Patent Description:
Antibiotic resistance (AMR) is a crisis that currently impacts human and animal health, involving the clinic, agriculture, and the environment. The World Health Organization along with public health and economic organizations across the globe recognize antibiotic resistance as one of the most pressing challenges of the <NUM>st Century (Laxminarayan et al. The crisis is the result of two interrelated elements. First, resistance genes are ancient, evolving in concert with the emergence of antibiotic production, presumably hundreds of millions of years ago (Forsberg et al. , <NUM>, Davies and Davies, <NUM>, Barlow & Hall, <NUM>, Perry et al. , <NUM>, D'Costa et al. , <NUM>, <NUM>). This challenge is amplified by the facile movement of AMR genes via horizontal gene transfer coupled with the movement of people and goods across the planet, thereby facilitating spread (Levy and Bonnie, <NUM>; Schwartz & Morris, <NUM>; Gaze et al. The second is the lack of new antibiotics available to counter the emergence of resistance (Brown & Wright, <NUM>; Silver, <NUM>). These two issues conspire to threaten modem medicine and food security. One of the significant gaps to address the antibiotic crisis is a lack of suitable tools to rapidly detect and identify the complete resistome (entire AMR gene contingent), in various environments and associated microbiomes.

Identifying the resistome of individual strains, microbiomes, and environmental settings (sediment, hospitals, etc.) provides critical information on the resistance gene census of a given sample e.g. infected sites, food and water supply, etc. (Surette and Wright, <NUM>; Allen et al. , <NUM>; Fitzpatrick and Walsh, <NUM>; Forsberg et al. , <NUM>; Luo et al. , <NUM>; Pal et al. This information can be used to guide antibiotic use and inform stewardship programs, track the spread and emergence of resistance, monitor the emergence of new resistance alleles associated with the use of antibiotics or other bioactive compounds, and enable molecular surveillance for public health decision making. Importantly, this strategy is highly scalable from the individual, to her/his local environments (i.e. hospital ward, barn, etc.) and even larger geographic regions (Van Schaik, <NUM>; Buelow et al. , <NUM>; Allen et al. , <NUM>; Lax and Gilbert, <NUM>; Nesme et al.

Profiling the resistomes of bacterial strains that are culturable is reasonably straightforward using whole genome sequencing or direct detection of selected genes, e.g. via polymerase chain reaction (PCR) or microarrays (Walsh and Duffy <NUM>; Mezger et al. , <NUM>; Zumla et al. , <NUM>; Pulido et al. These latter strategies can also be applied to metagenomes, as was showed to be possible through the identification of resistance genes for tetracycline, penicillin, and glycopeptide antibiotics in <NUM>,<NUM>-year old Beringian permafrost (D'Costa et al. A weakness of highly targeted or PCR based approaches is that they are rarely comprehensive despite the number of known resistance elements, let alone the continual emergence of variants and/or completely novel mechanisms (Boolchandani et al. , <NUM>, Boolchandani et al. , <NUM>; Crofts et al. Furthermore, non-targeted resistome survey methods in metagenomes require millions of sequencing reads, or deep sequencing, and careful filtering, recognizing that the vast majority of sequences will not encode antibiotic resistance determinants (Boolchandani et al. , <NUM>; Rowe and Winn, <NUM>).

A more appropriate approach for the identification of resistomes is the use of a probe and capture strategy (Gnirke et al. , <NUM>), as such methods have seen great success in enriching for targeted sequences in highly complex metagenomes. For example, this approach has been used to capture, sequence, and reconstruct human mitochondrial sequences as well as the genomes of infectious agents and extinct species from various environments including highly degraded archeological and historical samples (Wagner et al. , <NUM>; Patterson Ross et al. , <NUM>; Duggan et al. , <NUM>; Devault et al. , <NUM>; Enk et al. , <NUM>; Depledge et al. In a probe and capture experiment, target RNA 'baits' are designed to be complementary (to at least <NUM>% identity), to target DNA sequences of interest. Actual synthesized baits are biotin-labelled and are incubated with the DNA from metagenomic or genomic libraries, where they hybridize to related sequences, as shown in <FIG>. The targeted capture sequencing workflow begins with DNA isolation from a sample of interest (stool from a healthy donor in this example). In <FIG>, at step (a) DNA is fragmented through sonication and prepared as a sequencing library, and at steps (b) and (c) target sequences representing less than <NUM>% of the total DNA are and captured through hybridization with biotinylated probes and streptavidin-coated magnetic beads. At steps (d) and (e) the purified and amplified capture library fragments are sequenced and analysed for AMR sequence content by mapping to the Comprehensive Antibiotic Resistance Database (CARD). CARD is a curated collection of characterized, peer-reviewed resistance determinants and associated antibiotics, and provides data, models, and algorithms relating to the molecular basis of antimicrobial resistance. The CARD provides curated reference sequences and SNPs organized by the Antibiotic Resistance Ontology (ARO) and AMR gene detection models. Information about CARD is available online at https://card. Ontologies at CARD are available on the CARD website. These data are additionally associated with detection models, in the form of curated homology cut-offs and SNP maps, for prediction of resistome from molecular sequences. These models can be downloaded or can be used for analysis of genome sequences using the Resistance Gene Identifier ("RGI") for prediction of complete resistome from genomic and metagenomic data, either online or as a stand-alone tool. All data and software associated with CARD is protected by copyright; CARD is available to academic and government users and requires licenses for commercial use; details are available at https://card. For the avoidance of doubt, this patent application, and any patents to issue herefrom, do not grant any license in respect of CARD in whole or in part.

Targets are captured using streptavidin-coated magnetic bead separation, reactions pooled and sequenced on a next-generation sequencing (NGS) platform. This strategy offers excellent advantages for the sampling of resistomes in a variety of environments where resistance genes are generally rare and genetically diverse. Indeed, recently this approach has been explored for resistance gene capture by other groups (Lanza et al. , <NUM>, Noyes et al. , <NUM>, Allicock et al. However, these approaches target many other genes that are not rigorously associated with resistance, increasing the cost and the opportunity for false positive gene identification.

Forster, M. , Scientific Reports (<NUM>), <NUM>:<NUM> discloses a method eliminating false positive data on viral integrants as determined by paired-end sequencing.

Thus, the increasing sensitivity and lower cost of DNA sequencing holds promise for identifying AMR components at the genome level to allow precision medical and/or environmental intervention. However, this same increased sensitivity raises the risk of false positives, which may not only result in wasted effort to treat a non-existent problem, but also makes it worse. For example, a false positive identification of an AMR component may result in the unnecessary deployment of one of the limited number of antibiotics held "in reserve" because it is known to be effective against AMR. Such deployment can needlessly expose microbes to these "reserve" drugs, allowing them to develop resistance. Thus, the reduction of false positives when detecting AMR components is a crucial aspect of antibiotic stewardship.

In one aspect, the present disclosure is directed to a method for suppressing false positives (Type I Error) during analysis of sample biological materials. The method comprises, for each of at least one handling step during the analysis, obtaining at least one sample handling blank carrying a transfer substrate mixed with at least part of the sample biological materials, obtaining at least one control blank that is isolated from the sample biological materials and corresponding to the sample handling blank in that handling step, and replicating the handling applied to the at least one sample handling blank for the at least one control blank. Following completion of all handling steps, there is at least one final sample handling blank carrying the transfer substrates from the handling steps mixed with the at least part of the sample biological materials, and at least one final control blank carrying the transfer substrates from the handling steps and isolated from the sample biological materials. The method further comprises applying a hybridization probe solution containing at least one hybridization probe to each final sample handling blank to produce at least one baited final sample handling blank, and applying to each final control blank hybridization probe solution identical to that applied to each final sample handling blank to produce at least one baited final control blank. The method further comprises feeding each baited final sample handling blank into a DNA sequencer and sequencing sample bait-captured DNA carried by the baited final sample handling blank, and feeding each baited final control blank into the DNA sequencer and sequencing control bait-captured DNA carried by the baited final control blank. The method still further comprises comparing the sample bait-captured DNA to the control bait-captured DNA and discounting, from a final identified genetic sequence, genetic components that are common to the final sample handling blank and the final control blank and pass a statistical significance test.

The at least one handling step may comprise a plurality of handling steps including a collection step during which the sample biological materials are collected and at least one transfer step where the sample biological materials are transferred from a preceding sample handling blank to a subsequent sample handling blank.

The sample biological materials may be from a vertebrate, and may include at least one of blood, urine, feces, tissue, lymph fluid, spinal fluid and sputum.

The sample biological materials may be from at least one of a living organism, a cadaver of a formerly living organism, and an archaeological sample.

The sample biological materials may be from an invertebrate.

The sample biological materials may be from at least one environmental sample, which may comprise at least one of mud, soil, water, effluent, filter deposits and surface films.

In another aspect, the present disclosure is directed to a method for suppressing false positives (Type I Error) during analysis of sample biological materials. The method comprises, for at least one final sample handling blank carrying transfer substrate mixed with at least part of the sample biological materials, applying a hybridization probe solution containing at least one hybridization probe to each final sample handling blank to produce at least one baited final sample handling blank, and applying hybridization probe solution identical to that applied to each final sample handling blank to at least one final control blank, wherein the at least one final control blank carries transfer substrate identical to that applied to each sample handling blank and the at least one final control blank is isolated from the sample biological materials, to thereby produce at least one baited final control blank. The method further comprises feeding each baited final sample handling blank into a DNA sequencer and sequencing sample bait-captured DNA carried by the baited final sample handling blank, and feeding each baited final control blank into the DNA sequencer and sequencing control bait-captured DNA carried by the baited final control blank. The method still further comprises comparing the sample bait-captured DNA to the control bait-captured DNA and discounting, from a final identified genetic sequence, genetic components that are common to the final sample handling blank and the final control blank and pass a statistical significance test.

These and other features will become more apparent from the following description in which reference is made to the appended drawings wherein:.

The present disclosure describes a targeted method for the analysis of antibiotic resistomes. The efficacy of this probeset and strategy are tested using both a panel of previously sequenced pathogenic bacteria with known resistance genotypes and phenotypes, as well as previously uncharacterized human metagenomic stool samples. The method is readily applicable to both clinical and non-clinical settings.

The probeset used herein was based on stringently curated AMR gene (ARG) sequences from the Comprehensive Antibiotic Resistance Database (CARD), tiled at four-fold coverage across ARG sequences, combined with rigorous bioinformatic analysis to suppress off-target hybridization, enabling a cost-effective and sensitive method to sample the known resistance gene landscape (Jia et al.

