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PubMed2026

pSTRminer: integrated bioinformatic software for genome-wide identification and population-scale evaluation of polymorphic short tandem repeats.

Animal forensic genetics plays a critical role in criminal investigations by providing crucial evidence through domestic animal individualization and wildlife species identification. While human forensic genetics benefits from standardized short tandem repeats (STR) genotyping systems, animal forensic applications encounter significant challenges, including the limited availability of validated STR markers, the prevalence of error-prone dinucleotide STRs (di-STRs), and insufficient integration of population data. To address these challenges, we developed pSTRminer, an integrated bioinformatic tool that automates genome-wide STR mining and polymorphism evaluation. By applying pSTRminer to domestic cattle (Bos taurus), we identified 775,444 STRs de novo from the reference genome and genotyped them using whole-genome sequencing data from 60 Chinese and 111 African cattle to evaluate polymorphism across diverse genetic backgrounds. This led to the development of the cattle STR database (CSDB), comprising loci with a genotyping success rate ≥ 40% and polymorphism information content (PIC) ≥ 0.5. Experimental validation of 30 randomly selected tetranucleotide STRs (tetra-STRs) and 33 di-STRs via next-generation sequencing in a local Chinese cattle population (n = 145) confirmed marker reliability. Although tetra-STRs had lower average polymorphism levels, they exhibited significantly lower stutter ratios (p < 0.05), providing a viable path for identifying discriminative markers with fewer artifacts. Systematic screening revealed that certain tetra-STRs could surpass di-STRs in polymorphism. In conclusion, pSTRminer provides a scalable framework for developing standardized STR panels, facilitating the identification of robust and informative markers in forensic applications.

Bioinformatic software↗

PubMed2026

Clinical Variant Interpretation with the Integrative Genomics Viewer (IGV) for Molecular Pathologists.

The integrative genomics viewer (IGV) is a pivotal tool in clinical genomics, enabling the visualization and interpretation of complex sequencing data. Bringing clinical knowledge to bear with visual evaluation of sequencing results is the primary means by which molecular pathologists and other professionals assess and finalize cases. A variety of software tools can assist, but their relationship to the underlying data must be understood and applied systematically. This study includes essential background on next-generation sequencing (NGS) data file types (e.g., FASTQ, BAM, VCF) with a discussion of their format and purpose. We then describe features of IGV that derive nuances from these files. We utilize a series of curated practical cases based on clinical vignettes through which the reader will interact with clinical NGS sequencing data using the IGV software to review various types of clinically relevant variants relative to the human reference genome. These clinical vignettes have been curated to describe examples of some of the complexities of interpretation of genomic data, and how utilizing IGV as part of a routine workflow can provide additional interpretive information for variants beyond routine bioinformatic software algorithm variant calls. The visual inspection of genomic variants utilizing the tools within IGV can unmask subtle contextual cues (i.e., variant allele frequency, strand bias, tissue-specific context) that can influence the interpretation of genomic variants. Although this study focuses on using IGV for the detection and interpretation of somatic variants, the provided applications can be extrapolated for use in the germline setting, including analysis of complex variants and detection of mosaicism.

Humans↗

PubMed2025

NanoASV: a snakemake workflow for reproducible field-based Nanopore full-length 16S metabarcoding amplicon data analysis.

SUMMARY: NanoASV is a conda environment and snakemake-based workflow using state-of-the-art bioinformatics software to process full-length SSU rRNA (16S/18S) amplicons acquired with Oxford Nanopore Sequencing technology. Its strength lies in reproducibility, portability, and the possibility to run offline, allowing in-field analysis. It can be installed on the Nanopore MK1C sequencing device and process data locally. AVAILABILITY AND IMPLEMENTATION: Source code and documentation are freely available at https://github.com/ImagoXV/NanoASV and Zenodo archive at https://doi.org/10.5281/zenodo.14730742.

Software↗

PubMed2025

The challenge of chromatin model comparison and validation: A project from the first international 4D Nucleome Hackathon.

