Patent ID: 12221657

DETAILED DESCRIPTION

Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the spirit and scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope and spirit being indicated by the following claims.

Referring now to the drawings, and more particularly toFIG.1throughFIG.10, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method.

According to an embodiment of the disclosure, a system100for improving accuracy of amplicon based taxonomic profiling of microbial community is shown in the block diagram ofFIG.1. More specifically, the disclosure provides a method and system for improving the resolution of 16S rRNA gene-based taxonomic profiles corresponding to microbial environments. The other amplicon gene sequences can also be used. The method and system100may be extended to other marker genes/genetic elements used for taxonomic classification such as the bacterial CPN60 or other Heat-Shock proteins (HSPs), and the fungal ITS elements sequences. The present disclosure provides a combinatorial strategy which involves sequencing of one pair or multiple pairs of contiguously or non-contiguously located variable regions within the 16S rRNA gene and subsequent processing and analysing the resultant sequencing data using a novel in silico combinatorial approach to improve the resolution of taxonomic profiling. The disclosure also provides increased accuracy and depth of taxonomic classification of a microbiome.

In the present disclosure, the system100have been explained with the help of two experiments targeting two different combinations of variable regions. Though it should be appreciated that the system100can also be modified to involve more than two experiments to enable targeting even more combinations which might be relevant for the biological problem.

According to an embodiment of the disclosure, the system100is specifically using paired end sequencing. Paired-end sequencing protocols available with some of the NGS platforms allow sequencing of a stretch of DNA from both its ends. For example, Illumina HiSeq sequencing platforms can be used for paired-end sequencing to generate up to 2×250 bp reads. To this end, appropriate primers need to be designed against a desired stretch of the 16S rRNA gene, such that the targeted V-regions (either contiguously or non-contiguously placed) reside within this stretch, and are not far from either of its boundaries. Sequencing of the amplicon generated with these primers can then be performed with a paired-end sequencing protocol, whereby these (amplified) stretches of DNA are sequenced from both ends. Two reads sequenced from each such amplicon would cover the two targeted V-regions (one from each end). Since each of the sequenced reads from any given ‘pair’ targets a single V-region (situated at one of the ends of the amplicon), read-length limitations do not restrict capturing the entirety of the individual V-regions. Consequently, it becomes possible to sequence almost all possible pair wise combinations of V-regions, either arranged contiguously or non-contiguously. Paired-end sequencing protocols can therefore, in principle, be employed for sequencing various pair wise combinations of contiguous or non-contiguous V-regions in a single sequencing run.

According to an embodiment of the disclosure, the system100further comprises a sample collection module102, an input module104, a DNA extraction module106, a sequencer108, a first microbial abundance profile generation module110, a second microbial abundance profile generation module112, a memory114and one or more hardware processor116as shown in the block diagram ofFIG.1. The one or more hardware processors116works in communication with the memory114. The memory114further comprises a plurality of modules. The plurality of modules accesses the set of algorithms stored in the memory114to perform a certain functions. The memory114further comprises a computation table generation module118and a combined microbial abundance profile generation module120.

According to an embodiment of the disclosure the sample collection module102is configured to collect a biological sample from the environment. The biological sample can be collected from various places such as gut, swab, saliva from human body or any other place outside the human body. The input module104is configured to obtaining a first subsample and a second subsample from the collected biological sample. In an example, the input module104could be same as the sample collection module102. The sample collection module102and the input module104can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite.

According to an embodiment of the disclosure, the system100comprises the DNA extraction module106and the sequencer108. The DNA extraction module106is configured to extract DNA fragments from the first subsample and the second subsample using laboratory standardized protocol. The sequencer108is configured to sequence the extracted DNA from the first subsample and the second subsample. The sequencing for the first subsample and the second subsample is performed separately and can be performed in any order.

The sequencing of the extracted microbial DNA from the first subsample is performed using a sequencer to get DNA sequence data. The DNA sequence data comprises of a plurality of pairs of sequence fragments. Each pair of the plurality of pairs of sequence fragments is generated through paired-end sequencing of an amplicon that comprises a first combination informative regions within the amplicon, wherein the informative regions contain phylogenetically relevant information. It should be appreciated that going forward in this disclosure, the informative region can also be referred as the variable regions (V-regions) specific to 16s rRNA.

The first combination of informative regions are having contiguously or non-contiguously located informative regions.

Similarly, the sequencing of the extracted DNA from the second subsample is performed using the sequencer to get DNA sequence data. The DNA sequenced data comprises of a plurality of pairs of sequence fragments, wherein each pair of the plurality of pairs of sequence fragments is generated through paired-end sequencing of the amplicon that comprises a second combination of informative regions within the amplicon. The second combination of informative regions are having contiguously or non-contiguously located informative regions. The first combination or the second combination, can both include one or more informative regions. The second combination of informative regions are different from the first combination of informative regions. The amplicon sequencing experiment targets a phylogenetic marker gene. Though it should be appreciated that there could be overlap between the informative regions of the first combination and the second combination but they can never be exactly same. Although, the first combinations and the second combination of informative regions are always expected to be different, one of the informative regions in both combinations may be shared by both the combinations.

According to an embodiment of the disclosure, the system100further comprises the first microbial abundance profile generation module110and the second microbial abundance profile generation module112. The first microbial abundance profile generation module110is configured to generate the microbial taxonomic abundance profile of the first sequenced subsample by employing a taxonomic classification method, wherein the taxonomic classification method utilizing phylogenetically relevant information corresponding to the first combination of informative regions, wherein the microbial taxonomic abundance profile comprises of abundance values corresponding to one or more pair of sequence fragments comprising the first combination of informative regions classified into a plurality of taxonomic groups. Similarly, the second microbial abundance profile generation module112is configured to generate a microbial abundance profile of the second sequenced subsample by employing the taxonomic classification method, wherein the taxonomic classification method utilizing phylogenetically relevant information corresponding to the second combination of informative regions, wherein the microbial abundance profile comprises of abundance values corresponding to one or more pair of sequence fragments comprising the second combination of informative regions classified into the plurality of taxonomic groups.

According to an embodiment of the disclosure, the memory114further comprises the computation table generation module118. The computation table generation module118is configured to generate a computation table. The computation table generation module pre-compute taxonomic classification accuracies for all different possible combinations of informative regions for microbes belonging to the plurality of taxonomic groups, wherein the pre-computing is based on marker gene sequences of known taxonomic origin present in existing sequence databases, to generate a computation table. The computation table generally comprises thousands of rows and various combination of variable regions in the column. A detailed methodology and rationale employed for computing the taxonomic classification accuracies is explained in the later part of the disclosure. Due to the space and the size constraint, only a part of tables are shown below.

(a) The individual V-regions (targeted in the experiments) in resolving each of the taxonomic groups under consideration is shown in TABLE 1.

(b) Pairs of (contiguously or non-contiguously located) V-regions within the 16S rRNA gene in resolving each of the taxonomic groups under consideration is shown in TABLE 2.

TABLE 1Taxonomic classification accuracies (in terms of number ofcorrect assignments) obtained using different V-regions extractedfrom 16S rRNA sequences downloaded from RDP database.Accuracy of taxonomic assignments has been evaluated at theGenus level considering pre-annotated lineages available inRDP. The number of correct assignments that were obtainedusing full-length 16S rRNA genes is also indicated.No. of correct assignmentsobtained with-Total NumberFull lengthNAMEof sequencessequencesV1V2. . .V9Abiotrophia4403. . .0Acaricomes11010Acetanaerobacterium22120.... . ... . ................ . ... . ............Zymophilus100100050. . .0

TABLE 2Taxonomic classification accuracies obtained using different pair-wisecombinations of V-regions (both contiguous as well as non-contiguous)evaluated with sequences downloaded from RDP database. Accuracy oftaxonomic assignments has been evaluated at the species level consideringthe assignments obtained with full-length 16S sequences to be correct.The * symbol indicates combinations of contiguous V-regionsNAMEV1V2*V1 + V3. . .V2V3*. . .V8V9*Abiotrophia_defectiva_(T)75100. . .75. . .0Acaricomes_phytoseiuli_(T)1001001000Acetanarobacterium_elongatum_(T)100100100100.... . ... . ................ . ... . ............marine_bacterium_PP-203100100. . .100. . .100

According to an embodiment of the disclosure, the memory114further comprises the combined microbial abundance profile generation module120. The combined microbial abundance profile generation module120is configured to combine the microbial taxonomic abundance profiles of the first and the second sequenced subsample based on the computation table to generate a combined microbial taxonomic abundance profile. The combined microbial taxonomic abundance profile has a refined abundance value and has improved taxonomic classification accuracy as compared to the microbial taxonomic abundance profiles obtained individually for the first and the second subsample, or as compared to a microbial taxonomic abundance profile obtained for the entire biological sample or any other subsample of a biological sample using amplicon sequencing targeting any of the combinations of informative regions in the phylogenetic marker gene.

In operation, a flowchart200illustrating a method for improving accuracy of amplicon based taxonomic profiling of microbial community is shown inFIG.2A-2C. Initially at step202, the biological sample is collected from environment. The biological sample can be collected from any site not limited to human beings. At step204, a first subsample and a second subsample is obtained from the biological sample. In the next step206, DNA is extracted from the first subsample and the second subsample. At step208, the extracted microbial DNA from the first subsample is sequenced using the sequencer to get DNA sequence data. The DNA sequence data comprises of a plurality of pairs of sequence fragments, wherein each pair of the plurality of pairs of sequence fragments is generated through paired-end sequencing of an amplicon that comprises a first combination informative regions within the amplicon, wherein the informative regions contain phylogenetically relevant information. Similarly, at step210, the extracted DNA from the second subsample is sequenced using the sequencer to get DNA sequence data, wherein the DNA sequenced data comprises of a plurality of pairs of sequence fragments, wherein each pair of the plurality of pairs of sequence fragments is generated through paired-end sequencing of the amplicon that comprises a second combination of informative regions within the amplicon, wherein the second combination of informative regions are different from the first combination of informative regions, wherein the amplicon sequencing experiment targets a phylogenetic marker gene.

