Biology subjects

Bartholomaus, A.

Publications and source records attributed to Bartholomaus, A..

3 recordsLinked to original sources

bioRxiv2026

A new automated pipeline for whole genome shotgun sequencing analysis and hazard characterization of microbial pesticides

Microbial pesticides are increasingly important for sustainable crop protection, yet hazard analysis and risk assessment remain challenging and require specific characterization of properties relevant to both safety and biological activity. Whole-genome sequencing (WGS) can support the identification of microorganisms at high resolution and characterize potential hazards associated with infectivity, pathogenicity, antimicrobial resistance, and toxic metabolite production. However, the routine use of WGS in this context requires accessible, reproducible, and interpretable workflows. Here, we present a publicly available web-based workflow for WGS-supported hazard analysis of microbial biocontrol agents. The workflow accepts assembled genomes as well as short-read, long-read and hybrid sequencing data, and performs genome quality assessment, taxonomic assignment, genome annotation, AMR detection, mobile-element screening, pathogenicity prediction and secondary-metabolite analysis, and compiles the results into an HTML report. For bacterial agents, we implement a transparent rule-based risk-classification module integrating taxonomic identity, PathogenFinder2 predictions, CARD/RGI resistance evidence, WHO priority taxa, and medically important antimicrobial categories. Secondary-metabolite assessment combines antiSMASH with local BLAST searches and EFSA-aligned identity/coverage thresholds to support product-level interpretation of biosynthetic gene clusters. Comparison with the currently used MOpS workflow developed by EFSA demonstrates the added benefits of the proposed workflow for a guided hazard analysis of microbial pesticides. The workflow is intended as a community-accessible pre-assessment tool that complements regulatory platforms by improving transparency, reproducibility, and early identification of potential hazards in microbial biocontrol candidates, envisioned to make WGS an integral part of the risk assessment of microbial pesticides. Key pointsO_LIThe RATION-GUI provides a public, reproducible workflow for whole-genome sequencing- supported hazard characterization of bacterial and fungal microbial pesticides. C_LIO_LIThe workflow integrates genome quality, taxonomy, annotation, antimicrobial resistance, pathogenicity-related evidence, mobile elements, and secondary-metabolite potential. C_LIO_LIDomain-specific outputs are translated into structured hazard indicators, evidence summaries, and recommended follow-up actions without replacing expert regulatory judgment. C_LIO_LICase studies demonstrate how the workflow improves transparency, traceability, and interpretation of genomic evidence for microbial pesticide risk assessment. C_LI

bioinformatics↗

bioRxiv2022

Recovery of 447 Eukaryotic bins reveals major challenges for Eukaryote genome reconstruction from metagenomes.

An estimated 8.7 million eukaryotic species exist on our planet. However, recent tools for taxonomic classification of eukaryotes only dispose of 734 reference genomes. As most Eukaryotic genomes are yet to be sequenced, the mechanisms underlying their contribution to different ecosystem processes remain untapped. Although approaches to recover Prokaryotic genomes have become common in genome biology, few studies have tackled the recovery of Eukaryotic genomes from metagenomes. This study assessed the reconstruction of Eukaryotic genomes using 215 metagenomes from diverse environments using the EukRep pipeline. We obtained 447 eukaryotic bins from 15 classes (e.g., Saccharomycetes, Sordariomycetes, and Mamiellophyceae) and 16 orders (e.g., Mamiellales, Saccharomycetales, and Hypocreales). More than 73% of the obtained eukaryotic bins were recovered from samples whose biomes were classified as host-associated, aquatic and anthropogenic terrestrial. However, only 93 bins showed taxonomic classification to (9 unique) genera and 17 bins to (6 unique) species. A total of 193 bins contained completeness and contamination measures. Average completeness and contamination were 44.64% ({sigma}=27.41%) and 3.97% ({sigma}=6.53%), respectively. Micromonas commoda was the most frequent taxa found while Saccharomyces cerevisiae presented the highest completeness, possibly resulting from a more significant number of reference genomes. However, mapping eukaryotic bins to the chromosomes of the reference genomes suggests that completeness measures should consider both single-copy genes and chromosome coverage. Recovering eukaryotic genomes will benefit significantly from long-read sequencing, intron removal after assembly, and improved reference genomes databases.

bioinformatics↗

bioRxiv2020

smORFer: a modular algorithm to detect small ORFs in prokaryotes

Emerging evidence places small proteins ([≤] 50 amino acids) more centrally in physiological processes. Yet, the identification of functional small proteins and the systematic genome annotation of their cognate small open reading frames (smORFs) remains challenging both experimentally and computationally. Ribosome profiling or Ribo-Seq (that is a deep sequencing of ribosome-protected fragments) enables detecting of actively translated open-reading frames (ORFs) and empirical annotation of coding sequences (CDSs) using the in-register translation pattern that is characteristic for genuinely translating ribosomes. Multiple identifiers of ORFs that use 3-nt periodicity in Ribo-Seq data sets have been successful in eukaryotic smORF annotation. Yet, they have difficulties evaluating prokaryotic genomes due to the unique architecture of prokaryotic genomes (e.g. polycistronic messages, overlapping ORFs, leaderless translation, non-canonical initiation etc.). Here, we present our new algorithm, smORFer, which performs with high accuracy in prokaryotic organisms in detecting smORFs. The unique feature of smORFer is that it uses integrated approach and considers structural features of the genetic sequence along with in-register translation and uses Fourier transform to convert these parameters into a measurable score to faithfully select smORFs. The algorithm is executed in a modular way and dependent on the data available for a particular organism allows using different modules for smORF search.

bioinformatics↗