Biology subjects

Priess, C.

Publications and source records attributed to Priess, C..

3 recordsLinked to original sources

bioRxiv2023

GWAIS-Web: A Fast and Secure Web Service for Epistasis Detection in Genome-wide Association Interaction Studies

MotivationGenome-wide association interaction studies (GWAIS) are becoming increasingly important as estimates of genetic interactions at the genome-wide level using genome-wide data from hundreds of thousands of individuals from large biobanks showed that non-additive genetic variance plays a role in complex human traits in addition to additive genetic effects identified in genome-wide association studies (GWAS). However, a comprehensive genome-wide search for all combinations of second-order (SNPxSNP) or third-order (SNPxSNPxSNP) associations using millions of SNP markers is a very computationally intensive task, especially when hundreds of thousands or, in the near future, even millions of individuals can be studied with GWAS datasets. The runtime so far exceeds years, even if the search is performed on a multicore CPU server system. ResultsWe developed GWAIS-Web, a web service for fast analysis of genome-wide interactions with case-control GWAS datasets. By using a hybrid combination of graphics-processing units (GPUs) and field-programmable gate arrays (FPGAs), GWAIS-Web speeds up epistasis detection methods for binary traits by a factor of more than 2000, allowing an exhaustive SNP-SNP GWAIS with a GWAS data set of one million SNPs and 500,000 individuals to complete within one day, which would take more than five years on a regular CPU server system. The user can choose between different methods for epistasis detection, such as logistic regression, BOOST, mutual information (MI) and others, with calculations in double precision and including on-the-fly filtering of correlated results based on linkage disequilibrium (LD). Due to the underlying common data structure of GWAIS-Web, all methods can be combined and processed together on-the-fly without increasing the runtime. The user can choose between 2nd order (pairwise) and 3rd order tests and can also limit the search to selected chromosomal regions. GWAIS-Web offers a high level of security through optional 2-factor authentication, encrypted connections and the protection of GWAS/user account data in accordance with the European General Data Protection Regulation (GDPR). AvailabilityGWAIS-Web is freely available at https://hybridcomputing.ikmb.uni-kiel.de. The stand-alone software HybridGWAIS can be downloaded at https://github.com/ikmb/hybridgwais. Supplementary informationSupplementary data are available online.

genetics↗

bioRxiv2022

Predicting Peptide HLA-II Presentation Using Immunopeptidomics, Transcriptomics and Deep Multimodal Learning

The human leukocyte antigen (HLA) class II proteins present peptides to CD4+ T cells through an interaction with T cell receptors (TCRs). Thus, HLA proteins are key players in shaping immunogenicity and immunodominance. Nevertheless, factors governing peptide presentation by HLA-II proteins are still poorly understood. To address this problem, we profiled the blood transcriptome and immunopeptidome of 20 healthy individuals and integrated the profiles with publicly available immunopeptidomics datasets. In depth multi-omics analysis identified expression levels and subcellular locations as import sequence-independent features governing presentation. Levering this knowledge, we developed the Peptide Immune Annotator Multimodal (PIA-M) tool, as a novel pan multimodal transformer-based framework that utilises sequence-dependent along with sequence-independent features to model presentation by HLA-II proteins. PIA-M illustrated a consistently superior performance relative to existing tools across two independent test datasets (area under the curve: 0.93 vs. 0.84 and 0.95 vs. 0.86), respectively. Besides achieving a higher predictive accuracy, PIA-M with its Rust-based pre-processing engine, had significantly shorter runtimes. PIA-M is freely available with a permissive licence as a standalone pipeline and as a webserver (https://hybridcomputing.ikmb.uni-kiel.de/pia). In conclusion, PIA-M enables a new state-of-the-art accuracy in predicting peptide presentation by HLA-II proteins in vivo.

bioinformatics↗

bioRxiv2022

EagleImp-Web: A Fast and Secure Genotype Phasing and Imputation Web Service using Field-Programmable Gate Arrays

Imputation web servers have been developed that allow phasing and imputation of genome-wide data without the need for own computing resources. However, their terms of use, data sharing with third parties, the security architecture of the web service, and the algorithms and parameter settings used are only partially disclosed. We developed EagleImp-Web, a fast, secure and convenient web service for phasing and imputation of genome-wide data. The web service uses technical improvements in phasing and imputation algorithms and a field-programmable gate array (FPGA) accelerator design to reduce computation time without loss of phasing and imputation quality. Other key features include no exposure of user information and input/output data to third parties, high data security and fast secure download, user authentication through 2-factor authentication, full control in managing user accounts, and full transparency of algorithms and their settings. EagleImp-Web provides simple and convenient functionalities for monitoring running jobs and selecting parameter settings and output information. Due to the speed advantage over a purely CPU-based implementation, EagleImp-Web offers the user the ability to choose a more resource-intensive parameter setting in exchange for computation time to further improve phasing and imputation quality. EagleImp-Web is freely availabe at https://hybridcomputing.ikmb.uni-kiel.de.

bioinformatics↗