In the next step212, the microbial taxonomic abundance profile of the first sequenced subsample is generated by employing a taxonomic classification method. The taxonomic classification method utilizes phylogenetically relevant information corresponding to the first combination of informative regions. The microbial taxonomic abundance profile comprises of abundance values corresponding to one or more pair of sequence fragments comprising the first combination of informative regions classified into a plurality of taxonomic groups. Similarly at step214, the microbial abundance profile of the second sequenced subsample is generated by employing the taxonomic classification method. The taxonomic classification method utilizing phylogenetically relevant information corresponding to the second combination of informative regions. The microbial abundance profile comprises of abundance values corresponding to one or more pair of sequence fragments comprising the second combination of informative regions classified into the plurality of taxonomic groups.

At step216, the taxonomic classification accuracies for all different possible combinations of informative regions for microbes belonging to the plurality of taxonomic groups are pre-computed. The pre-computing is based on marker gene sequences of known taxonomic origin present in existing sequence databases, to generate a computation table. At finally at step218, the microbial abundance profiles of the first and the second sequenced subsample are combined based on the computation table to generate a combined microbial abundance profile. The combined microbial taxonomic abundance profile has a refined abundance value and has improved taxonomic classification accuracy as compared to the microbial taxonomic abundance profiles obtained individually for the first and the second subsample, or as compared to a microbial taxonomic abundance profile obtained for the entire biological sample or any other subsample of a biological sample using amplicon sequencing targeting any of the combinations of informative regions in the phylogenetic marker gene.

According to an embodiment of the disclosure, the system100can also be explained with the help experimental procedures and results. As mentioned earlier, the disclosure is using 16S rRNA as amplicon for the experimental procedures. Following are the steps involved in determining the combinatorial strategy for improving accuracy of amplicon based taxonomic profiling of microbial community as shown in the flowchart ofFIG.3.

A microbial community (M) is initially considered for metagenomic profiling by two paired-end sequencing experiments (Exand Ey). Each of these experiments can target 2 distinct V-regions (either arranged contiguously or non-contiguously on the 16S rRNA gene), using appropriate forward and reverse primers. In the current example, Extargets the V-region combination Va+Vb, and Eytargets Vc+Vd. For example, combinations of V-regions selected in the two experiments could be V1+V4 and V2+V6 in one scenario. Based on the taxonomic resolution efficiencies of different (combinations of) V-regions, Exand Eywill generate two different taxonomic abundance profiles Pxand Pyrespectively, each of which constitutes of estimated abundance values (Ti) for different taxonomic groups (i)—
Px≡{T1x,T2x,T3x, . . . ,Tnx}  Equation 1
Py≡{T1y,T2y,T3y, . . . ,Tny}  Equation 2

Subsequently, for each of the taxonomic groups (Ti), a refined estimate of its abundance (Tixy) can be arrived at by combining the observed abundances Tixand Tiy, such that the refined abundance Tixyis relatively closer to the estimate obtained with the experiment (either of Exor Ey) providing better classification accuracies for taxa ‘i’. Calculation of the refined estimate therefore takes into consideration the taxonomic classification accuracies of the combination of V-regions that had been used for the initial set of experiments Exand Eyusing the following equation:

Tixy=(WixWiy*Tix)+Tiy1+WixWiyEquation⁢⁢3
Wherein Wixand Wiyare the relative accuracies in taxonomic classification for a particular taxonomic group ‘i’, obtained using the specific combination of V-regions chosen for experiments Exand Eyrespectively. These taxonomic classification accuracies can be calculated from the evaluation results obtained from the computation table generated in step216(methodology for computation of the values provided in these tables has been described in the later part of the disclosure), as a ratio of the correct assignments obtained for particular taxa using a specific combination of V-regions, and the total number of correct assignments obtained using the same V-region combination. For example, considering that the combination of Va+Vbwas used in experiment Ex, Wixcan be calculated as:

Wix=Correct⁢⁢assignmentsfor⁢⁢taxon⁢⁢i⁢⁢using⁢⁢Va+VbTotal⁢⁢correct⁢⁢assignments⁢⁢using⁢⁢Va+VbEquation⁢⁢4

Similarly,

Wiy=Correct⁢⁢assignmentsfor⁢⁢taxon⁢⁢i⁢⁢using⁢⁢Vc+VdTotal⁢⁢correct⁢⁢assignments⁢⁢using⁢⁢Vc+VdEquation⁢⁢5

The denominator term representing “total correct assignments using Va+Vb” has been introduced to capture any additional specificity of the chosen Va+Vbregion toward a particular taxon ‘i’ in context of the overall taxonomic classification performance of Va+Vb. Other simple ways of calculating the “relative accuracy in taxonomic classification” or weight (Wix), e.g., in a case wherein the denominator term is omitted, would also work fine when V-region combinations with decent classification accuracy are chosen.

It may be noted here, that in the experiment(s) using paired-end sequencing to capture two different V-regions from the 16S rRNA gene, the correspondence between the pairs of V-regions originating from the same 16S rRNA gene is retained. This allows joining the different V-regions together into a single DNA string (separated appropriately by ambiguous nucleotide characters) and providing the same as an input to taxonomic classification tools, such as the RDP classifier. However, for V-regions targeted in separate sequencing experiments, cross-experiment correspondence between the sequenced V-regions with respect to their origin 16S rRNA gene cannot be identified. This necessitates the indirect strategy of combining information obtained from different V-regions (or their combinations) for refining the taxonomic abundance estimates, as described above.

To avoid variations arising from experimental workflows and sample handling/preparations, it would be ideal to perform a single PCR step for amplicon generation, using different sets of primers appropriate for the chosen combinations of V-regions (Va+Vb, and Vc+Vdin the given example). However, it also needs to be mentioned here that the designed primers may have different affinities for the targeted regions on 16S rRNA genes originating from different taxonomic groups. This may again result in unequal proportions of 16S rRNA sequence fragments amplified by the different sets of primers, which would subsequently be reflected in the sequencing outcome. In such a scenario, the combination strategy needs to factor in this difference in proportions, while arriving at a refined taxonomic abundance estimate. Alternately, the experiment may target a combination of 3 V-regions (e.g. Va+Vband Va+Vcor, Va+Vcand Vb+Vc), such that, either the forward primers or the reverse primers be common to the targeted combinations. This way, some equivalence in the proportions of fragments (targeting different taxonomic groups) can be maintained on account of the shared primer (for V-region) selected.

Further, it should be appreciated that if required the user can also obtain more than 2 subsamples, each targeting different V-regions. For example, there are 4 experiments—Ea, Eb, Ec, Ed. . . then the combinatorial formula can be written as—

Tiabcd=Wia*Tia+Wib*Tib+Wic*Tic+Wid*TidWia+Wib+Wic+Wid
Wherein the values for W and T can be calculated as mentioned earlier
Evaluation Results with Novel Combinatorial Strategy

Considering the fact that human gut is one of the most diverse and densely populated reservoir of microbes, the utility of the combinatorial strategy was assessed with a simulated metagenomic sample (namely GUT1—method of generating the same has been described in the later part of the disclosure) that was specifically generated for this purpose (along with 7 more simulated metagenomes pertaining to different human body sites). The taxonomic classification efficiency of the V-region combinations (at the species level) was assessed on the simulated metagenome GUT1. The V-region combinations V1+V4 and V1+V5 provided highest average classification accuracies for most of the host (human) associated environmental niches along with the simulated metagenome GUT1 as shown inFIG.4. Consequently, these V-region combinations were targeted for evaluating this combinatorial strategy wherein 5,000 sequence fragments corresponding to each of the V-region combinations (i.e. a total of 10,000 fragments) were sampled from the simulated metagenome. The results obtained with the combinatorial strategy were compared against the results obtained when each of the V-region combinations were targeted separately (with a sequencing depth of 10,000 reads in each case).

Results in Table 3 indicate that although the V1+V4 and V1+V5 regions can classify the reads with commendable accuracy, the abundance values provided for individual genera deviates from the actual (RDP) lineage by a certain extent. The combinatorial approach was observed to moderate these deviations to a significant extent, and relative abundance of individual genera ascertained by the combinatorial approach exhibited better coherence with the actual lineage. In quantitative terms, while the average deviations (from actual lineage) in relative taxonomic abundance predictions for V1+V4 and V1+V5 combination based approaches were 17.4% and 11.5% respectively, the combinatorial approach exhibited a significantly lower average deviation (6.9%) from the actual lineage. Similar improvements were also observed when this approach was tested on microbiomes pertaining to other host-associated/environmental. Given that the proposed combinatorial approach does not incur any significant additional sequencing cost and is a simple in silico extrapolation of the results obtained with standard pair-end sequencing, adoption of the same would be easy and would enable researchers to explore the taxonomic diversity of different environments with greater accuracy. While certain additional experimental costs for primers, multiplexing barcodes, additional PCR, and handling etc. are expected to be incurred to implement the proposed combinatorial strategy, the actual sequencing (reagents) cost, constituting the bulk of the total expenditure, remains the same. The additional pre-processing and handling efforts can at most be twice compared to the sample handling efforts needed for a single paired-end sequencing experiment. However, the potential benefits in terms of an improved taxonomic resolution are expected to outweigh any inhibitions arising due to the additional, but trivial, pre-processing and handling efforts.

