Parabacteroides mpnisolate, Parabacteroi  des nlaezlp340 , Parabacteroides, Paraeggerthella, Paraeggerthella hongkongensis, Paraeggerthella  nlaezlp797 , Paraeggerthella  nlaezlp896 , Paraprevotella, Paraprevotella clara, Paraprevotella, Paraprevotella xylaniphila, Parasutterella, Parasutterella excrementihominis, Parasutterella, Pectobacterium, Pectobacterium carotovorum, Pectobacterium wasabiae, Pediococcus, Pediococcus  te2r,  Pediococcus, Pedobacter, Pedobacter  b3N1b-b5 , Pedobacter daechungensis, Pedobacter, Peptostreptococcus, Peptostreptococcus anaerobius, Peptostreptococcus stomatis, Peptostreptococcus, Phascolarctobacterium, Phascolarctobacterium faecium, Phascolarctobacterium, Photobacterium, Photobacterium  MIE,  Pilibacter, Pilibacter, Planctomyces, Planctomyces , Planococcaceae_incertae_sedis, Planococcaceae_incertae_sedis,  Planomicrobium, Planomicrobium, Plesiomonas, Plesiomonas, Porphyrobacter, Porphyrobacter  KK348 , Porphyromonas, Porphyromonas asaccharolytica, Porphyromonas bennonis, Porphyromonas canine, Porphyromonas somerae, Porphyromonas, Prevotella, Prevotella  bacterium,  Prevotella  BI-42 , Prevotella bivia, Prevotella buccalis, Prevotella copri, Prevotella  DJF_B112 , Prevotella mpnisolate, Prevotella  oral,  Prevotella, Propionibacterium, Propionibacterium acnes, Propionibacterium freudenreichii, Propionibacterium  LG,  Propionibacterium, Proteiniborus, Proteiniborus, Proteiniphilum, Proteiniphilum, Proteus, Proteus  HS7514 , Providencia, Providencia, Pseudobutyrivibrio, Pseudobutyrivibrio bacterium, Pseudobutyrivibrio fibrisolvens, Pseudobutyrivibrio ruminis, Pseudobutyrivibrio, Pseudochrobactrum, Pseudochrobactrum, Pseudoflavonifractor, Pseudoflavonifractor  asf500 , Pseudoflavonifractor  bacterium,  Pseudoflavonifractor capillosus, Pseudoflavonifractor  NML,  Pseudoflavonifractor, Pseudomonas, Pseudomonas  1043 , Pseudomonas  10569 , Pseudomonas  127(39-zx),  Pseudomonas  12A_19 , Pseudomonas  145(38zx),  Pseudomonas  22010 , Pseudomonas  32010 , Pseudomonas  34t20 , Pseudomonas  3C_10 , Pseudomonas  4-5(2010),  Pseudomonas  4-9(2010),  Pseudomonas  6-13.J,  Pseudomonas  63596 , Pseudomonas  82010 , Pseudomonas  a001-142L,  Pseudomonas  al01-18-2 , Pseudomonas  a111-5 , Pseudomonas aeruginosa, Pseudomonas agarici, Pseudomonas amspl, Pseudomonas  AU2390 , Pseudomonas  AZ18R1 , Pseudomonas azotoformans, Pseudomonas  B122 , Pseudomonas  B65(2012),  Pseudomonas  bacterium,  Pseudomonas  BJSX,  Pseudomonas  BLH-8D5 , Pseudomonas  BWDY-29 , Pseudomonas  CA18 , Pseudomonas  Cantas12 , Pseudomonas  CB11 , Pseudomonas  CBZ-4 , Pseudomonas cedrina, Pseudomonas  CGMCC,  Pseudomonas  CL16 , Pseudomonas  CNE,  Pseudomonas corrugata, Pseudomonas cuatrocienegasensis, Pseudomonas  CYEB-7 , Pseudomonas  D5 , Pseudomonas  DAP37 , Pseudomonas  DB48 , Pseudomonas deceptionensis, Pseudomonas  Den-05 , Pseudomonas  DF7EH1 , Pseudomonas  DhA-91 , Pseudomonas  DVS14a,  Pseudomonas  DYJK4-9 , Pseudomonas  DZQS,  Pseudomonas  E11_ICE19B,  Pseudomonas  E2.2 , Pseudomonas  e2-CDC-TB4D2 , Pseudomonas  EM189 , Pseudomonas  enrichment,  Pseudomonas extremorientalis, Pseudomonas  FA1R/BE/F/GH37 , Pseudomonas  FA1R/BE/F/GH39 , Pseudomonas  FA1R/BE/F/GH94 , Pseudomonas  FLM05-3 , Pseudomonas fluorescens, Pseudomonas fragi, Pseudomonas  &#39;FSL,  Pseudomonas  G1013 , Pseudomonas  gingeri,  Pseudomonas  HC2-2 , Pseudomonas  HC2-4 , Pseudomonas  HC2-5 , Pseudomonas  HC4-8 , Pseudomonas  HC6-6 , Pseudomonas  Hg4-06 , Pseudomonas  HLB8-2 , Pseudomonas  HLS12-1 , Pseudomonas  HSF20-13 , Pseudomonas  HW08 , Pseudomonas  11-44 , Pseudomonas  IpA-92 , Pseudomonas  IV,  Pseudomonas  JCM,  Pseudomonas jessenii, Pseudomonas  JSPBS,  Pseudomonas  K3R3.1A,  Pseudomonas  KB40 , Pseudomonas  KB42 , Pseudomonas  KB44 , Pseudomonas  KB63 , Pseudomonas  KB73 , Pseudomonas  KK-21-4 , Pseudomonas  KOPRI,  Pseudomonas  L1R3.5 , Pseudomonas  LAB-27 , Pseudomonas  LAB-44 , Pseudomonas  Lc10-2 , Pseudomonas libanensis, Pseudomonas  Ln5C.7 , Pseudomonas  LS197 , Pseudomonas lundensis, Pseudomonas marginalis, Pseudomonas  MFY143 , Pseudomonas  MFY146 , Pseudomonas  MY1404 , Pseudomonas  MY1412 , Pseudomonas  MY1416 , Pseudomonas  MY1420 , Pseudomonas  N14zhy,  Pseudomonas  NBRC,  Pseudomonas  NCCP-506 , Pseudomonas  NFU20-14 , Pseudomonas  NJ-22 , Pseudomonas  NJ-24 , Pseudomonas  Nj-3 , Pseudomonas  Nj-55 , Pseudomonas  Nj-56 , Pseudomonas  Nj-59 , Pseudomonas  Nj-60 , Pseudomonas  Nj-62 , Pseudomonas  Nj-70 , Pseudomonas  NP41 , Pseudomonas  OCW4 , Pseudomonas  OW3-15-3-2 , Pseudomonas  P1(2010),  Pseudomonas  P2(2010),  Pseudomonas  P3(2010),  Pseudomonas  P4(2010),  Pseudomonas  PD,  Pseudomonas  PF1B4 , Pseudomonas  PF2M10 , Pseudomonas  PILH1 , Pseudomonas poae, Pseudomonas proteobacterium, Pseudomonas  ps4-12 , Pseudomonas  ps4-2 , Pseudomonas  ps4-28 , Pseudomonas  ps4-34 , Pseudomonas  ps4-4 , Pseudomonas psychrophila, Pseudomonas putida, Pseudomonas  R-35721 , Pseudomonas  R-37257 , Pseudomonas  R-37265 , Pseudomonas  R-37908 , Pseudomonas  RBE1CD-48 , Pseudomonas  RBE2CD-42 , Pseudomonas  regd9 , Pseudomonas  RKS7-3 , Pseudomonas  S2 , Pseudomonas  seawater,  Pseudomonas  SGb08 , Pseudomonas  SGb120 , Pseudomonas  SGb396 , Pseudomonas  sgn,  Pseudomonas  &#39;Shk,  Pseudomonas stutzeri, Pseudomonas syringae, Pseudomonas taetrolens, Pseudomonas tolaasii, Pseudomonas trivialis, Pseudomonas  TUT1023 , Pseudomonas, Pseudomonas  W15Feb26 , Pseudomonas  W15Feb4 , Pseudomonas  W15Feb6 , Pseudomonas  WD-3 , Pseudomonas  WR4-13 , Pseudomonas  WR7#2 , Pseudomonas  Y1000 , Pseudomonas  ZS29-8 , Psychrobacter, Psychrobacter  umb13d,  Psychrobacter, Pyramidobacter, Pyramidobacter piscolens, Pyramidobacter, Rahnella, Rahnella aquatilis, Rahnella carotovorum, Rahnella  GIST-WP4w1 , Rahnella  LR113 , Rahnella, Rahnella  Z2-S1 , Ralstonia, Ralstonia  bacterium,  Ralstonia, Raoultella, Raoultella  B19 , Raoultella  enrichment,  Raoultella planticola, Raoultella  sv6xvii,  Raoultella  SZ015 , Raoultella, Renibacterium, Renibacterium  G20 , Rhizobium, Rhizobium