Post-graduate theses
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Identifier |
000464362 |
Title |
A scalable and reproducible bioinformatics workflow for HLA type inference from NGS data |
Alternative Title |
Μια κλιμακούμενη και αναπαραγώγιμη ροή βιοπληροφορικής εργασίαέυρεσης τύπων HLA από δεδομένα αλληλούχισης επόμενης γενιάς |
Author
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Κολιαδήμα, Μαρία-Ευτυχία
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Thesis advisor
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Καντεράκης, Αλέξανδρος
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Reviewer
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Λατσούδη, Λένα
Ποταμιάς, Γεώργιος
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Abstract |
Human Leukocyte Antigen (HLA) group of genes is one of the most polymorphic regions in the
human genome with significant roles in antigen presentation (donor-recipient compatibility during
hematopoietic stem cell transplantation-HSCT) adaptive immune responses underlying disease
susceptibility (immunological & metabolic diseases and cancer) and drug response
(pharmacogenetics). Also, the high allelic diversity and significant linkage disequilibrium (LD) of
HLA have proven useful for singling out individuals and populations, since certain alleles show
distinctive profiles in populations. Thus, HLA is important in studying the origins and genetic
structure of human groups, as well as inferring relations between populations and ethnic groups.
Even though sequence-based typing (SBT) is the gold standard method for HLA typing, the
advancement of sequencing technologies has resulted in the growing need to use Next Generation
Sequencing (NGS) for accurate HLA calling. However, the high allelic and structural variation,
inter-gene sequence homologies and high LD make HLA analysis challenging. Recent advanced
algorithmic approaches including accurate SNP-based HLA imputation and HLA genome inference
from whole exome and genome sequencing (WES and WGS) data, have rendered the HLA region
significantly accessible. The result has been an explosion of in-silico NGS-based HLA typing
software solutions, each using different methods for imputation or inference. Despite the continued
growth in this bioinformatics field, a single workflow has not been developed enabling the
concomitant evaluation of the HLA typed alleles. In these thesis we compiled 4 open-source
genome inference algorithms (HLA*LA, HISAT-Genotype, HLA-HD and HLAScan) able to call
2nd-field alleles for Class I (HLA-A, -B, -C) and Class II genes (HLA-DRB1, -DQB1) using
Illumina short read NGS data.
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Language |
English |
Issue date |
2024-04-17 |
Collection
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School/Department--School of Medicine--Department of Medicine--Post-graduate theses
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Type of Work--Post-graduate theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/f/6/6/metadata-dlib-1714027370-402539-17539.tkl
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Views |
48 |
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