FIG.5is schematic depicting two amplicon sequencing experiments targeting different combinations of V-regions. Experiment A targets two contiguous V-regions (VXand VY). The resulting paired end reads (generated through 250 bp×2 paired end sequencing of the amplicon) are merged together utilizing the overlapping region. Subsequently, the merged read is used for taxonomic classification. Similarly, experiment B targets two non-contiguously placed V regions (VXand VZ). The resulting pair-end reads do not have any overlap and are concatenated together using eight ambiguous nucleotide characters (N). Subsequently, the concatenated read is used for taxonomic classification.

TABLE 3Utility of proposed combinatorial approach in obtaining refined taxonomic profiles comparedto taxonomic abundance estimates obtained with pair wise combinations of V-regions.Results in the table pertain to the simulated human gut metagenomic dataset GUT1.Abundance (%)AbundanceAbundanceAbundanceestimated with(%) estimated(%) estimated(%) estimatedcombinatorialwith full-with 10000with 10000approach usinglength 16SV1 + V4 paired-V1 + V5 paired-5000 V1 + V4 andSpeciesreadsend readsend reads5000 V1 + V5 readsFaecalibacteriumprausnitzii11.1712.2412.2510.97Bacteroides faecis10.6911.9711.2411.26Prevotellaamnii6.730.006.727.22Prevotellanigrescens6.476.986.766.90Megamonashypermegale5.356.063.534.61Bacteroides pyogenes4.234.444.334.52Bacteroides finegoldii3.984.034.133.97Alistipesputredinis3.453.733.713.48Roseburia hominis2.412.702.842.59Bacteroides nordii2.182.502.262.14Bacteroides eggerthii2.152.512.242.13Bacteroides helcogenes2.092.352.132.09Bacteroides caccae2.082.302.322.29Bacteroides massiliensis2.072.102.132.00Bacteroides coprocola2.042.432.272.20Bacteroides salyersiae2.042.262.012.10Bacteroides stercoris2.031.922.502.15Bacteroides uniformis2.022.032.041.92Bacteroides acidifaciens2.012.302.002.06Proteiniphilumacetatigenes2.012.212.182.06Bacteroides cellulosilyticus1.982.160.001.95Bacteroides intestinalis1.962.022.082.01Roseburiafaecis1.741.941.911.68Roseburia intestinalis1.742.161.911.84Parasutterellasecunda1.501.741.561.38Roseburiainulinivorans1.001.001.061.10Phascolarctobacteriumsuccinat0.990.820.780.80Parabacteroides distasonis0.901.031.040.73Parabacteroides merdae0.891.070.870.90Parasutterellaexcrementihomin0.820.990.840.74Dorealongicatena0.780.320.510.32Phascolarctobacterium faecium0.740.810.830.69Blautiaproducta0.700.550.860.61Escherichia/Shigella fergusonii0.690.590.000.64Escherichia/Shigella albertii0.570.560.620.70Escherichia/Shigella flexneri0.560.000.000.00Escherichia/Shigella0.530.500.540.57Dialisterinvisus0.470.580.500.45Megasphaeraelsdenii0.460.370.470.39Blautiaglucerasea0.450.410.480.60Blautiahydrogenotrophica0.430.440.460.51Blautiaschinkii0.430.470.540.43Mitsuokellaialaludinii0.390.400.420.34Collinsellaaerofaciens0.340.370.420.36Bifidobacterium longum0.320.400.370.36Bifidobacterium animalis0.320.250.320.29Ruminococcusflavefaciens0.300.210.250.17Blautiahansenii0.280.330.300.32Megasphaerasp. NMBHI-100.280.220.190.16Klebsiella pneumoniae0.250.210.290.27Cumulate Percentage—17.4011.476.85Deviation from abundanceestimated using full-length16S sequences

Similarly, Table 4, Table 5, Table 6, Table 7, Table 8, Table 9, Table 10 and Table 11 show the results of other simulated microbiome of GUT2, Sputum (oral), sub-gingival (oral), skin, soil, aquatic, vagina and nematode gut respectively. The same has also been shown inFIG.4.

TABLE 4Utility of proposed combinatorial approach in obtaining refined taxonomic profiles comparedto taxonomic abundance estimates obtained with pair-wise combinations of V-regions.Results in the table pertain to the simulated human gut microbiome dataset Gut2.Abundance (%)AbundanceAbundanceAbundanceestimated with(%) estimated(%) estimated(%) estimatedcombinatorialwith full-with 10000with 10000approach usinglength 16SV1 + V4 paired-V1 + V5 paired-5000 V1 + V4 andSpeciesreadsend readsend reads5000 V1 + V5 readsBacteroides faecis(T)13.2313.2313.3113.54Alistipes putredinis(T)9.149.249.859.54Faecalibacterium prausnitzii(T)8.428.678.648.18Bacteroides pyogenes(T)5.244.995.514.94Bacteroides finegoldii(T)4.894.935.334.72Parabacteroides merdae(T)3.573.813.703.38Parabacteroides distasonis(T)3.233.193.363.41Oscillibacter valericigenes(T)2.953.233.013.03Bacteroides acidifaciens(T)2.812.712.882.88Bacteroides salyersiae(T)2.722.852.982.60Bacteroides coprocola(T)2.652.292.822.79Bacteroides massiliensis(T)2.642.692.942.79Bacteroides intestinalis(T)2.632.602.772.35Bacteroides uniformis(T)2.622.892.742.69Bacteroides stercoris(T)2.592.532.602.88Bacteroides cellulosilyticus(T)2.582.450.002.60Bacteroides eggerthii(T)2.532.712.672.62Bacteroides caccae(T)2.502.742.722.69Proteiniphilum acetatgenes(T)2.482.702.202.81Bacteroides helcogenes(T)2.472.742.412.37Bacteroides nordii(T)2.412.682.512.46Ruminococcus flavefaciens(T)1.511.001.291.08Ruminococcus albus(T)1.451.241.301.26Roseburia hominis(T)1.081.111.101.12Odoribacter laneus(T)0.930.801.050.94Roseburia intestinalis(T)0.880.810.950.78Parasutterella secunda(T)0.861.080.950.76Phascolarctobacterium0.810.730.550.72succinatutensYIT 12067Roseburia faecis(T)0.790.740.730.75Dialister invisus(T)0.680.720.660.71Phascolarctobacterium0.600.630.620.54faecium(T)Prevotella amnii(T)0.550.000.740.58Prevotella nigrescens(T)0.540.570.510.52Roseburia inulinivorans(T)0.490.640.480.55Flavonifractor plautii(T)0.460.450.410.55Blautia producta(T)0.460.230.470.46Coprococcus catus(T)0.420.450.390.50Parasutterella excrementihominis(T)0.400.410.360.37Dialister pneumosintes(T)0.380.390.470.33Dorea longicatena(T)0.380.140.090.12Ruminococcus bromii(T)0.280.230.150.26Blautia hydrogenotrophica(T)0.250.240.270.27Coprococcus eutactus(T)0.240.280.250.24Blautia glucerasea(T)0.220.240.220.22Blautia schinkii(T)0.210.220.250.16Blautia wexlerae(T)0.210.190.170.26Butyriccoccus pullicaecorum(T)0.190.180.200.24Butyricmonas synergistica(T)0.150.150.100.20Blautia hansenii(T)0.140.110.190.12Ruminococcus faecis(T)0.110.110.130.11Cumulated Percentage—6.328.645.67Deviation from abundanceestimated using full-length16S sequences

TABLE 5Utility of proposed combinatorial approach in obtaining refined taxonomic profiles comparedto taxonomic abundance estimates obtained with pair-wise combinations of V-regions. Resultsin the table pertain to the simulated human gut microbiome dataset Sputum.Abundance (%)AbundanceAbundanceAbundanceestimated with(%) estimated(%) estimated(%) estimatedcombinatorialwith full-with 10000with 10000approach usinglength 16SV1 + V4 paired-V1 + V5 paired-5000 V1 + V4 andSpeciesreadsend readsend reads5000 V1 + V5 readsPrevotella amnii(T)8.050.009.608.81Prevotella nigrescens(T)7.859.659.087.95Streptococcus salivarius(T)7.648.488.918.24Streptococcus suis(T)6.197.297.156.59Granulicatella adiacens(T)5.816.723.325.43Fusobacterium nucleatum(T)4.594.594.824.34Streptococcus agalactiae(T)4.225.144.834.73Staphylococcus aureus(T)4.034.644.834.35Streptococcus pyogenes(T)3.393.520.193.27Neisseria meningitidis2.842.721.582.05Streptococcus gallolyticus(T)2.312.652.622.66Rothia dentocariosa(T)2.202.492.672.33Streptococcus dysgalactiae(T)2.042.402.422.35Veillonella parvula(T)2.041.982.242.23Propionibacterium acnes(T)1.922.072.122.41Rothia aeria(T)1.902.442.452.19Veillonella tobetsuensis(T)1.902.122.351.92Streptococcus pneumoniae(T)1.740.720.880.89Rothia mucilaginosa(T)1.651.951.201.47Haemophilus aegyptius(T)1.581.911.611.61Gemella sanguinis(T)1.501.591.611.61Gemella haemolysans(T)1.431.731.551.37Actinomyces neuii(T)1.391.601.861.68Gemella bergeri(T)1.381.321.531.76Gemella morbillorum(T)1.371.091.231.22Dolosigranulum pigrum(T)1.361.511.521.71Veillonella criceti(T)1.350.790.680.68Streptococcus egui(T)1.341.711.641.29Streptococcus infantarius(T)1.151.311.281.08Pelomonas saccharophila(T)1.100.280.110.38Rothia endophytica0.930.930.960.99Staphylococcus warneri(T)0.911.120.900.93Acinetobacter baumannii(T)0.850.930.900.94Veillonella atypica(T)0.770.000.000.00Rothia amarae(T)0.711.010.900.62Veillonella denticariosi(T)0.711.010.360.57Veillonella ratti(T)0.690.360.000.11Anoxybacillus rupiensis(T)0.680.870.850.74Actinomyces coleocanis(T)0.680.700.820.77Staphylococcus cohnii(T)0.600.670.570.50Streptococcus constellatus(T)0.590.830.660.60Peptostreptococcus russellii(T)0.550.590.760.62Solobacterium moorei(T)0.540.650.680.57Staphylococcus hominis(T)0.540.590.630.54Parvimonas micra(T)0.540.650.730.54Stenotrophomonas rhizophila(T)0.530.530.460.58Streptococcus iniae(T)0.500.620.540.39Gemella palaticanis(T)0.490.590.430.66Peptostreptococcus0.460.540.650.52anaerobius(T)Streptococcus pseudoporcinus(T)0.460.370.320.20Cumulated Percentage—24.3024.3512.64Deviation from abundanceestimated using full-length16S sequences

