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Gaël A. Millot

dblp:276/1067 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—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 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › epigenomics
hi-c data analysis
0.412020
Serpentine: a flexible 2D binning method for differential Hi-C analysis · Bioinform. 2020
Bioinformatics and computational biology › epigenomics
chromatin conformation analysis
0.112020
Serpentine: a flexible 2D binning method for differential Hi-C analysis · Bioinform. 2020

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

2d binning · 0.4
YearPublicationVenuePosition
2020 Serpentine: a flexible 2D binning method for differential Hi-C analysis
abstract
MOTIVATION: Hi-C contact maps reflect the relative contact frequencies between pairs of genomic loci, quantified through deep sequencing. Differential analyses of these maps enable downstream biological interpretations. However, the multi-fractal nature of the chromatin polymer inside the cellular envelope results in contact frequency values spanning several orders of magnitude: contacts between loci pairs separated by large genomic distances are much sparser than closer pairs. The same is true for poorly covered regions, such as repeated sequences. Both distant and poorly covered regions translate into low signal-to-noise ratios. There is no clear consensus to address this limitation. RESULTS: We present Serpentine, a fast, flexible procedure operating on raw data, which considers the contacts in each region of a contact map. Binning is performed only when necessary on noisy regions, preserving informative ones. This results in high-quality, low-noise contact maps that can be conveniently visualized for rigorous comparative analyses. AVAILABILITY AND IMPLEMENTATION: Serpentine is available on the PyPI repository and https://github.com/koszullab/serpentine; documentation and tutorials are provided at https://serpentine.readthedocs.io/en/latest/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Lyam Baudry, Gaël A. Millot, Agnes Thierry, Romain Koszul, Vittore F. Scolari
Bioinform.2