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HLA Typing pp 215-223 | Cite as

HLA Typing from Short-Read Sequencing Data with OptiType

  • András SzolekEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1802)

Abstract

The established standards for HLA genotyping rely on targeted DNA sequencing techniques. However, the increasing abundance of short-read sequencing data has prompted a demand for computational tools capable of HLA typing from general purpose sequencing data as well. OptiType is a software that performs HLA typing from short-read DNA and RNA sequencing data, and this chapter guides the user through its installation and usage.

Keywords

HLA class I typing RNA-Seq Whole-exome Seq OptiType Mapping In silico Bioinformatics 

References

  1. 1.
    Bauer DC, Zadoorian A, Wilson LOW et al (2016) Evaluation of computational programs to predict 5HLA6 genotypes from genomic sequencing data. Brief Bioinform pii:bbw097.  https://doi.org/10.1093/bib/bbw097CrossRefGoogle Scholar
  2. 2.
    Kiyotani K, Mai TH, Nakamura Y (2016) Comparison of exome-based 5HLA6 class I genotyping tools: identification of platform-specific genotyping errors. J Human Genet 62(3):397–405.  https://doi.org/10.1038/jhg.2016.141CrossRefGoogle Scholar
  3. 3.
    Szolek A, Schubert B, Mohr C et al (2014) OptiType: precision HLA typing from next-generation sequencing data. Bioinformatics 30(23):3310–3316.  https://doi.org/10.1093/bioinformatics/btu548CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Weese D, Holtgrewe M, Reinert K (2012) 5RazerS6 3: faster, fully sensitive read mapping. Bioinformatics 28(20):2592–2599.  https://doi.org/10.1093/bioinformatics/bts505CrossRefPubMedGoogle Scholar
  5. 5.
    Siragusa E, Weese D, Reinert K (2013) Fast and accurate read mapping with approximate seeds and multiple backtracking. Nucleic Acids Res 41(7):e78–e78.  https://doi.org/10.1093/nar/gkt005CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    van der Walt S, Colbert SC, Varoquaux G (2011) The 5NumPy6 array: a structure for efficient numerical computation. Comput Sci Eng 13(2):22–30.  https://doi.org/10.1109/mcse.2011.37CrossRefGoogle Scholar
  7. 7.
    Hart WE, Watson J-P, Woodruff DL (2011) Pyomo: modeling and solving mathematical programs in Python. Math Progr Comput 3(3):219–260.  https://doi.org/10.1007/s12532-011-0026-8CrossRefGoogle Scholar
  8. 8.
    McKinney W (2010) Data structures for statistical computing in python. In: van der Walt S, Millman J (eds) Proceedings of the 9th python in science conference. Creative Commons, Austin, TX, pp 51–56Google Scholar
  9. 9.
    Li H, Handsaker B, Wysoker A et al (2009) The sequence alignment/map format and 5SAMtools6. Bioinformatics 25(16):2078–2079.  https://doi.org/10.1093/bioinformatics/btp352CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Hunter JD (2007) Matplotlib: a 2D graphics environment. Comput Sci Eng 9(3):90–95.  https://doi.org/10.1109/mcse.2007.55CrossRefGoogle Scholar
  11. 11.
    Brandt DYC, Aguiar VRC, Bitarello BD et al (2015) Mapping bias overestimates reference allele frequencies at the 5HLA6 genes in the 1000 Genomes Project phase I data. G3 Genet 5(5):931–941.  https://doi.org/10.1534/g3.114.015784CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Applied Bioinformatics, Department for Computer ScienceUniversity of TübingenTübingenGermany

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