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Multiallelic Maximal Perfect Haplotype Blocks with Wildcards via PBWT

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Bioinformatics and Biomedical Engineering (IWBBIO 2023)

Abstract

Computing maximal perfect blocks of a given panel of haplotypes is a crucial task for efficiently solving problems such as polyploid haplotype reconstruction and finding identical-by-descent segments shared among individuals of a population. Unfortunately, the presence of missing data in the haplotype panel limits the usefulness of the notion of perfect blocks.

We propose a novel algorithm for computing maximal blocks in a panel with missing data (represented as wildcards). The algorithm is based on the Positional Burrows-Wheeler Transform (PBWT) and has been implemented in the tool Wild-pBWT, available at https://github.com/AlgoLab/Wild-pBWT/. Experimental comparison showed that Wild-pBWT is 10–15 times faster than another state-of-the-art approach, while using a negligible amount of memory.

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References

  1. Alanko, J., et al.: Finding all maximal perfect haplotype blocks in linear time. Algorithms Mol. Biol. 15 (2020). https://doi.org/10.1186/s13015-020-0163-6

  2. Baaijens, J.A., et al.: Computational graph pangenomics: a tutorial on data structures and their applications. Nat. Comput. 21(1), 81–108 (2022). https://doi.org/10.1007/s11047-022-09882-6

    Article  PubMed  PubMed Central  Google Scholar 

  3. Bonizzoni, P., et al.: Compressed data structures for population-scale positional burrows–wheeler transforms. bioRxiv (2022). https://doi.org/10.1101/2022.09.16.508250

  4. Cunha, L., Diekmann, Y., Kowada, L., Stoye, J.: Identifying maximal perfect haplotype blocks. In: Alves, R. (ed.) BSB 2018. LNCS, vol. 11228, pp. 26–37. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01722-4_3

    Chapter  Google Scholar 

  5. Durbin, R.: Efficient haplotype matching and storage using the positional Burrows-Wheeler transform (PBWT). Bioinformatics 30 (2014). https://doi.org/10.1093/bioinformatics/btu014

  6. Gog, S., Beller, T., Moffat, A., Petri, M.: From theory to practice: plug and play with succinct data structures. In: Gudmundsson, J., Katajainen, J. (eds.) SEA 2014. LNCS, vol. 8504, pp. 326–337. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07959-2_28

    Chapter  Google Scholar 

  7. Halldorsson, B.V., et al.: The sequences of 150,119 genomes in the UK Biobank. Nature 607 (2022). https://doi.org/10.1038/s41586-022-04965-x

  8. Kirsch-Gerweck, B., et al.: Haploblocks: efficient detection of positive selection in large population genomic datasets. Mol. Biol. Evol. 40 (2023). https://doi.org/10.1093/molbev/msad027

  9. Moeinzadeh, M.H., et al.: Ranbow: a fast and accurate method for polyploid haplotype reconstruction. PLOS Comput. Biol. 16 (2020). https://doi.org/10.1371/journal.pcbi.1007843

  10. Naseri, A., Zhi, D., Zhang, S.: Multi-allelic positional Burrows-Wheeler transform. BMC Bioinform. 20 (2019). https://doi.org/10.1186/s12859-019-2821-6

  11. Naseri, A., et al.: RaPID: ultra-fast, powerful, and accurate detection of segments identical by descent (IBD) in biobank-scale cohorts. Genome Biol. 20 (2019). https://doi.org/10.1186/s13059-019-1754-8

  12. Rubinacci, S., Delaneau, O., Marchini, J.: Genotype imputation using the positional Burrows-Wheeler transform. PLOS Genetics 16 (2020). https://doi.org/10.1371/journal.pgen.1009049

  13. Taliun, D., et al.: Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590 (2021). https://doi.org/10.1038/s41586-021-03205-y

  14. Vigna, S.: Broadword implementation of rank/select queries. In: McGeoch, C.C. (ed.) WEA 2008. LNCS, vol. 5038, pp. 154–168. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-68552-4_12

    Chapter  Google Scholar 

  15. Williams, L., Mumey, B.: Maximal perfect haplotype blocks with wildcards. iScience 23 (2020). https://doi.org/10.1016/j.isci.2020.101149

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Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 872539.

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Correspondence to Paola Bonizzoni .

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Bonizzoni, P., Della Vedova, G., Pirola, Y., Rizzi, R., Sgrò, M. (2023). Multiallelic Maximal Perfect Haplotype Blocks with Wildcards via PBWT. In: Rojas, I., Valenzuela, O., Rojas Ruiz, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2023. Lecture Notes in Computer Science(), vol 13919. Springer, Cham. https://doi.org/10.1007/978-3-031-34953-9_5

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  • DOI: https://doi.org/10.1007/978-3-031-34953-9_5

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  • Online ISBN: 978-3-031-34953-9

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