Demo Data from EDBT 2026 / reviewers in the wild / expert
Colby Chiang
dblp:254/3336
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2019
—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
genomics |
0.4 | 1 | 2019 | svtools: population-scale analysis of structural variation · Bioinform. 2019 |
Bioinformatics and computational biology › genomics › structural variation
structural variation analysis |
0.4 | 1 | 2019 | svtools: population-scale analysis of structural variation · Bioinform. 2019 |
Bioinformatics and computational biology › genomics
genome sequencing |
0.1 | 1 | 2019 | svtools: population-scale analysis of structural variation · Bioinform. 2019 |
Methods — techniques the papers use, named apart from their topics
joint variant analysis · 0.4cloud-based pipeline · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2019 | svtools: population-scale analysis of structural variationabstractSUMMARY: Large-scale human genetics studies are now employing whole genome sequencing with the goal of conducting comprehensive trait mapping analyses of all forms of genome variation. However, methods for structural variation (SV) analysis have lagged far behind those for smaller scale variants, and there is an urgent need to develop more efficient tools that scale to the size of human populations. Here, we present a fast and highly scalable software toolkit (svtools) and cloud-based pipeline for assembling high quality SV maps-including deletions, duplications, mobile element insertions, inversions and other rearrangements-in many thousands of human genomes. We show that this pipeline achieves similar variant detection performance to established per-sample methods (e.g. LUMPY), while providing fast and affordable joint analysis at the scale of ≥100 000 genomes. These tools will help enable the next generation of human genetics studies. AVAILABILITY AND IMPLEMENTATION: svtools is implemented in Python and freely available (MIT) from https://github.com/hall-lab/svtools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. David E. Larson, Haley J. Abel, Colby Chiang, Abhijit Badve, Indraniel Das, James M. Eldred, Ryan Layer, Ira M. Hall |
Bioinform. | 3 |