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Arvind Ravi

dblp:68/9350 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · unresolved

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

Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

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 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › gene expression analysis
gene expression quantification
0.512021
RNA-SeQC 2: efficient RNA-seq quality control and quantification for large cohorts · Bioinform. 2021
Bioinformatics and computational biology › transcriptomics
RNA-seq analysis
0.512021
RNA-SeQC 2: efficient RNA-seq quality control and quantification for large cohorts · Bioinform. 2021

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

quality metrics · 0.5
YearPublicationVenuePosition
2021 RNA-SeQC 2: efficient RNA-seq quality control and quantification for large cohorts
abstract
SUMMARY: Post-sequencing quality control is a crucial component of RNA sequencing (RNA-seq) data generation and analysis, as sample quality can be affected by sample storage, extraction and sequencing protocols. RNA-seq is increasingly applied to cohorts ranging from hundreds to tens of thousands of samples in size, but existing tools do not readily scale to these sizes, and were not designed for a wide range of sample types and qualities. Here, we describe RNA-SeQC 2, an efficient reimplementation of RNA-SeQC (DeLuca et al., 2012) that adds multiple metrics designed to characterize sample quality across a wide range of RNA-seq protocols. AVAILABILITY AND IMPLEMENTATION: The command-line tool, documentation and C++ source code are available at the GitHub repository https://github.com/getzlab/rnaseqc. Code and data for reproducing the figures in this paper are available at https://github.com/getzlab/rnaseqc2-paper. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Aaron Graubert, François Aguet, Arvind Ravi, Kristin G. Ardlie, Gad Getz
Bioinform.3