Biology subjects

Bole, M.

Publications and source records attributed to Bole, M..

2 recordsLinked to original sources

bioRxiv2026

PhaGAMeToo: A semi-automated workflow for merging structural and functional annotation of phage genomes and generation of a GenBank file

MotivationAnalysing and concatenating phage annotation is time-consuming. Further, the output of phage annotation tools cannot be directly submitted to public repositories. To deal with these issues, we developed PhaGAMeToo. This command-line workflow for Linux integrates the functional annotations of two major viral annotation tools (Pharokka and VIBRANT), enabling faster and more accurate functional annotation. Furthermore, the workflow provides merged annotations as submission-ready GenBank files. ResultsPhaGAMeToo uses three steps to generate submission-ready GenBank files. The user uses the reoriented viral genomes as inputs for Pharokka and VIBRANT. Pharokka and VIBRANT-generated files are parsed through the PhaGAMeToo workflow to produce a merged GenBank file. Further, PhaGAMeToo also enables the use of BLASTP to annotate hypothetical proteins not identified by Pharokka and VIBRANT. It then merges the results into a submission-ready GenBank file(s). We tested PhaGAMeToo in three different Use Cases. We analysed reference and uncultivated viral genomes manually curated or directly recovered using MuDoGeR in our Use Cases. In the Use Case 1, we analysed four different NCBI reference genomes. In the Use Cases 2 and 3, we analysed seven recently described huge phage genomes and 56 uncultivated viral genomes recovered from 30 soil metagenomes, respectively. Availability and implementationThe source code, documentation, and installation instructions for PhaGAMeToo are available at https://github.com/NFDI4Microbiota/PhaGAMeToo ContactRene.Kallies@uba.de; ebrardemircioglu25@hacettepe.edu.tr Supplementary informationSupplementary data will be made available upon publication.

bioinformatics↗

bioRxiv2026

BioAutoML-FAST: an automated machine-learning platform for reusable and benchmarked biological sequence models

The prediction of biological sequence properties has traditionally relied on alignment-based methods that assume evolutionary homology and depend on curated reference databases. This, in turn, limits scalability and sensitivity for large or heterogeneous datasets, remote homologs, short sequences, and rapidly evolving genomic regions. Although Machine-Learning (ML) approaches offer alignment-free alternatives, their broader adoption is limited by: (i) the lack of standardized, externally validated benchmark models across diverse datasets, and (ii) the technical expertise required for feature engineering, model selection, and evaluation. Automated machine learning (AutoML) alleviates these challenges by systematically optimizing representations and models with minimal user intervention. However, most existing frameworks prioritize task-specific model construction and lack mechanisms for preserving trained models as persistent, comparable benchmarks. We introduce BioAutoML-FAST, an end-to-end web platform for automated ML analysis of nucleotide and amino acid sequences. It supports both classification and regression tasks and automates feature extraction, model training, and evaluation without requiring prior user expertise. Uniquely, it serves as a community benchmarking resource, hosting a continuously expanding repository of reusable, standardized models (currently 60) for genomic, transcriptomic, and proteomic applications. Extensive validation on independent datasets demonstrates performance comparable to or exceeding that of state-of-the-art methods, including protein language models such as ESM-2. BioAutoMLFAST is available at https://bioautoml.icmc.usp.br/. This website is free and open to all users, and there is no login requirement.

bioinformatics↗