The computational modeling of chromatin structure is highly complex due to the hierarchical organization of chromatin, which reflects its diverse biophysical principles, as well as inherent dynamism, which underlies its complexity. Chromatin structure modeling can be based on diverse approaches and assumptions, making it essential to determine how different methods influence the modeling outcomes. We conducted a project at the NIH-funded 4D Nucleome Hackathon on March 18-21, 2024, at The University of Washington in Seattle, USA. The hackathon provided an amazing opportunity to gather an international, multi-institutional and unbiased group of experts to discuss, understand and undertake the challenges of chromatin model comparison and validation. Here we give an overview of the current state of the 3D chromatin field and discuss our efforts to run and validate the models. We used distance matrices to represent chromatin models and we calculated Spearman correlation coefficients to estimate differences between models, as well as between models and experimental data. In addition, we discuss challenges in chromatin structure modeling that include: 1) different aspects of chromatin biophysics and scales complicate model comparisons, 2) large diversity of experimental data (e.g., population-based, single-cell, protein-specific) that differ in mathematical properties, heatmap smoothness, noise and resolutions complicates model validation, 3) expertise in biology, bioinformatics, and physics is necessary to conduct comprehensive research on chromatin structure, 4) bioinformatic software, which is often developed in academic settings, is characterized by insufficient support and documentation. We also emphasize the importance of establishing guidelines for software development and standardization.

Chromatin↗

PubMed2026

MACS3: A Peak-calling Platform for Bulk and Single-cell Regulatory Genomics.

Since the original publication of Model-based Analysis for ChIP-Seq (MACS), the software has been widely used to identify enriched genomic regions in ChIP-seq, ATAC-seq, CUT&RUN, DNase-seq, and related regulatory genomics assays. Over the years, MACS has evolved substantially, with MACS version 3 (MACS3) now serving as the actively maintained implementation. MACS3 preserves the core MACS framework for fragment pileup, dynamic local background noise, statistical enrichment testing, and peak refinement, while adding functionality needed for contemporary bulk and single-cell workflows. It supports conventional bulk peak calling, paired-end and fragment-based file formats, modular signal processing, direct analysis of single-cell ATAC-seq fragment files, barcode-restricted pseudobulk and cluster-level peak calling, specialized ATAC-seq and variant-calling modules, as well as command-line and programmatic interfaces. MACS3 is distributed through standard software channels and supported by continuous testing across operating systems, Python versions, and CPU architectures. Here we describe the architecture, current capabilities, and recommended use of MACS3, providing an updated reference for applying the MACS framework in contemporary bulk and single-cell regulatory genomics workflows. MACS3 is open-source software available at https://github.com/macs3-project/MACS.

Bioinformatics software↗

PubMed2026

miRNA Target Prediction: An Overview of the Past and Current Tools.

MicroRNAs (miRNAs) are among the most studied molecules in recent years, and since their discovery, many miRNAs have been identified across various species. As members of the non-coding RNA family, miRNAs are key players in post-transcriptional gene regulation. These molecules can inhibit translation or promote degradation of messenger RNA (mRNA) by binding to the 3' untranslated region (UTR) of mRNA, thereby influencing almost all biological processes. To identify a miRNA's biological role, it is essential to predict the target sites to which it binds, a goal made possible through bioinformatics tools. This chapter discusses the bioinformatics tools commonly used for this purpose. Also, it analyzes the main factors considered in target prediction, such as seed match, free energy, conservation, site accessibility, multiple binding site contribution, and machine learning and deep learning approaches. Understanding the principles underlying these predictive methodologies is crucial for advancing one's biological research on miRNAs.

MicroRNAs↗

PubMed2025

Automated Machine Learning Tools to Build Regression Models for Schizosaccharomyces pombe Omics Data.

Machine learning is a powerful tool for analyzing biological data and making useful predictions. The surge of biological data from high-throughput omics technologies has raised the need for modeling approaches capable of tackling such amounts of data, which is pivotal to understanding the nature of complex molecular systems. Here, we show how to construct a simple model using automated machine learning (AutoML) to predict protein abundance in Schizosaccharomyces pombe, using data obtained from codon usage bias and quantitative proteomics.

Machine Learning↗

PubMed2025

BIWT: a bioinformatics walkthrough for embedding spatial multiomics in agent-based models for virtual cells.

