ArticleBioinformatics (Oxford, England)2023
GSEL: a fast, flexible python package for detecting signatures of diverse evolutionary forces on genomic regions.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Cell type-specific epigenetic regulatory circuitry of coronary artery disease loci.Nature communications · 2026Article
- The emergence of genetic variants linked to brain and cognitive traits in human evolution.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Genome-wide analyses of neonatal jaundice reveal a marked departure from adult bilirubin metabolism.Nature communications · 2024Article
- Gonomics: uniting high performance and readability for genomics with Go.Bioinformatics (Oxford, England) · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
summaryGSEL 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.