leguminosarum, Rhodococcus, Rhodococcus erythropolis, Rhodopirellula, Rhodopirellula, Riemerella, Riemerella anatipestifer, Rikenella, Rikenella, Robinsoniella, Robinsoniella peoriensis, Robinsoniella, Roseburia, Roseburia  11SE37 , Roseburia  bacterium,  Roseburia cecicola, Roseburia  DJF VR77 , Roseburia faecis, Roseburia fibrisolvens, Roseburia hominis, Roseburia intestinalis, Roseburia inulinivorans, Roseburia, Roseibacillus, Roseibacillus, Rothia, Rothia, Rubritalea, Rubritalea, Ruminococcus, Ruminococcus  25F6 , Ruminococcus albus, Ruminococcus  bacterium,  Ruminococcus bromii, Ruminococcus callidus, Ruminococcus champanellensis, Ruminococcus  DJF VR87 , Ruminococcus flavefaciens, Ruminococcus gauvreauii, Ruminococcus lactaris, Ruminococcus  NK3A76 , Ruminococcus, Ruminococcus  YE71 , Saccharofermentans, Saccharofermentans, Salinicoccus, Salinicoccus, Salinimicrobium, Salinimicrobium, Salmonella, Salmonella agglomerans, Salmonella bacterium, Salmonella enterica, Salmonella freundii, Salmonella hermannii, Salmonella paratyphi, Salmonella  SL0604 , Salmonella subterranea, Salmonella, Scardovia, Scardovia oral, Schwartzia, Schwartzia, Sedimenticola, Sedimenticola, Sediminibacter, Sediminibacter, Selenomonas, Selenomonas fecal, Selenomonas, Serpens, Serpens, Serratia, Serratia  1135 , Serratia  136-2 , Serratia  5.1R,  Serratia  AC-CS-1B,  Serratia  AC-CS-B2 , Serratia aquatilis, Serratia bacterium, Serratia  B S26 , Serratia carotovorum, Serratia  DAP6 , Serratia  enrichment,  Serratia  F2 , Serratia ficaria, Serratia fonticola, Serratia grimesii, Serratia  J145 , Serratia  JM983 , Serratia liquefaciens, Serratia marcescens, Serratia plymuthica, Serratia proteamaculans, Serratia proteolyticus, Serratia  ptz-16s,  Serratia quinivorans, Serratia  SBS,  Serratia  SS22 , Serratia  trout,  Serratia  UA-G004 , Serratia, Serratia  White,  Serratia  yellow,  Shewanella, Shewanella baltica, Shewanella, Slackia, Slackia  intestinal,  Slackia isoflavoniconvertens, Slackia  NATTS,  Slackia, Solibacillus, Solibacillus, Solobacterium, Solobacterium moorei, Solobacterium, Spartobacteria _genera_incertae_sedis,  Spartobacteria _genera_incertae_sedis,  Sphingobium, Sphingobium, Sphingomonas, Sphingomonas, Sporacetigenium, Sporacetigenium, Sporobacter, Sporobacter, Sporobacterium, Sporobacterium olearium, Staphylococcus, Staphylococcus epidermidis, Staphylococcus  PCA17 , Staphylococcus, Stenotrophomonas, Stenotrophomonas, Streptococcus, Streptococcus  1606-02B,  Streptococcus agalactiae, Streptococcus alactolyticus, Streptococcus anginosus, Streptococcus bacterium, Streptococcus bovis, Streptococcus  ChDC,  Streptococcus constellatus, Streptococcus  CR-314S,  Streptococcus criceti, Streptococcus cristatus, Streptococcus downei, Streptococcus dysgalactiae, Streptococcus enrichment, Streptococcus equi, Streptococcus equinus, Streptococcus  ES11 , Streptococcus eubacterium, Streptococcus fecal, Streptococcus gallinaceus, Streptococcus gallolyticus, Streptococcus gastrococcus, Streptococcus genomosp, Streptococcus gordonii, Streptococcus  I5 , Streptococcus infantarius, Streptococcus intermedius, Streptococcus  Je2 , Streptococcus  JS-CD2 , Streptococcus  LRC,  Streptococcus luteciae, Streptococcus lutetiensis, Streptococcus  M09-11185 , Streptococcus mitis, Streptococcus mutans, Streptococcus  NA,  Streptococcus  nlaezlc353 , Streptococcus  nlaezlp68 , Streptococcus  nlaezlp758 , Streptococcus  nlaezlp807 , Streptococcus  oral,  Streptococcus oralis, Streptococcus parasanguinis, Streptococcus phocae, Streptococcus pneumoniae, Streptococcus porcinus, Streptococcus pyogenes, Streptococcus  S16-08 , Streptococcus salivarius, Streptococcus sanguinis, Streptococcus sobrinus, Streptococcus suis, Streptococcus symbiont, Streptococcus thermophilus, Streptococcus  TW1 , Streptococcus, Streptococcus vestibularis, Streptococcus warneri, Streptococcus  XJ-RY-3 , Streptomyces, Streptomyces malaysiensis, Streptomyces  MVCS6 , Streptophyta, Streptophyta cordifolium, Streptophyta ginseng, Streptophyta hirsutum, Streptophyta oleracea, Streptophyta sativa, Streptophyta sativum, Streptophyta sativus, Streptophyta tabacum, Streptophyta , Subdivision3_genera_incertae_sedis, Subdivision3_genera_incertae_sedis,  Subdoligranulum, Subdoligranulum  bacterium,  Subdoligranulum  ic1393 , Subdoligranulum  ic1395 , Subdoligranulum, Subdoligranulum variabile, Succiniclasticum, Succiniclasticum, Sulfuricella, Sulfuricella, Sulfurospirillum, Sulfurospirillum, Sutterella, Sutterella, Sutterella wadsworthensis, Syntrophococcus, Syntrophococcus, Syntrophomonas, Syntrophomonas bryantii, Syntrophomonas, Syntrophus, Syntrophus, Tannerella, Tannerella, Tatumella, Tatumella, Thermofilum, Thermofilum, Thermogymnomonas, Thermogymnomonas, Thermovirga, Thermovirga, Thiomonas, Thiomonas  ML1-46 , Thorsellia, Thorsellia carsonella , TM7_genera_incertae_sedis, TM7_genera_incertae_sedis,  Trichococcus, Trichococcus, Turicibacter, Turicibacter sanguinis, Turicibacter, Vagococcus, Vagococcus  bfs11-15 , Vagococcus, Vampirovibrio, Vampirovibrio, Varibaculum, Varibaculum, Variovorax, Variovorax  KS2D-23 , Veillonella, Veillonella dispar, Veillonella  MSA12 , Veillonella  OK8 , Veillonella  oral,  Veillonella parvula, Veillonella tobetsuensis, Veillonella, Vibrio, Vibrio  3C1 , Vibrio, Victivallis, Victivallis, Victivallis vadensis, Vitellibacter, Vitellibacter, Wandonia, Wandonia haliotis, Weissella, Weissella cibaria, Weissella confusa, Weissella oryzae, Weissella, Yersinia, Yersinia  9gw38 , Yersinia  A125 , Yersinia aldovae, Yersinia aleksiciae, Yersinia  b702011 , Yersinia  bacterium,  Yersinia bercovieri, Yersinia enterocolitica, Yersinia entomophaga, Yersinia frederiksenii, Yersinia intermedia, Yersinia kristensenii, Yersinia  MAC,  Yersinia massiliensis, Yersinia mollaretii, Yersinia nurmii, Yersinia pekkanenii, Yersinia pestis, Yersinia pseudotuberculosis, Yersinia rohdei, Yersinia ruckeri, Yersinia  s10fe31 , Yersinia  s17fe31 , Yersinia  s4fe31 , Yersinia, Yersinia  YEM17B. 