SUMMARY: Whereas transcriptomic and spatial profiling offer static snapshots of tissue structure, mechanistic models use biological rules to predict how tissues evolve. We present the BioInformatics WalkThrough (BIWT) software to directly initialize spatial agent-based models from single-cell and spatial molecular data. We demonstrate how initialization strategies affect tumor-immune dynamics and spatial clustering, positioning BIWT as a software suite to generate data-driven virtual cells representing both experimental and clinical contexts. AVAILABILITY AND IMPLEMENTATION: The BIWT software is available at https://github.com/PhysiCell-Tools/PhysiCell-Studio. The sample dataset for running the BIWT is available at https://zenodo.org/records/16365625. The code and instructions for reproducing the use case example is available at https://github.com/drbergman/BIWT-Paper.

Software↗

PubMed2026

Phage bioinformatics tools: a review of computational approaches for bacteriophage research.

Rising clinical interest in phage therapy and the exponential growth of metagenomic sequence catalogues have driven a rapid expansion of bacteriophage bioinformatics. More than 80 dedicated tools, mostly published since 2020, now span identification, assembly, annotation, taxonomy, lifestyle prediction, defence-system detection, and host prediction. Aimed at experienced practitioners and developers, this review synthesizes the field through the lens of three successive computational paradigms: sequence homology, bounded by database completeness; machine learning, constrained by labelled training data; and foundation models, which now achieve Matthews correlation coefficients above 0.95 in identification tasks and, through structure-informed prediction, raise functional annotation to over half of phage genes. Furthermore, we map the upstream components, namely, gene callers, homology engines, protein language models, and structural search tools, that underpin most downstream pipelines, exposing shared infrastructure and ecosystem-level fragility when dependencies change. To translate this into practice, we propose web-based and command-line reference workflows calibrated to user expertise and sample types. Finally, we set an agenda for the next wave of tool development. Roughly half of phage genes still resist functional annotation despite structural methods; no broadly generalizable strain-level host predictor exists for phage therapy; varying true-positive rates (0%-97%) underscore the absence of standardized community benchmarks analogous to Critical Assessment of Structure Prediction or Critical Assessment of Metagenome Interpretation. As generative genome models begin designing synthetic phages, progress will depend less on producing standalone tools than on rigorous evaluation, interoperable infrastructure, and clinically meaningful prediction targets.

Computational Biology↗

PubMed2026

Community-driven advances in computational mass spectrometry: The perspective of EuBIC-MS members.

Advances in data acquisition, artificial intelligence, and integrative bioinformatics are driving the rapid evolution of computational mass spectrometry, and in turn, transforming modern proteomics, metabolomics, and lipidomics. These developments have greatly increased the scale and complexity of mass spectrometry data, underscoring the importance of evolving accurate, transparent, efficient and reproducible data processing workflows. Addressing these challenges requires collaborative innovation that brings together expertise in software engineering, statistics, and biology. The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS), an initiative of the European Proteomics Association (EuPA), fosters a culture of open, community-driven development through its biennial Developers Meetings and Winter Schools. This commentary summarizes the scientific background and outcomes of the EuBIC-MS Developers Meeting 2025, which took place in Novacella, Italy. Three keynote presentations highlighted major frontiers in the field: deep proteome and phosphoproteome profiling, text mining for protein-protein interaction extraction, and scalable proteomics for AI-driven drug discovery. Seven community-selected hackathons addressed emerging challenges such as single-cell proteomics data analysis, FAIR metadata extraction, deep learning frameworks, R-Python interoperability, and DIA validation. Together, these efforts demonstrate the potential for scientific and technical innovation to arise from open collaboration, and highlight how community-driven initiatives can accelerate progress in computational mass spectrometry. SIGNIFICANCE: Modern proteomics increasingly depends on computational advances to translate complex, high-dimensional data into biological knowledge. The EuBIC-MS Developers Meeting 2025 exemplifies how community-driven collaboration can directly accelerate this process by bringing together experts from bioinformatics, statistics, and experimental proteomics to co-develop open, interoperable, and reproducible analytical tools. By fostering shared software frameworks, transparent benchmarking, and collaborative problem solving, the EuBIC-MS community helps ensure that technological innovation translates into reliable biological insights. This collaborative model strengthens the foundation for quantitative, system-level understanding of proteomes and establishes a sustainable path for integrating artificial intelligence and next-generation data acquisition into routine biological discovery. This commentary shows some current highlights in the field of computational mass spectrometry and community-based approaches undertaken during the most recent Developers Meeting to solve these challenges. The approaches discussed and initiated during the meeting - ranging from deep proteome profiling and phosphosite mapping to text mining, single-cell data analysis, and FAIR metadata extraction - address key bottlenecks that currently limit the biological interpretability and comparability of proteomics data.

