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Rahulsimham Vegesna

dblp:121/1288 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › transcriptomics › RNA-seq analysis
fusion transcript detection
0.212014
PRADA: pipeline for RNA sequencing data analysis · Bioinform. 2014
Bioinformatics and computational biology › transcriptomics › transcriptome sequencing
RNA sequencing analysis
0.212014
PRADA: pipeline for RNA sequencing data analysis · Bioinform. 2014
Bioinformatics and computational biology › gene expression analysis
gene expression quantification
0.112014
PRADA: pipeline for RNA sequencing data analysis · Bioinform. 2014

Methods — techniques the papers use, named apart from their topics

dual-mapping strategy · 0.2
YearPublicationVenuePosition
2014 PRADA: pipeline for RNA sequencing data analysis
abstract
SUMMARY: Technological advances in high-throughput sequencing necessitate improved computational tools for processing and analyzing large-scale datasets in a systematic automated manner. For that purpose, we have developed PRADA (Pipeline for RNA-Sequencing Data Analysis), a flexible, modular and highly scalable software platform that provides many different types of information available by multifaceted analysis starting from raw paired-end RNA-seq data: gene expression levels, quality metrics, detection of unsupervised and supervised fusion transcripts, detection of intragenic fusion variants, homology scores and fusion frame classification. PRADA uses a dual-mapping strategy that increases sensitivity and refines the analytical endpoints. PRADA has been used extensively and successfully in the glioblastoma and renal clear cell projects of The Cancer Genome Atlas program. AVAILABILITY AND IMPLEMENTATION: http://sourceforge.net/projects/prada/ CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Wandaliz Torres-García, Siyuan Zheng, Andrey Sivachenko, Rahulsimham Vegesna, Qianghu Wang, Rong Yao, Michael F. Berger, John N. Weinstein, Gad Getz, Roel G. W. Verhaak
Bioinform.4