A set of <NUM>-mer nucleotide probes were custom designed and synthesized through the myBaits platform (Arbor Biosciences, Ann Arbor, Michigan). The probes span the protein homolog model of curated ARGs from CARD and represent nucleotide sequences (<NUM>) that are well-characterized in the literature as resistance-conferring. Many of the probes are highly specific to individual genes (<NUM>% nucleotide identity to reference ARG sequence) as shown in part (A) of <FIG>, but partial hybridization can allow for probes to target sequences that are divergent from the reference sequence. Part (A) of <FIG> shows an example of the process of designing probes against an antibiotic resistance gene (ndm-<NUM>). In the example, probes are <NUM> nucleotides each and tiled at a <NUM>-nucleotide sliding window. Resistance conferred through mutation (protein variant model in CARD) to genes encoding highly conserved proteins (including gyrA and <NUM> rRNA sequences) was purposefully not included in the design.

With <NUM>,<NUM> probes, this probeset is capable of targeting <NUM> nucleotide sequences implicated in resistance across all classes of antibiotics and a wide range of resistance gene families (see part (C) <FIG>). The majority (<NUM>%) of genes targeted by probes mirror the breakdown in CARD, dominated by antibiotic inactivation mechanisms and by the beta-lactamase proteins, reflecting their use in the clinic (part (C) of <FIG>). The next largest category of resistance elements targeted by the probeset are efflux pumps. The majority of the probes (<NUM>,<NUM>) target a single gene and the remainder range to a maximum of <NUM> genes (average <NUM> genes) due to sequence conservation within gene families (see <FIG>). For example, a single probe initially designed to target <NUM> nucleotides of the beta-lactamase gene blaSHV-<NUM> is predicted to also target an additional <NUM> genes including other members of the SHV, LEN, and OKP-A/-B beta-lactamases due to homology between these gene sequences. Thus, in some cases there is overlap in the utility of some <NUM>-mer probes. In addition to many beta-lactamase families, aminoglycoside-modifying enzymes (AAC(<NUM>) and AAC(<NUM>')) and quinolone resistance qnr genes are large families with probes designed to target upwards of <NUM> genes each. Remarkably, <NUM> of the <NUM> targeted genes (<NUM>%) are covered by at least <NUM> or more probes (see <FIG>).

At the individual determinant level, the number of probes per gene (average <NUM> probes per gene, range = <NUM> - <NUM>) and length coverage of a gene (average <NUM>% with a range of <NUM>% to <NUM>%) varies (<FIG>, part (B) of <FIG>). The majority of genes (<NUM>/<NUM>) have greater than <NUM>% length coverage by probes (part (B) of <FIG>). Members of the beta-lactamase families (blaCTX-M, blaTEM, blaOXA, blaGES, blaSHV) are among the genes with the highest probe coverage, not surprising given their preponderance in the dataset and their homology within families. <NUM>% of targeted gene sequences (<NUM>) have full-length coverage (<NUM>%) with an average depth of probe coverage of a gene of <NUM>. 47x (minimum <NUM>. 05x; maximum <NUM>. 83x) (part (B) of <FIG>; <FIG>). Only <NUM> sequences from CARD have no probe coverage due to filtering of candidate probes during the design. The average length of a targeted gene in CARD is <NUM> bp, and the average length of all genes targeted by probes is <NUM> bp (see <FIG> and <FIG>). Overall this probeset targets ~<NUM> megabases of antibiotic resistance nucleotide sequence and greater than <NUM>% of the nucleotide sequences curated in CARD. Additional metrics assessed included the guanosine and cytosine content of probes (average <NUM>% GC; range: <NUM> - <NUM>%) and target genes (average: <NUM>% GC; range: <NUM>% to <NUM>%), as well as the probe melting temperature (average: <NUM> ° C) (see <FIG>). Probe design in conjunction with verification with Arbor Biosciences encouraged compatibility in the probeset and promotes efficient capture.

To characterize the sensitivity and selectivity of this probeset, a series of control experiments was conducted using a panel of previously sequenced, assembled and annotated multi-drug resistant Gram-positive and Gram-negative bacteria isolated within the Hamilton Health Sciences Network. The proportion of the genomes targeted by the probeset as determined by mapping the entire probe contingent to each genome individually ranged from <NUM> - <NUM>% shown in Supplementary Table <NUM>.

Clinical bacterial isolates obtained through the Wright Clinical Collection. Bacterial genomes were sequenced, and draft genome assemblies were analyzed through the Resistance Gene Identifier in CARD to predict the number of resistance genes. The total probeset was mapped against the draft assembled genome and the number of genes with probe coverage, percentage of genome covered by probes and overlap between predicted RGI genes and probe coverage were determined.

ARGs probe-to-target regions were predicted by passing draft genome assemblies through the Resistance Gene Identifier (RGI) in CARD. Strains were predicted to have between <NUM> and <NUM> ARGs of which between <NUM> and <NUM> were targeted by probes, representing <NUM> unique genes among the strains tested (Supplementary Table <NUM>). Genomic DNA from four different strains was tested individually via enrichment on two different library preparations; these are referred to as Trial <NUM> and Trial <NUM> hereafter. Over <NUM>% of reads mapped to the respective draft bacterial genomes after removing those with low mapping quality scores, as shown in Supplementary Table <NUM>.

Strains were enriched individually in two trials with different library sizes. For each strain the regions predicted to be targeted by probes were determined through mapping the probeset to each individual genome). Enrichment results across two trials were determined by mapping trimmed and filtered reads to genome, calculating the percentage on-target and normalizing reads and depth per kb per million reads.

Furthermore, the majority (higher than <NUM>% in all cases) of reads mapped to the small proportion (<<NUM>%) of the genome that was predicted to be targeted by the probeset (Supplementary Table <NUM>); part (A) of <FIG> shows the percentage of reads on target for each strain tested in various sample types (either individual or pooled) for both enriched and shotgun samples. In <FIG>, each point on the graph represents a replicate experiment either as a genome that was enriched individually or when pooled with other genomes (Pool <NUM>, <NUM> and <NUM>) across both trials. The horizontal line for each strain represents the mean.

This enrichment approach is insensitive and tractable to different library preparation methods (NEBNext Ultra II versus modified Meyer and Kircher) and varying library insert sizes (average library fragment sizes range from <NUM> to <NUM>) as shown in Supplementary Table <NUM> (see also Meyer and Kircher, <NUM>).

The amount in nanograms of each library and the corresponding amount of probes used for enrichment. The average size of library fragments prior to enrichment was determined through the Agilent Bioanalyzer <NUM>. The number of clusters (paired-end reads) that were generated for each library when sequenced by Illumina's MiSeq V2 2x250. Blanks for each trial were included and sequenced on a separate run; many of the blank libraries did not generate peaks on the Bioanalyzer nor any signal by quantitative PCR therefore their values are N/A. In Phase <NUM>, three positive controls for enrichment were included with genomic DNA from Escherichia coli C0002 and varying library and probe amounts.

After subsampling reads between trials to equal depth to account for differences in sequencing between enriched libraries, there is a strong correlation between read count and read depth on targeted regions for bacterial strains enriched individually (Supplementary Table <NUM>). For all four strains across the two Trials and different library prep methods, the correlation between read counts mapping to probe-targeted regions is high (Pearson correlation <NUM> - <NUM>) (<FIG>). For <FIG>, reads from enrichment of individual genomes of Escherichia coli C0002 (A), Staphylococcus aureus C0018 (B), Klebsiella pneumonia C0050 (C) and Pseudomonas aeruginosa C0060 (D) in Trial <NUM> were subsampled to same depth as reads in Trial <NUM>. The reads were mapped to the respective bacterial genome, filtered for mapping quality and then the number of reads on each RGI and probe-targeted region were counted and normalized per kb per million reads. Pearson correlation coefficients are shown. In all cases, the length percent coverage of a gene by reads is <NUM>% (Supplementary Table <NUM>). Finally, the Pearson correlation for average read depth on probe-targeted regions between the two trials ranges from <NUM> to <NUM> for the four strains (results not shown).

The outcome was successful capture of the majority (><NUM>%) of antibiotic resistance genes targeted by the probeset from single-sourced bacterial genome libraries with at least <NUM> reads. When genomic DNA from multiple bacterial strains was pooled at varying ratios of <NUM> and/or <NUM> strains, with some strains representing less than <NUM>% of the total `mock' metagenome, there were recovered significantly more targeted genes with at least <NUM>, <NUM> or <NUM> reads mapping (mapping quality >=<NUM> and length >=<NUM>) compared to shotgun sequencing (part (B) of <FIG>; Supplementary Table <NUM>; Supplementary Table <NUM>). Part (B) of <FIG> shows the percent recovery of regions predicted to be targeted by probes for each strain tested in various sample types in both enriched and shotgun samples (<NUM> versus <NUM> versus <NUM> reads per probe-targeted region).

We pooled various nanogram amounts of genomic DNA from bacteria and estimated the percentage of each strain in the respective pools based on total genome size of each strain. With reads generated through shotgun sequencing and after enrichment, we calculated the percentage of reads mapping to a particular genome by mapping to a combined reference of the genomes used in a given pool and counting the reads that mapped to each respective genome (= reads mapping to genome A / reads mapping to all genomes).

Genomic DNA from individual strains was pooled in various ratios to produce "mock metagenomes" for enrichment. For each strain, the regions predicted be targeted by probes (determined through mapping the probeset to each individual genome) are considered the targeted region for analysis. Trimmed and filtered reads from paired enriched and shotgun pools were subsampled to same read depth. The resulting reads were mapped to the individual strain's genomes, counted on-target and normalized per kb per million reads mapping. Percentage on-target, percentage of probe-targeted regions with at least <NUM> reads as well as their percent coverage, average reads, and average depth were determined for each strain at the probe-targeted region level. The fold enrichment is based on all genes regardless of read counts.

In <NUM>/<NUM> cases, <NUM>% or more of the reads within the enriched samples mapped to probe-targeted regions within the individual bacterial genome regardless of pooling ratios (Supplementary Table <NUM>; part (A) of <FIG>). The one exception is Trial <NUM> Pool <NUM> (enrichment), where on-target mapping was not as effective (-<NUM>%) as the other pools for reasons that were not obvious; nevertheless, even this trial remained over <NUM>-fold better than the unenriched samples (Supplementary Table <NUM>). In all shotgun samples, the percentage of reads on target never exceeded <NUM>% and in <NUM>/<NUM> cases was less than <NUM>% of the total sequencing data (Supplementary Table <NUM>, part (A) of <FIG>). Furthermore, the average percent coverage of probe-targeted regions with at least <NUM>, <NUM> or <NUM> reads in all strains enriched individually or in pools is always higher than in the shotgun samples and ranges from <NUM>- to <NUM>-fold greater (part (C) of <FIG>, Supplementary Table <NUM>). Part (C) of <FIG> shows the average percent length coverage of probe-targeted regions with reads from strains tested individually and in pools in both enriched and shotgun samples (<NUM> versus <NUM> versus <NUM> reads). This does not include the average percent coverage of genes in samples that did not have any captured regions (values in panel B were zero).

