Demo Data from EDBT 2026 / reviewers in the wild / expert
Arvind Ravi
dblp:68/9350
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › gene expression analysis
gene expression quantification |
0.5 | 1 | 2021 | RNA-SeQC 2: efficient RNA-seq quality control and quantification for large cohorts · Bioinform. 2021 |
Bioinformatics and computational biology › transcriptomics
RNA-seq analysis |
0.5 | 1 | 2021 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | RNA-SeQC 2: efficient RNA-seq quality control and quantification for large cohortsabstractSUMMARY: 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 |