Abstract
The plethora of RNA-seq data which have been generated in the recent years constitutes an attractive resource to investigate HLA variation and its relationship with normal and disease phenotypes, such as cancer. However, next generation sequencing (NGS) brings new challenges to HLA analysis because of the mapping bias introduced by aligning short reads originated from polymorphic genes to a single reference genome. Here we describe HLApers, a pipeline which adapts widely used tools for analysis of standard RNA-seq data to infer HLA genotypes and estimate expression. By generating reliable expression estimates for each HLA allele that an individual carries, HLApers allows a better understanding of the relationship between HLA alleles and phenotypes manifested by an individual.
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Acknowledgements
Funding was provided by the National Institutes of Health, USA (GM 075091). VRCA was supported by a postdoc fellowship from the São Paulo Funding Agency (FAPESP, http://www.fapesp.br/en/) (2014/12123-2 and 2016/24734-1).
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Aguiar, V.R.C., Masotti, C., Camargo, A.A., Meyer, D. (2020). HLApers: HLA Typing and Quantification of Expression with Personalized Index. In: Boegel, S. (eds) Bioinformatics for Cancer Immunotherapy. Methods in Molecular Biology, vol 2120. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0327-7_7
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DOI: https://doi.org/10.1007/978-1-0716-0327-7_7
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