All enrichments resulted in an increased average number of read counts, a higher percentage of probe-targeted reads and higher percent coverage of these regions when compared to their shotgun controls (parts (B) and (C) of <FIG>). For all strains in all pooled libraries across both trials, the average normalized read count and depth of reads on probe-targeted ARGs from enriched libraries is over <NUM> times (<NUM> - <NUM>) higher than from its unenriched control (Supplementary Table <NUM>). In <NUM>/<NUM> cases, the fold-increase in read counts exceeded two orders of magnitude and was over four for some probe-targeted regions (Supplementary Table <NUM>). The one case that did not conform (from Trial <NUM> Pool <NUM>, see above) reflects a minor and non-reproducible variability in the quality of the capture for unknown reasons. Nonetheless, there is a clear distinction between the shotgun and enriched samples with the enriched data showing a more consistent agreement between normalized read counts per probe-targeted region. <FIG> shows the read counts per probe-targeted region within the Escherichia coli C0002 strain (part A) and Staphylococcus aureus C0018 strain (part B) across eight enriched samples and six shotgun samples. For <FIG>, among enriched and shotgun pairs, reads were subsampled to equal depths and mapped to the individual strain's genome. Read counts were normalized by number of reads mapping per target length in kilobases per million reads. The predicted number of probes for each region along the genome are shown in the panels below. The Y axes are in the logarithmic scale.

A similar trend is observed when the raw read counts for each sample are used (<FIG>). As shown in <FIG>, enrichment results in higher read counts on antibiotic resistance genes compared to shotgun sequencing. <FIG> shows raw read counts at each probe-targeted region within the Escherichia coli C0002 strain and <FIG> shows raw read counts at each probe-targeted region within the Staphylococcus aureus C0018 strain in enriched and shotgun samples including individual and "mock metagenomes" of multiple strains. Among enriched and shotgun pairs, reads were subsampled to equal depths and mapped to the individual strain's genome. The predicted number of probes for each region along the genome are shown in the panels below. The Y axes are in the logarithmic scale.

While over <NUM>% of the predicted genes are captured with at least <NUM> reads for C0002 in all the enriched samples, between <NUM> and <NUM> (all) of the probe-targeted regions have less than <NUM> reads in the shotgun data at the same sequencing depth (between <NUM>,<NUM> and <NUM>,<NUM> paired reads) as the enriched samples (Supplementary Tables <NUM>, <NUM>, <NUM>; <FIG>).

In order to determine the efficacy and reproducibility of the enrichment in more complex samples, enrichments were performed on replicates from metagenomic libraries with DNA isolated from a `healthy' individual's stool sample. Each library contained the same input concentration of DNA, and varying nanogram quantities of library and probes were used in nine combinations across three technical replicates (Supplementary Table <NUM>). To determine the fold-enrichment experiments were compared with traditional shotgun sequencing; <NUM> of the libraries (<NUM> in each set) were sequenced to a depth of over <NUM> million paired reads (Supplementary Table <NUM>). Resulting reads were subsampled to the same depth using seqtk, normalized as per the other experiments, and then mapped to CARD using the metagenomic mapping feature (rgi bwt) of RGI. Also included was a series of positive control enrichments with genomic DNA from E. coli C0002 that was used previously for enrichment in each set. In all cases, the results identified the same genes with a consistent number of reads mapping among these replicate enrichments (when subsampled to equal depths among sets) proving reproducibility regardless of probe and library ratio (Supplementary Table <NUM>; <FIG> and <FIG>).

Enrichment results from the positive control of E. coli C0002 control used in Phase <NUM>. Trimmed and deduplicated reads were mapped to CARD using RGIBWT, filtered by genes with probe coverage, an average read mapping quality >=<NUM>, and percent length coverage of a gene with reads >=<NUM>%.

Within each set, there was found an excellent correlation with previous results seen with E. coli C0002 in Trial <NUM> and <NUM> (Pearson correlations: ><NUM> for all pairs in Set <NUM>, ><NUM> for Set <NUM>, ><NUM> for Set <NUM>) (<FIG> and <FIG>). <FIG> and <FIG> show normalized read counts from C0002 control enrichments from three samples in each set (<FIG> corresponds to set <NUM>, <FIG> corresponds to set <NUM> and <FIG> corresponds to set <NUM>) to the two trials of individual enrichment. Genes with reads were filtered based on read mapping quality greater than or equal to <NUM>% and genes with probes mapping. Genes are ordered by sum of read counts from highest to lowest (left to right) with the ARO identifier shown along the X axis.

As will be described further below in the context of <FIG>, negative controls can be implemented to suppress false positives (Type I Error) during analysis. To track and measure the contamination in the lab and chemicals, a negative control of a blank DNA extraction was included and processed identically to the DNA used in Phase <NUM> and Phase <NUM> throughout library preparation, enrichment, and sequencing. A negative reagent control was also included throughout enrichment. For Phase <NUM> in both Trial <NUM> and Trial <NUM>, a negligible amount of library DNA was found in the Blank after enrichment and very few of the sequenced reads were associated with the indexes used for the Blank library (between <NUM>% and <NUM>% of sequenced reads; Supplementary Table <NUM>, Supplementary Table <NUM>).

After trimming and removing duplicates, more than <NUM>% of these reads mapped to CARD with only ten genes in Trial <NUM> with at least <NUM> reads each and percent length coverage (>=<NUM>), read mapping quality (>=<NUM>) and probes mapping (Supplementary Table <NUM>).

For Phase <NUM>, only the Blank from Set <NUM> produced sufficient reads to map to CARD (<NUM>% reads mapping), and <NUM> genes were identified (Supplementary Table <NUM>). Of these genes, two are found only in the blank sample, two are found in both shotgun and enriched libraries (tetQ and acrF), but <NUM> genes overlap between the blank and enriched libraries.

Across the enriched samples, with the full number of reads and no filters, an average of <NUM>% of reads map to CARD with on average <NUM> genes identified with at least <NUM> reads, compared to <NUM>% mapping in the shotgun libraries and <NUM> genes on average (<FIG>; Supplementary Table <NUM>).

For the enriched samples, trimmed and deduplicated reads were mapped to CARD using RGIBWT, filtered by genes with at least <NUM> reads, those with probes, an average read mapping quality >=<NUM>, and length coverage of a gene with reads >=<NUM>%. For the shotgun samples, trimmed and deduplicated reads were mapped to CARD using RGIBWT, filtered by genes with an average read mapping quality >=<NUM> and read length coverage of a gene >=<NUM>%. EN = enriched, UN = shotgun.

Significantly more genes with at least <NUM>, <NUM>, and <NUM> reads from each enriched sample were found as compared to the shotgun samples and that the average percent coverage of a gene by reads in the enriched samples is <NUM>-fold higher (<FIG>). In <FIG>, for the enriched and shotgun samples, the full number of reads for each sample were mapped to CARD using rgi bwt. <FIG> shows the percentage of reads mapping to CARD. For <FIG>, genes were counted with at least <NUM>, <NUM> and <NUM> reads and filtered for mapping quality (>=<NUM>), percent coverage by reads (>=<NUM>) and probes mapping (only for the enriched samples). <FIG> shows the average percent coverage of all genes with at least <NUM> reads in each sample after the same filters used in <FIG>.

Less than <NUM>% of reads (at between <NUM> million and <NUM> million reads) overall in the shotgun stool samples mapped to CARD, which is consistent with the expectation that resistance genes represent a minor proportion of the total gut microbiome in healthy individuals (Supplementary Table <NUM>). When subsampled to the same depth as their enriched pairs (between <NUM>,<NUM> and <NUM>,<NUM> reads), the results identified on average <NUM> (range: <NUM> - <NUM>) antibiotic resistance determinant with at least <NUM> reads after filtering in the shotgun samples (Supplementary Table <NUM>).

For the enriched samples, reads were subsampled to <NUM>,<NUM> reads and mapped to CARD using RGIBWT. Results were filtered by genes with at least <NUM> reads, those with probes, an average read mapping quality >=<NUM>, and length coverage of a gene with reads >=<NUM>%. For the shotgun samples, reads were subsampled to their paired enriched sample and mapped to CARD using RGIBWT. Results were filtered by genes with an average read mapping quality >=<NUM> and read length coverage of a gene >=<NUM>%. EN = enriched, UN = shotgun.

Conversely, when subsampled to the depth of the lowest enriched sample (<NUM>,<NUM> reads), on average <NUM> ARGs in the enriched libraries post-filtering with at least <NUM> reads were identified (Supplementary Table <NUM>). For further analysis of the shotgun data, the full number of reads was used and the probe-mapping filter was omitted to allow inclusion of genes that the probes do not target. Finally, as there were only a few genes with reads at <NUM>% read length coverage in the shotgun samples, the cut-off was reduced to a <NUM>% length coverage by reads filter for sufficient analyses.

The genes and their read counts that passed the chosen filters (at least <NUM> reads, <NUM>% gene length coverage by reads, mapping quality at least <NUM> and probes mapping) were combined within each set to compare between probe and library ratios in subsampled and full read samples through both enrichment and shotgun sequencing. With the full number of reads, <NUM>/<NUM> (<NUM>%) of genes detected overlap among all enriched libraries (n = <NUM>), while there were identified <NUM> genes of a total <NUM> (<NUM>%) in all the shotgun libraries (n = <NUM>, Supplementary Table <NUM>, <NUM>).

We calculated the overlap of genes with at least <NUM> reads passing the percent length coverage by reads (>=<NUM>%), average read mapping quality (>=<NUM>) and probe mapping (except for shotgun libraries) filters.

When subsampled to the lowest enriched read coverage (<NUM>,<NUM> reads), there are no genes that overlap between all six shotgun libraries, while <NUM>/<NUM> (<NUM>%) of genes overlap across all <NUM> enriched libraries (Supplementary Table <NUM>).

Libraries were subsampled to the same number of reads within sets and overall (<NUM>,<NUM> reads). Shotgun libraries were subsampled to the same number of reads as the lowest enriched library overall. Resulting genes with at least <NUM> reads were filtered for percent coverage by reads (>=<NUM>%), average mapping quality (>=<NUM>) and probe mapping (except for the shotgun samples).

