Balestrucci, A. | bioRxiv Science

bioRxiv2026

ParaDISM: Precise mapping of short reads to genes with highly homologous regions

BackgroundGenes with highly similar genomic copies (paralogs, tandem duplications and pseudogenes) pose a major challenge for Short-Read High Throughput Sequencing (srHTS). High sequence similarity makes it difficult to unambiguously identify the sequences of origin of short reads. This results in misalignment artifacts which can propagate through bioinformatic pipelines and increase error rates in variant calling. ResultsWe present ParaDISM, a pipeline that refines standard alignments to improve read placement and reduce misalignment-driven false variant calls in highly homologous sequences. ParaDISM assigns a read/read pair to a sequence only when supported by unambiguous sequence-specific evidence by using a multiple sequence alignment of reference sequences to identify disambiguating positions. An optional iterative refinement procedure calls variants from confidently assigned reads, updates the reference sequences, and processes remaining non-assigned reads. We evaluated the performance of ParaDISM both in terms of read alignment and the resulting short variant calls using extensive computational simulation experiments and the Genome in a Bottle HG002 benchmark. We applied ParaDISM to reanalyze two case studies: five public tumour exomes at the GNAQ/GNAQP1 locus, and 18 short-read sequencing datasets of patients diagnosed with Autosomal Dominant Polycystic Kidney Disease (16 exomes and 2 panel sequencing datasets). Compared to the standard aligners (bowtie2, bwa-mem and minimap2), ParaDISM reduced the number of misalignment artifacts and false variant calls, resulting in an increased specificity and precision of the results. ConclusionsParaDISM improves the precision of read placement and single-nucleotide variant calling in highly homologous reference sequences. By reducing the number of false variant calls caused by misalignment artifacts, ParaDISM provides a stronger level of evidence for the called variants compared to currently available approaches. The pipeline is open source and available under the MIT license at github.com/BioGeMT/ParaDISM.

bioinformatics↗

bioRxiv2026

Agentomics: An Agentic System that Autonomously Develops Novel State-of-the-art Solutions for Biomedical Machine Learning Tasks

MotivationExtracting knowledge from biomedical data is crucial for advancing our understanding of biological systems and developing novel therapeutics. The quantity, quality, and resolution of biomedical data constantly evolves, requiring the automation of biomedical machine learning (ML). Existing Automated ML tools lack flexibility, while Large Language Models (LLMs) struggle to consistently deliver reproducible machine learning codebases, and existing LLM Agent-powered solutions lag behind human-engineered ML models. ResultsHere, we introduce Agentomics, an autonomous LLM-powered agentic system for end-to-end ML experimentation. Given a biomedical dataset, Agentomics implements various ML modeling strategies, and produces a ready-to-use ML model. Agentomics introduces strict validation checkpoints for standard ML development steps, allowing gradual development on top of working code with defined interfaces and validated artifacts. Further, it offers native support for biomedical foundation models that can be leveraged during experimentation. The generic nature of Agentomics allows the user to create ML solutions for a large variety of datasets and use various LLMs. We evaluate Agentomics across 20 datasets from the domains of Protein Engineering, Drug Discovery, and Regulatory Genomics. When benchmarked against other agentic systems, Agentomics outperformed them in all tested domains. When benchmarked against human expert solutions, Agentomics generated novel state-of-the-art models for 11/20 established benchmark datasets. Availability and ImplementationAgentomics is implemented in Python. Source code and documentation are freely available at: https://github.com/BioGeMT/Agentomics-ML. Contactpanagiotis.alexiou@um.edu.mt

bioinformatics↗