     Accuracy and Sensitivity 
     The methods provided herein can provide strain classification of a genera, species or sub-strain level of one or more microbes in a sample with an accuracy of greater than 1%, 20%, 30%, 40%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.2%, 99.5%, 99.7%, or 99.9%. The methods provided herein can provide strain quantification of a genera, species or sub-strain level of one or more microbes in a sample with an accuracy of greater than 1%, 20%, 30%, 40%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.2%, 99.5%, 99.7%, or 99.9%. 
     In some applications a similar microbiome profile from a patient to a reference profile indicates an increased likelihood of one or more of: a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. In some applications, a dissimilar microbiome profile from a patient to a reference profile indicates one or more of: an increased likelihood of a poor clinical outcome, good clinical outcome, high risk of disease, low risk of disease, complete response, partial response, stable disease, non-response, and recommended treatments for disease management. 
     The methods provided herein can provide a health status of a subject with a specificity greater than 1%, 20%, 30%, 40%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.2%, 99.5%, 99.7%, or 99.9% ROC. The methods provided herein can provide a health status of a subject with sensitivity greater than 1%, 20%, 30%, 40%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 96%, 97%, 98%, 99%, 99.2%, 99.5%, 99.7%, or 99.9% ROC. 
     The diagnostic methods provided by the present disclosure for the diseases provided herein can have at least one of a sensitivity of 70% or greater and specificity of greater than 70% based on measurement of 15 or fewer microbes in the biological sample. Such diagnostic method can have at least one of a sensitivity greater than 70% and specificity greater than 70% based on measurement of no more than 2 microbes, 3 or fewer microbes, 4 or fewer microbes, 5 or fewer microbes, 6 or fewer microbes, 7 or fewer microbes, 8 or fewer microbes, 9 or fewer microbes, 10 or fewer microbes, 11 or fewer microbes, no more than 12 microbes, 13 or fewer microbes, 14 or fewer microbes, 15 or fewer microbes, 16 or fewer microbes, 18 or fewer microbes, 19 or fewer microbes, 20 or fewer microbes, 25 or fewer microbes, 30 or fewer microbes, 35 or fewer microbes, 40 or fewer microbes, 45 or fewer microbes, 50 or fewer microbes, 55 or fewer microbes, 60 or fewer microbes, 65 or fewer microbes, 70 or fewer microbes, 75 or fewer microbes, 80 or fewer microbes, 85 or fewer microbes, 90 or fewer microbes, or 100 or fewer microbes, 200 or fewer microbes, 300 or fewer microbes, 400 or fewer microbes, 500 or fewer microbes, 600 or fewer microbes, 700 or fewer microbes or 800 or fewer microbes. 
     The microbial profile for a subject provided by the present disclosure can have an accuracy of 70% or greater based on measurement of 15 or fewer microbes in the biological sample. Such profiling method can have at least an accuracy greater than 70% based on measurement of no more than 2 microbes, 3 or fewer microbes, 4 or fewer microbes, 5 or fewer microbes, 6 or fewer microbes, 7 or fewer microbes, 8 or fewer microbes, 9 or fewer microbes, 10 or fewer microbes, 11 or fewer microbes, no more than 12 microbes, 13 or fewer microbes, 14 or fewer microbes, 15 or fewer microbes, 16 or fewer microbes, 18 or fewer microbes, 19 or fewer microbes, 20 or fewer microbes, 25 or fewer microbes, 30 or fewer microbes, 35 or fewer microbes, 40 or fewer microbes, 45 or fewer microbes, 50 or fewer microbes, 55 or fewer microbes, 60 or fewer microbes, 65 or fewer microbes, 70 or fewer microbes, 75 or fewer microbes, 80 or fewer microbes, 85 or fewer microbes, 90 or fewer microbes, or 100 or fewer microbes, 200 or fewer microbes, 300 or fewer microbes, 400 or fewer microbes, 500 or fewer microbes, 600 or fewer microbes, 700 or fewer microbes or 800 or fewer microbes. 
     III. Computer Systems 
     The current disclosure provides computer systems for implementing any of the methods described herein. A computer system may be used to implement one or more steps including, sample collection, sample processing, detecting, quantifying one or more microbes, generating a profile data, comparing said data to a reference, generating a subject-specific microbiome profile, comparing the subject-specific profile to a reference profile, receiving medical history, receiving medical records, receiving and storing data obtained by one or more methods described herein, analyzing said data, generating a report, and reporting results to a receiver. 
     For example, provided herein are computer systems for detecting a presence or absence of a microbes. Also provided herein are computer systems for detecting a presence or absence of bacteria, fungi, archaea or other elements that comprise and maintain a microbiome. 
     Computer systems described herein may comprise computer-executable code for performing any of the algorithms described herein. Computer systems described herein may comprise computer-executable code for performing any of the algorithms and using the database as herein. 
