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Indel sensitive and comprehensive variant/mutation detection from RNA sequencing data for precision medicine

Overview of attention for article published in BMC Medical Genomics, September 2018
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Mentioned by

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3 tweeters

Citations

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3 Dimensions

Readers on

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36 Mendeley
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Title
Indel sensitive and comprehensive variant/mutation detection from RNA sequencing data for precision medicine
Published in
BMC Medical Genomics, September 2018
DOI 10.1186/s12920-018-0391-5
Pubmed ID
Authors

Naresh Prodduturi, Aditya Bhagwate, Jean-Pierre A. Kocher, Zhifu Sun

Abstract

RNA-seq is the most commonly used sequencing application. Not only does it measure gene expression but it is also an excellent media to detect important structural variants such as single nucleotide variants (SNVs), insertion/deletion (Indels) or fusion transcripts. However, detection of these variants is challenging and complex from RNA-seq. Here we describe a sensitive and accurate analytical pipeline which detects various mutations at once for translational precision medicine. The pipeline incorporates most sensitive aligners for Indels in RNA-Seq, the best practice for data preprocessing and variant calling, and STAR-fusion is for chimeric transcripts. Variants/mutations are annotated, and key genes can be extracted for further investigation and clinical actions. Three datasets were used to evaluate the performance of the pipeline for SNVs, indels and fusion transcripts. For the well-defined variants from NA12878 by GIAB project, about 95% and 80% of sensitivities were obtained for SNVs and indels, respectively, in matching RNA-seq. Comparison with other variant specific tools showed good performance of the pipeline. For the lung cancer dataset with 41 known and oncogenic mutations, 39 were detected by the pipeline with STAR aligner and all by the GSNAP aligner. An actionable EML4 and ALK fusion was also detected in one of the tumors, which also demonstrated outlier ALK expression. For 9 fusions spiked-into RNA-seq libraries with different concentrations, the pipeline was able to detect all in unfiltered results although some at very low concentrations may be missed when filtering was applied. The new RNA-seq workflow is an accurate and comprehensive mutation profiler from RNA-seq. Key or actionable mutations are reliably detected from RNA-seq, which makes it a practical alternative source for personalized medicine.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 36 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 8 22%
Student > Ph. D. Student 7 19%
Researcher 5 14%
Student > Postgraduate 3 8%
Student > Bachelor 3 8%
Other 4 11%
Unknown 6 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 47%
Agricultural and Biological Sciences 4 11%
Computer Science 3 8%
Unspecified 1 3%
Medicine and Dentistry 1 3%
Other 1 3%
Unknown 9 25%

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 02 August 2019.
All research outputs
#9,129,743
of 15,565,939 outputs
Outputs from BMC Medical Genomics
#419
of 812 outputs
Outputs of similar age
#143,436
of 274,556 outputs
Outputs of similar age from BMC Medical Genomics
#4
of 9 outputs
Altmetric has tracked 15,565,939 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 812 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 45th percentile – i.e., 45% 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 274,556 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.