TABLE 6Utility of proposed combinatorial approach in obtaining refined taxonomic profiles comparedto taxonomic abundance estimates obtained with pair-wise combinations of V-regions. Resultsin the table pertain to the simulated human gut microbiome dataset Sub-gingival.Abundance (%)AbundanceAbundanceAbundanceestimated with(%) estimated(%) estimated(%) estimatedcombinatorialwith full-with 10000with 10000approach usinglength 16SV1 + V4 paired-V1 + V5 paired-5000 V1 + V4 andSpeciesreadsend readsend reads5000 V1 + V5 readsFusobacterium nucleatum(T)19.1621.8519.7419.69Prevotella nigrescens(T)11.1712.8812.6612.57Prevotella amnii(T)10.270.0011.4210.43Parvimonas micra(T)4.415.505.134.83Streptococcus salivarius(T)4.405.365.074.52Streptococcus suis(T)3.634.473.804.26Streptococcus agalactiae(T)2.853.323.283.13Streptococcus pyogenes(T)2.192.680.162.40Capnocytophaga canimorsus(T)2.122.612.131.91Granulicatella adiacens(T)1.852.001.031.59Porphyromonas crevioricanis(T)1.842.361.991.84Campylobacter lari(T)1.770.420.420.60Treponema maltophilum(T)1.571.151.621.31Acinetobacter baumannii(T)1.451.661.391.37Streptococcus gallolyticus(T)1.451.871.571.81Fusobacterium necrophorum(T)1.381.681.651.56Streptococcus dysgalactiae(T)1.371.741.691.35Neisseria meningitidis1.311.500.730.96Leptotrichia buccalis(T)1.251.531.231.12Porphyromonas somerae(T)1.251.481.471.19Enhydrobacter aerosaccus(T)1.181.371.171.43Aggregatibacter aphrophilus(T)1.161.101.201.19Actinomyces neuii(T)1.131.371.231.29Filifactor villosus(T)1.111.101.291.17Fusobacterium varium(T)1.071.211.271.19Reyranella massiliensis1.071.340.971.14Streptococcus pneumoniae(T)1.070.630.540.53Treponema lecithinolyticum(T)0.971.231.001.03Veillonella parvula(T)0.940.890.940.85Streptococcus egui(T)0.900.950.970.79Treponema amylovorum(T)0.891.111.000.78Veillonella tobetsuensis(T)0.820.970.930.95Fusobacterium mortiferum(T)0.750.450.270.24Treponema socranskii(T)0.710.970.810.82Streptococcus infantarius(T)0.700.690.570.72Porphyromonas asaccharolytica0.690.870.760.75DSM 20707 (T)Leptotrichia wadei(T)0.680.900.730.65Porphyromonas endodontalis(T)0.650.000.000.00Acinetobacter calcoaceticus(T)0.640.650.450.69Leptotrichia goodfellowii(T)0.640.860.610.72Veillonella criceti(T)0.630.370.250.28Actinomyces coleocanis(T)0.600.690.760.60Porphyromonas gulae(T)0.600.000.000.00Sphingobacterium0.570.400.520.57spiritivorum(T)Catonella morbi(T)0.570.790.640.50Porphyromonas cansulci(T)0.540.600.670.53Rothia aeria(T)0.530.660.630.56Leptotrichia hofstadii(T)0.510.560.720.66Acinetobacter lwoffii(T)0.490.520.400.40Capnocytophaga cynodegmi(T)0.490.660.490.55Cumulated Percentage—29.1816.1711.42Deviation from abundanceestimated using full-length16S sequences

TABLE 7Utility of proposed combinatorial approach in obtaining refined taxonomic profiles comparedto taxonomic abundance estimates obtained with pair-wise combinations of V-regions.Results in the table pertain to the simulated human gut microbiome dataset Skin.Abundance (%)AbundanceAbundanceAbundanceestimated with(%) estimated(%) estimated(%) estimatedcombinatorialwith full-with 10000with 10000approach usinglength 16SV1 + V4 paired-V1 + V5 paired-5000 V1 + V4 andSpeciesreadsend readsend reads5000 V1 + V5 readsStaphylococcus aureus(T)20.2122.7624.0622.83Propionibacterium acnes(T)11.2813.1113.4112.33Staphylococcus warneri(T)3.814.264.434.44Methylobacterium populi(T)3.280.410.550.55Cupriavidus taiwanensis(T)2.962.702.562.66Schlegelella thermodepolymerans(T)2.923.893.683.44Staphylococcus cohnii(T)2.552.743.132.64Staphylococcus hominis(T)2.452.832.802.91Cupriavidus basilensis(T)2.402.702.872.56Uruburuella suis(T)2.392.652.842.88Corynebacterium diphtheriae(T)2.352.912.762.35Corynebacterium glutamicum(T)2.272.612.983.09Cupriavidus respiraculi(T)2.062.351.852.00Staphylococcus sciuri(T)1.882.222.091.84Micrococcus yunnanensis(T)1.550.000.000.00Methylobacterium komagatae(T)1.471.411.121.14Streptococcus salivarius(T)1.471.501.521.44Methylobacterium goesingense(T)1.451.741.571.70Dermacoccus nishinomiyaensis(T)1.381.891.541.76Corynebacterium bovis(T)1.321.501.651.53Staphylococcus equorum(T)1.281.651.461.43Streptococcus suis(T)1.261.331.591.57Methylobacterium hispanicum(T)1.230.000.000.00Finegoldia magna(T)1.201.301.371.43Schlegelella aquatica(T)1.200.001.481.92Ralstonia syzygii1.180.260.330.43Cupriavidus pauculus(T)1.131.411.301.27Staphylococcus capitis(T)1.131.281.281.31Geobacillus stearothermophilus(T)1.051.000.841.05Wautersia numazuensis(T)1.040.130.090.15Methylobacterium mesophilicum(T)0.991.090.880.78Staphylococcus pasteuri(T)0.951.091.101.45Cupriavidus campinensis(T)0.931.040.951.18Staphylococcus carnosus(T)0.930.960.660.91Cupriavidus alkaliphilus(T)0.911.060.130.84Methylobacterium rhodesianum(T)0.901.061.171.05Methylobacterium marchantiae(T)0.870.590.710.69Lactobacillus plantarum(T)0.861.150.901.33Corynebacterium ulcerans(T)0.840.330.620.34Propionibacterium acidipropionici(T)0.840.000.000.00Propionibacterium acidifaciens(T)0.820.910.950.92Staphylococcus succinus(T)0.820.910.951.01Propionibacterium freudenreichii(T)0.821.060.930.99Geobacillus thermodenitrificans(T)0.800.630.600.53Cupriavidussp. ASC-640.790.630.570.65Geobacillus thermoleovorans(T)0.780.570.020.39Methylobacterium brachiatum(T)0.770.000.000.00Stenotrophomonas rhizophila(T)0.770.980.710.82Reyranella massiliensis0.760.700.900.73Streptococcus pyogenes(T)0.740.720.110.75Cumulated Percentage—24.1526.9922.65Deviation from abundanceestimated using full-length16S sequences