       FIG. 1  depicts an exemplary computer system  100  adapted to implement a method described herein. The system  100  includes a central computer server  101  that is programmed to implement exemplary methods described herein. The server  101  includes a central processing unit (CPU, also “processor”)  105  which can be a single core processor, a multi core processor, or plurality of processors for parallel processing. The server  101  also includes memory  110  (e.g. random access memory, read-only memory, flash memory); electronic storage unit  115  (e.g. hard disk); communications interface  120  (e.g. network adaptor) for communicating with one or more other systems; and peripheral devices  125  which may include cache, other memory, data storage, and/or electronic display adaptors. The memory  110 , storage unit  115 , interface  120 , and peripheral devices  125  are in communication with the processor  105  through a communications bus (solid lines), such as a motherboard. The storage unit  115  can be a data storage unit for storing data. The server  101  is operatively coupled to a computer network (“network”)  130  with the aid of the communications interface  120 . The network  130  can be the Internet, an intranet and/or an extranet, an intranet and/or extranet that is in communication with the Internet, a telecommunication or data network. The network  130  in some cases, with the aid of the server  101 , can implement a peer-to-peer network, which may enable devices coupled to the server  101  to behave as a client or a server. 
     The storage unit  115  can store files, such as subject reports, and/or communications with the caregiver, sequencing data, data about individuals, or any aspect of data associated with the invention. 
     The server can communicate with one or more remote computer systems through the network  130 . The one or more remote computer systems may be, for example, personal computers, laptops, tablets, telephones, Smart phones, or personal digital assistants. 
     In some applications the computer system  100  includes a single server  101 . In other situations, the system includes multiple servers in communication with one another through an intranet, extranet and/or the internet. 
     The server  101  can be adapted to store measurement data or a database as provided herein, patient information from the subject, such as, for example, polymorphisms, mutations, medical history, family history, demographic data and/or other clinical or personal information of potential relevance to a particular application. Such information can be stored on the storage unit  115  or the server  101  and such data can be transmitted through a network. 
     Methods as described herein can be implemented by way of machine (or computer processor) executable code (or software) stored on an electronic storage location of the server  101 , such as, for example, on the memory  110 , or electronic storage unit  115 . During use, the code can be executed by the processor  105 . In some cases, the code can be retrieved from the storage unit  115  and stored on the memory  110  for ready access by the processor  105 . In some situations, the electronic storage unit  115  can be precluded, and machine-executable instructions are stored on memory  110 . Alternatively, the code can be executed on a second computer system  140 . 
     Aspects of the systems and methods provided herein, such as the server  101 , can be embodied in programming. Various aspects of the technology may be thought of as “products” or “articles of manufacture” typically in the form of machine (or processor) executable code and/or associated data that is carried on or embodied in a type of machine readable medium. Machine-executable code can be stored on an electronic storage unit, such memory (e.g., read-only memory, random-access memory, flash memory) or a hard disk. “Storage” type media can include any or all of the tangible memory of the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives and the like, which may provide non-transitory storage at any time for the software programming. All or portions of the software may at times be communicated through the Internet or various other telecommunication networks. Such communications, for example, may enable loading of the software from one computer or processor into another, for example, from a management server or host computer into the computer platform of an application server. Thus, another type of media that may bear the software elements includes optical, electrical, and electromagnetic waves, such as used across physical interfaces between local devices, through wired and optical landline networks and over various air-links. The physical elements that carry such waves, such as wired or wireless likes, optical links, or the like, also may be considered as media bearing the software. As used herein, unless restricted to non-transitory, tangible “storage” media, terms such as computer or machine “readable medium” can refer to any medium that participates in providing instructions to a processor for execution 
     Computer systems described herein may comprise computer-executable code for performing any of the algorithms or algorithms-based methods described herein. In some applications the algorithms described herein will make use of a memory unit that is comprised of at least one database. 
     Data relating to the present disclosure can be transmitted over a network or connections for reception and/or review by a receiver. The receiver can be but is not limited to the subject to whom the report pertains; or to a caregiver thereof, e.g., a health care provider, manager, other health care professional, or other caretaker; a person or entity that performed and/or ordered the analysis. The receiver can also be a local or remote system for storing such reports (e.g. servers or other systems of a “cloud computing” architecture). In one embodiment, a computer-readable medium includes a medium suitable for transmission of a result of an analysis of a biological sample using the methods described herein. 
     A. Databases 
     Computer systems disclosed herein may comprise a memory unit. The memory unit can be configured to receive data comprising extracting data from a pubic database, detecting, quantifying and profiling one or more microbiomes. The microbiome profile can be any organism known to comprise a microbiome. Examples of such organisms are provided herein. 
     There are several public microbe (bacteria, fungi, and archaea) and viral protein and genome databases known in the art. The present methods of the disclosure can be used with such public databases. Examples of public databases include but are not limited to Biocyc, Ensembl Bacteria, The Integrated Microbial Genomes, MicrobesOnline, Microbial Genomes from Genome Channel, Microbial Genomes at NCBI, RCSB protein database, Sanger Centre Bacterial Genomes, Ribosomal Database Project (RDP), or DOE JGI Microbial Genomics Database. 
     The current disclosure also provides for a database that has additional or more accurate microbe information such as the composition of particular microbiomes in a particular cohort, or microbiome reference profiles of a particular cohort. Such database can include but are not limited to additional or more accurate sequences comprising the 16S subunit of ribosome for a given microbe strain, additional or more accurate sequence comprising the 23S subunit of ribosome for a given microbe strain, additional or more accurate information of the sequence comprising the intergenic region between the 16S subunit and 23S subunit of ribosome, additional or more accurate information of the sequence comprising variable regions in the 16S ribosome for a particular strain, additional or more accurate information of the sequence comprising variable regions in the 23S subunit of ribosome for a particular strain, additional or more accurate information of the sequence comprising variable regions with a high accuracy in strain resolution at the genus level, additional or more accurate information of the sequence comprising variable regions with a high accuracy in strain resolution at the species level, or additional or more accurate information of the sequence comprising variable regions with a high accuracy in strain resolution at the sub-type level. 
     Such a database that has additional or more accurate genome information can be comprised of sequence reads greater than 500 base pair, 600 base pair, 700 base pair, 800 base pair, 900 base pair, 1000 base pair, 1100 base pair, 1200 base pair, 1300 base pair, 1400 base pair, 16S subunit of ribosome for a given microbe strain. Such a database can be comprised of sequence reads greater than 500 base pair, 600 base pair, 700 base pair, 800 base pair, 900 base pair, 1000 base pair, 1100 base pair, 1200 base pair, 1300 base pair, 1400 base pair, 16S or 23S subunit of ribosome for a given microbe strain. 
     Such a database can be comprised of sequence reads greater than 500 base pair, 600 base pair, 700 base pair, 800 base pair, 900 base pair, 1000 base pair, 1100 base pair, 1200 base pair, 1300 base pair, 1400 base pair, comprising the intergenic region between the 16S subunit and 23S subunit of ribosome for a given bacterial strain. 
     Such a database can be comprised of sequence reads greater than 500 base pair, 600 base pair, 700 base pair, 800 base pair, 900 base pair, 1000 base pair, 1100 base pair, 1200 base pair, 1300 base pair, 1400 base pair, 1500 base pair comprising the variable regions in the 16S ribosome. Such a database can be comprised of sequence reads greater than 500 base pair, 600 base pair, 700 base pair, 800 base pair, 900 base pair, 1000 base pair, 1100 base pair, 1200 base pair, 1300 base pair, 1400 base pair, 1500 base pair comprising the variable regions in the 16S or 23S ribosome. 
