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PopGLen-A Snakemake pipeline for performing population genomic analyses using genotype likelihood-based methods

Nolen, Zachary J LU orcid (2025) In Bioinformatics
Abstract

SUMMARY: PopGLen is a Snakemake workflow for performing population genomic analyses within a genotype-likelihood framework, integrating steps for raw sequence processing of both historical and modern DNA, quality control, multiple filtering schemes, and population genomic analysis. Currently, the population genomic analyses included allow for estimating linkage disequilibrium, kinship, genetic diversity, genetic differentiation, population structure, inbreeding, and allele frequencies. Through Snakemake, it is highly scalable, and all steps of the workflow are automated, with results compiled into an HTML report. PopGLen provides an efficient, customizable, and reproducible option for analyzing population genomic datasets across a wide... (More)

SUMMARY: PopGLen is a Snakemake workflow for performing population genomic analyses within a genotype-likelihood framework, integrating steps for raw sequence processing of both historical and modern DNA, quality control, multiple filtering schemes, and population genomic analysis. Currently, the population genomic analyses included allow for estimating linkage disequilibrium, kinship, genetic diversity, genetic differentiation, population structure, inbreeding, and allele frequencies. Through Snakemake, it is highly scalable, and all steps of the workflow are automated, with results compiled into an HTML report. PopGLen provides an efficient, customizable, and reproducible option for analyzing population genomic datasets across a wide variety of organisms.

AVAILABILITY AND IMPLEMENTATION: PopGLen is available under GPLv3 with code, documentation, and a tutorial at https://github.com/zjnolen/PopGLen. An example HTML report using the tutorial dataset is included in the supplementary material.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
epub
subject
in
Bioinformatics
article number
btaf105
publisher
Oxford University Press
external identifiers
  • pmid:40067089
ISSN
1367-4803
DOI
10.1093/bioinformatics/btaf105
language
English
LU publication?
yes
additional info
© The Author(s) 2025. Published by Oxford University Press.
id
49db595e-02bb-4540-93f5-7d8e44f16c1d
date added to LUP
2025-03-13 09:45:05
date last changed
2025-04-04 14:31:06
@article{49db595e-02bb-4540-93f5-7d8e44f16c1d,
  abstract     = {{<p>SUMMARY: PopGLen is a Snakemake workflow for performing population genomic analyses within a genotype-likelihood framework, integrating steps for raw sequence processing of both historical and modern DNA, quality control, multiple filtering schemes, and population genomic analysis. Currently, the population genomic analyses included allow for estimating linkage disequilibrium, kinship, genetic diversity, genetic differentiation, population structure, inbreeding, and allele frequencies. Through Snakemake, it is highly scalable, and all steps of the workflow are automated, with results compiled into an HTML report. PopGLen provides an efficient, customizable, and reproducible option for analyzing population genomic datasets across a wide variety of organisms.</p><p>AVAILABILITY AND IMPLEMENTATION: PopGLen is available under GPLv3 with code, documentation, and a tutorial at https://github.com/zjnolen/PopGLen. An example HTML report using the tutorial dataset is included in the supplementary material.</p><p>SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.</p>}},
  author       = {{Nolen, Zachary J}},
  issn         = {{1367-4803}},
  language     = {{eng}},
  month        = {{03}},
  publisher    = {{Oxford University Press}},
  series       = {{Bioinformatics}},
  title        = {{PopGLen-A Snakemake pipeline for performing population genomic analyses using genotype likelihood-based methods}},
  url          = {{http://dx.doi.org/10.1093/bioinformatics/btaf105}},
  doi          = {{10.1093/bioinformatics/btaf105}},
  year         = {{2025}},
}