TABLE 8Utility of proposed combinatorial approach in obtaining refined taxonomic profilescompared to taxonomic abundance estimates obtained with pair-wise combinations ofV-regions. Results in the table pertain to the simulated Soil microbiome dataset.Abundance (%)AbundanceAbundanceAbundanceestimated with(%) estimated(%) estimated(%) estimatedcombinatorialwith full-with 10000with 10000approach usinglength 16SV1 + V4 paired-V1 + V5 paired-5000 V1 + V4 andSpeciesreadsend readsend reads5000 V1 + V5 readsBradyrhizobium pachyrhizi(T)18.7216.9014.8314.67Rhodomicrobium vannielii5.924.247.936.47Gemmata obscuriglobus(T)5.237.607.116.93Gemmatimonas aurantiaca(T)5.167.896.416.78Ktedonobacter racemifer(T)5.037.876.947.11Bradyrhizobium diazoefficiens4.290.002.593.15USDA 110Bradyrhizobium japonicum(T)3.970.000.000.00Bradyrhizobium liaoningense(T)3.900.000.000.00Aguisphaera giovannonii(T)3.805.344.855.62Gaiella occulta(T)2.233.192.992.52Mycobacterium leprae(T)2.173.142.422.72Bradyrhizobium canariense(T)2.163.194.893.99Phenylobacterium2.091.541.561.72muchangponenseBradyrhizobiumsp. OO991.930.000.000.00Bradyrhizobium rifense1.610.000.000.00Burkholderia fungorum(T)1.521.161.921.82Phenylobacterium composti(T)1.452.441.882.24Bradyrhizobiumsp. LMTR 211.441.911.751.86Pedomicrobium ferrugineum(T)1.422.111.941.79Pedomicrobium australicum(T)1.332.240.001.75Pedomicrobium manganicum(T)1.332.241.791.47Massilia aurea(T)1.291.961.881.70Thermoleophilum album(T)1.231.981.641.75Domibacillus robiginosus(T)1.151.871.881.80Acidisoma tundrae(T)1.111.671.271.70Domibacillussp. NIO-10161.071.801.521.40Acidisoma sibiricum(T)0.981.451.261.15Dyella japonica(T)0.980.400.320.34Opitutus terrae(T)0.931.051.031.60Bradyrhizobium iriomotense(T)0.880.661.100.82Tumebacillus ginsengisoli(T)0.880.921.291.19Burkholderia phenoliruptrix(T)0.861.561.200.94Burkholderia unamae(T)0.850.570.680.63Burkholderia phytofirmans(T)0.840.620.670.54Pedomicrobium americanum(T)0.821.142.802.06Burkholderia bannensis0.760.020.040.02Bradyrhizobium denitrificans(T)0.730.700.910.84Rhodopila globiformis(T)0.721.190.950.97Sinomonas atrocyanea(T)0.710.220.290.24Burkholderia tuberum(T)0.660.830.670.82Burkholderia mimosarum(T)0.660.440.380.39Microvirgasp. BR32990.661.030.800.90Vampirovibrio chlorellavorus(T)0.610.940.950.82Burkholderia sediminicola(T)0.610.000.380.26Legionella pneumophila(T)0.580.830.870.79Burkholderia udeis0.570.090.060.10Chromobacterium vaccinii(T)0.540.730.820.66Segetibacter koreensis(T)0.540.700.700.98Phenylobacterium falsum(T)0.530.790.760.74Phenylobacterium immobile(T)0.530.831.081.23Cumulated Percentage—47.9644.0540.99Deviation from abundanceestimated using full-length16S sequences

TABLE 9Utility of proposed combinatorial approach in obtaining refined taxonomic profilescompared to taxonomic abundance estimates obtained with pair-wise combinations ofV-regions. Results in the table pertain to the simulated Aquatic microbiome dataset.Abundance (%)AbundanceAbundanceAbundanceestimated with(%) estimated(%) estimated(%) estimatedcombinatorialwith full-with 10000with 10000approach usinglength 16SV1 + V4 paired-V1 + V5 paired-5000 V1 + V4 andSpeciesreadsend readsend reads5000 V1 + V5 readsPolynucleobacter necessarius(T)31.1736.2136.3036.01Polynucleobacter cosmopolitanus(T)12.6814.4813.5513.85Mycobacterium leprae5.916.746.676.84Luteolibacter algae(T)3.904.734.644.50Rhodoferax saidenbachensisED163.132.000.821.78Polynucleobacter acidiphobus(T)2.603.042.952.83Acidovorax delafieldii(T)2.350.580.370.46Rhodoferax antarcticus(T)2.092.362.262.29Acidovorax temperans(T)1.850.580.660.54Methylophilus methylotrophus(T)1.690.860.590.80Rhodoferax fermentans(T)1.691.871.852.17Opitutus terrae(T)1.601.851.912.03Luteolibacter pohnpeiensis(T)1.561.871.851.86Haliscomenobacter hydrossis(T)1.351.591.631.54Acidovorax cattleyae(T)1.271.502.511.82Mycobacterium iranicum(T)1.261.481.441.45Mycobacterium novocastrense(T)1.161.271.401.35Mycobacterium marinum(T)1.130.000.000.00Methylomonas methanica(T)1.030.060.040.09Mycobacterium tuberculosis(T)1.010.000.000.00Microbacterium paraoxydans(T)0.991.041.100.83Algoriphagus namhaensis0.971.131.001.10Polynucleobacter rarus(T)0.961.081.090.95Mycobacterium cookii(T)0.951.201.071.11Acidovorax caeni(T)0.931.131.031.08Mycobacterium arupense(T)0.930.000.000.00Flavobacterium degerlachei(T)0.800.330.720.59Methylomonas koyamae(T)0.780.590.940.95Acidovorax avenae(T)0.720.000.000.00Methylophilus leisingeri(T)0.710.821.410.95Rhodomicrobium vannielii0.710.390.880.71Fluviicola taffensis0.700.870.840.89Comamonas testosteroni(T)0.670.650.680.76Beijerinckia indica(T)0.640.770.760.81Algoriphagus antarcticus(T)0.620.730.690.72Acidovorax radicis(T)0.610.000.000.00Methylocystis rosea(T)0.570.581.070.75Methylomonas scandinavica(T)0.570.340.660.47Methylophilus flavus(T)0.570.360.350.37Stenotrophomonas rhizophila(T)0.550.610.500.53Methylocystis hirsuta(T)0.530.470.000.27Comamonas jiangduensis(T)0.510.060.060.09Algoriphagus halophilus(T)0.500.610.600.61Algoriphagus lutimaris(T)0.490.520.600.45Verrucomicrobium spinosum(T)0.450.520.540.60Acidovorax konjaci(T)0.440.130.100.18Aguisphaera giovannonii(T)0.440.550.560.60Belnapia moabensis(T)0.440.520.500.54Caulobacter henricii(T)0.400.470.340.43Prosthecobacter vanneervenii(T)0.400.490.440.44Cumulated Percentage—25.2627.3924.93Deviation from abundanceestimated using full-length16S sequences

TABLE 10Utility of proposed combinatorial approach in obtaining refined taxonomic profiles compared to taxonomicabundance estimates obtained with pair-wise combinations of V-regions. Results in the table pertainto the simulated human gut microbiome dataset Vagina. While the chosen pairs of V-regions appearto provide sub-optimal performance for the Vaginal microbiome dataset, using a different set of V-region pairs (e.g. V1 + V5 and V1 + V8) improves the results of the combinatorial approach.Abundance (%)AbundanceAbundanceAbundanceestimated with(%) estimated(%) estimated(%) estimatedcombinatorialwith full-with 10000with 10000approach usinglength 16SV1 + V4 paired-V1 + V5 paired-5000 V1 + V4 andSpeciesreadsend readsend reads5000 V1 + V5 readsLactobacillus plantarum(T)24.1126.5024.9724.72Lactobacillus paracasei(T)14.1415.4314.9014.73Lactobacillus fermentum8.949.459.229.75Lactobacillus delbrueckii(T)7.718.437.837.95Prevotella amnii(T)4.990.005.345.30Prevotella nigrescens(T)4.825.375.125.18Sneathia sanguinegens(T)4.394.804.614.43Atopobium rimae(T)2.701.060.930.96Lactobacillus reuteri(T)2.262.542.392.33Lactobacillus diolivorans(T)2.102.292.452.34Lactobacillus farraginis(T)1.601.631.381.38Lactobacillus sakei(T)1.591.811.541.51Lactobacillus amylovorus(T)1.221.401.291.43Lactobacillus kimchii(T)1.130.370.420.29Lactobacillus gasseri(T)1.041.180.991.03Atopobium minutum(T)0.981.081.041.11Lactobacillus kefiri(T)0.971.170.970.94Lactobacillus futsaii0.910.890.910.84Lactobacillus kefiranofaciens(T)0.860.990.890.80Lactobacillus farciminis0.790.180.180.18Finegoldia magna(T)0.760.800.850.75Lactobacillus buchneri(T)0.730.900.780.80Parvimonas micra(T)0.730.900.870.75Lactobacillus mucosae(T)0.640.680.730.69Lactobacillus animalis(T)0.610.680.570.76Lactobacillus parabuchneri(T)0.580.710.590.46Lactobacillus florum(T)0.560.660.720.61Lactobacillus kunkeei(T)0.550.620.550.61Dialister invisus(T)0.550.550.580.68Streptococcus salivarius(T)0.550.500.570.40Lactobacillus coryniformis(T)0.490.590.460.53Aerococcus viridans0.450.230.070.13Lactobacillus vaccinostercus(T)0.450.550.510.48Lactobacillus ingluviei(T)0.410.420.280.34Anaerococcus murdochii(T)0.400.540.300.26Lactobacillus helveticus(T)0.360.000.330.31Anaerococcus vaginalis(T)0.360.440.350.41Streptococcus suis(T)0.360.320.400.33Lactobacillus paracollinoides(T)0.330.300.290.35Dialister pneumosintes(T)0.320.350.350.33Lactobacillus vaginalis(T)0.300.310.220.46Lactobacillus oeni(T)0.280.370.270.30Mobiluncus curtisii(T)0.270.300.250.36Lactobacillus crustorum(T)0.250.210.230.23Lactobacillus rossiae(T)0.250.150.360.15Ureaplasma urealyticum(T)0.250.300.280.30Lactobacillus harbinensis(T)0.240.230.170.18Lactobacillus acetotolerans(T)0.240.220.220.28Streptococcus agalactiae(T)0.240.280.280.29Lactobacillus sunkii(T)0.230.300.210.25Cumulated Percentage—17.808.839.50Deviation from abundanceestimated using full-length16S sequences

