Benchmarking NGS-Based HLA Typing Algorithms.

Knowledge of the expected accuracy of HLA typing algorithms is important when choosing between algorithms and when evaluating the HLA typing predictions of an algorithm. This chapter guides the reader through an example benchmarking study that evaluates the performances of four NGS-based HLA typing algorithms as well as outlining factors to consider, when designing and running such a benchmarking study. The code related to this benchmarking workflow can be found at https://github.

DNA immunization with predicted T-cell epitopes protects against lethal SARS-CoV-2 infection in K18-hACE2 mice.

The global SARS-CoV-2 pandemic caused significant social and economic disruption worldwide, despite highly effective vaccines being developed at an unprecedented speed. Because the first licensed vaccines target only single B-cell antigens, antigenic drift could lead to loss of efficacy against emerging SARS-CoV-2 variants. Improving B-cell vaccines by including multiple T-cell epitopes could solve this problem.

Benchmarking freely available HLA typing algorithms across varying genes, coverages and typing resolutions.

Identifying the specific human leukocyte antigen (HLA) allele combination of an individual is crucial in organ donation, risk assessment of autoimmune and infectious diseases and cancer immunotherapy. However, due to the high genetic polymorphism in this region, HLA typing requires specialized methods. We investigated the performance of five next-generation sequencing (NGS) based HLA typing tools with a non-restricted license namely HLA*LA, Optitype, HISAT-genotype, Kourami and STC-Seq.

Promoters with Consistent Expression throughout the Murine Gut.

Advanced microbial therapeutics have great potential as a novel modality to diagnose and treat a wide range of diseases. Yet, to realize this potential, robust parts for regulating gene expression and consequent therapeutic activity in situ are needed. In this study, we characterized the expression level of more than 8000 variants of the sigma factor 70 (σ70) promoter in a range of different environmental conditions and growth states using fluorescence-activated cell sorting and deep sequencing.

NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning.

The ability to predict local structural features of a protein from the primary sequence is of paramount importance for unraveling its function in absence of experimental structural information. Two main factors affect the utility of potential prediction tools: their accuracy must enable extraction of reliable structural information on the proteins of interest, and their runtime must be low to keep pace with sequencing data being generated at a constantly increasing speed. Here, we present NetSurfP-2.

LYRA, a webserver for lymphocyte receptor structural modeling.

The accurate structural modeling of B- and T-cell receptors is fundamental to gain a detailed insight in the mechanisms underlying immunity and in developing new drugs and therapies. The LYRA (LYmphocyte Receptor Automated modeling) web server (http://www.cbs.