Comparing among subsampled enriched libraries (<NUM>,<NUM> reads), the majority (<NUM>/<NUM>) of genes missing in at least one sample are those with on average less than twenty reads across the <NUM> libraries (Supplementary Tables <NUM>; <FIG>). For <FIG>, enriched reads from <NUM> libraries were subsampled to <NUM>,<NUM> reads, mapped to CARD through rgi bwt. The reads were mapped to CARD through rgi bwt and filtered for genes with probes mapping, with greater than or equal to <NUM>% length coverage by reads and an average read mapping quality >=<NUM>. Read counts were log-transformed and combined into a heatmap ordered by average read counts across the <NUM> enriched samples. The order of genes with higher read counts is consistent among enriched samples (<FIG>). This phenomenon with the shotgun samples is also seen at the full number of reads where there is a high agreement in read counts for genes expected or known to be present in higher abundance (i.e. gene copy number) and a more significant discrepancy between reads targeting lower abundance genes (<FIG>). For <FIG>, the full number of reads from the <NUM> enriched and shotgun pairs were mapped to CARD through rgi bwt. The results were filtered for genes with greater than or equal to <NUM>% read length coverage and an average read mapping quality >= <NUM>. Read counts were normalized by kb of gene and reads available for mapping, log-transformed and combined into a heatmap. Genes are ordered by sum of read counts. ARO numbers from CARD are shown on the right-hand side of the heatmap.

Thus, enrichment does not in some way bias the prevalence of rank order of AMR in these samples. Finally, both methods resulted in excellent correlation among technical replicates individually (Pearson correlation <NUM> for shotgun and <NUM> for enriched; <FIG> and <FIG>).

It was found that enrichment exceeded shotgun sequencing by identifying more unique antibiotic resistance genes at much lower sequencing depths. The enriched samples provided a more diverse representation of ARGs at less than <NUM>,<NUM> paired reads compared to over <NUM> million reads in the shotgun samples (<FIG>). For <FIG>, the AmrPlusPlus Rarefaction Analyzer was used with subsampling every <NUM>% of the total reads and a gene read length of at least <NUM>% to identify antibiotic resistance genes. The solid lines show individual sequencing experiments and the dotted lines are the logarithmic extrapolations beyond the experimental sequencing depth.

With the full number of reads in both methods (between <NUM>- and <NUM>-fold more in the shotgun samples than the enriched samples), the average fold-enrichment is greater than <NUM>-fold and there are still <NUM> to <NUM> fewer genes in the shotgun samples (part (A) of <FIG>; Supplementary Table <NUM>). For the enriched and shotgun samples, the full number of reads for each sample were mapped to CARD using rgi bwt and the results were filtered for genes with probes mapping, with reads with an average mapping quality >=<NUM> and a percent length coverage of a gene by reads greater than or equal to <NUM>%. In part (A) of <FIG>, read counts were normalized per kilobase of reference gene per million reads sequenced (RPKM) and log transformed to produce the heatmap. The rows are grouped based on resistance mechanisms as annotated in CARD (not all mechanisms and classes are shown). ABC = ATP-binding cassette antibiotic efflux pump; MFS = major facilitator superfamily antibiotic efflux pump; RND = resistance-nodulation cell division antibiotic efflux pump; MLS = macrolides, lincosamides, streptogramins. ii) The number of reads used for mapping in each sample.

In most cases, there are only a few genes found via shotgun that are missing in the enriched paired sample (between <NUM> and <NUM>; <NUM> unique genes). Only between <NUM> to <NUM> genes in each sample is predicted to be targeted by probes for a total of <NUM> unique genes not identified in the enriched counterpart of each pair (Supplementary Table <NUM>). Of these, only one, novA (ARO: <NUM>), is missing from all enriched samples but is present in all shotgun samples with ><NUM> reads, mapping quality >=<NUM> and percent length coverage by reads >=<NUM>%. The other <NUM> genes (macB (ARO: <NUM>), vanRG (ARO: <NUM>), vanSG (ARO: <NUM>), smeE (ARO: <NUM>), cfxA6 (ARO: <NUM>), cepA (ARO: <NUM>)) are found in only a few shotgun samples with less than <NUM> reads and less than <NUM>% read length coverage on average (Supplementary Table <NUM>; Supplementary Table <NUM>).

When combined, the enriched libraries cluster separately from the shotgun libraries with a stronger correlation (<NUM> compared to <NUM> for the shotgun libraries; <FIG>).

Supplementary Table <NUM> compares genes with reads for shotgun and enriched stool library pairs. The full number of reads from shotgun and enriched pairs were mapped to CARD using rgi bwt. Results samples were filtered for gene with at least <NUM> reads, those probes mapping (only for the enriched samples), average read mapping quality >=<NUM> and average read length coverage >=<NUM>%. Filtered genes and their normalized read counts (RPM) from each enriched/shotgun pair were combined to compare and determine the fold-enrichment.

The overlap was then compared between all <NUM> enriched samples and the six shotgun-sequenced libraries and included genes found through shotgun without any probes mapping. There were found a total of <NUM> genes with at least <NUM> reads between all libraries of which, <NUM> are overlapping between methods, <NUM> are unique to the enriched libraries, and <NUM> are unique to the shotgun libraries (part (B) of <FIG>; Supplementary Table <NUM>). In part (B) of <FIG>, on the left, overlap of genes found with at least <NUM> reads, a percent coverage greater than or equal to <NUM>% and an average mapping quality of reads greater than or equal to <NUM> in the <NUM> enriched and <NUM> shotgun samples. Between all samples, enriched or shotgun sequenced, there were <NUM> genes with reads passing these filters; <NUM> overlap, <NUM> are unique to the enriched, and <NUM> are unique to the shotgun samples. On the right, of the <NUM> genes only identified through shotgun sequencing, only <NUM> of these genes are predicted to be targeted by probes.

Of the <NUM> genes not found in any enriched library, only <NUM> are predicted to be targeted by probes, while the remaining were not in CARD when the probes were initially designed (<NUM>) or had probes that were removed during design and filtering (<NUM>). Of the four genes with predicted probes, cfxA6 is present in all enriched samples but was filtered out by mapping quality; vanSG is only present in <NUM>/<NUM> shotgun samples at less than <NUM>% gene length coverage by reads; cepA is found in enriched samples but at less than <NUM> reads; finally, there were identified novA in all shotgun samples but in only a few enriched samples at less than <NUM> reads and less than <NUM>% read length coverage. Despite the few genes that are missing from the enriched samples, even with over <NUM>-fold more sequencing depth, shotgun sequencing did not provide the same resolution as enrichment.

Increased interest in targeted capture approaches has resulted in the design of probesets for the detection of viruses, bacteria, and more recently, antibiotic resistance elements (Depledge et al. , <NUM>; Allicock et al. , <NUM>; Lanza et al. , <NUM>; Noyes et al. Although this study is not the first to employ targeted capture for antibiotic resistance genes, focus was placed on a rigorous probe design, reduced input library and probe concentrations, and robust validation to produce a cost-effective alternative to shotgun sequencing. Finally, there are many considerations when designing a probeset including choosing an appropriate reference database and how the probe sequences are determined (Mercer et al. , <NUM>; Metsky et al. , <NUM>; Enk et al. , <NUM>; Phillippy, <NUM>; Douglas et al.

In ancient genomic studies, many samples yield negligible, if any, endogenous DNA molecules to analyse often requiring extensive pre-screening (Pääbo et al. , <NUM>, Damgaard et al. In many samples, the target sequences represent <<NUM>% of the total DNA or may be inherently difficult to extract (i.e. Mycobacterium tuberculosis from direct clinical samples for sequencing) and in many cases the sample itself (eg. , blood, stool, soil) contains inhibitors of downstream steps in library generation (Votintseva et al. , <NUM>; Rantakokko-Jalava, & Jalava, <NUM>; Schrader et al. , <NUM>; Levy-Booth et al. Since microbial DNA and the target antibiotic resistance gene fragments can represent rare components in clinical and environmental samples, prior experience with ancient DNA samples guided experimental design. Given the random fragmentation that occurs through sonication and the nature of sequencing library preparation, it is difficult to predict the exact nature of all DNA molecules that will comprise the final library used in hybridization (in terms of number and length of antibiotic resistance element present on each fragment and the proportion of the library that contains resistance elements). As shown, even with a single DNA extract from an individual stool sample followed by multiple library preparations and sequencing on different days, the composition of antibiotic resistance elements recovered through shotgun sequencing of replicate libraries varies (only <NUM>% of genes overlap between all samples). There was also observed some variability in enrichment with <NUM>% of genes overlapping between the <NUM> libraries with <NUM> reads or more.

Others have suggested designing one probe per gene or tiling probes across a gene without overlap (1X coverage) (Noyes et al. With BacCapSeq, over <NUM> million probes were designed to target protein-coding sequences from bacterial pathogens (including AMR from CARD and virulence factors) with an average <NUM>-nucleotide distance between probes along their targets (Allicock et al. This inter-probe distance and random distribution of probes across sequences from various pathogens may reduce specificity for individual organisms and reduce on-target efficiency. Furthermore, while a well-designed probe per gene may reduce off-target sequencing, this approach risks falsely excluding genes if the specific DNA fragment targeted by that probe is not by chance included in the library or is in a very low concentration and thus simply missed due to selection and bias during DNA extraction and library preparation. In order to successfully identify a gene present in low concentration using a spaced probe tiling strategy, one may require multiple DNA extractions, library preparations, and enrichment reactions along with deeper sequencing. A tiling approach with dense and highly overlapping probes, similar to the probe design herein, increases the likelihood of capturing DNA molecules resulting in efficient enrichment and higher recovery but comes at the increased cost of production (Clark et al.

CARD was chosen as the reference database for the probe design and analysis due to its rigorous curation of antibiotic resistance determinants. The protein variant and protein overexpression model of the database was excluded as the genes included (gyrA, EF-Tu genes, efflux pump regulators, etc.) are likely to be found across many families of bacteria and were thought likely to overwhelm the probeset and sequencing effort with abundant, non-mutant antibiotic susceptible alleles. Instead, as the approach is focused on mobile genetic elements and acquired resistance genes that are often unique to individual families of bacteria, there was focus on CARD's protein homolog models targeting over <NUM> antibiotic resistance genes. There was extensive filtering of candidate probes against the human genome, other eukaryote, archaeal, and weakly matching bacterial sequences to provide a probeset that is bacterial ARG specific and avoids off-target hybridization. Focusing on one highly curated database of antibiotic resistance determinants (CARD) increases the likelihood of capturing bona fide sequences that are associated with known resistance and reduces the overall cost of the probe set and sequencing effort. Noyes et al. (<NUM>) increased the copy number of probes for large resistance genes families (beta-lactamases, etc.) where individual probes can target upwards of <NUM> genes, strategically increasing the concentration of those particular probes to promote equal affinity of each target gene in case there are multiple variants in a metagenome, yet the results suggest this is not necessary as enrichment did not bias the rank prevalence of AMR in the samples.