Mass Spectrometry↗

PubMed2025

Spatial Genomic Approaches to Investigate HOX Genes in Mouse Brain Tissues.

Spatial transcriptomic tools are an upcoming and powerful way to investigate targeted gene expression patterns within tissues. These tools offer the unique advantage of visualizing and understanding gene expression while preserving tissue integrity, thereby maintaining the spatial context of genes. Curio is a robust spatial transcriptomic tool that facilitates high throughput comprehensive spatial gene expression analysis across the entir e transcriptome with high efficiency. Here, we present a bioinformatics protocol for performing whole transcriptome gene expression analysis of mouse brain tissue using Curio. Specifically, we demonstrate using computational techniques to visualize expression patterns of various HOX genes in the mouse brain.

Animals↗

PubMed2026

map3C: a computational tool for processing multiomic single-cell Hi-C data.

SUMMARY: The emergence of multiomic single-cell Hi-C (scHi-C) methods, which simultaneously profile chromatin conformation and other modalities such as gene expression or DNA methylation, creates tremendous opportunities for studying the genome's structure-function relationships. Existing tools for processing multiomic scHi-C datasets lack certain key functions for downstream bioinformatics analysis. We present map3C, a software tool that incorporates additional key functions. Specifically, we demonstrate that map3C facilitates multiomic scHi-C processing, quality control, and identification of structural variant locations in the genome. AVAILABILITY AND IMPLEMENTATION: map3C is available at https://github.com/luogenomics/map3C and is archived at https://doi.org/10.5281/zenodo.20724719.

Software↗

PubMed2026

Identification of NLRP3 and TIPE2 as asthma biomarkers via integrative bioinformatics and Mendelian randomization.

Asthma is a chronic inflammatory airway disease imposing a substantial global health burden. NLRP3 is an immune sensor involved in infection and cellular stress responses. Recent studies suggest that NLRP3 may be involved in the pathogenesis of asthma. We hypothesized that genetic variation in NLRP3 may contribute to asthma susceptibility. However, the causal relationship between NLRP3 and asthma still remains unclear. In this study, bioinformatics analysis using asthma data and R software was performed to identify NLRP3-related genes. We performed weighted gene co-expression network analysis to identify co-expressed genes, resulting in 12 candidate genes. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analyses were used to identify the functions of these candidate genes, revealing their involvement in cellular metabolism. Mendelian randomization analysis of the 12 candidate genes identified 2 biomarkers: NLRP3 and TNFAIP8L2 (TIPE2). We validated their diagnostic value for asthma using the GSE182503 dataset, with area under the curve values of 0.83 and 0.66 for NLRP3 and TIPE2, respectively. This project discusses how NLRP3 promotes asthma pathogenesis, whereas TIPE2 may alleviate it, and explores the potential interplay between them. NLRP3 and TIPE2 may serve as diagnostic biomarkers for asthma: NLRP3 may promote, whereas TIPE2 may alleviate asthma development. Both genes represent potential diagnostic biomarkers and therapeutic targets that warrant further functional investigation.

Asthma↗

PubMed2026

Meta-PseU: A meta-classifier for robust prediction of RNA pseudouridine modification sites from long sequences.

BACKGROUND AND OBJECTIVES: Pseudouridine (Ψ) represents one of the most abundant and conserved RNA modifications. Ψ provides an additional hydrogen-bond donor that enhances RNA structural stability and modulates translation. It participates in diverse biological processes, including RNA-protein interactions, splicing, translational control, and stress responses. Aberrant pseudouridylation is implicated in cancer, neurodegenerative disorders, and autoimmune diseases. Despite its biological importance, experimental identification of Ψ sites remains time-consuming and costly, limiting the feasibility of transcriptome-wide profiling. Computational approaches have therefore become essential complements to experimental techniques. However, state-of-the-art machine-learning and deep-learning predictors often suffer from limited generalizability due to small training datasets. To overcome these issues, we aim at constructing new long-sequence datasets and developing a novel Ψ site predictor. METHODS: New long-sequence datasets were constructed as benchmarks for RNA Ψ-site prediction. The Ψ modification sites in RMBase 3.0 were mapped to the reference genomes across three species of human, mouse, and yeast, and the RNA sequences with a length of 201 were generated by extending the upstream and downstream from the mapped, central sites. To eliminate sequence redundancy, the sequences were clustered using CD-HIT with a 70% sequence identity threshold. We developed Meta-PseU, a logistic regression-based meta-classifier that considered 118 machine learning and deep learning classifiers. The datasets and programs are freely accessible at https://github.com/kuratahiroyuki/MetaPseU. RESULTS: By optimizing model configuration, we proposed the Meta-PseU model stacking 32 machine learning and deep learning classifiers out of 118 classifiers. Meta-PseU substantially improved model generalizability, overcoming a key limitation of existing approaches. It greatly outperformed state-of-the-art predictors and achieved increasing accuracy with increasing sequence length. CONCLUSIONS: Long-sequence datasets were newly constructed as benchmarks for RNA Ψ-site prediction. Meta-PseU offers a new framework for robust Ψ-site identification by using long sequences.

