Integrated Web Application (Snips2HLA-HsG) Development for Sample Preparation and Model Creation for HLA Allele Prediction with the SNP Data Using HIBAG Package of Bioconductor and R Programming

Sivaprakasam, Balamurugan and Sadagopan, Prasanna (2024) Integrated Web Application (Snips2HLA-HsG) Development for Sample Preparation and Model Creation for HLA Allele Prediction with the SNP Data Using HIBAG Package of Bioconductor and R Programming. OBM Genetics, 08 (02). pp. 1-16. ISSN 25775790

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Abstract

Integrated Web Application (Snips2HLA-HsG) Development for Sample Preparation and Model Creation for HLA Allele Prediction with the SNP Data Using HIBAG Package of Bioconductor and R Programming Balamurugan Sivaprakasam https://orcid.org/0000-0002-4766-837X Prasanna Sadagopan

The present study introduces Snips2HLA-HsG, an integrated application designed for SNP genotype analysis and HLA allele type prediction. Leveraging attribute bagging, a powerful ensemble classifier technique from the Bioconductor HIBAG package, Snips2HLA-HsG offers a comprehensive response for genetic analysis. Accessible via <a href="https://snips2hla.shinyapps.io/hla_home/">https://snips2hla.shinyapps.io/hla_home/</a>, the application distinguishes itself by prioritizing user-friendliness and integrating all-purpose functionalities, including sample preparation, model generation, HLA prediction, and accuracy assessment. In contrast to the fragmented landscape of existing HLA imputation software, this study addresses the need for an integrated, user-centric platform. By streamlining processes and enhancing accessibility, Snips2HLA-HsG ensures usability, even for biologists with limited computer proficiency. Future updates will address the choice between one or ten classifiers, aiming to optimize server utility and meet research needs effectively by adding more classifiers to utilize multiple cores for faster calculations. Looking ahead, Snips2HLA-HsG will undergo regular updates and maintenance to ensure continued effectiveness and relevance in genetic research. Maintenance efforts will focus on resolving issues or bugs and providing ongoing user support.
06 14 2024 06 14 2024 1 16 10.21926/obm.genet.2402243 https://www.lidsen.com/journals/genetics/genetics-08-02-243 10.1007/s00281-021-00901-9 Naito T, Okada Y. HLA imputation and its application to genetic and molecular fine-mapping of the MHC region in autoimmune diseases. Semin Immunopathol. 2022; 44: 15-28. 10.4414/smw.2020.20214 Sanchez-Mazas A. A review of HLA allele and SNP associations with highly prevalent infectious diseases in human populations. Swiss Med Wkly. 2020; 150: w20214. 10.1016/j.humimm.2020.10.004 Gonzalez-Galarza FF, McCabe A, Dos Santos EJ, Jones AR, Middleton D. A snapshot of human leukocyte antigen (HLA) diversity using data from the allele frequency net database. Hum Immunol. 2021; 82: 496-504. 10.1101/gr.4413105 Thorisson GA, Smith AV, Krishnan L, Stein LD. The international HapMap project web site. Genome Res. 2005; 15: 1592-1593. 10.1016/j.ajhg.2007.09.001 Leslie S, Donnelly P, McVean G. A statistical method for predicting classical HLA alleles from SNP data. Am J Hum Genet. 2008; 82: 48-56. 10.1097/JBR.0000000000000044 Gao J, Zhu C, Zhu Z, Tang L, Liu L, Wen L, et al. The human leukocyte antigen and genetic susceptibility in human diseases. J BioX Res. 2019; 2: 112-120. 10.3389/fgene.2021.774916 Douillard V, Castelli EC, Mack SJ, Hollenbach JA, Gourraud PA, Vince N, et al. Approaching genetics through the MHC lens: Tools and methods for HLA research. Front Genet. 2021; 12: 774916. 