     Such a database can further comprise of additional or more accurate proteome information can be comprised of sequence reads greater than 500 amino acids, 600 amino acids, 700 amino acids, 800 amino acids, 900 amino acids, 1000 amino acids, 1100 amino acids, 1200 amino acids, 1300 amino acids, 1400 amino acids, 16S subunit of ribosome for a given microbe strain. Such a database can be comprised of sequence reads greater than 500 amino acids, 600 amino acids, 700 amino acids, 800 amino acids, 900 amino acids, 1000 amino acids, 1100 amino acids, 1200 amino acids, 1300 amino acids, 1400 amino acids, 16S or 23S subunit of ribosome for a given microbe strain. 
     Such a database can be comprised of sequence reads greater than 500 amino acids, 600 amino acids, 700 amino acids, 800 amino acids, 900 amino acids, 1000 amino acids, 1100 amino acids, 1200 amino acids, 1300 amino acids, 1400 amino acids, comprising the intergenic region between the 16S subunit and 23S subunit of ribosome for a given bacterial strain. 
     Such a database can be comprised of sequence reads greater than 500 amino acids, 600 amino acids, 700 amino acids, 800 amino acids, 900 amino acids, 1000 amino acids, 1100 amino acids, 1200 amino acids, 1300 amino acids, 1400 amino acids, 1500 amino acids comprising the variable regions in the 16S ribosome. 
     Such a database can be comprised of sequence reads greater than 500 amino acids, 600 amino acids, 700 amino acids, 800 amino acids, 900 amino acids, 1000 amino acids, 1100 amino acids, 1200 amino acids, 1300 amino acids, 1400 amino acids, 1500 amino acids comprising the variable regions in the 16S or 23S ribosome. 
     The database maybe located on central server containing the computer-executable code that allows access to a user. The user can connect to the central server through a physical connection or cloud-based connection depending on the application. In some applications a portion of the database and necessary executable code will be supplied to as user on appropriate storage media. 
     B. Computer Generated Report 
     The computer system can further comprise computer-executable code for providing a report communicating the detecting, measuring, or determining a profile of a microbiome from a subject. Measuring, or determining a profile of a microbiome can include the use of a database as provided herein. 
     Computer systems disclosed herein may comprise computer-executable code for performing at least one of: generating a cohort-generalized microbiome profile or a subject-specific microbiome profile based upon the measurement data from a biological sample from the subject, comparing the cohort-generalized microbiome profile or subject-specific microbiome profile to at least one reference and determining the health status of a subject. 
     In some applications the computer system can access the computer-executable code by having a connection to a central server that contained the computer-executable code to generate a report comprising at least one clinical recommendation such as for example, disease state, diagnosis, prognosis, treatment suggestions or procedures for clinical management in a subject which can be retrieved by a health worker or clinician via said central server. The connection to a central server containing the computer-executable code can be a physical connection or cloud-based connection depending on the application. 
     IV. Kits 
     The disclosure provides kits. A kit described herein can comprise one or more compositions, reagents, buffers, components for measuring or detecting one or more microbes or microbiomal profiles by a method provided herein. A kit as can further comprise instructions for practicing any of the methods provided herein. For example, instructions can include specifics sample preparation steps for the biological samples as provided herein and it for measuring or detecting. Likewise, the contents of the kit will be tailored to its particular application and sample type. 
     The kits can further comprise reagents to enable the detection by such applications as PCR, DNA/RNA array, protein array, sequencing, mass spectrometry, immunohistochemistry, laser cell microdissection, high-content cell screening, flow cytometry, which are suitable with the methods described herein for detection and determination of a subject&#39;s prognosis, prediction of response, and diagnosis. 
     Kits can further comprise a software package for measuring or determining of a microbiome profile as, which as described herein, can include reference microbiome profiles or other health related data. In some applications the kits software package including connection to a central server to conduct for measuring or determining and can generate a report comprising at least one clinical recommendation such as for example, disease state, diagnosis, prognosis, treatment suggestions or procedures for clinical management in a subject which can be retrieved by a health worker or clinician via said central server. 
     In some applications, kits can further comprise a report. The report can be a paper or an electronic report. The report can be generated by computer software (e.g. computer-executable code) provided with the kit, or by a computer sever which the user uploads to a website wherein the computer server generates the report. 
     In some applications the kit can provide for profiling more than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 150, 200, 250, 300, 350, 400, 450, or 500 microbes at the genera, species, sub-strain level or a combination thereof. 
     In general, kits will comprise of a packaging material. As used herein, the term “packaging material” can refer to a physical structure housing the components of the kit. The packaging material can maintain sterility of the kit components, and can be made of material commonly used for such purposes (e.g., paper, corrugated fiber, glass, plastic, foil, ampules, etc.). Depending on the applications, kits can also include such materials as a buffering agent, a preservative, or a protein or nucleic acid stabilizing agents or any such materials that are necessary for the stable transport of biological samples and reagents. 
     V. Microbial-Based Therapeutics and Cosmetics 
     Provided herein are compositions that may be administered as therapeutics or cosmetics or as a cosmetic. One or more, or combination thereof the microorganisums provided herein can be used to created various formulations for treating a subject. The present disclosure provides therapeutic or cosmetic formulations for the following conditions IBD, preterm labor, obesity, diabetic foot ulcers, bacteremia, acne, infantile colic, type 2 diabetes,  C. difficile , IBS, asthma, autism, psoriasis, allergies, cardiovascular disease, cancer, depression, cystic fibrosis, multiple sclerosis, urinary tract infection, radiation enteropathy, drug metabolism, chronic fatigue, and type 1 diabetes. 
     A. Formulations 
     Formulations provided herein can include the addition of one or more genetic elements to the therapeutics or cosmetics in order to enchance stability. Formulations provided herein can include those suitable for oral including buccal and sub-lingual, rectal, intranasal, topical, transdermal, transdermal patch, pulmonary, vaginal, suppository, or parenteral (including intramuscular, intraarterial, intrathecal, intradermal, intraperitoneal, subcutaneous and intravenous) administration or in a form suitable for administration by aerosolization, inhalation or insufflation. General information on drug delivery systems can be found in Ansel et al., Pharmaceutical Dosage Forms and Drug Delivery Systems (Lippencott Williams &amp; Wilkins, Baltimore Md. (1999). 
     The appropriate quantity of a therapeutic or cosmetic composition to be administered, the number of treatments, and unit dose will vary according to the subject and the disease state of the subject. The person responsible for administration will determine the appropriate dose, number of treatments, etc. for the subject. 
     In various applications, the therapeutic or cosmetic composition can include carriers and excipients (including but not limited to buffers, carbohydrates, mannitol, proteins, polypeptides or amino acids such as glycine, antioxidants, bacteriostats, chelating agents, suspending agents, thickening agents and/or preservatives), water, oils including those of petroleum, animal, vegetable or synthetic origin, such as peanut oil, soybean oil, mineral oil, sesame oil and the like, saline solutions, aqueous dextrose and glycerol solutions, flavoring agents, coloring agents, detackifiers and other acceptable additives, adjuvants, or binders, other pharmaceutically acceptable auxiliary substances as required to approximate physiological conditions, such as pH buffering agents, tonicity adjusting agents, emulsifying agents, wetting agents and the like. Examples of excipients include starch, glucose, lactose, sucrose, gelatin, malt, rice, flour, chalk, silica gel, sodium stearate, glycerol monostearate, talc, sodium chloride, dried skim milk, glycerol, propylene, glycol, water, ethanol and the like. 
