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LDkit: a parallel computing toolkit for linkage disequilibrium analysis
BACKGROUND: Linkage disequilibrium (LD) analysis is broadly utilized in genetics to understand the evolutionary and demographic history and helps geneticists identify genes associated with interested inherited traits, such as diseases. There are some tools for linkage disequilibrium analysis either...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7565767/ https://www.ncbi.nlm.nih.gov/pubmed/33066733 http://dx.doi.org/10.1186/s12859-020-03754-5 |
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author | Tang, You Li, Zhuo Wang, Chao Liu, Yuxin Yu, Helong Wang, Aoxue Zhou, Yao |
author_facet | Tang, You Li, Zhuo Wang, Chao Liu, Yuxin Yu, Helong Wang, Aoxue Zhou, Yao |
author_sort | Tang, You |
collection | PubMed |
description | BACKGROUND: Linkage disequilibrium (LD) analysis is broadly utilized in genetics to understand the evolutionary and demographic history and helps geneticists identify genes associated with interested inherited traits, such as diseases. There are some tools for linkage disequilibrium analysis either in a local or online way; however, there has been no such tool supporting both graphical user interface (GUI) and parallel computing. RESULTS: We developed a GUI software called LDkit for LD analysis, which supports parallel computing. The LDkit supports both variant call format (VCF) and PLINK ‘ped + map’ format. At the same time, users could also just analyze a subset of individuals from the whole population. The LDkit reads the data by block and then paralleled the computation process by monitoring the usage of processes. Assessment on the Human 1000 genome data showed that when paralleled with 32 threads, the running time was reduced to less than 6 minutes from ~77 minutes using the chromosome 22 dataset with 1,103,547 SNPs and 2504 individuals. CONCLUSIONS: The software LDkit can be effectively used to calculate and plot LD decay, LD block, and linkage disequilibrium analysis between a site and a given region. Most importantly, both graphical user interface (GUI) and stand-alone packages are available for users’ convenience. LDkit was written in JAVA language under cross-platform support. |
format | Online Article Text |
id | pubmed-7565767 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-75657672020-10-20 LDkit: a parallel computing toolkit for linkage disequilibrium analysis Tang, You Li, Zhuo Wang, Chao Liu, Yuxin Yu, Helong Wang, Aoxue Zhou, Yao BMC Bioinformatics Software BACKGROUND: Linkage disequilibrium (LD) analysis is broadly utilized in genetics to understand the evolutionary and demographic history and helps geneticists identify genes associated with interested inherited traits, such as diseases. There are some tools for linkage disequilibrium analysis either in a local or online way; however, there has been no such tool supporting both graphical user interface (GUI) and parallel computing. RESULTS: We developed a GUI software called LDkit for LD analysis, which supports parallel computing. The LDkit supports both variant call format (VCF) and PLINK ‘ped + map’ format. At the same time, users could also just analyze a subset of individuals from the whole population. The LDkit reads the data by block and then paralleled the computation process by monitoring the usage of processes. Assessment on the Human 1000 genome data showed that when paralleled with 32 threads, the running time was reduced to less than 6 minutes from ~77 minutes using the chromosome 22 dataset with 1,103,547 SNPs and 2504 individuals. CONCLUSIONS: The software LDkit can be effectively used to calculate and plot LD decay, LD block, and linkage disequilibrium analysis between a site and a given region. Most importantly, both graphical user interface (GUI) and stand-alone packages are available for users’ convenience. LDkit was written in JAVA language under cross-platform support. BioMed Central 2020-10-16 /pmc/articles/PMC7565767/ /pubmed/33066733 http://dx.doi.org/10.1186/s12859-020-03754-5 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Tang, You Li, Zhuo Wang, Chao Liu, Yuxin Yu, Helong Wang, Aoxue Zhou, Yao LDkit: a parallel computing toolkit for linkage disequilibrium analysis |
title | LDkit: a parallel computing toolkit for linkage disequilibrium analysis |
title_full | LDkit: a parallel computing toolkit for linkage disequilibrium analysis |
title_fullStr | LDkit: a parallel computing toolkit for linkage disequilibrium analysis |
title_full_unstemmed | LDkit: a parallel computing toolkit for linkage disequilibrium analysis |
title_short | LDkit: a parallel computing toolkit for linkage disequilibrium analysis |
title_sort | ldkit: a parallel computing toolkit for linkage disequilibrium analysis |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7565767/ https://www.ncbi.nlm.nih.gov/pubmed/33066733 http://dx.doi.org/10.1186/s12859-020-03754-5 |
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