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Section: Mathematical & Computational Biology
Topic: Biophysics and computational biology, Genetics/Genomics

HMMploidy: inference of ploidy levels from short-read sequencing data

Soraggi, Samuele1, 2  ; Rhodes, Johanna3 ; Altinkaya, Isin2, 4, 5 ; Tarrant, Oliver2; Balloux, Francois6 ; Fisher, Matthew C3 ; Fumagalli, Matteo2, 7  
1 Bioinformatics Research Center (BiRC), University of Aarhus, 8000 Aarhus, Denmark
2 Department of Life Sciences Silwood Park, Imperial College London, Ascot, SL5 7PY, UK
3 MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK
4 Department of Biology, Hacettepe University, 06800 Beytepe Campus, Ankara, Turkey
5 GLOBE, Section for Geogenetics, Øster Voldgade 5-7, 1350, Copenhagen, Denmark
6 UCL Genetics Institute, University College London, London, WC1E 6BT, UK
7 School of Biological and Behavioural Sciences, Queen Mary University of London, London, E1 4NS, UK

10.24072/pcjournal.178 - Peer Community Journal, Volume 2 (2022), article no. e60.

Get full text PDF Peer reviewed and recommended by PCI

The inference of ploidy levels from genomic data is important to understand molecular mechanisms underpinning genome evolution. However, current methods based on allele frequency and sequencing depth variation do not have power to infer ploidy levels at low- and mid-depth sequencing data, as they do not account for data uncertainty. Here we introduce HMMploidy, a novel tool that leverages the information from multiple samples and combines the information from sequencing depth and genotype likelihoods. We demonstrate that HMMploidy outperforms existing methods in most tested scenarios, especially at low-depth with large sample size. We apply HMMploidy to sequencing data from the pathogenic fungus Cryptococcus neoformans and retrieve pervasive patterns of aneuploidy, even when artificially downsampling the sequencing data. We envisage that HMMploidy will have wide applicability to low-depth sequencing data from polyploid and aneuploid species.

Published online:
DOI: 10.24072/pcjournal.178
Type: Software tool
Soraggi, Samuele 1, 2; Rhodes, Johanna 3; Altinkaya, Isin 2, 4, 5; Tarrant, Oliver 2; Balloux, Francois 6; Fisher, Matthew C 3; Fumagalli, Matteo 2, 7

1 Bioinformatics Research Center (BiRC), University of Aarhus, 8000 Aarhus, Denmark
2 Department of Life Sciences Silwood Park, Imperial College London, Ascot, SL5 7PY, UK
3 MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, London, W2 1PG, UK
4 Department of Biology, Hacettepe University, 06800 Beytepe Campus, Ankara, Turkey
5 GLOBE, Section for Geogenetics, Øster Voldgade 5-7, 1350, Copenhagen, Denmark
6 UCL Genetics Institute, University College London, London, WC1E 6BT, UK
7 School of Biological and Behavioural Sciences, Queen Mary University of London, London, E1 4NS, UK
License: CC-BY 4.0
Copyrights: The authors retain unrestricted copyrights and publishing rights 
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     title = {HMMploidy: inference of ploidy levels from short-read sequencing data},
     journal = {Peer Community Journal},
     eid = {e60},
     publisher = {Peer Community In},
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     year = {2022},
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Soraggi, Samuele; Rhodes, Johanna; Altinkaya, Isin; Tarrant, Oliver; Balloux, Francois; Fisher, Matthew C; Fumagalli, Matteo. HMMploidy: inference of ploidy levels from short-read sequencing data. Peer Community Journal, Volume 2 (2022), article  no. e60. doi : 10.24072/pcjournal.178. https://peercommunityjournal.org/articles/10.24072/pcjournal.178/

Peer reviewed and recommended by PCI : 10.24072/pci.mcb.100010

Conflict of interest of the recommender and peer reviewers:
The recommender in charge of the evaluation of the article and the reviewers declared that they have no conflict of interest (as defined in the code of conduct of PCI) with the authors or with the content of the article.

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