From trash to treasure: detecting unexpected contamination in unmapped NGS data

Sangiovanni, Mara; Granata, Ilaria; Thind, Amarinder Singh; Guarracino, Mario Rosario

doi:10.1186/s12859-019-2684-x

Cited by 71 publications

(61 citation statements)

References 64 publications

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“…The lower value can be explained by ~5% Kraken-II FP rate when run under its default setting. The upper value is consistent with ~10% contamination level, a scenario that can happen in real sequencing projects (Merchant, Wood, & Salzberg, 2014;Sangiovanni, Granata, Thind, & Guarracino, 2019).…”

Section: Usefulness Of Theoretical Modelssupporting

confidence: 79%

The impact of contaminants on the accuracy of genome skimming and the effectiveness of exclusion read filters

Rachtman

Balaban

Bafna

et al. 2020

Molecular Ecology Resources

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The ability to detect the identity of a sample obtained from its environment is a cornerstone of molecular ecological research. Thanks to the falling price of shotgun sequencing, genome skimming, the acquisition of short reads spread across the genome at low coverage, is emerging as an alternative to traditional barcoding. By obtaining far more data across the whole genome, skimming has the promise to increase the precision of sample identification beyond traditional barcoding while keeping the costs manageable. While methods for assembly‐free sample identification based on genome skims are now available, little is known about how these methods react to the presence of DNA from organisms other than the target species. In this paper, we show that the accuracy of distances computed between a pair of genome skims based on k‐mer similarity can degrade dramatically if the skims include contaminant reads; i.e., any reads originating from other organisms. We establish a theoretical model of the impact of contamination. We then suggest and evaluate a solution to the contamination problem: Query reads in a genome skim against an extensive database of possible contaminants (e.g., all microbial organisms) and filter out any read that matches. We evaluate the effectiveness of this strategy when implemented using Kraken‐II, in detailed analyses. Our results show substantial improvements in accuracy as a result of filtering but also point to limitations, including a need for relatively close matches in the contaminant database.

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Section: Usefulness Of Theoretical Modelssupporting

confidence: 79%

The impact of contaminants on the accuracy of genome skimming and the effectiveness of exclusion read filters

Rachtman

Balaban

Bafna

et al. 2020

Molecular Ecology Resources

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show abstract

“…This observation implies the presence of high sequence similarity at the species level. We calculated the ratios by running PathSeq [18], FastQ Screen [28], and DecontaMiner [29] (Additional file 2). Of note, comparing existing pipelines is not straightforward because different aligners are employed and databases are inaccessible in some cases.…”

Section: Resultsmentioning

confidence: 99%

“…The network data were visualized by using software Cytoscape (V.3.5.1). PathSeq [18], FastQ Screen [28], and DecontaMiner [29] were installed with their reference databases. Because FastQ Screen accepts limited number of genomes, the input reads were mapped to ten specific genomes only.…”

Section: Methodsmentioning

confidence: 99%

A systematic sequencing-based approach for microbial contaminant detection and functional inference

et al. 2019

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Background Microbial contamination poses a major difficulty for successful data analysis in biological and biomedical research. Computational approaches utilizing next-generation sequencing (NGS) data offer promising diagnostics to assess the presence of contaminants. However, as host cells are often contaminated by multiple microorganisms, these approaches require careful attention to intra- and interspecies sequence similarities, which have not yet been fully addressed. Results We present a computational approach that rigorously investigates the genomic origins of sequenced reads, including those mapped to multiple species that have been discarded in previous studies. Through the analysis of large-scale synthetic and public NGS samples, we estimate that 1000–100,000 contaminating microbial reads are detected per million host reads sequenced by RNA-seq. The microbe catalog we established included Cutibacterium as a prevalent contaminant, suggesting that contamination mostly originates from the laboratory environment. Importantly, by applying a systematic method to infer the functional impact of contamination, we revealed that host-contaminant interactions cause profound changes in the host molecular landscapes, as exemplified by changes in inflammatory and apoptotic pathways during Mycoplasma infection of lymphoma cells. Conclusions We provide a computational method for profiling microbial contamination on NGS data and suggest that sources of contamination in laboratory reagents and the experimental environment alter the molecular landscape of host cells leading to phenotypic changes. These findings reinforce the concept that precise determination of the origins and functional impacts of contamination is imperative for quality research and illustrate the usefulness of the proposed approach to comprehensively characterize contamination landscapes.

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Section: Methodsmentioning

confidence: 99%

A systematic NGS-based approach for contaminant detection and functional inference

Park

Onizuka

Seki

et al. 2019

Preprint

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BackgroundMicrobial contamination impedes successful biological and biomedical research. Computational approaches utilizing next-generation sequencing (NGS) data offer promising diagnostics to assess the presence of contaminants. However, as host cells are often contaminated by multiple microorganisms, these approaches require careful attention to intra- and interspecies sequence similarities, which have not yet been fully addressed.ResultsWe present a computational approach that rigorously investigates the genomic origins of sequenced reads, including those mapped to multiple species that have been discarded in previous studies. Through the analysis of large-scale synthetic and public NGS samples, we approximated that 1,000−100,000 microbial reads prevail when one million host reads are sequenced by RNA-seq. The microbe catalog we established included Cutibacterium as a prevalent contaminant, suggesting that contamination mostly originates from the laboratory environment. Importantly, by applying a systematic method to infer the functional impact of contamination, we revealed that host-contaminant interactions cause profound changes in the host molecular landscapes, as exemplified by changes in inflammatory and apoptotic pathways during Mycoplasma infection.ConclusionsThese findings reinforce the concept that precise determination of the origins and functional impacts of contamination is imperative for quality research and illustrate the usefulness of the proposed approach to comprehensively characterize contamination landscapes.

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From trash to treasure: detecting unexpected contamination in unmapped NGS data

Cited by 71 publications

References 64 publications

The impact of contaminants on the accuracy of genome skimming and the effectiveness of exclusion read filters

The impact of contaminants on the accuracy of genome skimming and the effectiveness of exclusion read filters

A systematic sequencing-based approach for microbial contaminant detection and functional inference

A systematic NGS-based approach for contaminant detection and functional inference

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