Cargando…
Local ancestry prediction with PyLAE
SUMMARY: We developed PyLAE, a new tool for determining local ancestry along a genome using whole-genome sequencing data or high-density genotyping experiments. PyLAE can process an arbitrarily large number of ancestral populations (with or without an informative prior). Since PyLAE does not involve...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8679960/ https://www.ncbi.nlm.nih.gov/pubmed/35003914 http://dx.doi.org/10.7717/peerj.12502 |
_version_ | 1784616642859237376 |
---|---|
author | Moshkov, Nikita Smetanin, Aleksandr Tatarinova, Tatiana V. |
author_facet | Moshkov, Nikita Smetanin, Aleksandr Tatarinova, Tatiana V. |
author_sort | Moshkov, Nikita |
collection | PubMed |
description | SUMMARY: We developed PyLAE, a new tool for determining local ancestry along a genome using whole-genome sequencing data or high-density genotyping experiments. PyLAE can process an arbitrarily large number of ancestral populations (with or without an informative prior). Since PyLAE does not involve estimating many parameters, it can process thousands of genomes within a day. PyLAE can run on phased or unphased genomic data. We have shown how PyLAE can be applied to the identification of differentially enriched pathways between populations. The local ancestry approach results in higher enrichment scores compared to whole-genome approaches. We benchmarked PyLAE using the 1000 Genomes dataset, comparing the aggregated predictions with the global admixture results and the current gold standard program RFMix. Computational efficiency, minimal requirements for data pre-processing, straightforward presentation of results, and ease of installation make PyLAE a valuable tool to study admixed populations. AVAILABILITY AND IMPLEMENTATION: The source code and installation manual are available at https://github.com/smetam/pylae. |
format | Online Article Text |
id | pubmed-8679960 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86799602022-01-06 Local ancestry prediction with PyLAE Moshkov, Nikita Smetanin, Aleksandr Tatarinova, Tatiana V. PeerJ Bioinformatics SUMMARY: We developed PyLAE, a new tool for determining local ancestry along a genome using whole-genome sequencing data or high-density genotyping experiments. PyLAE can process an arbitrarily large number of ancestral populations (with or without an informative prior). Since PyLAE does not involve estimating many parameters, it can process thousands of genomes within a day. PyLAE can run on phased or unphased genomic data. We have shown how PyLAE can be applied to the identification of differentially enriched pathways between populations. The local ancestry approach results in higher enrichment scores compared to whole-genome approaches. We benchmarked PyLAE using the 1000 Genomes dataset, comparing the aggregated predictions with the global admixture results and the current gold standard program RFMix. Computational efficiency, minimal requirements for data pre-processing, straightforward presentation of results, and ease of installation make PyLAE a valuable tool to study admixed populations. AVAILABILITY AND IMPLEMENTATION: The source code and installation manual are available at https://github.com/smetam/pylae. PeerJ Inc. 2021-12-14 /pmc/articles/PMC8679960/ /pubmed/35003914 http://dx.doi.org/10.7717/peerj.12502 Text en © 2021 Moshkov et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Moshkov, Nikita Smetanin, Aleksandr Tatarinova, Tatiana V. Local ancestry prediction with PyLAE |
title | Local ancestry prediction with PyLAE |
title_full | Local ancestry prediction with PyLAE |
title_fullStr | Local ancestry prediction with PyLAE |
title_full_unstemmed | Local ancestry prediction with PyLAE |
title_short | Local ancestry prediction with PyLAE |
title_sort | local ancestry prediction with pylae |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8679960/ https://www.ncbi.nlm.nih.gov/pubmed/35003914 http://dx.doi.org/10.7717/peerj.12502 |
work_keys_str_mv | AT moshkovnikita localancestrypredictionwithpylae AT smetaninaleksandr localancestrypredictionwithpylae AT tatarinovatatianav localancestrypredictionwithpylae |