     In some applications, the therapeutic or cosmetic composition is substantially free of preservatives. In other applications, the composition may contain at least one preservative. General methodology on pharmaceutical dosage forms is found in Ansel et al., Pharmaceutical Dosage Forms and Drug Delivery Systems (Lippencott Williams &amp; Wilkins, Baltimore Md. (1999). It will be recognized that, while any suitable carrier known to those of ordinary skill in the art may be employed to administer the compositions provided herein, the type of carrier will vary depending on the mode of administration. A thorough discussion of acceptable carriers/excipients can be found in Remington&#39;s Pharmaceutical Sciences, Gennaro, A R, ed., 20th edition, 2000: Williams and Wilkins PA, USA. 
     A therapeutic or cosmetic composition may also be encapsulated within liposomes using well-known technology. Biodegradable microspheres may also be employed as carriers for the therapeutics or cosmetics compositions provided herein. Suitable biodegradable microspheres are disclosed, for example, in U.S. Pat. Nos. 4,897,268; 5,075,109; 5,928,647; 5,811,128; 5,820,883; 5,853,763; 5,814,344 and 5,942,252. 
     The composition may be administered in liposomes or microspheres or microparticles. Methods for preparing liposomes and microspheres for administration to a patient are well known to those of skill in the art. U.S. Pat. No. 4,789,734, the contents of which are hereby incorporated by reference, describes methods for encapsulating biological materials in liposomes. A review of known methods is provided by G. Gregoriadis, Chapter 14, “Liposomes,” Drug Carriers in Biology and Medicine, pp. 2.sup.87-341 (Academic Press, 1979). 
     Microspheres formed of polymers or proteins are well known to those skilled in the art, and can be tailored for passage through the gastrointestinal tract directly into the blood stream. Alternatively, the compound can be incorporated and the microspheres, or composite of microspheres, implanted for slow release over a period of time ranging from days to months. See, for example, U.S. Pat. Nos. 4,906,474, 4,925,673 and 3,625,214, and Jein, TIPS 19:155-157 (1998), the contents of which are hereby incorporated by reference. 
     The compositions provided herein may be formulated as a sterile solution or suspension, in suitable vehicles, well known in the art. The therapeutic or cosmetic compositions may be sterilized by conventional techniques or may be sterile filtered. The resulting aqueous solutions may be packaged for use as is, or lyophilized, the lyophilized preparation being combined with a sterile solution prior to administration. Suitable formulations and additional carriers are described in Remington “The Science and Practice of Pharmacy” (20th Ed., Lippincott Williams &amp; Wilkins, Baltimore Md.), the teachings of which are incorporated by reference in their entirety herein. 
     VI. Indications 
     As mentioned above, research indicates that microbiomes can have an effect on a subject&#39;s disease status and clinical treatment response. For example, obese and lean individuals can be categorized based on differences in specific species of microbes (Future Microbiol. (2012) 7(1): 91-109. Additionally, in another example the administration of  Lactobacillus  gasseri SBT2055 to lean individuals had observable weight loss (Micr. Path. (2012) 53(2): 100-108; Eur J Clin Nutr (2010) 64:636-43). On the other hand for obese individuals it was  Lactobacillus plantarum  that had the biggest weight loss effect (Micr. Path. (2012) 53(2): 100-108; Eur J Clin Nutr (2010) 64:636-43). These studies indicate that specifically tailoring treatment to a subjects&#39; microbiome may have beneficial effects on a subject&#39;s health status or ameliorate certain symptoms associated with the onset of an indication. 
     Furthermore, in non-obese individuals, the acetate producing fermentation reaction is limited by the partial pressure of hydrogen which creates a thermodynamic roadblock to further fermentation (Dolfing &amp; Tiedje, 54 Appl. Environ. Microbiol. 1871-3 1988; Schink, 61 Microbiol. Mol. Bio. Rev. 262-80 1997). Obese individuals on the other hand have hydrogen-oxidizing methanogens that can consume the hydrogen and thus allow the acetate producing fermentation reaction to continue. The gut of obese individuals actually uniquely contains hydrogen-oxidizing methanogenic Archaea (Zhang et al PNAS 106:2365-70 2009) and higher levels of Prevotellaceae (Bacteroidetes phylum) (Turnbaugh 2006), which are known to perform the carbohydrate fermentation reaction to acetate and hydrogen. 
     Indications that can be used with the present disclosure include, but are not limited to the following: IBD, preterm labor, obesity, diabetic foot ulcers, bacteremia, acne, infantile colic, type 2 diabetes,  C. difficile , IBS, asthma, autism, psoriasis, allergies, cardiovascular disease, cancer, depression, cystic fibrosis, multiple sclerosis, urinary tract infection, radiation enteropathy, drug metabolism, chronic fatigue, and type 1 diabetes. 
     Inflammatory bowel disease (IBD) involves chronic inflammation of all or part of the digestive tract. IBD primarily includes ulcerative colitis and Crohn&#39;s disease. IBD can be painful and debilitating, and sometimes leads to life-threatening complications. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for IBD, ulcerative colitis or Crohn&#39;s disease. The present disclosure also provides therapeutic or cosmetic formulations for treatment of inflammatory bowel disease conditions. 
     Preterm labor occurs when contractions begin to open the cervix before 37 weeks of pregnancy. The earlier premature birth happens, the greater the health risks for the developing baby. Many premature babies need special care in the neonatal intensive care unit. Premature babies can also have long-term mental and physical disabilities. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for preterm labor. The present disclosure also provides therapeutic or cosmetic formulations for treatment of preterm labor. 
     Obesity is a complex disorder involving an excessive amount of body fat. Obesity increases the risk of diseases and health problems such as heart disease, diabetes and high blood pressure. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for obesity. The present disclosure also provides therapeutic or cosmetic formulations for treatment of obesity conditions. 
     Peripheral neuropathy is the most common form of diabetic neuropathy. The feet and legs are often affected first, followed by the hands and arms. Possible signs and symptoms of peripheral neuropathy can include serious foot problems, such as ulcers, infections, deformities, and bone and joint pain. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for diabetic neuropathy. The present disclosure also provides therapeutic or cosmetic formulations for treatment of diabetic neuropathy conditions. 
     Bacteremia or septicemia refers to the presence of bacteria in the blood. A diagnosis of bacteremia is usually confirmed by a blood culture. Treatment usually requires hospitalization and intravenous antibiotics. Without prompt treatment, bacteremia can quickly progress to severe sepsis. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for bacteremia, which can include the antibiotic susceptibilities of the infection. The present disclosure also provides therapeutic or cosmetic formulations for treatment of bacteremia. 
     Acne is a skin condition that occurs when the hair follicles become plugged with oil and dead skin cells. Acne most commonly appears on the face, neck, chest, back and shoulders. Depending on its severity of the acne, this condition can cause emotional distress and lead to scarring of the skin. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of that includes a report that gives guidance on health status or treatment modalities for acne. The present disclosure also provides therapeutic or cosmetic formulations for treatment of acne conditions. 
     Infantile colic physicians and parents use the term colic to describe an infant with excessive crying, irritability, or fussiness. Babies with colic often cry more than three hours a day, three days a week for three weeks or longer. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for infantile colic. The present disclosure also provides therapeutic or cosmetic formulations for treatment of the above-mentioned condition. 
