Demo Data from VLDB 2026 / reviewers in the wild / expert
Rahulsimham Vegesna
dblp:121/1288
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › transcriptomics › RNA-seq analysis
fusion transcript detection |
0.2 | 1 | 2014 | PRADA: pipeline for RNA sequencing data analysis · Bioinform. 2014 |
Bioinformatics and computational biology › transcriptomics › transcriptome sequencing
RNA sequencing analysis |
0.2 | 1 | 2014 | PRADA: pipeline for RNA sequencing data analysis · Bioinform. 2014 |
Bioinformatics and computational biology › gene expression analysis
gene expression quantification |
0.1 | 1 | 2014 | PRADA: pipeline for RNA sequencing data analysis · Bioinform. 2014 |
Methods — techniques the papers use, named apart from their topics
dual-mapping strategy · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2014 | PRADA: pipeline for RNA sequencing data analysisabstractSUMMARY: 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 |