Biology subjects

Collatz, M.

Publications and source records attributed to Collatz, M..

3 recordsLinked to original sources

bioRxiv2026

AssayBLAST v2: Major update improving reliability and reporting of the in silico analysis of molecular multi-parameter assays

IntroductionAccurate in silico evaluation of primers and probes is essential for the rational design of molecular multi-parameter assays. We present Assay-BLAST v2 to automate and simplify this process for extensive assay designs. ResultsA newly integrated strand and proximity check enables precise validation of corresponding oligonucleotides, ensuring correct orientation and spacing for efficient amplification. Based on predicted oligonucleotide interactions, Assay-BLAST v2 estimates amplification outcomes, offering a computational benchmark for downstream wet-lab validation and performance correlation. Additionally, the updated software integrates an adaptive BLAST parameter optimization that dynamically scales with database size, thereby improving both analytical sensitivity and computational performance. These improvements are supported by a comparative evaluation against the previous version of AssayBLAST. ConclusionsCollectively, these enhancements streamline the assay development workflow, reduce costs associated with suboptimal primer and probe synthesis, and increase the robustness and reliability of molecular diagnostics and research applications.

bioinformatics↗

bioRxiv2020

EpiDope: A Deep neural network for linear B-cell epitope prediction

By binding to specific structures on antigenic proteins, the so-called epitopes, B-cell antibodies can neutralize pathogens. The identification of B-cell epitopes is of great value for the development of specific serodiagnostic assays and the optimization of medical therapy. However, identifying diagnostically or therapeutically relevant epitopes is a challenging task that usually involves extensive laboratory work. In this study, we show that the time, cost and labor-intensive process of epitope detection in the lab can be significantly shortened by using in silico prediction. Here we present EpiDope, a python tool which uses a deep neural network to detect B-cell epitope regions on individual protein sequences (github.com/mcollatz/EpiDope). With an area under the curve (AUC) between 0.67 {+/-} 0.07 in the ROC curve, EpiDope exceeds all other currently used B-cell prediction tools. Moreover, for AUC10% (AUC for a false-positive rate < 0.1), EpiDope improves the prediction accuracy in comparison to other state-of-the-art methods. Our software is shown to reliably predict linear B-cell epitopes of a given protein sequence, thus contributing to a significant reduction of laboratory experiments and costs required for the conventional approach.

bioinformatics↗

bioRxiv2019

A comprehensive annotation and differential expression analysis of short and long non-coding RNAs in 16 bat genomes

Although bats are increasingly becoming the focus of scientific studies due to their unique properties, these exceptional animals are still among the least studied mammals. Assembly quality and completeness of bat genomes vary a lot and especially non-coding RNA (ncRNA) annotations are incomplete or simply missing. Accordingly, standard bioinformatics pipelines for gene expression analysis often ignore ncRNAs such as microRNAs or long antisense RNAs. The main cause of this problem is the use of incomplete genome annotations. We present a complete screening for ncRNAs within 16 bat genomes. NcRNAs affect a remarkable variety of vital biological functions, including gene expression regulation, RNA processing, RNA interference and, as recently described, regulatory processes in viral infections. Within all investigated bat assemblies we annotated 667 ncRNA families including 162 snoRNAs and 193 miRNAs as well as rRNAs, tRNAs, several snRNAs and IncRNAs, and other structural ncRNA elements. We validated our ncRNA candidates by six RNA-Seq data sets and show significant expression patterns that have never been described before in a bat species on such a large scale. Our annotations will be usable as a resource (Electronic Supplement) for deeper studying of bat evolution, ncRNAs repertoire, gene expression and regulation, ecology, and important host-virus interactions. Supplementary informationis available at rna.uni-jena.de/supplements/bats, the Open Science Framework (doi.org/10.17605/OSF.IO/4CMDN), and GitHub (github.com/rnajena/bats_ncrna).

bioinformatics↗