     Type 2 diabetes, once known as adult-onset or noninsulin-dependent diabetes, is a chronic condition that affects the way the body metabolizes glucose. With type 2 diabetes, the body either resists the effects of insulin or doesn&#39;t produce enough insulin to maintain a normal glucose level. Untreated, type 2 diabetes can be life-threatening. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for type 2 diabetes. The present disclosure also provides therapeutic or cosmetic formulations for treatment of type 2 diabetes. 
       Clostridium difficile , often called “ C. difficile ” or “ C. diff ”, is a bacterium that can cause symptoms ranging from diarrhea to life-threatening inflammation of the colon. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for infections such as  C. difficile . The present disclosure also provides therapeutic or cosmetic formulations for treatment of  Clostridium difficile  infections. 
     Asthma is a condition in which the airways narrow and swell and produce extra mucus. This can make breathing difficult and trigger coughing, wheezing and shortness of breath. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for asthma. The present disclosure also provides therapeutic or cosmetic formulations for treatment of asthma. 
     Autism spectrum disorder is a serious neurodevelopmental disorder that impairs a child&#39;s ability to communicate and interact with others. It also includes restricted repetitive behaviors, interests and activities. Autism spectrum disorder (ASD) is now defined by the American Psychiatric Association&#39;s Diagnosis and Statistical Manual of Mental Disorders (DSM-5) as a single disorder that includes disorders that were previously considered separate—autism, Asperger&#39;s syndrome, childhood disintegrative disorder and pervasive developmental disorder not otherwise specified. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for autism spectrum disorder. The present disclosure also provides therapeutic or cosmetic formulations for treatment of autism spectrum disorders. 
     Psoriasis is a common, persistent, long-lasting (chronic) skin condition that changes the life cycle of skin cells. Psoriasis causes cells to build up rapidly on the surface of the skin. The extra skin cells form thick, silvery scales and itchy, dry, red patches that are sometimes painful. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for Psoriasis. The present disclosure also provides therapeutic or cosmetic formulations for treatment of psoriasis or a similar skin condition. 
     Allergies occur when the immune system reacts to a foreign substance such as pollen, bee venom or pet dander. When you come into contact with the allergen, the immune system&#39;s reaction can inflame the skin, sinuses, airways or digestive system. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for allergies. The present disclosure also provides therapeutic or cosmetic formulations for treatment of allergies. 
     Cardiovascular diseases can affect the heart, arteries and veins of the body. Examples of some cardiovascular disease include but are not limited to heart valve disease, coronary artery disease, congenital heart disease in adults and congenital heart spontaneous coronary artery dissection, heart failure, heart rhythm disorders (arrhythmias). The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for cardiovascular disease. The present disclosure also provides therapeutic or cosmetic formulations for treatment of the above-mentioned cardiovascular conditions. 
     Cancer refers to any one of a large number of proliferative diseases characterized by the development of abnormal cells that divide uncontrollably and have the ability to infiltrate and destroy normal body tissues and organs. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for cancer or other proliferative diseases. The present disclosure also provides therapeutic or cosmetic formulations for treatment of cancer. 
     Depression also called major depression, major depressive disorder or clinical depression is a mood disorder that causes a persistent feeling of sadness and loss of interest. It can affect how a person feels, thinks and behaves and can lead to a variety of emotional and physical problems. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for depression disorders. The present disclosure also provides therapeutic or cosmetic formulations for treatment of the above-mentioned depression conditions. 
     Cystic fibrosis is a life-threatening genetic disorder that causes severe damage to the lungs and digestive system. Cystic fibrosis affects the cells that produce secreted fluids such as mucus, sweat and digestive juices that act as lubricants in the body. These secreted fluids are normally thin and slippery but in cystic fibrosis the secretions to become thick and sticky resulting in plugging up tubes, ducts and passageways, especially in the lungs and pancreas. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for cystic fibrosis. The present disclosure also provides therapeutic or cosmetic formulations for treatment of cystic fibrosis. 
     Multiple sclerosis is a disease in which the immune system attacks the protective sheath (myelin) that covers the nerves. Myelin damage disrupts communication between the brain and the rest of the body. Ultimately, the nerves themselves may deteriorate a process that&#39;s currently irreversible. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for multiple sclerosis. The present disclosure also provides therapeutic or cosmetic formulations for treatment of multiple sclerosis. 
     Urinary tract infection is an infection in any part of the urinary system (e.g. kidneys, ureters, bladder and urethra). Most infections involve the lower urinary tract—the bladder and the urethra. Infection limited to the bladder can be painful and annoying. However, serious consequences can occur if a UTI spreads to the kidneys. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for urinary tract infections. The present disclosure also provides therapeutic or cosmetic formulations for treatment of infections in any part of the urinary system. 
     Radiation enteropathy is radiation-induced GI injuries of the colon and rectum to the small bowel. Radiotherapy is a mainstay of oncological treatment for a variety of malignant diseases and is commonly administered to the abdomen and pelvis of patients with gastrointestinal (GI), urological and gynaecological cancers. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for radiation enteropathy. The present disclosure also provides therapeutic or cosmetic formulations for treatment of radiation-induced injury conditions. 
     Drug metabolism refers to the rate at which the body breaks down as drug after administration. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for drug metabolism in a patient. The present disclosure also provides therapeutic or cosmetic formulations for treatment of drug metabolism conditions. 
     Chronic fatigue syndrome is a complicated disorder characterized by extreme fatigue that can&#39;t be explained by any underlying medical condition. The fatigue may worsen with physical or mental activity, but doesn&#39;t improve with rest. The cause of chronic fatigue syndrome is currently unknown. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for chronic fatigue syndrome. The present disclosure also provides therapeutic or cosmetic formulations for treatment of CFS conditions. 
     Type 1 diabetes, once known as juvenile diabetes or insulin-dependent diabetes, is a chronic condition in which the pancreas produces little or no insulin, a hormone needed to allow sugar (glucose) to enter cells to produce energy. Various factors may contribute to type 1 diabetes, including genetics and exposure to certain viruses. Although type 1 diabetes typically appears during childhood or adolescence, it also can develop in adults. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for type 1 diabetes. The present disclosure also provides therapeutic or cosmetic formulations for treatment of type 1 diabetes. 
     Dental cavities are caused by the conversion of sugar from food to elongated, sticky sugar chains through a bacterially produced glucansucrase enzyme. Attempts to directly inhibit this enzyme have failed because it is evolutionarily closely related to amylase, which is the enzyme used to break down starch. A more effective approach would be to reduce the proportion of  Streptococcus mutans , which is the bacteria associated with tooth decay. This would leave intact the useful enzymes used by the body to break down starches while simultaneously minimizing the rate of cavity formation. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for tooth decay. The present disclosure also provides provides therapeutic or cosmetic formulations for treatment of tooth decay conditions. 
     Halitosis is a dental condition in which excessively bad breath is produced by the microbial flora present in an individual&#39;s mouth. It is known that the most problematic types are the gram-negative bacteria (e.g.  Prevotella intermedia, Porphyromonas gingivalis, Treponema denticola ). By using the methods outlined in this disclosure, a strain level list of the most problematic bacteria, as well as the most protective, will enable a directed therapeutic/cosmetic formulation to treat halitosis. The methods, compositions, systems and kits of the present disclosure provide for a diagnostic assay of at least one microbiome that includes a report that gives guidance on health status or treatment modalities for halitosis. The present disclosure also provides therapeutic or cosmetic formulations for treatment of halitosis. 
