Microbial Contamination in Next Generation Sequencing: Implications for Sequence-Based Analysis of Clinical Samples

Strong, Michael J.; Xu, Guiyin; Morici, Lisa A.; BonDurant, Sandra Splinter; Baddoo, Melody; Lin, Zhen; Fewell, Claire; Taylor, Christopher M.; Flemington, Erik K.

doi:10.1371/journal.ppat.1004437

Cited by 164 publications

(147 citation statements)

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“…Manual BLAST analysis of the corresponding EBV reads excluded the possibility of misalignment of human reads to the EBV genome. We therefore suspect that similar to our contention regarding low read numbers detected for other viruses, these low numbers of EBV reads most likely reflect cross-contamination during sample processing and/or sequencing (58).…”

Section: Resultssupporting

confidence: 70%

“…Nevertheless, the low read numbers in these cases suggest that they are most likely due to cross-contamination during sequencing rather than transcription from endogenous viruses (see M. J. Strong et al [58] and below). In contrast, more than 10,000 reads were found for EBV, KSHV, HTLV-1 and murine retroviruses (see Table S2 in the supplemental material and Fig.…”

Section: Resultsmentioning

confidence: 99%

“…We also noted that the alignment profile of all murine type C retrovirus reads within the DEL cluster are similar and that single nucleotide differences with the aligned genome are nearly identical across all samples. We have previously addressed the impact of sample cross-contamination during the sequencing process (58). Because the file names are sequential, we suggest that they were likely processed in a batch and that samples may have been contaminated with DEL se-quences at some point in the sequencing pipeline.…”

Section: Resultsmentioning

confidence: 99%

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High-Throughput RNA Sequencing-Based Virome Analysis of 50 Lymphoma Cell Lines from the Cancer Cell Line Encyclopedia Project

Cao¹,

Strong²,

Wang³

et al. 2015

J Virol

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Using high-throughput RNA sequencing data from 50 common lymphoma cell culture models from the Cancer Cell Line Encyclopedia project, we performed an unbiased global interrogation for the presence of a panel of 740 viruses and strains known to infect human and other mammalian cells. This led to the findings of previously identified infections by Epstein-Barr virus (EBV), Kaposi's sarcoma herpesvirus (KSHV), and human T-lymphotropic virus type 1 (HTLV-1). In addition, we also found a previously unreported infection of one cell line (DEL) with a murine leukemia virus. High expression of murine leukemia virus (MuLV) transcripts was observed in DEL cells, and we identified four transcriptionally active integration sites, one being in the TNFRSF6B gene. We also found low levels of MuLV reads in a number of other cell lines and provided evidence suggesting crosscontamination during sequencing. Analysis of HTLV-1 integrations in two cell lines, HuT 102 and MJ, identified 14 and 66 transcriptionally active integration sites with potentially activating integrations in immune regulatory genes, including interleukin-15 (IL-15), IL-6ST, STAT5B, HIVEP1, and IL-9R. Although KSHV and EBV do not typically integrate into the genome, we investigated a previously identified integration of EBV into the BACH2 locus in Raji cells. This analysis identified a BACH2 disruption mechanism involving splice donor sequestration. Through viral gene expression analysis, we detected expression of stable intronic RNAs from the EBV BamHI W repeats that may be part of long transcripts spanning the repeat region. We also observed transcripts at the EBV vIL-10 locus exclusively in the Hodgkin's lymphoma cell line, Hs 611.T, the expression of which were uncoupled from other lytic genes. Assessment of the KSHV viral transcriptome in BCP-1 cells showed expression of the viral immune regulators, K2/vIL-6, K4/vIL-8-like vCCL1, and K5/E2-ubiquitin ligase 1 that was significantly higher than expression of the latency-associated nuclear antigen. Together, this investigation sheds light into the virus composition across these lymphoma model systems and provides insights into common viral mechanistic principles. IMPORTANCEViruses cause cancer in humans. In lymphomas the Epstein-Barr virus (EBV), Kaposi's sarcoma herpesvirus (KSHV) and human T-lymphotropic virus type 1 are major contributors to oncogenesis. We assessed virus-host interactions using a high throughput sequencing method that facilitates the discovery of new virus-host associations and the investigation into how the viruses alter their host environment. We found a previously unknown murine leukemia virus infection in one cell line. We identified cellular genes, including cytokine regulators, that are disrupted by virus integration, and we determined mechanisms through which virus integration causes deregulation of cellular gene expression. Investigation into the KSHV transcriptome in the BCP-1 cell line revealed high-level expression of immune signaling genes. EBV transcriptome analysis sh...