TABLE 11Utility of proposed combinatorial approach in obtaining refined taxonomic profiles comparedto taxonomic abundance estimates obtained with pair-wise combinations of V-regions.Results in the table pertain to the simulated Nematode-gut microbiome dataset.Abundance (%)AbundanceAbundanceAbundanceestimated with(%) estimated(%) estimated(%) estimatedcombinatorialwith full-with 10000with 10000approach usinglength 16SV1 + V4 paired-V1 + V5 paired-5000 V1 + V4 andSpeciesreadsend readsend reads5000 V1 + V5 readsAcinetobacter baumannii(T)8.4010.448.599.11Cellvibrio vulgaris(T)7.930.0010.139.66Cellvibrio japonicus(T)6.248.527.977.38Cellvibrio fibrivorans(T)4.602.026.163.87Reyranella massiliensis3.585.315.244.08Pseudoalteromonas tetraodonis(T)3.584.764.623.65Enhydrobacter aerosaccus(T)3.574.914.394.58Acinetobacter calcoaceticus(T)3.474.212.823.22Cellvibrio mixtus(T)3.174.014.073.37Cellvibriosp. E503.064.040.003.43Marinomonas primoryensis(T)2.620.000.000.00Acinetobacter lwoffii(T)2.422.672.342.18Escherichia/Shigella flexneri(T)2.150.000.000.00Escherichia/Shigella fergusonii(T)2.152.890.002.37Escherichia/Shigella dysenteriae(T)2.122.542.762.57Staphylococcus aureus(T)2.092.822.622.43Escherichia/Shigella albertii(T)2.012.722.872.30Alkanindiges illinoisensis(T)1.942.492.622.37Pseudomonas aeruginosa(T)1.762.472.302.02Arcobacter butzleri(T)1.742.142.161.66Cellvibrio fulvus(T)1.742.272.322.04Propionibacterium acnes(T)1.702.292.302.02Cellvibrio gandavensis(T)1.632.271.751.92Cellvibrio ostraviensis(T)1.602.192.042.04Marinomonas arctica(T)1.561.941.791.94Oceanospirillum maris(T)1.452.242.001.83Peredibacter starrii(T)1.391.841.791.54Pseudoalteromonas arctica(T)1.310.000.000.00Delftia lacustris(T)1.250.000.000.00Oleispira antarctica(T)1.171.541.541.52Oceanospirillum beijerinckii(T)1.081.501.471.44Acinetobacter junii(T)1.061.571.151.10Listonella anguillarum(T)1.010.220.780.54Brevundimonas naejangsanensis(T)1.011.001.191.33Pseudoalteromonas shioyasakiensis0.960.951.190.93Oceanospirillum linum0.890.721.101.22Vibrio cholerae(T)0.870.000.020.08Brevundimonas diminuta(T)0.821.100.020.97Persicirhabdus sediminis(T)0.821.371.060.97Leucobacter chromiiresistens(T)0.820.000.000.00Acinetobacter radioresistens(T)0.801.200.900.97Brevundimonas terrae(T)0.770.350.340.35Vibrio rotiferianus(T)0.770.550.020.59Acinetobacter guillouiae(T)0.760.370.830.66Leucobacter tardus(T)0.761.020.850.95Brevundimonas bullata(T)0.721.000.230.76Leucobacter komagatae(T)0.710.170.230.23Microbacteriaceae bacteriumDSM 270640.670.520.210.56Acinetobacter nectaris(T)0.660.850.620.67Brevundimonas intermedia(T)0.640.000.620.59Cumulated Percentage—45.7437.2624.19Deviation from abundanceestimated using full-length16S sequences

Method for Generation of the Computation Table

Rationale and methodology employed for pre-computing/pre-estimating the accuracies of(a) the individual V-regions (targeted in the experiments) in resolving each of the taxonomic groups under consideration.(b) Pairs of (contiguously or non-contiguously located) V-regions within the 16S rRNA gene in resolving each of the taxonomic groups under consideration.

As a one-time procedure, the following steps were performed for pre-computing the discriminating capability i.e. the accuracy of different V-regions (or combinations of various possible pairs of the same) with respect to different taxonomic lineages. The pre-generated set of accuracy values are required for solving Equation 3.

Rationale and Procedure

Full length bacterial 16S rRNA gene sequences (along with their annotated lineages) present in the RDP database (release 11.3) were retrieved. The RDP hierarchy browser was used for this purpose with the following filters—Strain=‘Both’; Source=‘Isolates’; Size ‘>=1200’; Taxonomy=‘NCBI’; Quality=‘Good’, which resulted in a downloaded set of 232,163 sequences. Further, sequences not containing any of the nine V-regions (V1-V9) were filtered out from the set of sequences, leaving a total of 84,711 16S rRNA sequences belonging to 11,810 species. Subsequently, both full-length as well as different portions of the 16S rRNA gene sequences were extracted in silico to represent outcomes of amplicon sequencing experiments, and were provided as input to the Wang classifier (algorithm used in RDP classifier), for taxonomic classification. The current version of RDP classifier 16S training set was used as the reference database for these taxonomic assignment steps, and the taxonomic hierarchy information of the reference sequences were appropriately used while training the Wang classifier in order to enable obtaining taxonomic classifications resolved up to species level. Only a subset (57,632 sequences) of the originally downloaded full-length 16S rRNA gene sequences, which could be classified at species level with >=80% bootstrap confidence threshold, was later used as a pool for randomly drawing sequences during creation of mock/simulated metagenomic datasets (as described later in this section).

For evaluating the discriminating ability of individual V-regions and their combinations, the regions of interest were parsed out from corresponding full-length 16S rRNA gene sequences using an in house modified version of the V-xtractor program, and submitted as query sequences to the Wang classifier, after appropriate pre-processing. First, the effectiveness of individual V-regions in resolving between different taxonomic groups was evaluated. For this purpose, different V-regions from all the 16S rRNA gene sequences, downloaded from the RDP database, were extracted. Subsequently, each of these individual V-regions were subjected to taxonomic classification with the Wang classifier, and the resultant assignments at the genus level were checked for accuracy and specificity against the taxonomic attributes provided by RDP for the corresponding full-length sequences.

The taxonomic classification accuracies of different V-regions in resolving different taxonomic groups are depicted inFIG.6. The taxonomic classification accuracies (at genus level) obtained with V-regions have been cumulated and depicted at the ‘phylum level’ in the figure, and placed in context with the taxonomic classification accuracies which would have been obtained with full length 16S rRNA gene sequences.

Except for V1, V5 and V9, all other V-regions were observed to have certain utility in taxonomic classification, even when targeted individually. It was also evident from the plot that some V-regions provide comparatively higher taxonomic classification accuracies of classification for specific taxonomic groups. For example, the V4 region has the highest accuracy while classifying sequences pertaining to the phylum Bacteroidetes (75.9%), whereas the V2 region classifies best with respect to the phylum Firmicutes (68.2%). A detailed list of taxonomic classification accuracies in taxonomic classification obtained with different V-regions at genus level is also calculated and collated in a table (not provided in the disclosure due to large size).

Given these observations, it would seem logical for a microbiome study design to sequence two (or more) V-regions from a 16S rRNA gene fragment which have complementary abilities with respect to classification of different taxonomic groups. Furthermore, the choice of the combination of V-regions could also be guided by the environment from where the metagenomic sample is being collected, given that diverse environments may be differentially enriched with different taxonomic groups. A preferred combination of V-regions cannot always be expected to be situated in a contiguous stretch on the 16S rRNA gene. Given the read length limitations of NGS technologies, targeting an amplicon constituting the preferred regions becomes difficult in reality.

The length distributions of V-regions and C-regions (constant/conserved regions flanking the V-regions) across different bacterial taxonomic groups are provided inFIG.7. These distributions indicate that while individual V-regions and contiguous stretches like V2-V3 (median length 297 bp) or V3-V4 (median length 254 bp) can easily be targeted with short read sequencing techniques like Illumina HiSeq/MiSeq, sequencing longer contiguous stretches encompassing more than two V-regions, such as V2-V3-V4 (median length 482 bp) and V4-V5-V6 (median length 453 bp) necessitates sequencing platforms that can generate longer read lengths.

Paired-end sequencing protocols available with some of the NGS platforms allow sequencing of a stretch of DNA from both its ends. For example, Illumina HiSeq sequencing platforms can be used for paired-end sequencing to generate up to 2×250 bp reads. The current work proposes, and evaluates in silico, the utilization of paired-end sequencing protocols for sequencing various pair wise combinations of non-contiguous V-regions in a single sequencing run. To this end, appropriate primers need to be designed against a desired stretch of the 16S rRNA gene, such that the targeted V-regions (either contiguously or non-contiguously placed) reside within this stretch, and are not far from either of its boundaries. Sequencing of the amplicons generated with these primers can then be performed with a paired-end sequencing protocol, whereby these (amplified) stretches of DNA are sequenced from both ends. Two reads sequenced from each such amplicon would cover the two targeted V-regions (one from each end). Since each of the sequenced reads from any given ‘pair’ targets a single V-region (situated at one of the ends of the amplicon), read-length limitations do not restrict capturing the entirety of the individual V-regions. Consequently, it becomes possible to sequence almost all possible pair wise combinations of V-regions, either arranged contiguously or non-contiguously. The results pertaining to the in silico evaluation of the effectiveness of different combinations of V-regions (see Methods), in providing accurate taxonomic classifications (at the species level) for sequences listed in the RDP database, is depicted inFIG.8.