Other approaches targeting ARGs have additionally included species identifiers, plasmid markers and biocide or metal resistance (Lanza et al. , <NUM>; Noyes et al. , <NUM>; Allicock et al. These probesets range in target capacity from <NUM> genes (<NUM> Mb) (Noyes et al. , <NUM>) to over <NUM>,<NUM> genes (<NUM> Mb) (Lanza et al. , <NUM>) and comprise up to <NUM> million probes (Allicock et al. The presently described approach is more conservative in probe design (<NUM> Mb for <NUM> genes), but this allows for more probes per gene (<NUM>% of genes with greater than <NUM> probes) and increased depth of probe coverage (<NUM>. 47X average) which it is believed increases specificity and sensitivity. There was also a similar gene probe coverage to Lanza et al. with <NUM>% of targeted genes having greater than <NUM>% probe coverage where they have <NUM>% of genes covered by at least <NUM>% (Lanza et al. These alternative approaches also target a wide range of genes which can expand the amount of information obtained but increases the cost of synthesis and sequencing. As more information on environmental resistance mechanisms and new determinants emerge in resistomes, further additions to the probeset will need to be validated. In future benchmarking analysis experiments, such as those performed here, the probeset will need to be compared alongside other probe design approaches in order to inform the ideal design of a targeted-capture probeset for antibiotic resistance as has been done in other cases (Metsky et al. , <NUM>; Ávila-Arcos, <NUM>).

Additional metrics were assessed apart from probe design that can impact enrichment including library preparation method, input library amount, and probe to library ratio. The trials tested significantly lower inputs (<NUM> ng to <NUM> ng) than recommended (up to <NUM>µg of DNA for metagenomic samples) setting this approach apart from other targeted capture methods of AMR genes (Noyes et al. , <NUM>; Lanza et al. Others have looked at reducing the amount of input DNA from the recommended amount of <NUM> ng to <NUM> ng and saw no significant differences in results (Shearer et al. Despite a <NUM>-fold drop in DNA input (<NUM> ng vs the recommended <NUM> ng), there were observed no visible differences in the order of genes captured in the stool sample and normalized read counts were comparable among different library and probe amounts, suggesting that this approach is robust to substantial fluctuations yet still identifies substantially all antibiotic resistance genes in samples with low DNA yield. Thus, the enrichment is robust and amenable to different library preparation methods and DNA fragment sizes, despite what others have shown (Enk et al. , <NUM>, Clark et al. , <NUM>, Jones et al. , <NUM>, Ávila-Arcos, <NUM>).

Many variables can affect the outcome of the sequencing results, including DNA extraction, library preparation, sequencing depth, enrichment methods and analysis. Factors influencing metagenome characterization include (but are not limited to) sample collection (Franzosa et al. , <NUM>), DNA extraction (Mackenzie et al. , <NUM>), choice of library preparation (Jones et al. , <NUM>), and excessive PCR amplification of indexed libraries (Probst et al. , <NUM>) and can lead to misinterpretation of data or loss of information, including variability in high GC sequences (Jones et al. In comparative metagenomics, these variables make comparisons among samples difficult unless all methods are performed at the same time, using the same reagents and libraries sequenced to the same depth. It was attempted to reduce bias and assess enrichment by using the same DNA extract, library preparations, and enrichment in triplicate. Even among replicate libraries and shotgun sequencing runs, the differences in the number of genes identified at various sequence depths highlights the inherent variability in metagenomics (<FIG>).

Other attempts at standardization include using mock controls and spike-in controls which may allow for more accurate abundance calculations and account for variations in upstream methods (Pollock et al. , <NUM>; Mercer et al. , <NUM>; Jones et al. , <NUM>; Eisenhofer et al. In the mock controls, a positive control (E. coli C0002) was included for enrichment to ensure the methodology and probes were performing optimally at the time of hybridization.

Advantageously, negative controls can be implemented to suppress false positives (Type I Error) during analysis. Referring to <FIG>, an illustrative method for suppressing false positives during analysis of sample biological materials is shown in pictorial form. The sample biological materials may be, for example, one or more of blood, urine, feces, tissue, lymph fluid, spinal fluid and sputum, and may come, for example from a vertebrate, such as a human being, a livestock animal such as a cow, pig, goat, horse, etc., or from a domestic companion animal, such as a cat, dog, ferret, etc., or from an invertebrate (e.g. shrimp, crab, prawn, lobster etc.). The sample biological materials may be from a living organism, a cadaver of a formerly living organism, or an archaeological sample. The sample biological materials may also be from at least one environmental sample, including, mud, soil, water, effluent (e.g. wastewater, sludge, sewage or the like), filter deposits and surface films.

The analysis comprises one or more handling steps, where the term "handling" includes initial collection of the sample biological materials, as well as transfer steps, for example from one carrier to another. For each handling step during the analysis, there is obtained at least one sample handling blank <NUM> carrying a transfer substrate <NUM> mixed with at least part of the sample biological material <NUM>. The term "transfer substrate", as used in this context refers to a single reagent or a mixture of reagents, which may be mixed with water or another suitable substance. For example, buffers, reaction buffers, water, purification beads, or other reagents/solutions in the experiment, would be included within the meaning of "transfer substrate". The sample handling blank <NUM> is a reservoir or vehicle for the sample, and may be, for example, a test tube, a slide, or another suitable carrier. Additionally, for each handling step during the analysis, there is obtained at least one control blank <NUM> that will serve as a negative control. The control blank <NUM> corresponds to the sample handling blank <NUM> in that handling step, in that it is the same type of blank, preferably taken from the same batch of blanks (e.g. the same box of test tubes or slides) and carries the same transfer substrate <NUM> from same batch of transfer substrate (e.g. reagents from the same manufacturer and the same container). Importantly, the control blank <NUM> is isolated from the sample biological materials <NUM>, as shown by the dashed box <NUM>, so that the control blank <NUM> is not exposed to any of the sample biological materials <NUM>. The control blank <NUM> is a "negative control" or a sample that is carried through the experiment without any addition of "biological materials" but including all other reagents. Any handling (e.g. agitation, centrifuge, light exposure, heating, cooling, etc.) applied to the sample handling blank(s) <NUM> is replicated for the control blank(s) <NUM> while isolation is maintained. Isolation, in this context, means that any cross-contamination of the sample biological material <NUM> onto the control blank <NUM> is avoided; isolation does not otherwise preclude side-by-side processing so as to enable identification of potential contaminants that enter the reaction from the surrounding environment. The control blank <NUM> is isolated from the sample biological materials <NUM> but not necessarily from the surrounding environment.

While <FIG> shows only a single handling step <NUM>, it will be appreciated that there may be additional handling steps. For example, there may be an initial a collection step during which the sample biological materials are collected on a sample handling blank, and then one or more transfer steps where the sample biological materials are transferred from a preceding sample handling blank to a subsequent sample handling blank. For example, part of a surface film may be scraped off a surface using a sterile scraper (a first sample handling blank) and then transferred to a test tube with reagent (a second sample handling blank). Each step performed with a sample handling blank is replicated with control blank. So, for example, a sterile scraper from the same batch as was used to scrape the surface film, but isolated therefrom (a first control blank) would be brought into contact with a sterile test tube from the same batch as that which received the film, containing reagent from the same batch, but isolated from the film (a second control blank).

Following completion of all handling steps, there will be at least one final sample handling blank <NUM> carrying an admixture <NUM> of the transfer substrate(s) <NUM> from the handling steps <NUM> mixed with the sample biological materials <NUM>, and at least one final control blank <NUM> carrying the transfer substrate(s) <NUM> from the handling steps and isolated from the sample biological materials <NUM>.

A hybridization probe solution <NUM> containing at least one hybridization probe is then applied to each final sample handling blank <NUM> to produce at least one baited final sample handling blank <NUM>. The hybridization probe solution <NUM> comprises probes that hybridize to target DNA, which may be, for example AMR genes or other target DNA. The identical hybridization probe solution <NUM> is also applied to each final control blank <NUM>, hybridization probe solution identical to that applied to each final sample handling blank to produce at least one baited final control blank <NUM>. The terms "bait" and "baited" refer to a nucleotide probe that is complementary to a sequence of interest (target) and aimed at enriching that target through hybridization (complementarity of nucleotide base of target and bait/probe). The bait(s) may each be an oligonucleotide of <NUM> basepair lengths. All of the results above and the AMR gene enrichment are now published at https://doi. org/<NUM>/AAC. <NUM>-<NUM>.

Each baited final sample handling blank <NUM> is fed into a DNA sequencer <NUM>, for example an Illumina DNA sequencer to sequence sample bait-captured DNA <NUM> carried by the baited final sample handling blank <NUM>. Likewise, each baited final control blank <NUM> is also fed into the DNA sequencer <NUM> to sequence control bait-captured DNA <NUM> carried by the baited final control blank <NUM>. The sample bait-captured DNA <NUM> is then compared <NUM> to the control bait-captured DNA <NUM> to generate a final identified genetic sequence <NUM>. Genetic components that are common to the final sample handling blank <NUM> and the final control blank <NUM> and that pass a statistical significance test are discounted and excluded from the final identified genetic sequence <NUM>. The statistical significance test may include, for example, deduplication, mapping quality and length cut-offs (i.e. percent length coverage and the average depth of coverage of each probe-targeted region), linear normalization based on total sequencing effort, rarefaction analysis, and comparison of total mapped read counts for different bait / sample ratios. In some embodiments, MAPQ statistical cut-offs will be used to exclude spurious alignment of DNA sequences to AMR reference sequences, i.e. bwa-mem MAPQ < <NUM>, thus suppressing false positive results. In addition, measures of depth of read coverage and gene completeness may be used relative to AMR reference sequences, for example requiring alignment of at least <NUM> sequencing reads and at least <NUM>% coverage of AMR reference sequences by mapped reads for prediction of an AMR gene for a specific sample. Lastly, detection under the above criteria of any AMR gene in a control/blank may be interpreted as laboratory contamination and that gene may be excluded from consideration in experimental samples.

