Biology subjects

Kohlbacher, O.

Publications and source records attributed to Kohlbacher, O..

24 records · Page 2Linked to original sources

bioRxiv2019

The HLA Ligand Atlas. A resource of natural HLA ligands presented on benign tissues.

The human leukocyte antigen (HLA) complex controls adaptive immunity by presenting defined fractions of the intracellular and extracellular protein content to immune cells. Here, we describe the HLA Ligand Atlas, an extensive collection of mostly matched HLA-I and -II ligandomes from 225 benign samples (29 tissues, 21 subjects). The initial release covers 51 HLA-I and 86 HLA-II allotypes presenting 89,853 HLA-I- and 140,861 HLA-II ligands. We observe that the immunopeptidomes differ considerably between tissues and individuals on both source protein and HLA-ligand level. 1,407 HLA-I ligands stem from non-canonical genomic regions. We highlight the importance of comparatively analyzing both benign and malignant tissues to inform tumor association, based on a case study in three glioblastoma patients. The resource provides insights into applied and basic immune-associated questions in the context of cancer immunotherapy, infection, transplantation, allergy, and autoimmunity. It is publicly available at www.hla-ligand-atlas.org.

immunology↗

bioRxiv2019

EPIFANY - A method for efficient high-confidence protein inference

Accurate protein inference under the presence of shared peptides is still one of the key problems in bottom-up proteomics. Most protein inference tools employing simple heuristic inference strategies are efficient, but exhibit reduced accuracy. More advanced probabilistic methods often exhibit better inference quality but tend to be too slow for large data sets.\n\nHere we present a novel protein inference method, EPIFANY, combining a loopy belief propagation algorithm with convolution trees for efficient processing of Bayesian networks. We demonstrate that EPIFANY combines the reliable protein inference of Bayesian methods with significantly shorter runtimes. On the 2016 iPRG protein inference benchmark data EPIFANY is the only tested method which finds all true-positive proteins at a 5% protein FDR without strict pre-filtering on PSM level, yielding an increase in identification performance (+10% in the number of true positives and +35% in partial AUC) compared to previous approaches. Even very large data sets with hundreds of thousands of spectra (which are intractable with other Bayesian and some non-Bayesian tools) can be processed with EPIFANY within minutes. The increased inference quality including shared peptides results in better protein inference results and thus increased robustness of the biological hypotheses generated.\n\nEPIFANY is available as open-source software for all major platforms at https://OpenMS.de/epifany.

bioinformatics↗

bioRxiv2019

FLASHDeconv: Ultrafast, high-quality feature deconvolution for top-down proteomics

Feature deconvolution, the determination of intact proteoform masses, is crucial for top-down proteomics, but currently suffers from long runtimes and quality issues. We present FLASHDeconv, an algorithm based on a simple transformation of mass spectra, which turns deconvolution into the search for constant patterns thus greatly accelerating the process. We show higher deconvolution quality and two to three orders of magnitude faster execution speed than existing approaches.

bioinformatics↗

bioRxiv2019

Where Natural Protein Sequences Stand out From Randomness

Approaches based on molecular evolution have organized natural proteins into a hierarchy of families, superfamilies, and folds, which are often pictured as islands in a great sea of unrealized and generally non-functional polypeptides. In contrast, approaches based on information theory have substantiated a mostly random scatter of natural proteins in global sequence space. We evaluate these opposing views by analyzing fragments of a given length derived from either a natural dataset or different random models. For this, we compile distances in sequence space between fragments within each dataset and compare the resulting distance distributions between sets. Even for 100-mers, more than 95% of distances can be accounted for by a random sequence model that incorporates the natural amino acid frequency of proteins. When further accounting for the specific residue composition of the respective fragments, which would include biophysical constraints of protein folding, more than 99% of all distances can be modeled. Thus, while the local space surrounding a protein is almost entirely shaped by common descent, the global distribution of proteins in sequence space is close to random, only constrained by divergent evolution through the requirement that all intermediates connecting two forms in evolution must be functional. Significance StatementWhen generating new proteins by evolution or design, can the entire sequence space be used, or do viable sequences mainly occur only in some areas of this space? As a result of divergent evolution, natural proteins mostly form families that occupy local areas of sequence space, suggesting the latter. Theoretical work however indicates that these local areas are highly diffuse and do not dramatically affect the statistics of sequence distribution, such that natural proteins can be considered to effectively cover global space randomly, though extremely sparsely. By comparing the distance distribution of natural sequences to that of various random models, we find that they are indeed distributed largely randomly, provided that the amino acid composition of natural proteins is respected.

evolutionary biology↗

bioRxiv2018

A computational platform for high-throughput analysis of RNA sequences and modifications by mass spectrometry

The field of epitranscriptomics is growing in importance, with chemical modification of RNA being associated with a wide variety of biological phenomena. A pivotal challenge in this area is the identification of modified RNA residues within their sequence contexts. Next-generation sequencing approaches are generally unable to capture modifications, although workarounds for some epigenetic marks exist. Mass spectrometry (MS) offers a comprehensive solution by using analogous approaches to shotgun proteomics. However, software support for the analysis of RNA MS data is inadequate at present and does not allow high-throughput processing. In particular, existing software solutions lack the raw performance and statistical grounding to efficiently handle the large variety of modifications present on RNA. We present a free and open-source database search engine for RNA MS data, called NucleicAcidSearchEngine (NASE), that addresses these shortcomings. We demonstrate the capability of NASE to reliably identify a wide range of modified RNA sequences in three original datasets of varying complexity. In a human tRNA sample, we characterize over 20 different modification types simultaneously and find many cases of incomplete modification.

bioinformatics↗

bioRxiv2017

Genetic variation in human drug-related genes

Variability in drug efficacy and adverse effects are observed in clinical practice. While the extent of genetic variability in classical pharmacokinetic genes is rather well understood, the role of genetic variation in drug targets is typically less studied. Based on 60,706 human exomes from the ExAC dataset, we performed an in-depth computational analysis of the prevalence of functional-variants in in 806 drug-related genes, including 628 known drug targets. We find that most genetic variants in these genes are very rare (f < 0.1%) and thus likely not observed in clinical trials. Overall, however, four in five patients are likely to carry a functional-variant in a target for commonly prescribed drugs and many of these might alter drug efficacy. We further computed the likelihood of 1,236 FDA approved drugs to be affected by functional-variants in their targets and show that the patient-risk varies for many drugs with respect to geographic ancestry. A focused analysis of oncological drug targets indicates that the probability of a patient carrying germline variants in oncological drug targets is with 44% high enough to suggest that not only somatic alterations, but also germline variants carried over into the tumor genome should be included in therapeutic decision-making.

bioinformatics↗