Title |
Inference of high resolution HLA types using genome-wide RNA or DNA sequencing reads
|
---|---|
Published in |
BMC Genomics, May 2014
|
DOI | 10.1186/1471-2164-15-325 |
Pubmed ID | |
Authors |
Yu Bai, Min Ni, Blerta Cooper, Yi Wei, Wen Fury |
Abstract |
Accurate HLA typing at amino acid level (four-digit resolution) is critical in hematopoietic and organ transplantations, pathogenesis studies of autoimmune and infectious diseases, as well as the development of immunoncology therapies. With the rapid adoption of genome-wide sequencing in biomedical research, HLA typing based on transcriptome and whole exome/genome sequencing data becomes increasingly attractive due to its high throughput and convenience. However, unlike targeted amplicon sequencing, genome-wide sequencing often employs a reduced read length and coverage that impose great challenges in resolving the highly homologous HLA alleles. Though several algorithms exist and have been applied to four-digit typing, some deliver low to moderate accuracies, some output ambiguous predictions. Moreover, few methods suit diverse read lengths and depths, and both RNA and DNA sequencing inputs. New algorithms are therefore needed to leverage the accuracy and flexibility of HLA typing at high resolution using genome-wide sequencing data. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Brazil | 3 | 2% |
United States | 3 | 2% |
United Kingdom | 2 | 1% |
Italy | 1 | <1% |
India | 1 | <1% |
Germany | 1 | <1% |
Argentina | 1 | <1% |
Sweden | 1 | <1% |
Unknown | 157 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 50 | 29% |
Student > Ph. D. Student | 35 | 21% |
Student > Master | 15 | 9% |
Student > Bachelor | 13 | 8% |
Professor | 8 | 5% |
Other | 27 | 16% |
Unknown | 22 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 48 | 28% |
Biochemistry, Genetics and Molecular Biology | 42 | 25% |
Medicine and Dentistry | 17 | 10% |
Computer Science | 12 | 7% |
Immunology and Microbiology | 12 | 7% |
Other | 11 | 6% |
Unknown | 28 | 16% |