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

Abstract

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

Transmission Dynamics of Human Herpesvirus 6 and 7 from Whole Genome Sequences of Families

“…2C). We demonstrated previously that this strategy can detect viral experimental and computational contamination in WGS [8]. Similarly to our previous findings, viruses associated with sample cell type or sequencing plate are likely contaminants or reagents used in the immortalization, storage, or sequencing pipelines.…”

“…As described in our previous work [8], one reason for biological source-dependent abundance is that different reagents and pipelines are used in LCL versus whole blood prep and storage pipelines and may lead to different contamination profiles. This is probably the case for EBV (gamma herpes 4) enriched in LCLs (acquired during the EBV-induced immortalization step) and its relatives, as well as the non-herpesviruses enriched in whole blood samples.…”

“…The iHART dataset [50] contains whole genome sequences from whole blood (WB) or lymphoblastoid cell lines (LCLs) from over 4,500 individuals from over 1,000 different nuclear families with multiplex autism. Originally curated to understand the genetic determinants of autism, the iHART dataset has become valuable not only for autism research, but because its unique family structure allows for understanding of inheritance patterns that cannot be done with case-control cohorts [47,9,10,8]. In this study, we utilize the the family structure to better understand intra-family viral transmission and integration patterns.…”

An Algorithm for Sequence Location Approximation using Nuclear Families (ASLAN) Validates Regions of the Telomere-to-Telomere Assembly and Identifies New Hotspots for Genetic Diversity

“…On a population-scale, we therefore understand much less about the role these regions play in health and disease, and the amount of genetic diversity present in these regions [16]. Reads originating from these regions - as well as from viruses and bacteria in hosts or NGS reagents [6, 5] - collectively make up the unmapped read space.…”

De novo identification of microbial contaminants in low microbial biomass microbiomes with Squeegee

“…A variety of sources can introduce microbial contamination. External sources include personnel, the laboratory environment, and kits and reagents used for collecting and processing samples 2,3,[11][12][13][14][15][16][17][18][19][20] . Internal sources of contamination may include human error, such as sample mislabeling or inadvertent mixing 3,11,17,21 .…”