10.1093/bioinformatics/btr061 Dilthey AT, Moutsianas L, Leslie S, McVean G. HLA* IMP-an integrated framework for imputing classical HLA alleles from SNP genotypes. Bioinformatics. 2011; 27: 968-972. 10.1038/nrg3054 Browning SR, Browning BL. Haplotype phasing: Existing methods and new developments. Nat Rev Genet. 2011; 12: 703-714. 10.1371/journal.pone.0064683 Jia X, Han B, Onengut-Gumuscu S, Chen WM, Concannon PJ, Rich SS, et al. Imputing amino acid polymorphisms in human leukocyte antigens. PLoS One. 2013; 8: e64683. 10.1038/tpj.2013.18 Zheng X, Shen J, Cox C, Wakefield JC, Ehm MG, Nelson MR, et al. HIBAG-HLA genotype imputation with attribute bagging. Pharmacogenomics J. 2014; 14: 192-200. 10.1186/s12859-017-1746-1 Jeanmougin M, Noirel J, Coulonges C, Zagury JF. HLA-check: Evaluating HLA data from SNP information. BMC Bioinformatics. 2017; 18: 334. 10.1093/bioinformatics/bty730 Shen JJ, Yang C, Wang YF, Wang TY, Guo M, Lau YL, et al. HLA-IMPUTER: An easy to use web application for HLA imputation and association analysis using population-specific reference panels. Bioinformatics. 2019; 35: 1244-1246. 10.1038/s41467-021-21975-x Naito T, Suzuki K, Hirata J, Kamatani Y, Matsuda K, Toda T, et al. A deep learning method for HLA imputation and trans-ethnic MHC fine-mapping of type 1 diabetes. Nat Commun. 2021; 12: 1639. 10.1038/s41467-021-21541-5 Cook S, Choi W, Lim H, Luo Y, Kim K, Jia X, et al. Accurate imputation of human leukocyte antigens with CookHLA. Nat Commun. 2021; 12: 1264. 10.1186/gm403 Boegel S, Löwer M, Schäfer M, Bukur T, De Graaf J, Boisguérin V, et al. HLA typing from RNA-seq sequence reads. Genome Med. 2013; 4: 102. 10.1371/journal.pcbi.1002877 Dilthey A, Leslie S, Moutsianas L, Shen J, Cox C, Nelson MR, et al. Multi-population classical HLA type imputation. PLoS Comput Biol. 2013; 9: e1002877. 10.1038/s41596-023-00853-4 Sakaue S, Gurajala S, Curtis M, Luo Y, Choi W, Ishigaki K, et al. Tutorial: A statistical genetics guide to identifying HLA alleles driving complex disease. Nat Protoc. 2023; 18: 2625-2641. 10.5530/ajbls.2023.12.29 Sivaprakasam B, Sadagopan P. HLA allele type prediction: A review on concepts, methods and algorithms. Asian J Biol Life Sci. 2023; 12: 206-215. 10.1101/2023.01.23.525129 Nanjala R, Mbiyavanga M, Hashim S, de Villiers S, Mulder N. Assessing HLA imputation accuracy in a west African population. bioRxiv. 2023. doi: 10.1101/2023.01.23.525129. 10.1007/978-1-0716-0199-0_3 Chang CC. Data management and summary statistics with PLINK. In: Statistical population genomics. Methods in Molecular Biology. New York, NY: Humana Press; 2020. pp. 57-73. 10.1093/bioinformatics/btp183 Li MX, Jiang L, Kao PY, Sham PC, Song YQ. IGG3: A tool to rapidly integrate large genotype datasets for whole-genome imputation and individual-level meta-analysis. Bioinformatics. 2009; 25: 1449-1450. 10.1007/978-1-4939-8546-3_11 Zheng X. Imputation-based HLA typing with SNPs in GWAS studies. In: HLA typing. Methods in Molecular Biology. New York, NY: Humana Press; 2018. pp. 163-176. 10.1093/bfgp/elw027 Zheng-Bradley X, Flicek P. Applications of the 1000 genomes project resources. Brief Funct Genomics. 2017; 16: 163-170. 10.1186/s12864-019-5957-x Belsare S, Levy-Sakin M, Mostovoy Y, Durinck S, Chaudhuri S, Xiao M, et al. Evaluating the quality of the 1000 genomes project data. BMC Genomics. 2019; 20: 620.

Item Type: Article
Subjects: Computer Science > Web Technologies
Divisions: Computer Science
Depositing User: Mr IR Admin
Date Deposited: 08 Oct 2024 05:16
Last Modified: 08 Oct 2024 05:16
URI: https://ir.vistas.ac.in/id/eprint/9399

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