Including a negative control/control blank provides an idea of background contamination that should be considered when using the bait method on experimental samples and analyzing the sequence data. For example, one could compare all samples processed to a control blank/negative control using linear normalized counts of sequencing reads based on total sequencing effort after deduplication. The reads may be mapped to a reference of probe-targeted regions. Similarities between the blank sample and experimental samples may be flagged to consider removing these results as contamination. If there is overlap between the targeted regions captured in a control blank and sample handling blank and that overlap represents ≥<NUM>% of the reads mapping to that probe-targeted region that region could be considered as a contaminant. Also, if reads from the control blank map to a probe-targeted region and in ><NUM>% of the samples processed there are also reads mapping to that same probe-targeted region it could be considered as contamination.

Thus, the present approach also introduced negative controls, including a blank DNA extraction and blank enrichment sample (water with reagents), to measure the extent of exogenous DNA contamination that is ubiquitous in all laboratory settings and reagents (Eisenhofer et al. , <NUM>; Salter et al. , <NUM>; Minich et al. Only <NUM> - <NUM>% of reads (post-enrichment) from the negative controls had the corresponding Illumina index sequence, the remainder having indexes from experimental samples, suggesting that DNA exchange among samples during enrichment or cross-contamination is the primary concern in the method (Supplementary Table <NUM>; Supplementary Table <NUM>). Notably, the genes identified in the Blank results not arising from cross-contamination and also found in the enriched and shotgun results are commonly associated with bacteria identified in negative controls in microbiome studies (mainly Escherichia coli) and encode efflux systems or other intrinsic resistance determinants (mdtEFHOP, emrKY, cpxA, acrDEFS, pmrF, eptA, tolC). The two genes that were unique to the Blank results (drfa17 had <NUM> reads covering <NUM>%; aph(<NUM>")-Ib <NUM> reads with <NUM>% coverage) are associated with mobile genetic elements in Enterobacteriaceae and the latter has been previously associated with laboratory reagent contamination (Sandalli et al. , <NUM>; Wally et al. Despite standard methods to control for contamination (i.e. filter pipettes, PCR cabinets, and sterile DNA/RNA-free consumables), there was still found to be limited contamination likely stemming from reagents and/or the surrounding laboratory environment, further highlighting the importance of negative controls in all targeted capture experiments and meticulous reporting and publishing of a laboratory based `resistome' (Supplementary Table <NUM>; de Goffau et al. , <NUM>; Salter et al. , <NUM>; Eisenhofer et al. The de Goffau et al. reference highlights the importance of reporting the reagent microbiome (contamination that is often found in reagents that are commonly used in all experiments) as in certain studies it can skew results and lead to false-positives. The Salter et al. reference reports frequent contamination in microbiome analyses and how studies should report results alongside `background' controls so that "erroneous conclusions are not drawn from culture-independent investigations". The Eisenhofer et al. reference is an opinion article highlighting criteria that should be reported on controls in microbiome research. However, although these references suggest reporting contamination or including controls, they do not suggest including blank controls as described in the present disclosure. Because enrichment/targeted capture is so sensitive to the less abundant targets which could include slight amounts of contamination it is very important to include blank controls and report these results alongside experimental results.

As can be seen from the above description, the methods described herein represent significantly more than merely using categories to organize, store and transmit information and organizing information through mathematical correlations. The methods are in fact an improvement to the technology of genetic analysis of sample biological materials, as they provide for suppression of false positives (Type I Error), which facilitates improved accuracy. Moreover, the methods are applied using physical steps carried out on physical blanks and by using a particular machine, namely a DNA sequencer. As such, the methods are confined to genetic analysis of sample biological materials and represent a technical improvement thereto.

There are many available reference databases for mapping along with a variety of analytical tools (Arango-Argoty et al. , <NUM>; Asante et al. , <NUM>; Boolchandani et al. , <NUM>; Rowe and Winn, <NUM>; Berglund et al. , <NUM>; Hunt et al. , <NUM>; Inouye et al. Similar to other targeted capture approaches for ARGs, Bowtie2 was used for mapping the sequencing reads against the reference database from which the probes were designed (Noyes et al. , <NUM>; Lanza et al. One important factor with AMR genes is the sequence similarity between families and classes of antibiotic resistance determinants as well as with genes that do not necessarily confer resistance. The difficulty in separating uncharacterized determinants from known sequences has not been well-established. Previous attempts have used a percentage read coverage of genes filter or no filters when reporting resistance genes obtained through enrichment (Lanza et al. , <NUM>; Noyes et al. Read count (<NUM> vs <NUM> vs <NUM>), read mapping quality, percent coverage by reads, and probe coverage of genes were assessed before reporting the presence or absence of resistance genes. In order to be able to make comparisons between the shotgun and enriched samples, reliance was placed on what are considered very permissive thresholds for the shotgun data (<NUM>% length coverage by reads and average read mapping quality of <NUM>), which have not been rigorously evaluated for the correct identification or reporting of antibiotic resistance genes from metagenomic sequencing data. However, it is notable that the thresholds for the shotgun data were to obtain reasonable results at all.

Mapping quality (MAPQ) in Bowtie2 is related to the likelihood that an alignment represents the correct match of that read to the reference (Langmead and Salzberg, <NUM>). A mapping quality value of zero indicates that a read maps with low identity and/or that it maps to multiple locations (as the number of possible mapping locations increases the map quality decreases). In the case of the CARD reference database, there are many gene families (blaCTX-M, blaTEM, blaOXA) that are very similar in nucleotide sequence identity and therefore a read belonging to one member has the potential to map to multiple genes. This feature results in an inflated number of genes with reads and consequently reduces the mapping quality for many reads. Lanza et al. describe this phenomenon as the mapping allele network (Lanza et al. The read mapping filter was kept high, with a cut-off of <NUM> (maximum MAPQ <NUM>), when mapping to the respective genomes for each bacterial genome enrichment (Trial <NUM> and Trial <NUM>). In the pooled mock metagenomic samples, because of the similarity between genes in two strains of the same species (i.e. Pool <NUM> contains two E. coli genomes - C0002, C0094), a mapping quality cut-off of <NUM> was used based on the distribution of read mapping quality. Consequently, a high mapping quality cut-off may result in inflated false-negative results, removing potential genes because the reads map to many members of AMR gene families.

The procedure included assessment and correction for duplicate removal and differences in sequencing depth. Removal of duplicates allows for more accurate assessment of fold enrichment and removes bias introduced via amplification (Metsky et al. The probeset is predicted to target <NUM> genes from CARD, but in reality, the probes likely target many more divergent sequences. Others have shown that their probesets maintained up to <NUM>-fold enrichment with sequences that were <NUM>% similar to the target and that probes can be designed to tolerate up to <NUM> mismatches across a <NUM>-nucleotide probe (Noyes et al. , <NUM>; Metsky et al. More extensive databases, including CARD's Resistome and Variants data which contains over <NUM>,<NUM> predicted AMR allele sequences (CARD R&V version <NUM>. <NUM>), may provide additional information for variant and pathogen-of-origin identification.

The enrichment of resistance genes in the human gut microbiome samples resulted in a higher average percentage on-target (<NUM>%) when compared to other published capture-based methods, <NUM> (<NUM> - <NUM>%) (Lanza et al. , <NUM>), and a median of <NUM> (<NUM>% - <NUM>%) (Noyes et al. Overall, the probeset and method identified a greater diversity of antibiotic resistance genes in the human gut microbiome despite having been sequenced at <NUM> - <NUM>-fold lower depth when compared to their shotgun sequenced correlate. Similar to other studies with probesets for AMR, there was found to be an average fold-enrichment of <NUM> - <NUM> for enriched samples and an average of <NUM>% of genes detected between each pair of enriched and shotgun samples were identified in the enriched library. There was identified an average of <NUM> % (<NUM> - <NUM>) of genes from the shotgun samples in their paired enriched library. Noyes et al. reported a higher overlap with genes detected by both shotgun and enrichment approaches (<NUM>%) and Lanza et al. showed a slightly lower overlap of <NUM>%. Other research illustrates that enrichment maintains the frequency and rank order of genes when compared to shotgun results, similar to the enriched library results (Metsky et al. With a reduced depth of sequencing, it is evident that enrichment offers more valuable information in both the number of genes with reads as well as the depth and breadth of coverage of those genes (<FIG>). Only a few genes were absent in the enriched libraries when compared to the shotgun libraries. In the case of novA, which is <NUM>% GC, perhaps the probeset or hybridization conditions were not sufficient to capture the genes by the method described herein. The variant of the vanS (<NUM>% GC) sensor from vancomycin resistance gene clusters that could not be identified was covered by less than <NUM> reads in the shotgun samples, suggesting a very low abundance in the metagenome. Finally, the beta-lactamase genes cepA and cfxA6 had been excluded from the enriched results after filtering due to low mapping quality or less than <NUM> reads. The low mapping quality suggests that reads are mapping to other beta-lactamase genes in the reference database.

As enriched libraries only require a small proportion of a sequencing run, it was possible to sequence more libraries on a single run, which is much more cost-effective and time-efficient than deep shotgun sequencing. Although shotgun sequencing can provide additional information on other functions and genes of interest, targeted-capture provides a more robust, reproducible profile of a subset of genes from a metagenome at a fraction of the cost. Targeted capture provides many advantages to shotgun metagenomics when only a specific set of genes is in question across multiple samples.

This study presents a focused ARG probe-capture method and analysis approach validated against pure bacterial genomes, mock metagenomic libraries, and the gut microbiota as represented by human stool. Rigorous measurement of the performance of the probe design and methods was conducted to satisfy many of the parameters routinely discussed in targeted capture (Mamanova et al. These metrics include sensitivity and specificity (consistently high percentage of reads on target and recovery of probe-targeted sequences), uniform recovery of ARGs across bacterial genomes, reproducibility between library preparations, reduced cost and reduced amounts of input DNA. The targeted capture is reproducible with individual DNA samples isolated from multidrug-resistant bacteria and increased the recovery of probe-targeted regions in mock metagenomes compared to shotgun sequencing, with an associated reduction in cost. It is also easily scalable, as newly discovered ARGs can be easily added to the probeset iteratively. With a small amount of DNA from a single stool sample, enrichment uncovers more information about the antibiotic resistance determinants in the gut microbiome at a significantly lower depth of sequencing when compared to the shotgun sequencing results from the same sample. This probeset provides a cost-effective and efficient approach to identify antibiotic resistance determinants in metagenomes allowing for a higher-throughput when compared to a shotgun sequencing approach. The method reveals the resistome from a variety of environments including the human gut microbiome, unearthing the realities of antibiotic resistance now ubiquituous in commensal and pathogenic milieu. The importance of suppressing false positives during analysis of sample biological materials is also emphasized.

