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Abin Abraham

dblp:340/6044 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2023
0000-0002-9951-2879ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author · 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 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › functional genomics › functional enrichment analysis
genomic region enrichment analysis
0.712023
GSEL: a fast, flexible python package for detecting signatures of diverse evolutionary forces on genomic regions · Bioinform. 2023
Bioinformatics and computational biology
population genetics
0.712023
GSEL: a fast, flexible python package for detecting signatures of diverse evolutionary forces on genomic regions · Bioinform. 2023

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

linkage disequilibrium analysis · 0.7empirical null distribution · 0.7
YearPublicationVenuePosition
2023 GSEL: a fast, flexible python package for detecting signatures of diverse evolutionary forces on genomic regions
abstract
SUMMARY: GSEL is a computational framework for calculating the enrichment of signatures of diverse evolutionary forces in a set of genomic regions. GSEL can flexibly integrate any sequence-based evolutionary metric and analyze sets of human genomic regions identified by genome-wide assays (e.g. GWAS, eQTL, *-seq). The core of GSEL's approach is the generation of empirical null distributions tailored to the allele frequency and linkage disequilibrium structure of the regions of interest. We illustrate the application of GSEL to variants identified from a GWAS of body mass index, a highly polygenic trait. AVAILABILITY AND IMPLEMENTATION: GSEL is implemented as a fast, flexible and user-friendly python package. It is available with demonstration data at https://github.com/abraham-abin13/gsel_vec. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Abin Abraham, Abigail L. Labella, Mary Lauren Benton, Antonis Rokas, John A. Capra
Bioinform.1