Dataset Information

MutScan: fast detection and visualization of target mutations by scanning FASTQ data.

ABSTRACT: BACKGROUND:Some types of clinical genetic tests, such as cancer testing using circulating tumor DNA (ctDNA), require sensitive detection of known target mutations. However, conventional next-generation sequencing (NGS) data analysis pipelines typically involve different steps of filtering, which may cause miss-detection of key mutations with low frequencies. Variant validation is also indicated for key mutations detected by bioinformatics pipelines. Typically, this process can be executed using alignment visualization tools such as IGV or GenomeBrowse. However, these tools are too heavy and therefore unsuitable for validating mutations in ultra-deep sequencing data. RESULT:We developed MutScan to address problems of sensitive detection and efficient validation for target mutations. MutScan involves highly optimized string-searching algorithms, which can scan input FASTQ files to grab all reads that support target mutations. The collected supporting reads for each target mutation will be piled up and visualized using web technologies such as HTML and JavaScript. Algorithms such as rolling hash and bloom filter are applied to accelerate scanning and make MutScan applicable to detect or visualize target mutations in a very fast way. CONCLUSION:MutScan is a tool for the detection and visualization of target mutations by only scanning FASTQ raw data directly. Compared to conventional pipelines, this offers a very high performance, executing about 20 times faster, and offering maximal sensitivity since it can grab mutations with even one single supporting read. MutScan visualizes detected mutations by generating interactive pile-ups using web technologies. These can serve to validate target mutations, thus avoiding false positives. Furthermore, MutScan can visualize all mutation records in a VCF file to HTML pages for cloud-friendly VCF validation. MutScan is an open source tool available at GitHub: https://github.com/OpenGene/MutScan.

SUBMITTER: Chen S

PROVIDER: S-EPMC5778627 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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Publications

MutScan: fast detection and visualization of target mutations by scanning FASTQ data.

Chen Shifu S Huang Tanxiao T Wen Tiexiang T Li Hong H Xu Mingyan M Gu Jia J

BMC bioinformatics 20180122 1

<h4>Background</h4>Some types of clinical genetic tests, such as cancer testing using circulating tumor DNA (ctDNA), require sensitive detection of known target mutations. However, conventional next-generation sequencing (NGS) data analysis pipelines typically involve different steps of filtering, which may cause miss-detection of key mutations with low frequencies. Variant validation is also indicated for key mutations detected by bioinformatics pipelines. Typically, this process can be execute ...[more]

PMID: 29357822

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Project description:Abstract Target?site mutations and detoxification gene overexpression are two major mechanisms conferring insecticide resistance. Molecular assays applied to detect these resistance genetic markers are time?consuming and with high false?positive rates. RNA?Seq data contains information on the variations within expressed genomic regions and expression of detoxification genes. However, there is no corresponding method to detect resistance markers at present. Here, we collected 66 reported resistance mutations of four insecticide targets (AChE, VGSC, RyR, and nAChR) from 82 insect species. Next, we obtained 403 sequences of the four target genes and 12,665 sequences of three kinds of detoxification genes including P450s, GSTs, and CCEs. Then, we developed a Perl program, FastD, to detect target?site mutations and overexpressed detoxification genes from RNA?Seq data and constructed a web server for FastD (http://www.insect-genome.com/fastd). The estimation of FastD on simulated RNA?Seq data showed high sensitivity and specificity. We applied FastD to detect resistant markers in 15 populations of six insects, Plutella xylostella, Aphis gossypii, Anopheles arabiensis, Musca domestica, Leptinotarsa decemlineata and Apis mellifera. Results showed that 11 RyR mutations in P. xylostella, one nAChR mutation in A. gossypii, one VGSC mutation in A. arabiensis and five VGSC mutations in M. domestica were found to be with frequency difference >40% between resistant and susceptible populations including previously confirmed mutations G4946E in RyR, R81T in nAChR and L1014F in VGSC. And 49 detoxification genes were found to be overexpressed in resistant populations compared with susceptible populations including previously confirmed detoxification genes CYP6BG1, CYP6CY22, CYP6CY13, CYP6P3, CYP6M2, CYP6P4 and CYP4G16. The candidate target?site mutations and detoxification genes were worth further validation. Resistance estimates according to confirmed markers were consistent with population phenotypes, confirming the reliability of this program in predicting population resistance at omics?level. We developed a program called FastD to detect the insecticide target?site mutations and overexpressed detoxification genes from RNA?Seq data and constructed a corresponding web server for FastD (http://www.insect-genome.com/fastd) in this article.

Dataset Information

MutScan: fast detection and visualization of target mutations by scanning FASTQ data.

Publications

MutScan: fast detection and visualization of target mutations by scanning FASTQ data.

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