     VII. Examples 
     Example 1: Comparison of Microbe Classification Accuracy 
     As more bacterial strains are identified, it has become clear that just sequencing a few of the hypervariable regions does not provide enough distinction. There are classes of bacteria now characterized that share homologous hypervariable regions. Therefore, the need to sequence the entire 16S and/or 23S and include all of the hypervariable regions is necessary to make accurate classification calls. Comparison simulations experiments were conducted with the methods of the present disclosure and the current methods in the field to determine if the methods of the present disclosure provide increased accuracy for microbe classification. 
     Using long read length sequence coverage of the 16S and/or 23S ribosomal subunits, allows for each sequence read yield an unambiguous assignment of bacterial identification. The current shorter read length platforms, covers only 1-3 hypervariable regions  FIG. 2A . These shorter read length platforms are currently performing at, or below, a 60-80% classification accuracy. A read length that can span the entirety of the 16S operon will perform at a 90-99% classification accuracy  FIG. 2B . The plot shown in  FIG. 2B  presents classification accuracy based upon sequencing shorter hyper-variable regions (lower lines) vs. sequencing the entire 16S subunit in a single sequence read (upper line). The plots are produced using calculations derived from data from the Ribosomal Database Project. Moreover, even accounting for higher error rates in certain longer read sequencing technologies still yields substantially better classification accuracy. By extension, sequencing the entirety of the ribosomal RNA operon (rRNA that includes—16S, intergenic, and 23 S regions) would increase the classification capability of the method. 
     Example 2: Affect of Diet on a Subject&#39;s Gut Microbiome and Obesity (Prophetic Example) 
     Using the methods provided herein, experiments were conducted to determine if a particular diet regimen can changes a subjects&#39; gut microbiome profile.  FIG. 3  depicts changes measured and detected in an individual&#39;s gut microbiome profile to changes in diet using the methods described herein. Weight (dashed line) is a lagging indicator, when compared to the change in the microbiome structure. 
     A report containing individualized dieting recommendation was generated based on quantitative microbiome profile using the computer system shown in  FIG. 1  was provided to a healthcare professional. 
     Example 3: Comparison of Microbe Resolution at Strain Level of a Microbiome 
     Comparison simulation experiments were conducted with the methods of the present disclosure to determine if the methods of the present disclosure provide increased microbe classification accuracy at the strain level compared to the current methods being used in the field. 
     Samples were collected in the form of skin swabs. Briefly, a sterile swab is first dipped into a tube containing sterile 1×PBS to wet; the swab was then swiped across the area of interest 10-20 times; next the swab was gently dipped into 300 uL of lysis buffer (described herein) in a sterile 1.5 mL tube; and the swab is left in the microcentrifuge tube until nucleic acid extraction. The subsequent extraction of DNA from human skin microbiome samples includes the removal of the exonuclease was conducted followed by adapter ligation at various higher concentrations. Next, size-selection using Ampure and Blue Pippen approaches were performed to enrich for the expected length amplicon species. After extraction, the nucleic acid samples were selected for the proper size. Next, PCR amplification reactions were conducted to prepare the libraries for sequencing. Forward and reverse primers were selected based on empirical data that indicate which sets had minimal self-complimentarity. Finally samples were sequenced using long read length sequence technology. 
       FIG. 4A  depicts strain level resolution data with the methods provided for  Nitrosomonas  genus, percentage strain classification accuracy is shown in the Y-axis.  FIG. 4B  depicts strain level resolution data with the methods provided for  Staphylococcus  genus, percentage strain classification accuracy is shown in the Y-axis.  FIG. 6A  standard resolution of a microbiome using current technology  FIG. 6B  resolution of a microbiome using a method described herein. 
     Example 4: Study of Microbial-Based Therapeutic Application on Acne (Prophetic Example) 
     Experiments using the methods provided herein were conducted to determine if the application of  Nitrosomonas  to the skin can have beneficial effects on a person suffering from acne. 
     Samples were prepared for skin samples using the following steps a sterile swab is first dipped into a tube containing sterile 1×PBS to wet; the swab is swiped across the area of interest 10-20 times with enough vigor that the skin is slightly pink/red colored afterwards; the swab is gently dipped into 300 uL of Lysis Buffer (described below) in a sterile 1.5 mL tube; the swab is left in the microcentrifuge tube for shipping and then processed for nucleic acid extraction and long read sequence analysis using the methods provided herein. 
       FIGS. 5A and 5B  depict expected results from the study.  FIG. 5A  depicts how the methods of this disclosure can be used to quantify the successful application of the microbial therapeutic/cosmetic over time. The addition of a non-commensal strain to the consortia, aids in distinguishing applied strains from the background variation of individual microbiomes for that site (e.g. skin, gut, mouth, etc.)  FIG. 5B  depicts how the methods in this disclosure can be used to discover stabilizing commensal strains. Those strains that when present, are correlated to the longevity of the applied consortia, would become candidates for expanding the initial consortia to produce formulations with increased stability and efficacy 701.301 CLEAN COPY 
     Example 5: Association Study with Disease and Healthy Controls Subjects (Prophetic Example) 
     Comparison experiments were conducted to determine to if the present methods of the disclosure provide more insights to correlation of a microbiome that can distinguish healthy from diseased microbiome profiles over traditional current methods. 
     Nucleic acids were extracted from biological samples from the human microbiome. Briefly, removal of the exonuclease was conducted followed by adapter ligation at various higher concentrations. Next, size-selection using Ampure and Blue Pippen approaches were performed to enrich for the expected length amplicon species (e.g. full length 16S is about 1500 bp). After the samples were selected for the proper size, amplification reactions were conducted to prepare the libraries for sequencing. Forward and reverse primers were selected based on empirical data that indicate which sets had minimal self-complimentarity. Finally samples were sequenced using long read length sequence technology. 
     Using the methods above we expect to identify at set of microbes in a microbiome that would distinguish healthy from diseased subjects. The following protocol, as provide herein, can be used to assess the differences between healthy from diseased individuals. Example of diseases that can be used with the methods described include IBD, preterm labor, obesity, diabetic foot ulcers, bacteremia, acne, infantile colic, type 2 diabetes,  C. difficile , IBS, asthma, autism, psoriasis, allergies, cardiovascular disease, cancer, depression, cystic fibrosis, multiple sclerosis, urinary tract infection, radiation enteropathy, drug metabolism, chronic fatigue, type 1 diabetes, halitosis, and tooth decay. 
       FIGS. 7A and 7B  show the expected results of the comparison association study.  FIG. 7A  depicts the expected results from an association study with a disease and healthy controls patients&#39; microbiomes using current technology, indicating no obvious correlation of a microbiome profile with a disease state. 
     In contrast,  FIG. 7B  depicts the expected results from an association study with disease and healthy controls patients&#39; microbiomes using a method described herein revealing a correlation of a microbiome that can distinguish healthy controls&#39; microbiome profiles from disease subjects&#39; microbiome profiles (as shown  FIG. 8 ). 
     While preferred embodiments of the present disclosure have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the disclosure. It should be understood that various alternatives to the embodiments of the disclosure described herein may be employed in practicing the disclosure. It is intended that the following claims define the scope of the disclosure and that methods and structures within the scope of these claims and their equivalents be covered thereby.