The reference for probe design was the protein homolog model of antibiotic resistance determinants (n = <NUM>,<NUM>) from the Comprehensive Antibiotic Resistance Database (version <NUM>. <NUM> of CARD released December <NUM>, <NUM>; Jia et al. Using PanArray (v1. <NUM>), there were designed probes of <NUM> nucleotide length across all genes with a sliding window of <NUM> nucleotides and acceptance of <NUM> mismatch across probes (Phillippy, <NUM>). To prevent off-target hybridization between the probes and non-bacterial sequences, the candidate set of probe sequences (n = <NUM>,<NUM>) was compared against the human reference genome and GenBank's non-redundant nucleotide database through BLAST (blastn) (Altschup et al. , <NUM>; Benson et al. Probes with high sequence similarity (><NUM>%) and probes with high-scoring segment pairs (HSPs) greater than <NUM> nucleotides of a possible <NUM> were discarded (n=<NUM>). The procedure identified and discarded <NUM> probes with human hits, <NUM> probes with eukaryotic hits, <NUM> that were similar to viral references, and <NUM> that were similar to archaeal sequences. Probes with HSPs less than <NUM> nucleotides of a possible <NUM> to bacterial sequences were additionally discarded, resulting in a set of <NUM>,<NUM> probes. The candidate list was further filtered to omit probes that had bacterial HSPs that were <<NUM>% identity, resulting in a candidate list of <NUM>,<NUM> probes.

Probe sequences, along with <NUM>-<NUM> nucleotide(s) upstream and downstream of the probe location on the target gene, were sent to Arbor Biosciences (Ann Arbor, MI) for probe design. Additional <NUM> nucleotide probes were created across the candidate probe and flanking sequences at four times tiling density, resulting in <NUM>,<NUM> probes. Sequences with <NUM>% identity over <NUM>% length were collapsed using USEARCH (usearch -cluster_fast - query_cov <NUM> -target_cov <NUM> -id <NUM> -centroids) resulting in a set of <NUM>,<NUM> final probes (Edgar, <NUM>). Filtering similar to as described above was performed against the human genome; no probes were found to be similar. Arbor Biosciences (Ann Arbor, MI) synthesized this final set of <NUM>,<NUM><NUM>-nt biotinylated ssRNA probes through the custom myBaits kit.

To predict the genes that can be targeted by the probes, a Bowtie2 (settings used: bowtie2 --end-to-end -N <NUM> '-L <NUM>' -a) alignment was performed to compare the set of <NUM>,<NUM> probe sequences to the <NUM>,<NUM> nucleotide reference sequences of the protein homolog models in CARD (version <NUM>. <NUM> released <NUM>-<NUM>-<NUM>). Probes were mapped to all possible locations and the resulting alignment file was manipulated through samtools and bedtools to determine the number of instances that a probe mapped to a nucleotide sequence in CARD (samtools view -b, samtools sort, Langmead and Salzberg, <NUM>; Li et al. , <NUM>; Quinlan and Hall, <NUM>). The length coverage of each gene in CARD (i.e. fraction of the gene sequence with corresponding probes) was calculated (bedtools genomecov -ibam), and genes with zero coverage were determined (Quinlan and Hall, <NUM>). Furthermore, it was determined that the depth of coverage of each gene in CARD (i.e. the number of probes mapped to the gene) from the alignment (bedtools coverage -mean; Quinlan and Hall, <NUM>). The GC content of probe sequences and nucleotide sequences in CARD was calculated using a Python3 script from https://gist. com/wdecoster/8204dba7e504725e5bb249ca77bb2788. Melting temperature (Tm) was determined using OligoArray function melt. pl (-n RNA, -t <NUM> -C <NUM>. 89e-<NUM>) (Rouillard et al. Finally, the mechanisms and drug classes of each resistance gene were determined using annotations found in CARD. Prism <NUM> for macOS (https://www. com) was used to generate plots in <FIG>.

Clinical bacterial isolates were obtained from the IIDR Clinical Isolate Collection which consists of strains from the core clinical laboratory at Hamilton Health Sciences Centre (Supplementary Table <NUM>). Isolates were received from the clinical microbiology lab and grown on BHI plates at <NUM> for <NUM> hours. A colony was inoculated into <NUM> LB and grown at <NUM> with aeration for <NUM> hours, at which point genomic DNA was isolated using the Invitrogen Purelink Genomic DNA kit (Carlsbad, CA). If DNA was not isolated the same day, cell pellets were stored at -<NUM>. While genomic DNA from all other strains was only isolated once, DNA from a cell pellet of Pseudomonas aeruginosa C0060 was extracted additionally using the Invitrogen PureLink Genomic Kit (Carlsbad, CA) with a varied genomic lysis/binding buffer (<NUM> EDTA, <NUM> Tris-HCl, <NUM> GuSCN, <NUM>% Triton-X-<NUM>, <NUM>% Tween-<NUM>, pH <NUM>). The quantity of purified DNA was measured via absorbance (Thermo Fisher Nanodrop, Waltham, MA) and visualized for purity using agarose gel electrophoresis. A human stool sample was obtained from a healthy volunteer for the purpose of culturing the microbiome with consent (HiREB#<NUM>-T). DNA was extracted the same day following a modified protocol as described in Whelan et al. Briefly, samples were bead beat, centrifuged, and the supernatant further processed using the MagMax Express <NUM>-Deep Well Magnetic Particle Processor from Applied Biosystems (Foster City, CA) with the multi-sample kit (Life Technologies #<NUM>). DNA was stored at -<NUM> until used for library preparation.

Library preparation for genome sequencing of the clinical bacterial genomes was completed by the McMaster Genomics Facility in the Farncombe Institute at McMaster University (Hamilton, ON) using the New England Biolabs (Ipswich, MA) Nextera DNA library preparation kit. Libraries were sequenced using an Illumina HiSeq <NUM> or Illumina MiSeq v3 platform using V2 (<NUM> x <NUM> bp) chemistry. Paired sequencing reads were processed through Trimmomatic v0. <NUM> to remove adaptors, checked for quality using FASTQC (http://www. bioinformatics. uk/projects/fastqc/), and de novo assembled using SPAdes v <NUM>. <NUM> (Bolger et al. , <NUM>; Bankevich et al. The Livermore Metagenomics Analysis Toolkit (LMAT) v <NUM>. <NUM> was used to identify the bacterial species and screen for contamination or mixed culture, while the Resistance Gene Identifier (RGI; version <NUM>. <NUM>) from CARD was used on the SPAdes contigs to identify Perfect (<NUM>% match) and Strict (<<NUM>% match but within CARD similarity cut-offs) hits to CARD's curated antibiotic resistance genes (Ames et al.

Two phases of experiments were performed, the first with genomic DNA from cultured multi-drug resistant bacteria (Phase <NUM>) and the second with metagenomic DNA from a human stool sample (Phase <NUM>). The two trials in Phase <NUM> differ in their library preparation methods as described below (the major difference being library fragment size by sonication). In both trials, genomic DNA from strains was tested individually (Escherichia coli C0002, Pseudomonas aeruginosa C0060, Klebsiella pneumoniae C0050, and Staphylococcus aureus C0018) (Supplementary Table <NUM> and <NUM>). In addition, varying nanogram amounts (based on absorbance) of each genome were combined prior to library preparation to create "mock metagenomes" referred to as Pool <NUM> (C0002, C0018, C0050, C0060), Pool <NUM> (C0002, C0018, C0050, C0060), and Pool <NUM> (C0002, C0018, C0050, C0060, Klebsiella pneumoniae C0006, Staphylococcus aureus C0033, Escherichia coli C0094, Pseudomonas aeruginosa C0292). Amounts of each strain in each Pool varied between trials (Supplementary Table <NUM>). Phase <NUM> consists of <NUM> replicates referred to as Set <NUM>, Set <NUM>, and Set <NUM> wherein DNA extract from one individual human stool sample was split evenly into each Set. From these aliquots, there were generated <NUM> individually indexed sequencing libraries and performed capture with varying library and probe ratios (Supplementary Table <NUM>). In all trials and sets, a blank DNA extract was carried throughout library preparation and enrichment, while an additional negative reagent control was introduced during enrichment.

Library preparations were performed in a PCR clean hood, using bleached equipment, and UV-irradiated before use to prevent non-endogenous DNA contamination. Trial <NUM> used the NEBNext Ultra II DNA library preparation kit (New England Biolabs, Ipswich, MA) through the McMaster Genomics Facility. Based on absorbance and fluorometer values (QuantiFluor, Promega, Madison, WI), approximately <NUM> microgram of individual bacterial genomic DNA or pools of genomic DNA was sonicated to <NUM> base pairs (bp) and there were prepared dual-indexed libraries with a size selection for <NUM>-<NUM> bp inserts. A negative control consisting of a DNA extraction blank was included throughout the process. Post-library quality and quantity verification was performed using a High Sensitivity DNA Kit for the Agilent <NUM> Bioanalyzer (Agilent Technologies, Santa Clara, CA) and quantitative PCR using the KAPA SYBR Fast qPCR master mix for Bio-Rad machines (Roche Canada) using primers for the distal ends of Illumina adapters and the following cycling conditions: <NUM>) <NUM> for <NUM>; <NUM>) <NUM> for <NUM> sec; <NUM>) <NUM> for <NUM> sec; <NUM>) Repeat <NUM>-<NUM> for <NUM> cycles total; <NUM>) <NUM> for <NUM> <NUM>) <NUM> hold. Illumina's PhiX control library (Illumina, San Diego, CA) was used as a standard for quantification. To increase the concentration of some libraries, samples were lyophilized and re-suspended in a smaller volume of nuclease-free water to provide approximately <NUM> nanograms of DNA for enrichment in an appropriate volume.

In Trial <NUM>, the same genomic DNA, except for P. aeruginosa C0060 which was re-isolated, was used for library construction through a modified protocol (Supplementary material; Meyer and Kircher, <NUM>). Briefly, blunt end repair, adapter ligation, a library size-selection, and indexing PCR were performed on ~<NUM> nanograms of sonicated DNA (<NUM>-<NUM> bp) again including a negative control of a blank DNA extraction throughout the process. The McMaster Genomics Facility performed library quality control as described above.

One DNA extract from a donor stool sample was divided into three <NUM>µL aliquots of approximately <NUM> nanograms each (based on fluorometer QuantiFluor results). DNA was sonicated to <NUM> bp and split into <NUM> individual library reactions (<NUM> ng in <NUM>µL). Dual-indexes libraries (NEBNExt Ultra II library kits, New England Biolabs, Ipswich, MA) were prepared with a size-selection for <NUM>-<NUM> bp library fragments and <NUM> (Set <NUM>), <NUM> (Set <NUM>), or <NUM> cycles (Set <NUM>) of amplification. The McMaster Genomics Facility performed library quality control (Agilent Bioanalyzer <NUM> and quantitative PCR as described above). Positive control libraries were generated using Escherichia coli C0002 genomic DNA (<NUM> ng of sonicated DNA) and a negative control with a blank DNA extract.

