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Sequencing facility and DNA source associated patterns of virus-mappable reads in whole-genome sequencing data.

ABSTRACT: Numerous viral sequences have been reported in the whole-genome sequencing (WGS) data of human blood. However, it is not clear to what degree the virus-mappable reads represent true viral sequences rather than random-mapping or noise originating from sample preparation, sequencing processes, or other sources. Identification of patterns of virus-mappable reads may generate novel indicators for evaluating the origins of these viral sequences. We characterized paired-end unmapped reads and reads aligned to viral references in human WGS datasets, then compared patterns of the virus-mappable reads among DNA sources and sequencing facilities which produced these datasets. We then examined potential origins of the source- and facility-associated viral reads. The proportions of clean unmapped reads among the seven sequencing facilities were significantly different (P < 2 × 10^-16). We identified 260,339 reads that were mappable to a total of 99 viral references in 2535 samples. The majority (86.7%) of these virus-mappable reads (corresponding to 47 viral references), which can be classified into four groups based on their distinct patterns, were strongly associated with sequencing facility or DNA source (adjusted P value <0.01). Possible origins of these reads include artificial sequences in library preparation, recombinant vectors in cell culture, and phages co-contaminated with their host bacteria. The sequencing facility-associated virus-mappable reads and patterns were repeatedly observed in other datasets produced in the same facilities. We have constructed an analytic framework and profiled the unmapped reads mappable to viral references. The results provide a new understanding of sequencing facility- and DNA source-associated batch effects in deep sequencing data and may facilitate improved bioinformatics filtering of reads.

SUBMITTER: Chen X

PROVIDER: S-EPMC7856238 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Campylobacter spp. are a leading and increasing cause of gastrointestinal infections worldwide. Source attribution, which apportions human infection cases to different animal species and food reservoirs, has been instrumental in control- and evidence-based intervention efforts. The rapid increase in whole-genome sequencing data provides an opportunity for higher-resolution source attribution models. Important challenges, including the high dimension and complex structure of WGS data, have inspired concerted research efforts to develop new models. We propose network analysis models as an accurate, high-resolution source attribution approach for the sources of human campylobacteriosis. A weighted network analysis approach was used in this study for source attribution comparing different WGS data inputs. The compared model inputs consisted of cgMLST and wgMLST distance matrices from 717 human and 717 animal isolates from cattle, chickens, dogs, ducks, pigs and turkeys. SNP distance matrices from 720 human and 720 animal isolates were also used. The data were collected from 2015 to 2017 in Denmark, with the animal sources consisting of domestic and imports from 7 European countries. Clusters consisted of network nodes representing respective genomes and links representing distances between genomes. Based on the results, animal sources were the main driving factor for cluster formation, followed by type of species and sampling year. The coherence source clustering (CSC) values based on animal sources were 78%, 81% and 78% for cgMLST, wgMLST and SNP, respectively. The CSC values based on Campylobacter species were 78%, 79% and 69% for cgMLST, wgMLST and SNP, respectively. Including human isolates in the network resulted in 88%, 77% and 88% of the total human isolates being clustered with the different animal sources for cgMLST, wgMLST and SNP, respectively. Between 12% and 23% of human isolates were not attributed to any animal source. Most of the human genomes were attributed to chickens from Denmark, with an average attribution percentage of 52.8%, 52.2% and 51.2% for cgMLST, wgMLST and SNP distance matrices respectively, while ducks from Denmark showed the least attribution of 0% for all three distance matrices. The best-performing model was the one using wgMLST distance matrix as input data, which had a CSC value of 81%. Results from our study show that the weighted network-based approach for source attribution is reliable and can be used as an alternative method for source attribution considering the high performance of the model. The model is also robust across the different Campylobacter species, animal sources and WGS data types used as input.

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Sequencing facility and DNA source associated patterns of virus-mappable reads in whole-genome sequencing data.

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