Taxonomic classification accuracies provided by several combinations of non-contiguously placed V-region pairs, namely V1+V3 (77.7%), V1+V4 (77.4%), V1+V8 (76.6%), V2+V5 (73.6%), etc., were sufficiently high, and exceeded the taxonomic classification accuracies provided by combination of adjacently placed V-regions by a fair margin. It was also significant to note that many of the individual V-regions, which had very low taxonomic discriminating ability of their own (FIG.6), could provide significant taxonomic classification accuracies when paired up with other V-regions. For example, while V1 and V5 provided very low taxonomic classification accuracies when targeted alone, the combination of V1+V5 could provide a significantly high taxonomic classification accuracy of 73.4%. Furthermore, although the individual V-regions were observed to have differential abilities in classifying sequences originating from different phyla (FIG.6), their combinations were much more coherent in this regard, and could classify sequences from all phyla with almost equivalent efficiency (FIG.9). Results indicate the potential utility of targeting pairs of non-contiguously placed V-regions to improve taxonomic classification accuracy. Additionally, the results also suggest that for exploring the taxonomic diversity of a particular environment, which may be expected to be enriched with particular groups of bacteria, an appropriate combination of V-regions sensitive to the same bacterial groups may be chosen.

It may be noted in this context, that reads generated during amplicon sequencing may often encompass flanking ‘constant’ regions in addition to the targeted V-region(s), depending on choice of primers and the maximum read-length attainable by the sequencing technology. Consequently, our evaluation exercise, pertaining to combination of V-regions, aimed at mimicking 250 bp×2 paired-end sequencing, wherein the extracted regions (representing sequenced reads) also encompass such flanking regions. To achieve this, regions from the full length 16S rRNA genes were extracted in such a way that either of the 250 bp reads (constituting a read-pair) contained one of the target V-regions, flanked in both directions by equivalent portions (lengths) of the surrounding ‘constant’ regions. HMMs corresponding to constant regions surrounding the V-regions, as provided by the V-xtractor program were used for this purpose. In case two adjacent V-regions were targeted, there was a significant chance of finding an overlap between two reads constituting a pair. This overlap was utilized to join the pair of reads together into a single sequence before submitting the same as a query to the Wang classifier. In contrast, on sequencing two distantly separated non-contiguous V-regions, no overlap between the read pairs could be expected. Accordingly, the pair of reads in this case were concatenated using a string of eight consecutive ‘N’s, while preserving their orientation, prior to processing with Wang classifier. Given that Wang classifier (or RDP classifier) utilizes 8-mer nucleotide frequencies during taxonomic assignment, joining two non-overlapping sequenced fragments with 8 ambiguous nucleotides (N) ensures avoiding generation of spurious 8-mers consisting nucleotides from non-adjacent regions of the gene. Taxonomic assignments generated by the Wang classifier at a predetermined taxonomic level with a confidence threshold score of >=80% were used for all downstream comparative analyses.

The utility of all possible pair wise combinations of V regions, either arranged contiguously or non-contiguously, were investigated in silico in terms of accuracy of taxonomic classifications provided by each such combination. As mentioned earlier, sequence fragments mimicking outcomes of 250 bp×2 paired-end sequencing, which target different contiguous/non-contiguous combinations of V-regions, were derived from the downloaded 16S rRNA gene sequences. These fragments were subsequently subjected to taxonomic classification with the Wang classifier and the assignments obtained at species level were checked for accuracy and specificity against the pre-annotated taxonomic attributes of their source (full-length) 16S rRNA genes.

To assess the utility of the proposed non-contiguous combination of V-regions on a microbiome dataset, while avoiding any bias arising out of the proportion of sequences pertaining to different bacterial groups currently catalogued in reference databases like RDP, taxonomic classification exercises were further performed with mock and simulated metagenomic datasets.

Each of the mock microbiome datasets were constructed using 10,000 randomly selected 16S rRNA gene sequences from one of the five randomized 16S gene pools. Each of these gene pools consisted of sequences downloaded from the RDP database, wherein the proportion of sequences selected from different organisms were also randomized (see Methods). The results, in terms of classification accuracy at the species level, are depicted in Table 12. It was interesting to note that 18 out of the 20 combinations of V-regions, which could provide classification accuracy >=60% on average, constituted of non-contiguous V-regions. The best performing combination of adjacent V-regions was V2-V3, which on average provided 69.1% classification accuracy. In comparison, the combination of the non-contiguously placed V-regions V1+V4 demonstrated a high average classification accuracy of 77.2%.

TABLE 12Taxonomic classification accuracies obtained using different pair-wisecombinations of V-regions (both contiguous as well as non-contiguous) evaluated formock microbiome datasets, each constituting of 10,000 randomly selected 16S rRNAgenes from five different 16S gene pools. Accuracy of taxonomic assignments has beenevaluated at the species level considering the assignments obtained with full-length 16Ssequences to be correct. Top 20 combinations in terms of average classification accuracyhave been depicted.Classification accuracy (%) at species level averaged over five mockdatasets from each 16S gene poolMock dataMock dataMock dataMock dataMock dataCombinationfrom 16Sfrom 16Sfrom 16Sfrom 16Sfrom 16SAverageof V-regiongene pool 1gene pool 2gene pool 3gene pool 4gene pool 5accuracyV1 + V477.2979.4772.7975.980.4877.19V1 + V374.6978.1677.5274.7680.0877.04V1 + V876.0377.9673.2475.7279.3276.46V1 + V777.278.3370.3777.3478.676.37V1 + V672.4677.3469.7378.2576.974.94V1 + V570.8974.2469.1673.3776.472.81V1 + V971.7471.4171.3373.9575.5772.8V2 + V469.0775.0772.7670.9973.5572.29V2 + V868.2674.673.3370.6673.2772.02V2 + V666.8474.5472.672.1972.6771.77V2 + V768.3472.7672.7371.1771.371.26V2V3*61.5371.5272.0366.3173.9269.06V2 + V965.0368.8571.666.3271.8168.72V1V2*64.270.2966.8165.4472.467.83V3 + V868.4761.869.6666.5967.8266.87V3 + V768.4161.671.0566.865.9366.76V2 + V561.3868.1968.4265.3669.3466.54V3 + V663.2659.9168.5367.0465.1564.78V3 + V963.6355.8567.265.9463.8363.29V3 + V560.9456.7465.7962.9162.4961.77

The specific combinations of V-regions, which provided comparatively higher accuracies of taxonomic classification with the RDP database sequences, were made subject to this further evaluation wherein 5 mock 16S metagenomic gene pools were created from randomly selected sets of 50 organisms (genus) listed in RDP database (Table 13).

