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TAP: a targeted clinical genomics pipeline for detecting transcript variants using RNA-seq data

Overview of attention for article published in BMC Medical Genomics, September 2018
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Title
TAP: a targeted clinical genomics pipeline for detecting transcript variants using RNA-seq data
Published in
BMC Medical Genomics, September 2018
DOI 10.1186/s12920-018-0402-6
Pubmed ID
Authors

Readman Chiu, Ka Ming Nip, Justin Chu, Inanc Birol

Abstract

RNA-seq is a powerful and cost-effective technology for molecular diagnostics of cancer and other diseases, and it can reach its full potential when coupled with validated clinical-grade informatics tools. Despite recent advances in long-read sequencing, transcriptome assembly of short reads remains a useful and cost-effective methodology for unveiling transcript-level rearrangements and novel isoforms. One of the major concerns for adopting the proven de novo assembly approach for RNA-seq data in clinical settings has been the analysis turnaround time. To address this concern, we have developed a targeted approach to expedite assembly and analysis of RNA-seq data. Here we present our Targeted Assembly Pipeline (TAP), which consists of four stages: 1) alignment-free gene-level classification of RNA-seq reads using BioBloomTools, 2) de novo assembly of individual targets using Trans-ABySS, 3) alignment of assembled contigs to the reference genome and transcriptome with GMAP and BWA and 4) structural and splicing variant detection using PAVFinder. We show that PAVFinder is a robust gene fusion detection tool when compared to established methods such as Tophat-Fusion and deFuse on simulated data of 448 events. Using the Leucegene acute myeloid leukemia (AML) RNA-seq data and a set of 580 COSMIC target genes, TAP identified a wide range of hallmark molecular anomalies including gene fusions, tandem duplications, insertions and deletions in agreement with published literature results. Moreover, also in this dataset, TAP captured AML-specific splicing variants such as skipped exons and novel splice sites reported in studies elsewhere. Running time of TAP on 100-150 million read pairs and a 580-gene set is one to 2 hours on a 48-core machine. We demonstrated that TAP is a fast and robust RNA-seq variant detection pipeline that is potentially amenable to clinical applications. TAP is available at http://www.bcgsc.ca/platform/bioinfo/software/pavfinder.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 30 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 27%
Researcher 5 17%
Student > Master 4 13%
Student > Doctoral Student 2 7%
Student > Bachelor 2 7%
Other 3 10%
Unknown 6 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 33%
Agricultural and Biological Sciences 6 20%
Medicine and Dentistry 4 13%
Unspecified 1 3%
Immunology and Microbiology 1 3%
Other 1 3%
Unknown 7 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 17 September 2018.
All research outputs
#10,760,292
of 13,526,754 outputs
Outputs from BMC Medical Genomics
#526
of 686 outputs
Outputs of similar age
#198,472
of 264,683 outputs
Outputs of similar age from BMC Medical Genomics
#6
of 11 outputs
Altmetric has tracked 13,526,754 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 686 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 12th percentile – i.e., 12% 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 264,683 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.