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Vincent Matthys

dblp:307/2518 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

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

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › genomics
genome organization
0.412019
Boost-HiC: computational enhancement of long-range contacts in chromosomal contact maps · Bioinform. 2019
Bioinformatics and computational biology › epigenomics
3d genome organization
0.112019
Boost-HiC: computational enhancement of long-range contacts in chromosomal contact maps · Bioinform. 2019

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

sparse inference · 0.4matrix completion · 0.4
YearPublicationVenuePosition
2019 Boost-HiC: computational enhancement of long-range contacts in chromosomal contact maps
abstract
MOTIVATION: Genome-wide chromosomal contact maps are widely used to uncover the 3D organization of genomes. They rely on collecting millions of contacting pairs of genomic loci. Contacts at short range are usually well measured in experiments, while there is a lot of missing information about long-range contacts. RESULTS: We propose to use the sparse information contained in raw contact maps to infer high-confidence contact counts between all pairs of loci. Our algorithmic procedure, Boost-HiC, enables the detection of Hi-C patterns such as chromosomal compartments at a resolution that would be otherwise only attainable by sequencing a hundred times deeper the experimental Hi-C library. Boost-HiC can also be used to compare contact maps at an improved resolution. AVAILABILITY AND IMPLEMENTATION: Boost-HiC is available at https://github.com/LeopoldC/Boost-HiC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Leopold Carron, Jean Baptiste Morlot, Vincent Matthys, Annick Lesne, Julien Mozziconacci
Bioinform.3