TABLE 13Source genera for 16S rRNA sequences included in five 16S gene pools.Randomly drawn sequences from these pools were used in generation of mock datasets.Genera16S gene16S gene16S gene16S gene16S genepool 1pool 2pool 3pool 4pool 5AcetobacteriumNoNoNoYesNoAchromobacterYesNoYesNoNoAcidiphiliumNoNoNoNoYesAcidithiobacillusNoYesNoNoNoAcidovoraxYesNoNoNoNoAcinetobacterYesNoNoYesNoActinobacillusYesNoNoNoNoActinomaduraNoNoYesNoYesAggregatibacterNoYesNoYesYesAgromycesNoNoNoNoYesAlcaligenesYesNoNoNoNoAlcanivoraxNoYesNoNoNoAlicyclobacillusNoNoNoNoYesAlkalibacteriumNoNoNoNoYesAlteromonasNoNoNoNoYesArcobacterYesNoNoNoYesArthrobacterYesYesYesYesNoAsaiaNoNoYesNoNoAzoarcusYesNoNoNoNoAzospirillumNoYesYesNoNoBacillusNoNoYesNoNoBifidobacteriumNoYesNoNoYesBorreliaNoNoNoYesYesBoseaNoNoYesYesNoBrachybacteriumNoNoYesNoNoBradyrhizobiumNoYesYesYesNoBrevibacillusYesNoNoNoNoBrevundimonasYesNoNoNoNoBrucellaYesNoNoNoNoBuchneraYesNoYesNoYesBurkholderiaNoNoNoYesNoButyrivibrioYesNoYesYesNoCampylobacterYesNoNoNoNoCarnobacteriumNoYesNoNoNoCaulobacterNoYesYesNoNoCellulomonasYesNoNoNoYesChromobacteriumYesNoNoNoYesChromohalobacterNoNoYesNoNoChryseobacteriumNoNoNoYesNoCitrobacterYesNoNoYesNoColwelliaNoNoYesYesNoComamonasNoNoNoNoYesCorallococcusNoYesNoYesNoCorynebacteriumNoNoNoNoYesCronobacterYesNoNoYesNoCurtobacteriumYesNoNoYesYesDeinococcusNoYesYesNoYesDelftiaNoNoYesNoYesDesulfosporosinusYesNoNoYesNoDesulfotomaculumNoNoYesYesNoEdwardsiellaNoNoYesYesNoEnterococcusNoYesNoNoYesErythrobacterYesNoNoNoYesEubacteriumNoNoYesNoNoExiguobacteriumNoYesNoNoNoFlavobacteriumYesYesNoYesNoFrancisellaYesYesNoNoNoFusobacteriumNoNoNoNoYesGallibacteriumNoNoYesNoNoGeobacillusNoNoYesYesNoGlaciecolaNoNoNoYesYesGluconobacterNoYesNoNoNoHaemophilusNoYesNoNoNoHalobacillusNoNoNoYesNoHalomonasNoYesNoYesYesHelicobacterYesNoNoNoNoHerbaspirillumNoNoNoYesNoHydrogenophagaNoNoYesNoNoIdiomarinaNoNoNoYesNoKitasatosporaNoYesNoNoNoKlebsiellaYesNoNoNoNoKocuriaNoNoYesNoNoKomagataeibacterNoNoNoNoYesLactobacillusNoNoYesNoYesLactococcusYesNoNoYesNoLegionellaNoYesNoNoNoLeifsoniaNoYesNoNoYesLeptospiraNoYesNoNoNoLeucobacterNoYesYesNoYesLeuconostocYesYesNoYesNoListeriaYesNoNoNoNoLoktanellaNoYesYesNoNoLysinibacillusYesNoNoNoNoLysobacterNoNoNoNoYesMarinobacterNoYesNoNoYesMarinobacteriumNoNoYesNoNoMarinomonasNoNoNoYesYesMassiliaYesNoYesYesNoMethylobacteriumNoNoNoYesYesMicrobisporaYesNoNoNoNoMicromonosporaYesYesNoNoNoMoraxellaNoNoYesNoNoMoritellaNoNoNoNoYesMycoplasmaNoYesNoNoNoNeisseriaYesYesNoNoYesNitrosomonasNoNoYesNoNoNocardioidesYesYesNoYesNoNovosphingobiumYesNoYesNoNoOceanobacillusYesNoYesYesNoPaenibacillusYesYesNoYesNoPandoraeaNoNoNoYesNoPantoeaNoNoNoYesYesParacoccusNoNoNoNoYesPectobacteriumNoNoYesNoYesPediococcusNoNoNoNoYesPhotobacteriumNoNoYesNoYesPhotorhabdusNoYesYesNoNoPhyllobacteriumNoYesNoNoNoPlanococcusNoNoNoYesNoPolaribacterNoNoYesNoNoPolynucleobacterYesNoNoNoNoProteusYesNoYesNoNoPseudomonasNoNoNoNoYesPseudoxanthomonasNoYesNoNoNoPsychrobacterNoNoYesNoNoRahnellaYesNoNoNoNoRalstoniaNoNoYesYesNoRhizobiumNoYesYesNoYesRhodopirellulaNoYesYesYesYesRickettsiaNoNoYesYesNoRuegeriaNoNoNoNoYesRuminococcusNoYesNoNoNoSalmonellaNoYesNoYesYesSelenomonasNoNoYesNoNoSerratiaNoYesNoYesNoShewanellaYesNoNoNoYesSorangiumYesNoYesNoNoSphingobiumNoYesNoYesYesSpiroplasmaYesNoNoNoNoSporolactobacillusNoNoYesYesNoStaphylococcusNoYesNoYesYesStenotrophomonasYesYesNoYesNoStreptococcusNoYesNoNoNoStreptomycesNoYesYesNoNoStreptosporangiumNoNoNoNoYesTaylorellaYesYesNoNoNoThalassospiraNoYesYesNoNoThermoanaerobacterYesNoYesNoNoThermoanaerobacteriumNoNoNoYesNoThermusNoNoNoYesNoThiomonasYesNoYesNoNoTrueperellaNoYesNoYesYesVibrioYesNoNoNoYesVirgibacillusYesNoYesNoNoWeissellaNoYesNoNoYesXanthomonasNoYesNoYesNoXenorhabdusYesNoNoYesNoXylellaNoYesNoNoNo

To obtain reads for building the mock metagenomic datasets corresponding to these pools, each time 10,000 16S rRNA genes were drawn randomly from a gene pool, such that the proportion of 16S rRNA genes drawn from any of the organisms are also randomized. 5 such datasets (with 10,000 reads each) corresponding to each of the 5 gene pools (a total of 25 mock datasets) were constructed for comparative evaluation. Different contiguous as well as non-contiguous combinations of V-regions were subsequently extracted from each of the 16S rRNA genes belonging to these mock datasets, and subjected to taxonomic analysis using Wang classifier, following the classification methodology described above. Taxonomic abundance values (obtained using different combinations of V-regions) were averaged over 5 mock datasets pertaining to the same gene pool. The averaged abundance values for each of the mock gene pools were compared against each other and the pre-annotated taxonomic attributes of their source (full-length) 16S rRNA genes, to assess the utility of the chosen combinations of V-regions. The results, in terms of classification accuracy at the species level, are depicted in Table 12. It was interesting to note that 18 out of the 20 combinations of V-regions, which could provide classification accuracy >=60% on average, constituted of non-contiguous V-regions. The best performing combination of adjacent V-regions was V2-V3, which on average provided 69.1% classification accuracy. In comparison, the combination of the non-contiguously placed V-regions V1+V4 demonstrated a high average classification accuracy of 77.2%.

Nine more simulated microbiomes mimicking different environmental and host associated niches—namely, gut, skin, vaginal, sub-gingival (oral), sputum (oral), nematode gut, soil, and aquatic were also generated. Taxonomic abundance estimates for eight of these environmental microbiomes were derived from datasets used in an earlier in silico study evaluating functional potential of diverse metagenomes Taxonomic abundance estimates for the aquatic microbiome was derived from a recent study. To populate these simulated microbiomes, sequences from RDP database were randomly drawn, while making sure that the proportions of 16S rRNA genes drawn from different genera were roughly similar to the proportions observed earlier for these environments. The taxonomic classification efficiency of the V-region combinations (at the species level) was also assessed on this set of simulated microbiome. The efficiency of the proposed non-contiguous combination of V-regions was further tested on nine additional simulated metagenomes mimicking different environmental and host associated niches as shown in Table 14. The data in the Table 14 have been collected from various sources in the art.

TABLE 14Summary of datasets used for deriving taxonomic profiles pertaining to variousenvironmental and host associated niches used togenerate simulated metagenomesmimicking the respective environments. Genera level abundances, averaged over alldatasets pertaining to an environment, were used to draw respective proportions 16SrRNA genes from the set of downloaded RDP sequences while constructing the simulatedmetagenomics datasetsNo. ofSample IdentifierEnvironmentSamplesGut1 (Prebiotics)Gut(Human)283Gut2 (HMP)Gut (Human)306SputumOral Cavity (Human)68SkinSkin (Human)149Sub-gingivalOral Cavity (Human)91VaginalVagina (Human)394SoilSoil18NematodeNematode Gut (Litoditis36Marina)AquaticAquatic20

Results pertaining to the human associated simulated metagenomes, namely, gut, skin, vaginal, subgingival (oral) and sputum (oral) are depicted inFIG.4. It was interesting to note that optimal classification of reads from the simulated metagenomes pertaining to different niches could be obtained with different combinations of non-contiguous V-regions.

The combination of V1+V4 regions provided the maximum accuracy of classification for skin (60.2%) and one of the gut (86.0%) metagenomes (GUT2), whereas metagenomes pertaining to vaginal and sub-gingival niches were best resolved by the combination V1+V9 (with accuracies of 83.3% and 78.6% respectively). Optimal classification of sputum metagenomic samples (72.1%) could be obtained by another non-contiguous combination, viz. V1+V5 regions, which could also provide relatively more accurate classification for the GUT1 metagenome (82.5%). These results further reiterate the need of choosing an optimal combination of V-regions, preferably non-contiguous, for a specific sampled environment.

It was also noted that the high variability in taxonomic classification accuracies of individual V-region combinations while classifying samples pertaining to different environments. For example, while the combination V2+V4 could classify one of the gut microbiomes (Gut2) with 85.93% accuracy, the classification results were not as high when the same combination was used to classify the aquatic microbiome (69.2%). On the other hand, the combination V2+V7 was observed to provide decent classification for the simulated aquatic microbiome (72.8%), while performing not so well for the simulated gut microbiome datasets (65.8% for Gut1 and 70.9% for Gut2). These results further reiterate the need of choosing an optimal combination of V-regions, preferably non-contiguous, for a specific sampled environment.

It may be noted here that the paired-end reads generated for in silico evaluation of the utility of different combinations of V-regions were based on HMMs pertaining to the flanking constant regions, as provided by the V-xtractor program. Actual primer design may not always allow generation of reads identical to the in silico experiment, and results from a sequencing experiment may slightly vary from the validation results presented. A comparison of the paired-end reads generated in the in silico experiments with respect to those which may be obtained by using different sets of primers currently available for 16S rRNA amplicon sequencing as shown inFIG.10.

It may be mentioned here that assessment of primer specificity on all sequences from RDP database (a total of 232,163 sequences having length >=1,200 bp) revealed that the combinations/pairs (either contiguous or non-contiguous) involving the V1-region could potentially amplify a lower fraction of sequences compared to other combinations. Apparently, the fraction of sequences that can be amplified by the said combinations is limited by the specificity/universality of the primer for V1-region. The presence of many incomplete/truncated SSU rRNA sequences in RDP database, which might be missing the V1 primer binding sites may also contribute to this observation. The overall results, however, do not indicate any significant deviations in the specificity (fraction of bacterial sequences amplified) of primer pairs targeting non-contiguous V-regions, when compared to the primers targeting contiguously placed V-regions.

The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words “comprising,” “having,” “containing,” and “including,” and other similar forms are intended to be equivalent in meaning and be open ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items, or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.

Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include random access memory (RAM), read-only memory (ROM), volatile memory, nonvolatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.

It is intended that the disclosure and examples be considered as exemplary only, with a true scope and spirit of disclosed embodiments being indicated by the following claims.