Demo Data from EDBT 2026 / reviewers in the wild / expert
Richard Lupat
dblp:30/11329
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2012
0000-0002-6435-7100ORCID · reported
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 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › cancer genomics › copy number analysis
copy number variation detection |
0.1 | 1 | 2012 | CONTRA: copy number analysis for targeted resequencing · Bioinform. 2012 |
Bioinformatics and computational biology › genomics
genomic variant analysis |
0.1 | 1 | 2012 | CONTRA: copy number analysis for targeted resequencing · Bioinform. 2012 |
Bioinformatics and computational biology › genomics › high-throughput sequencing
targeted resequencing |
0.1 | 1 | 2012 | CONTRA: copy number analysis for targeted resequencing · Bioinform. 2012 |
Bioinformatics and computational biology › sequence analysis
sequencing data analysis |
0.0 | 1 | 2012 | CONTRA: copy number analysis for targeted resequencing · Bioinform. 2012 |
Methods — techniques the papers use, named apart from their topics
binning and interpolation · 0.1GC-content bias correction · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | CONTRA: copy number analysis for targeted resequencingabstractAbstract Motivation: In light of the increasing adoption of targeted resequencing (TR) as a cost-effective strategy to identify disease-causing variants, a robust method for copy number variation (CNV) analysis is needed to maximize the value of this promising technology. Results: We present a method for CNV detection for TR data, including whole-exome capture data. Our method calls copy number gains and losses for each target region based on normalized depth of coverage. Our key strategies include the use of base-level log-ratios to remove GC-content bias, correction for an imbalanced library size effect on log-ratios, and the estimation of log-ratio variations via binning and interpolation. Our methods are made available via CONTRA (COpy Number Targeted Resequencing Analysis), a software package that takes standard alignment formats (BAM/SAM) and outputs in variant call format (VCF4.0), for easy integration with other next-generation sequencing analysis packages. We assessed our methods using samples from seven different target enrichment assays, and evaluated our results using simulated data and real germline data with known CNV genotypes. Availability and implementation: Source code and sample data are freely available under GNU license (GPLv3) at http://contra-cnv.sourceforge.net/ Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online. Jason Li 0002, Richard Lupat, Kaushalya C. Amarasinghe, Ella R. Thompson, Maria A. Doyle, Georgina L. Ryland, Richard W. Tothill, Saman K. Halgamuge, Ian G. Campbell, Kylie L. Gorringe |
Bioinform. | 2 |