Enrichments were performed in a PCR clean hood, with a water bath, thermal cyclers and heat blocks located nearby. The probeset was provided by Arbor Biosciences (Ann Arbor, MI) and diluted with deionized water. For enrichment of bacterial genomes in Trial <NUM>, there were used <NUM> ng of probes and <NUM> ng of each library following the MYBaits Manual V3 (Arbor Biosciences, Ann Arbor, MI) at a hybridization temperature of <NUM> for <NUM> hours (see supplementary methods for more details). After hybridization and capture with Dynabeads MyOne Streptavidin C1 beads (Thermo Fisher, Waltham, MA), the resulting enriched library was amplified through <NUM> cycles of PCR (cycling conditions in Supplementary materials) using the KAPA HiFi HotStart polymerase with library non-specific primers (Kapa Library Amplification Primer Mix (10X), Sigma-Aldrich, St. Louis, MO). A <NUM>µL aliquot of this library was amplified in an additional PCR reaction for <NUM> cycles (same conditions as above) and then purified. The capture in Trial <NUM> was performed the same as Trial <NUM> but applied <NUM> cycles of amplification post-capture (PCR conditions in Supplementary details). The McMaster Genomics Facility performed library quality control as described above. Libraries were pooled in equimolar amounts and sequenced to an average of <NUM>,<NUM> clusters by MiSeq V2 (2x250 bp reads). Pre-enrichment libraries for the "mock metagenomes" were sequenced on a separate MiSeq V2 (2x250 bp reads) run from the enriched libraries to an average of <NUM>,<NUM> clusters each. From both Trial <NUM> and Trial <NUM>, negative controls of blank extractions carried through library preparation and enrichment were sequenced on separate individual MiSeq <NUM> x <NUM> bp runs. After de-multiplexing, all possible index combinations were retrieved to identify potential cross-contamination of libraries as well as exogenous bacterial contamination.

Based on qPCR values and the average fragment sizes of each library generated from the human stool DNA extract, varying nanogram amounts of probes (<NUM>, <NUM>, <NUM>, <NUM>, <NUM> ng) and library (<NUM>, <NUM>, <NUM> ng) were combined for enrichment (Supplementary Table <NUM>). Along with the Negative Control - Blank library, additional negative controls were introduced during enrichment using dH<NUM>O to replace the volume normally required for library input. Capture probes were diluted with deionized and then prepared at the appropriate concentrations for each probe:library ratio. Enrichment was performed following the MYBaits Manual V4 (Arbor Biosciences, Ann Arbor, MI) at a hybridization temperature of <NUM> for <NUM> hours. After hybridization and capture with Dynabeads (Thermo Fisher, Waltham, MA), the resulting enriched library was amplified through <NUM> cycles of PCR using the KAPA HiFi HotStart ReadyMix polymerase with library non-specific primers and the following conditions: <NUM>) <NUM> <NUM> sec; <NUM>) <NUM> <NUM> sec; <NUM>) <NUM> for <NUM> sec; <NUM>) <NUM> for <NUM> sec; <NUM>) Repeat step <NUM> - <NUM> for <NUM> cycles total; <NUM>) <NUM> for <NUM>; <NUM>) <NUM> hold (Sigma-Aldrich, St. Louis, MO). The resulting products were purified using KAPA Pure Beads at a 1X volume ratio and eluted in <NUM> Tris, pH <NUM>. Purified libraries were quantified through qPCR using 10X SYBR Select Master Mix (Applied Biosystems, Foster City California) for BioRad Cfx machines, Illumina specific primers (10X primer mix from KAPA) and Illumina's PhiX Control Library as a standard. Cycling conditions were as follows: <NUM>) <NUM> for <NUM>; <NUM>) <NUM> for <NUM>; <NUM>) <NUM> for <NUM> sec; <NUM>) <NUM> for <NUM> sec; Repeat <NUM> - <NUM> for <NUM> cycles total. Enriched libraries were pooled in equimolar amounts based on qPCR values and the McMaster Metagenomic Sequencing facility performed library quality control as described above. Finally, the enriched libraries (average of <NUM>,<NUM> clusters) and the pre-enrichment libraries (average of <NUM>,<NUM>,<NUM> clusters) were sequenced by MiSeq V2 2x250 bp. The negative controls of blank extractions carried through library preparation and enrichment were sequenced on separate individual MiSeq <NUM> x <NUM> bp runs. After de-multiplexing, all possible index combinations were retrieved.

In order to identify probe-targeted regions and coordinates that overlap with predicted resistance genes based on RGI results for the individual bacterial strains, the probeset was aligned to the draft reference genome sequence using Bowtie2 version <NUM>. <NUM> (Langmead and Salzberg, <NUM>). Skewer version <NUM>. <NUM> (skewer -m pe -q <NUM> -Q <NUM>) was used to trim sequencing reads (enriched or shotgun), bbmap version <NUM> dedupe2. sh to remove duplicates, and mapped reads to the bacterial genomes using Bowtie2 version <NUM>. <NUM> (--very-sensitive-local unique sites only) (Jiang et al. , <NUM>; https://sourceforge. net/projects/bbmap/; Langmead and Salzberg, <NUM>). Aligned reads were filtered based on mapping quality (>= <NUM>) and length (>= <NUM> bp) using various tools: samtools version <NUM>, bamtools version <NUM>. <NUM>, and bedtools version <NUM>. <NUM> (Li et al. , <NUM>, Barnett et al. , <NUM>, Quinlan and Hall, <NUM>). It was determined that the number of reads mapping to the reference genome overall and the number of reads mapping within a predicted probe-targeted region using genomic coordinates and bedtools (intersectBed; Quinlan and Hall, <NUM>). The percent length coverage and the average depth of coverage of each probe-targeted region with at least one read was determined using bedtools coverage (-counts, -meant and default function) (Quinlan and Hall, <NUM>). Read counts were normalized by the number of reads mapping per kb of targeted region per total number of mapping reads to a particular genome. The number of genes with at least <NUM>, <NUM> or at least <NUM> reads were counted and their percent length coverage by reads was determined.

The enriched and shotgun reads for the human stool sample were processed in the same way as for the bacterial isolates. Subsampling of reads was performed using seqtk version <NUM>-r94 (seqtk sample -s100; https://github. com/lh3/seqtk). The bwt feature in RGI (beta of version <NUM>. <NUM>; http://github. com/arpcard/rgi) was used to map trimmed reads using Bowtie2 version <NUM>. <NUM> to the CARD (version <NUM>. <NUM>) generating alignments and results without any filters (Langmead and Salzberg, <NUM>). The gene mapping and allele mapping files were parsed to determine the number of genes in CARD with reads mapping (at least <NUM>, at least <NUM>, and at least <NUM> reads) under various filters. After plotting mapping quality for each read in every sample across the <NUM> sets, an average mapping quality (mapq) filter of <NUM> was chosen. A percent length coverage filter of a gene by reads of <NUM>, <NUM> and <NUM>% was assessed and the most permissive (<NUM>%) was chosen for comparison between the shotgun and enriched samples. Finally, a filter was used to check for the probes mapping to the reference sequences in most comparisons except to identify genes in the shotgun samples that would not be captured by the probeset. The same analysis process was repeated for the Negative Controls - Blank libraries after dividing the reads generated after enrichment among the index combinations used in the respective Phase, Trial or Set. In Set <NUM>, there were very few reads associated with the Blank library after enrichment, so the raw sequencing reads were used for analysis. For the Negative Control in Set <NUM>, deduplication was omitted, and the process could not identify any reads associated with the Blank indexes after sequencing for Set <NUM>. Read counts were normalized using the All Mapped Reads column in the gene mapping file and the reference length in kb along with the total number of reads available for mapping (per million) (RPKM). Hierarchical clustering was performed using Gene Cluster <NUM> and Java Tree View v <NUM>. 6r4 (http://bonsai. jp/~mdehoon/software/cluster/software. htm) using a log transformation and clustering arrays with an uncentered correlation (Pearson) and average linkage. For rarefaction analysis, the procedure first aligned trimmed reads against CARD (version <NUM>. <NUM>) using Bowtie2, followed by filtering for mapping quality >= <NUM> (Langmead and Salzberg, <NUM>). This file along with an annotation file for CARD was analyzed with the AmrPlusPlus Rarefaction Analyzer (http://megares. org/amrplusplus; Lakin et al. , <NUM>) with subsampling every <NUM>% of total reads and a gene read length coverage of at least <NUM>%. The average number of genes identified through after rarefaction was plotted and fit to a logarithmic curve to allow for simplified extrapolation. The heatmaps and figures were generated in Prism <NUM> for macOS (https://www.

The following references are cited herein, without any admission that any of them is relevant to the claimed invention or constitutes citable prior art:.

Raw sequencing reads (FASTQ) for IIDR Clinical Isolate Collection bacterial isolate genome assembly were deposited in NCBI BioProject PRJNA532924. All metagenomic sequencing results, enriched or shotgun, were deposited in NCBI BioProject PRJNA540073. The probeset sequences and annotations are available at the CARD website (http://card.

Claim 1:
A method for suppressing false positives (Type I Error) during analysis of sample biological materials, the method comprising:
for each of at least one handling step during the analysis:
obtaining at least one sample handling blank carrying a transfer substrate mixed with at least part of the sample biological materials;
obtaining at least one control blank that is isolated from the sample biological materials and corresponding to the sample handling blank in that handling step; and
replicating the handling applied to the at least one sample handling blank for the at least one control blank;
whereby, following completion of all handling steps, there is:
at least one final sample handling blank carrying the transfer substrates from the handling steps mixed with the at least part of the sample biological materials; and
at least one final control blank carrying the transfer substrates from the handling steps and isolated from the sample biological materials;
then:
applying a hybridization probe solution containing at least one hybridization probe to each final sample handling blank to produce at least one baited final sample handling blank; and
applying to each final control blank, hybridization probe solution identical to that applied to each final sample handling blank to produce at least one baited final control blank;
then:
feeding each baited final sample handling blank into a DNA sequencer and sequencing sample bait-captured DNA carried by the baited final sample handling blank; and
feeding each baited final control blank into the DNA sequencer and sequencing control bait-captured DNA carried by the baited final control blank;
then comparing the sample bait-captured DNA to the control bait-captured DNA and discounting, from a final identified genetic sequence, genetic components that:
are common to the final sample handling blank and the final control blank; and
pass a statistical significance test.