Evidence map›Paper›PMID 29357822›Full record

ArticleBMC bioinformatics2018

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

Shifu Chen, Tanxiao Huang, Tiexiang Wen, Hong Li, Mingyan Xu, Jia Gu

Abstract read
In one paragraph

Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
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  4. Review
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  6. Article
  7. Article
  8. UniqueNAR cancer · 2020
    Article
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Shifu ChenShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. sf.chen@siat.ac.cn.
Tanxiao HuangHaploX Biotechnology, Shenzhen, China.
Tiexiang WenShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Hong LiHaploX Biotechnology, Shenzhen, China.
Mingyan XuHaploX Biotechnology, Shenzhen, China.
Jia GuShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China. jia.gu@siat.ac.cn.

Funding

National Natural Science Foundation of China 61472411Special Funds for Future Industries of Shenzhen JSGG20160229123927512SZSTI Entrepreneurship Funds of Shenzhen CYZZ20150527145115656Technology Development and Creative Design Program of Nanshan Shenzhen KC2015JSJS0028AThe national 863 Program of China 2015AA043203
6 · The paper itself

Abstract

backgroundSome 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.

resultWe 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.

conclusionMutScan 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.

Indexed as

AlgorithmsSearch EngineCirculating Tumor DNAHigh-Throughput Nucleotide SequencingHumansMutationNeoplasmsSequence Analysis, DNACirculating Tumor DNAFast detectionMutation scanMutScanVariant visualization

Identifiers

PMID29357822
PMCPMC5778627

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.