Comprehensive Analysis to Improve the Validation Rate for Single Nucleotide Variants Detected by Next-Generation Sequencing

Park, Mi-Hyun; Rhee, Hwanseok; Park, Jung Hoon; Woo, Hae-Mi; Choi, Byung‐Ok; Kim, Boyoung; Chung, Ki Wha; Cho, Yoobok; Kim, Hyung Jin; Jung, Ji‐Won; Koo, Soo Kyung

doi:10.1371/journal.pone.0086664

Cited by 21 publications

(25 citation statements)

References 19 publications

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“…Furthermore, to examine the percentage of mutations truly induced by γ rays, we verified the mutation by Sanger sequencing. The false-positive detection rates of SBSs and Indels are similar to those in previous studies (Park et al, 2014).…”

Section: Type Of γ Irradiation-induced Mutationssupporting

confidence: 88%

Frequency and type of inheritable mutations induced by γ rays in rice as revealed by whole genome sequencing

Zheng

Hairui

et al. 2016

J. Zhejiang Univ. Sci. B

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Mutation breeding is based on the induction of genetic variations; hence knowledge of the frequency and type of induced mutations is of paramount importance for the design and implementation of a mutation breeding program. Although γ ray irradiation has been widely used since the 1960s in the breeding of about 200 economically important plant species, molecular elucidation of its genetic effects has so far been achieved largely by analysis of target genes or genomic regions. In the present study, the whole genomes of six γ-irradiated M 2 rice plants were sequenced; a total of 144-188 million high-quality (Q>20) reads were generated for each M 2 plant, resulting in genome coverage of >45 times for each plant. Single base substitution (SBS) and short insertion/deletion (Indel) mutations were detected at the average frequency of 7.5×10 −6 -9.8×10 −6 in the six M 2 rice plants (SBS being about 4 times more frequent than Indels). Structural and copy number variations, though less frequent than SBS and Indel, were also identified and validated. The mutations were scattered in all genomic regions across 12 rice chromosomes without apparent hotspots. The present study is the first genome-wide single-nucleotide resolution study on the feature and frequency of γ irradiation-induced mutations in a seed propagated crop; the findings are of practical importance for mutation breeding of rice and other crop species.

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Section: Type Of γ Irradiation-induced Mutationssupporting

confidence: 88%

Frequency and type of inheritable mutations induced by γ rays in rice as revealed by whole genome sequencing

Zheng

Hairui

et al. 2016

J. Zhejiang Univ. Sci. B

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“…Benchmarking studies indicate that a tool's performance strongly depends on the underlying study design, and thus no technique outcompetes all others under all circumstances (see, for example, O' Rawe et al, 2013;Park et al, 2014). In fact, no single method captures all existing variation and many tools provide contrasting and complementary information (Liu et al, 2013a;Neuman et al, 2013;O'Rawe et al, 2013;Yu and Sun, 2013;Cheng et al, 2014;Pirooznia et al, 2014).…”

Section: Discovery and Genotypingmentioning

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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“…; Park et al . ). In such cases, new sequence reads can be mapped to an available reference sequence.…”

Section: Introductionmentioning

confidence: 97%

SNP discovery in nonmodel organisms: strand bias and base‐substitution errors reduce conversion rates

Silva

Barendse

Kijas

et al. 2014

Molecular Ecology Resources

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Single nucleotide polymorphisms (SNPs) have become the marker of choice for genetic studies in organisms of conservation, commercial or biological interest. Most SNP discovery projects in nonmodel organisms apply a strategy for identifying putative SNPs based on filtering rules that account for random sequencing errors. Here, we analyse data used to develop 4723 novel SNPs for the commercially important deep-sea fish, orange roughy (Hoplostethus atlanticus), to assess the impact of not accounting for systematic sequencing errors when filtering identified polymorphisms when discovering SNPs. We used SAMtools to identify polymorphisms in a velvet assembly of genomic DNA sequence data from seven individuals. The resulting set of polymorphisms were filtered to minimize 'bycatch'-polymorphisms caused by sequencing or assembly error. An Illumina Infinium SNP chip was used to genotype a final set of 7714 polymorphisms across 1734 individuals. Five predictors were examined for their effect on the probability of obtaining an assayable SNP: depth of coverage, number of reads that support a variant, polymorphism type (e.g. A/C), strand-bias and Illumina SNP probe design score. Our results indicate that filtering out systematic sequencing errors could substantially improve the efficiency of SNP discovery. We show that BLASTX can be used as an efficient tool to identify single-copy genomic regions in the absence of a reference genome. The results have implications for research aiming to identify assayable SNPs and build SNP genotyping assays for nonmodel organisms.

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Comprehensive Analysis to Improve the Validation Rate for Single Nucleotide Variants Detected by Next-Generation Sequencing

Cited by 21 publications

References 19 publications

Frequency and type of inheritable mutations induced by γ rays in rice as revealed by whole genome sequencing

Frequency and type of inheritable mutations induced by γ rays in rice as revealed by whole genome sequencing

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

SNP discovery in nonmodel organisms: strand bias and base‐substitution errors reduce conversion rates

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