Comprehensive analysis to improve the validation rate for single nucleotide variants detected by next-generation sequencing

Next-generation sequencing (NGS) has enabled the high-throughput discovery of germline and somatic mutations. However, NGS-based variant detection is still prone to errors, resulting in inaccurate variant calls. Here, we categorized the variants detected by NGS according to total read depth (TD) and...

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Published in:PloS one 2014-01, Vol.9 (1), p.e86664-e86664
Main Authors: Park, Mi-Hyun, Rhee, Hwanseok, Park, Jung Hoon, Woo, Hae-Mi, Choi, Byung-Ok, Kim, Bo-Young, Chung, Ki Wha, Cho, Yoo-Bok, Kim, Hyung Jin, Jung, Ji-Won, Koo, Soo Kyung
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Bias
Bioinformatics
Biology
Charcot-Marie-Tooth Disease - genetics
Computer Science
Cost analysis
Deoxyribonucleic acid
Disease
DNA
DNA sequencing
Exome
Filtration
Genomes
High-Throughput Nucleotide Sequencing
Humans
Medicine
Mutation
Polymorphism, Single Nucleotide
Researchers
Single-nucleotide polymorphism
Statistical analysis
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cited_by cdi_FETCH-LOGICAL-c692t-eaccc3b3ec6a3346f07a88353ac32735e440ec2970aab2d33a60d736ff886aac3
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container_title PloS one
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creator Park, Mi-Hyun
Rhee, Hwanseok
Park, Jung Hoon
Woo, Hae-Mi
Choi, Byung-Ok
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Cho, Yoo-Bok
Kim, Hyung Jin
Jung, Ji-Won
Koo, Soo Kyung
description Next-generation sequencing (NGS) has enabled the high-throughput discovery of germline and somatic mutations. However, NGS-based variant detection is still prone to errors, resulting in inaccurate variant calls. Here, we categorized the variants detected by NGS according to total read depth (TD) and SNP quality (SNPQ), and performed Sanger sequencing with 348 selected non-synonymous single nucleotide variants (SNVs) for validation. Using the SAMtools and GATK algorithms, the validation rate was positively correlated with SNPQ but showed no correlation with TD. In addition, common variants called by both programs had a higher validation rate than caller-specific variants. We further examined several parameters to improve the validation rate, and found that strand bias (SB) was a key parameter. SB in NGS data showed a strong difference between the variants passing validation and those that failed validation, showing a validation rate of more than 92% (filtering cutoff value: alternate allele forward [AF] ≥ 20 and AF
doi_str_mv 10.1371/journal.pone.0086664
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However, NGS-based variant detection is still prone to errors, resulting in inaccurate variant calls. Here, we categorized the variants detected by NGS according to total read depth (TD) and SNP quality (SNPQ), and performed Sanger sequencing with 348 selected non-synonymous single nucleotide variants (SNVs) for validation. Using the SAMtools and GATK algorithms, the validation rate was positively correlated with SNPQ but showed no correlation with TD. In addition, common variants called by both programs had a higher validation rate than caller-specific variants. We further examined several parameters to improve the validation rate, and found that strand bias (SB) was a key parameter. SB in NGS data showed a strong difference between the variants passing validation and those that failed validation, showing a validation rate of more than 92% (filtering cutoff value: alternate allele forward [AF] ≥ 20 and AF&lt;80 in SAMtools, SB&lt;-10 in GATK). 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subjects Algorithms
Analysis
Bias
Bioinformatics
Biology
Charcot-Marie-Tooth Disease - genetics
Computer Science
Cost analysis
Deoxyribonucleic acid
Disease
DNA
DNA sequencing
Exome
Filtration
Genomes
High-Throughput Nucleotide Sequencing
Humans
Medicine
Mutation
Polymorphism, Single Nucleotide
Researchers
Single-nucleotide polymorphism
Statistical analysis
title Comprehensive analysis to improve the validation rate for single nucleotide variants detected by next-generation sequencing
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