Case report: Identification of intra-laboratory blood culture contamination with Staphylococcus aureus by whole genome sequencing

Ashokan, Anushia; Papanicolas, Lito E.; Leong, Lex E. X.; Theodossi, Maria; Daniel, Santhosh; Wesselingh, Steven; Rogers, Geraint B.; Gordon, David L.

doi:10.1016/j.diagmicrobio.2019.02.016

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Mycoplasma contamination has been found in many cell lines 43 , and Bradyrhizobium was the most common contaminant found in WGS from the 1000 genomes project 19 . Staphylococcus , Acinetobacter , Streptococcus , and Pseudomonas have been identified as possible contaminants in several WGS studies 44 – 46 . Despite numerous studies cataloguing bacterial contamination in NGS, the same species seem to persist in sequencing contamination.…”

Section: Discussionmentioning

confidence: 99%

The human “contaminome”: bacterial, viral, and computational contamination in whole genome sequences from 1000 families

Chrisman

Jung

et al. 2022

Sci Rep

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The unmapped readspace of whole genome sequencing data tends to be large but is often ignored. We posit that it contains valuable signals of both human infection and contamination. Using unmapped and poorly aligned reads from whole genome sequences (WGS) of over 1000 families and nearly 5000 individuals, we present insights into common viral, bacterial, and computational contamination that plague whole genome sequencing studies. We present several notable results: (1) In addition to known contaminants such as Epstein-Barr virus and phiX, sequences from whole blood and lymphocyte cell lines contain many other contaminants, likely originating from storage, prep, and sequencing pipelines. (2) Sequencing plate and biological sample source of a sample strongly influence contamination profile. And, (3) Y-chromosome fragments not on the human reference genome commonly mismap to bacterial reference genomes. Both experiment-derived and computational contamination is prominent in next-generation sequencing data. Such contamination can compromise results from WGS as well as metagenomics studies, and standard protocols for identifying and removing contamination should be developed to ensure the fidelity of sequencing-based studies.

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Section: Discussionmentioning

confidence: 99%

The human “contaminome”: bacterial, viral, and computational contamination in whole genome sequences from 1000 families

Chrisman

Jung

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Mycoplasma contamination has been found in many cell lines [45], and Bradyrhizobium was the most common contaminant found in WGS from the 1000 genomes project [20]. Staphylococcus, Acinetobacter, Streptococcus , and Pseudomonas have been identified as possible contaminants in several WGS studies [46, 47, 48]. Despite numerous studies cataloguing bacterial contamination in NGS, the same species seem to persist in sequencing contamination.…”

Section: Discussionmentioning

confidence: 99%

The Human “Contaminome”: Bacterial, Viral, and Computational Contamination in Whole Genome Sequences from 1,000 Families

Chrisman

Jung

et al. 2022

Preprint

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BackgroundThe unmapped readspace of whole genome sequencing data tends to be large but is often ignored. We posit that it contains valuable signals of both human infection and contamination. Using unmapped and poorly aligned reads from whole genome sequences (WGS) of over 1,000 families and 5,000 individuals, we present insights into common viral, bacterial, and computational contamination that plague whole genome sequencing studies.ResultsWe present several notable results: (1) In addition to known contaminants such as Epstein-Barr virus and phiX, sequences from whole blood and lymphocyte cell lines contain many other contaminants, likely originating from storage, prep, and sequencing pipelines. (2) Sequencing plate and biological sample source of a sample strongly influence contamination profile. And, (3) Y-chromosome fragments not on the human reference genome commonly mismap to bacterial reference genomes.ConclusionBoth experiment-derived and computational contamination is prominent in next-generation sequencing data. Such contamination can compromise results from WGS as well as metagenomics studies, and standard protocols for identifying and removing contamination should be developed to ensure the fidelity of sequencing-based studies.genomics WGS contamination

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Case report: Identification of intra-laboratory blood culture contamination with Staphylococcus aureus by whole genome sequencing

Cited by 2 publications

References 8 publications

The human “contaminome”: bacterial, viral, and computational contamination in whole genome sequences from 1000 families

The human “contaminome”: bacterial, viral, and computational contamination in whole genome sequences from 1000 families

The Human “Contaminome”: Bacterial, Viral, and Computational Contamination in Whole Genome Sequences from 1,000 Families

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