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Section: Resultssupporting

confidence: 70%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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High-Throughput RNA Sequencing-Based Virome Analysis of 50 Lymphoma Cell Lines from the Cancer Cell Line Encyclopedia Project

Cao¹,

Strong²,

Wang³

et al. 2015

J Virol

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“…However, sequences of bacterial origin can contaminate eukaryotic genome assembly results due to their occurrence in samples [17,18], DNA extraction kits [19], or laboratory environments [20,21]. One of the major challenges of working with eukaryotic genomes is the extent of repeat regions that complicate the assembly process [22].…”

Section: Introductionmentioning

confidence: 99%

Identifying contamination with advanced visualization and analysis practices: metagenomic approaches for eukaryotic genome assemblies

Delmont

Eren

2016

Preprint

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View the peer-reviewed version (peerj.com/articles/1839), which is the preferred citable publication unless you specifically need to cite this preprint.Delmont TO, Eren AM. 2016. Identifying contamination with advanced visualization and analysis practices: metagenomic approaches for eukaryotic genome assemblies. PeerJ 4:e1839 https://doi.org/10.7717/peerj.1839Identifying contamination with advanced visualization and analysis practices: metagenomic approaches for eukaryotic genome assemblies High-throughput sequencing provides a fast and cost effective mean to recover genomes of organisms from all domains of life. However, adequate curation of the assembly results against potential contamination of non-target organisms requires advanced bioinformatics approaches and practices. Here, we re-analyzed the sequencing data generated for the tardigrade Hypsibius dujardini using approaches routinely employed by microbial ecologists who reconstruct bacterial and archaeal genomes from metagenomic data. We created a holistic display of the eukaryotic genome assembly using DNA data originating from two groups and eleven sequencing libraries. By using bacterial single-copy genes, k- AbstractHigh-throughput sequencing provides a fast and cost effective mean to recover genomes of organisms from all domains of life. However, adequate curation of the assembly results against potential contamination of non-target organisms requires advanced bioinformatics approaches and practices. Here, we re-analyzed the sequencing data generated for the tardigrade Hypsibius dujardini using approaches routinely employed by microbial ecologists who reconstruct bacterial and archaeal genomes from metagenomic data. We created a holistic display of the eukaryotic genome assembly using DNA data originating from two groups and eleven sequencing libraries. By using bacterial single-copy genes, k-mer frequencies, and coverage values of scaffolds we could identify and characterize multiple near-complete bacterial genomes, and curate a 182 Mbp draft genome for H. dujardini supported by RNA-Seq data. Our results indicate that most contaminant scaffolds were assembled from Moleculo long-read libraries, and most of these contaminants have differed between library preparations. Our re-analysis shows that visualization and curation of eukaryotic genome assemblies can benefit from tools designed to address the needs of today's microbiologists, who are constantly challenged by the difficulties associated with the identification of distinct microbial genomes in complex environmental metagenomes.

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“…A potential concern is the growth of a wild frontier of microbial genome and microbiome analyses performed by non‐experts, hand‐cranking data through pipelines that they do not fully understand and then naively interpreting results without engaging the healthy scepticism of the seasoned expert (Bhatt et al ., 2013; Branton et al ., 2013; Laurence et al ., 2014; Salter et al ., 2014; Strong et al ., 2014; Ackelsberg et al ., 2015; Afshinnekoo et al ., 2015). Eternal vigilance is likely to be the price of containing the equivalent of microbial genomic astrology!…”

mentioning

confidence: 99%

Microbial bioinformatics 2020

Pallen

2016

Microbial Biotechnology

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SummaryMicrobial bioinformatics in 2020 will remain a vibrant, creative discipline, adding value to the ever‐growing flood of new sequence data, while embracing novel technologies and fresh approaches. Databases and search strategies will struggle to cope and manual curation will not be sustainable during the scale‐up to the million‐microbial‐genome era. Microbial taxonomy will have to adapt to a situation in which most microorganisms are discovered and characterised through the analysis of sequences. Genome sequencing will become a routine approach in clinical and research laboratories, with fresh demands for interpretable user‐friendly outputs. The “internet of things” will penetrate healthcare systems, so that even a piece of hospital plumbing might have its own IP address that can be integrated with pathogen genome sequences. Microbiome mania will continue, but the tide will turn from molecular barcoding towards metagenomics. Crowd‐sourced analyses will collide with cloud computing, but eternal vigilance will be the price of preventing the misinterpretation and overselling of microbial sequence data. Output from hand‐held sequencers will be analysed on mobile devices. Open‐source training materials will address the need for the development of a skilled labour force. As we boldly go into the third decade of the twenty‐first century, microbial sequence space will remain the final frontier!

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Microbial Contamination in Next Generation Sequencing: Implications for Sequence-Based Analysis of Clinical Samples

Cited by 164 publications

References 33 publications

High-Throughput RNA Sequencing-Based Virome Analysis of 50 Lymphoma Cell Lines from the Cancer Cell Line Encyclopedia Project

High-Throughput RNA Sequencing-Based Virome Analysis of 50 Lymphoma Cell Lines from the Cancer Cell Line Encyclopedia Project

Identifying contamination with advanced visualization and analysis practices: metagenomic approaches for eukaryotic genome assemblies

Microbial bioinformatics 2020

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