Detecting and Estimating Contamination of Human DNA Samples in Sequencing and Array-Based Genotype Data

Jun, Goo; Flickinger, Matthew; Hetrick, Kurt N.; Romm, Jane; Doheny, Kimberly F.; Abecasis, Gonçalo R.; Boehnke, Michael; Kang, Hyun Min

doi:10.1016/j.ajhg.2012.09.004

Cited by 446 publications

(393 citation statements)

References 11 publications

(6 reference statements)

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“…In addition, adapter sequences can be automatically detected and removed from genomic data sets (often simultaneously with a quality trimming of the data) using one of the software packages described in Table 2. Different mitigation techniques also exist for the removal of genomic contaminants, and some of the most popular ones include ContEst (Cibulskis et al, 2011), DeconSeq (Schmieder and Edwards, 2011a), QC-Chain (Zhou et al, 2013) as well as the set of methods developed by Jun et al, 2012 used in the 1000 Genomes Project.…”

Section: Quality Assessmentmentioning

confidence: 99%

From next-generation resequencing reads to a high-quality variant data set

Pfeifer

2016

Heredity

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Sequencing has revolutionized biology by permitting the analysis of genomic variation at an unprecedented resolution. Highthroughput sequencing is fast and inexpensive, making it accessible for a wide range of research topics. However, the produced data contain subtle but complex types of errors, biases and uncertainties that impose several statistical and computational challenges to the reliable detection of variants. To tap the full potential of high-throughput sequencing, a thorough understanding of the data produced as well as the available methodologies is required. Here, I review several commonly used methods for generating and processing next-generation resequencing data, discuss the influence of errors and biases together with their resulting implications for downstream analyses and provide general guidelines and recommendations for producing high-quality single-nucleotide polymorphism data sets from raw reads by highlighting several sophisticated reference-based methods representing the current state of the art. INTRODUCTIONSequencing has entered the scientific zeitgeist and the demand for novel sequences has never been greater, with applications spanning comparative genomics, clinical diagnostics and metagenomics, as well as agricultural, evolutionary, forensic and medical genetic studies. Several hundred species have already been completely sequenced (among them reference genomes for human and numerous major model organisms) (Pagani et al., 2012), shifting the focus of many scientific studies toward resequencing analyses in order to identify and catalog genetic variation in different individuals of a population. These population-scale studies permit insights into natural variation, inference on the demographic and selective history of a population, and variants identified in phenotypically distinct individuals can be used to dissect the relationship between genotype and phenotype.Until 2005, Sanger sequencing was the dominant technology, but it was prohibitively expensive and time consuming to routinely perform sequencing on a scale required to reach the scientific goals of many modern research projects. For these reasons, several massively parallel, high-throughput 'next-generation' sequencing (NGS) technologies have since been developed, permitting the analyses of genomes and their variation by being hundreds of times faster and over a thousand times cheaper than traditional Sanger sequencing (Metzker, 2010). Whereas the major limiting factor in the Sanger sequencing era was the experimental production of sequence data, data generation using NGS platforms is straightforward, shifting the bottleneck to downstream analyses, with computational costs often surpassing those of data production (Mardis, 2010). In fact, a multitude of bioinformatic algorithms is necessary to efficiently analyze the generated data sets and to answer biologically relevant questions. Thereby, the large amount of data with shorter read lengths, higher per-base error rates

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Section: Quality Assessmentmentioning

confidence: 99%

From next-generation resequencing reads to a high-quality variant data set

Pfeifer

2016

Heredity

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“…There are several methods to detect cross-sample contamination. However, all of them are usually supported by the additional information about mutations in other samples in a batch [226] or known genotypes [222].…”

Section: What Is Done In the Area To Solve Cross-contaminationmentioning

confidence: 99%

“…Contaminated samples often have unusually high levels of heterozygosity [222,223]. It is advised either to exclude contaminated samples from analysis, or model sample contamination during analysis to obtain more accurate SNP and genotype calls.…”

Section: Cross-contamination Of Samplesmentioning

confidence: 99%

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Computational Errors and Biases in Short Read Next Generation Sequencing

Abnizova¹,

Boekhorst²,

Orlov³

2017

J Proteomics Bioinform

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“…And Jun et al [5] demonstrates a likelihood-based method that could detectDNA sample contamination either using sequence data alone or with array-based genotypes. Although both methods are sensitive for estimating levels of contamination as low as 1%~1.5%, neither of them considers the genomic feature of large proportion of repeated sequences and pseudoautosomal (PAR) gene region on sex chromosomes.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Detecting Contaminated Sample Based on Illumina Sequencing Data

Huang¹,

Li²,

Jin³

et al. 2014

IJBBB

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Abstract-Illumina sequencing platform is widely used in genetics research. Due to the complex andlong-term library construction and DNA sequencing, samples can be contaminated with different sources, which can lead to false-positive SNP calling. To identify the contaminated samples, we built a model of mappability score to quantitatively measurethe accessibility of different parts ofhuman genome. By characterizing the genomic region with high probability of uniqueness and counting the discordant reads against genotypes on the unique region, we could detect outliers as the contaminated samples in a population scale. Totest the effectiveness of our method, we manually mixed the sequencing reads of two clean samples. With the prior knowledge of mixture rate, we concluded that ourmethodis quite sensitive for female samples contaminated even slightly by male samples, accurate for male samples with moderate contamination by female samples and powerful for severe cross-individual contamination with the same gender. This method is easily understood but fairly effective in population-scale sample quality control.Index Terms-Contamination, mappability score, sample quality control, unique region.

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Detecting and Estimating Contamination of Human DNA Samples in Sequencing and Array-Based Genotype Data

Cited by 446 publications

References 11 publications

From next-generation resequencing reads to a high-quality variant data set

From next-generation resequencing reads to a high-quality variant data set

Computational Errors and Biases in Short Read Next Generation Sequencing

A Novel Method for Detecting Contaminated Sample Based on Illumina Sequencing Data

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