Demo Data from EDBT 2026 / reviewers in the wild / expert
Vincenza Colonna
dblp:284/6149
· 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 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology › genomics
computational genomics |
0.5 | 1 | 2021 | Efficient dynamic variation graphs · Bioinform. 2021 |
Bioinformatics and computational biology › comparative genomics
pangenomics |
0.5 | 1 | 2021 | Efficient dynamic variation graphs · Bioinform. 2021 |
Bioinformatics and computational biology › genomics
variation graph |
0.5 | 1 | 2021 | Efficient dynamic variation graphs · Bioinform. 2021 |
Methods — techniques the papers use, named apart from their topics
python bindings · 0.5c++ library · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Efficient dynamic variation graphsabstractMOTIVATION: Pangenomics is a growing field within computational genomics. Many pangenomic analyses use bidirected sequence graphs as their core data model. However, implementing and correctly using this data model can be difficult, and the scale of pangenomic datasets can be challenging to work at. These challenges have impeded progress in this field. RESULTS: Here, we present a stack of two C++ libraries, libbdsg and libhandlegraph, which use a simple, field-proven interface, designed to expose elementary features of these graphs while preventing common graph manipulation mistakes. The libraries also provide a Python binding. Using a diverse collection of pangenome graphs, we demonstrate that these tools allow for efficient construction and manipulation of large genome graphs with dense variation. For instance, the speed and memory usage are up to an order of magnitude better than the prior graph implementation in the VG toolkit, which has now transitioned to using libbdsg's implementations. AVAILABILITY AND IMPLEMENTATION: libhandlegraph and libbdsg are available under an MIT License from https://github.com/vgteam/libhandlegraph and https://github.com/vgteam/libbdsg. Jordan Eizenga, Adam M. Novak, Emily Kobayashi, Flavia Villani, Cecilia Cisar, Simon Heumos, Glenn Hickey, Vincenza Colonna, Benedict Paten, Erik Garrison |
Bioinform. | 8 |