Pseudouridine↗

PubMed2024

Functional Analysis of MS-Based Proteomics Data: From Protein Groups to Networks.

Mass spectrometry-based proteomics allows the quantification of thousands of proteins, protein variants, and their modifications, in many biological samples. These are derived from the measurement of peptide relative quantities, and it is not always possible to distinguish proteins with similar sequences due to the absence of protein-specific peptides. In such cases, peptide signals are reported in protein groups that can correspond to several genes. Here, we show that multi-gene protein groups have a limited impact on GO-term enrichment, but selecting only one gene per group affects network analysis. We thus present the Cytoscape app Proteo Visualizer (https://apps.cytoscape.org/apps/ProteoVisualizer) that is designed for retrieving protein interaction networks from STRING using protein groups as input and thus allows visualization and network analysis of bottom-up MS-based proteomics data sets.

Proteomics↗

PubMed2026

Protocol to predict gene expression from transcriptomic data using PREDICT.

Linking DNA sequence variation to context-specific transcriptional programs is a critical challenge in regulatory genomics, especially for non-model organisms. Here, we present PREDICT, a modular Python package for discovering cis-regulatory elements and transcription factor binding motifs. We describe steps to identify enriched k-mers from differentially expressed genes, map them to known motifs, quantify their impact on gene expression, and visualize motif co-occurrences. PREDICT provides a robust, k-mer-based approach to uncover regulatory logic in diverse genomic systems. For complete details on the use and execution of this protocol, please refer to Yen et al. and Liu et al.1,2.

Gene Expression Profiling↗

PubMed2026

Influence of nicotine on protein expression around hydrophilic osseointegrated implants: A proteomic study in male rats.

OBJECTIVE: To ensure the success of dental implant treatment, various factors must be considered, including osseointegration and systemic conditions. There is evidence in the literature that smokers may exhibit alterations in tissue healing, which can compromise the success of implant rehabilitation. Therefore, this study aimed to investigate the influence of nicotine on the protein profile of bone tissue around hydrophilic implants during the osseointegration process in rats. DESIGN: Bone tissue samples from the control and nicotine groups (n = 3 per group) were subjected to protein extraction, mass spectrometry, and bioinformatic analyses. Protein identification was performed using Proteome Discoverer 2.1 software and the SEQUEST algorithm, and the protein data were compared with those of a protein database of Rattus norvegicus obtained from UniProt. RESULTS: A total of 740 proteins were detected in both the control group and the nicotine-exposed group. Among them, the proteins biglycan, periostin and histone H4 were highlighted because of their higher abundance in the healthy implant group, while they were reduced in the nicotine-exposed group. CONCLUSIONS: Nicotine has the potential to alter the protein profile of bone tissue around hydrophilic implants during osseointegration, which may impair tissue remodeling and healing.

Animals↗

PubMed2025

Prediction and Evaluation of Protein Aggregation with Computational Methods.

Protein and peptide aggregation has recently become one of the most studied biomedical problems due to its central role in several neurodegenerative disorders and of biotechnological importance. Multiple in silico methods, databases, tools, and algorithms have been developed to predict aggregation of proteins and peptides to better understand fundamental mechanisms of various aggregation diseases. Here, we attempt to provide a brief overview of bioinformatic methods and tools to better understand molecular mechanisms of aggregation disorders. Furthermore, through a better understanding of protein aggregation mechanisms, it might be possible to design novel therapeutic agents to treat and hopefully prevent protein aggregation diseases.

Computational Biology↗