PEPR: pipelines for evaluating prokaryotic references

Olson, Nathan D.; Zook, Justin M.; Samarov, Daniel V.; Jackson, Scott A.; Salit, Marc

doi:10.1007/s00216-015-9299-5

“…(2) it leverages efficient whole genome read mapping algorithms. Additionally, PathoScope was successfully used in our pilot study (https://doi.org/10.6084/m9.figshare.1200090.v1) and as part of the pipeline developed to characterize the NIST microbial genomic DNA reference material (Olson et al, 2016).…”

Section: Assessment Of Taxonomic Compositionmentioning

confidence: 99%

Peer Review #2 of "Challenging a bioinformatic tool’s ability to detect microbial contaminants using in silico whole genome sequencing data (v0.2)"

2017

0

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High sensitivity methods such as next generation sequencing and polymerase chain reaction (PCR) are adversely impacted by organismal and DNA contaminants. Current methods for detecting contaminants in microbial materials (genomic DNA and cultures) are not sensitive enough and require either a known or culturable contaminant. Whole genome sequencing (WGS) is a promising approach for detecting contaminants due to its sensitivity and lack of need for a priori assumptions about the contaminant. Prior to applying WGS, we must first understand its limitations for detecting contaminants and potential for false positives. Herein we demonstrate and characterize a WGS-based approach to detect organismal contaminants using an existing metagenomic taxonomic classification algorithm. Simulated WGS datasets from ten genera as individuals and binary mixtures of eight organisms at varying ratios were analyzed to evaluate the role of contaminant concentration and taxonomy on detection. For the individual genomes the false positive contaminants reported depended on the genus with Staphylococcus, Escherichia, and Shigella having the highest proportion of false positives. For nearly all binary mixtures the contaminant was detected in the in-silico datasets at the equivalent of 1 in 1,000 cells. Though F. tularensis was not detected in any of the simulated contaminant mixtures and Y. pestis was only detected at the equivalent of 1 in 10 cells. Once a WGS method for detecting contaminants is characterized, it can be applied to evaluate microbial material purity, in effort to ensure that contaminants in microbial materials used to validate pathogen detection assays, generate genome assemblies for database submission, and benchmark sequencing methods.

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“…Characterizing these contaminants in order to focus efforts on reducing their level is critical to ensuring high-quality microbial materials are used to populate sequence databases ( Parks et al, 2015 ), for mock communities to validate metagenomic methods ( Bokulich et al, 2016 ), to validate biodetection assays ( Ieven, Finch & van Belkum, 2013 ; Coates, Brunelle & Davenport, 2011 ), and for basic research using model systems ( Shrestha et al, 2013 ). Furthermore, tools to assess general contaminants are also needed for the characterization of microbial genomic reference materials ( Olson et al, 2016 ), where contaminant profiles allow users to properly determine whether the material is suitable for their application. Contaminants in microbial materials, as found in non-axenic cellular materials or genomic materials with foreign DNA, have been addressed when processing the sequencing data but not for general material characterization ( Shrestha et al, 2013 ; Tennessen et al, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

Challenging a bioinformatic tool’s ability to detect microbial contaminants usingin silicowhole genome sequencing data

Olson

¹

,

Zook

²

,

Morrow

³

et al. 2017

PeerJ

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High sensitivity methods such as next generation sequencing and polymerase chain reaction (PCR) are adversely impacted by organismal and DNA contaminants. Current methods for detecting contaminants in microbial materials (genomic DNA and cultures) are not sensitive enough and require either a known or culturable contaminant. Whole genome sequencing (WGS) is a promising approach for detecting contaminants due to its sensitivity and lack of need for a priori assumptions about the contaminant. Prior to applying WGS, we must first understand its limitations for detecting contaminants and potential for false positives. Herein we demonstrate and characterize a WGS-based approach to detect organismal contaminants using an existing metagenomic taxonomic classification algorithm. Simulated WGS datasets from ten genera as individuals and binary mixtures of eight organisms at varying ratios were analyzed to evaluate the role of contaminant concentration and taxonomy on detection. For the individual genomes the false positive contaminants reported depended on the genus, with Staphylococcus, Escherichia, and Shigella having the highest proportion of false positives. For nearly all binary mixtures the contaminant was detected in the in-silico datasets at the equivalent of 1 in 1,000 cells, though F. tularensis was not detected in any of the simulated contaminant mixtures and Y. pestis was only detected at the equivalent of one in 10 cells. Once a WGS method for detecting contaminants is characterized, it can be applied to evaluate microbial material purity, in efforts to ensure that contaminants are characterized in microbial materials used to validate pathogen detection assays, generate genome assemblies for database submission, and benchmark sequencing methods.

show abstract

“…Characterizing these contaminants in order to focus efforts on reducing their level is critical to ensuring high-quality microbial materials are used to populate sequence databases (Parks et al, 2015), for mock communities to validate metagenomic methods (Bokulich et al, 2016), to validate biodetection assays (Ieven et al, 2013;Coates et al, 2011), and for basic research using model systems (Shrestha et al, 2013). Furthermore, tools to assess general contaminants are also needed for the characterization of microbial genomic reference materials (Olson et al, 2016), where contaminant profiles allow users to properly determine whether the material is suitable for their application. Contaminants in microbial materials, as found in non-axenic cellular materials or genomic materials with foreign DNA, have been addressed when processing the sequencing data but not for general material characterization (Shrestha et al, 2013;Tennessen et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "Challenging a bioinformatic tool’s ability to detect microbial contaminants using in silico whole genome sequencing data (v0.1)"

Kumar¹

2017

0

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High sensitivity methods such as next generation sequencing and polymerase chain reaction (PCR) are adversely impacted by organismal and DNA contaminants. Current methods for detecting contaminants in microbial materials (genomic DNA and cultures) are not sensitive enough and require either a known or culturable contaminant. Whole genome sequencing (WGS) is a promising approach for detecting contaminants due to its sensitivity and lack of need for a priori assumptions about the contaminant. Prior to applying WGS, we must first understand its limitations for detecting contaminants and potential for false positives. Herein we demonstrate and characterize a WGS-based approach to detect organismal contaminants using an existing metagenomic taxonomic classification algorithm. Simulated WGS datasets from ten genera as individuals and binary mixtures of eight organisms at varying ratios were analyzed to evaluate the role of contaminant concentration and taxonomy on detection. For the individual genomes the false positive contaminants reported depended on the genus with Staphylococcus, Escherichia, and Shigella having the highest proportion of false positives. For nearly all binary mixtures the contaminant was detected in the in-silico datasets at the equivalent of 1 in 1,000 cells. Though F. tularensis was not detected in any of the simulated contaminant mixtures and Y. pestis was only detected at the equivalent of 1 in 10 cells. Once a WGS method for detecting contaminants is characterized, it can be applied to evaluate microbial material purity, in effort to ensure that contaminants in microbial materials used to validate pathogen detection assays, generate genome assemblies for database submission, and benchmark sequencing methods.

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PEPR: pipelines for evaluating prokaryotic references

Cited by 8 publications

References 24 publications

Peer Review #2 of "Challenging a bioinformatic tool’s ability to detect microbial contaminants using in silico whole genome sequencing data (v0.2)"

Peer Review #2 of "Challenging a bioinformatic tool’s ability to detect microbial contaminants using in silico whole genome sequencing data (v0.2)"

Challenging a bioinformatic tool’s ability to detect microbial contaminants usingin silicowhole genome sequencing data

Peer Review #1 of "Challenging a bioinformatic tool’s ability to detect microbial contaminants using in silico whole genome sequencing data (v0.1)"

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