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

Overview of attention for article published in BMC Bioinformatics, January 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

twitter
7 tweeters

Citations

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13 Dimensions

Readers on

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42 Mendeley
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Title
MutScan: fast detection and visualization of target mutations by scanning FASTQ data
Published in
BMC Bioinformatics, January 2018
DOI 10.1186/s12859-018-2024-6
Pubmed ID
Authors

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

Abstract

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. 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. 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.

Twitter Demographics

The data shown below were collected from the profiles of 7 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 42 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 36%
Student > Bachelor 5 12%
Student > Master 4 10%
Student > Ph. D. Student 3 7%
Other 2 5%
Other 2 5%
Unknown 11 26%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 13 31%
Agricultural and Biological Sciences 5 12%
Medicine and Dentistry 4 10%
Computer Science 2 5%
Chemical Engineering 1 2%
Other 5 12%
Unknown 12 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 07 November 2018.
All research outputs
#7,421,804
of 13,751,259 outputs
Outputs from BMC Bioinformatics
#2,777
of 5,119 outputs
Outputs of similar age
#151,492
of 352,248 outputs
Outputs of similar age from BMC Bioinformatics
#7
of 18 outputs
Altmetric has tracked 13,751,259 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,119 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 43rd percentile – i.e., 43% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 352,248 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.