SEARCH · Search PubMed

Results for “Bioinformatics software”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2Linked to original sources

PubMed2026

VIJB: a companion of the JBROWSE genome browser for the visually impaired people.

MOTIVATION: The availability of touch-sensitive and haptic devices has been a keystone development for the inclusion of visually impaired people (VIPs) in modern, highly digitized work environments. Braille displays have proven efficient and versatile enough to parse large and complex text files, making bioinformatics and text-heavy programming accessible to VIPs. However, the complex graphical objects -combining numerous datasets- typically generated during data integration remain challenging, even with the aid of descriptive AI. This is particularly true in functional genomics. Here, we present VIJB, a simple application that displays the multilayered output of the JBROWSE genome browser on a Braille reader, enabling VIPs to fully participate in data integration in functional genomics. AVAILABILITY AND IMPLEMENTATION: VIJB is programmed in Python and relies on the scientific library NumPy, the braillegraph and pyBigWig libraries, and the TABIX software. The architecture is summarized in Supplementary Material 1, available as supplementary data at Bioinformatics online. VIJB is available for download at the GitHub repository https://GitHub.com/NiBuMNHN/VIJB and is licenced under the GPL 3.0.

Persons with Visual Disabilities↗

PubMed2025

A Comprehensive Bioinformatics Approach to Analysis of Variants: Variant Calling, Annotation, and Prioritization.

Next-Generation Sequencing (NGS), also known as high-throughput sequencing technologies, has enabled rapid and efficient sequencing of large amounts of DNA and RNA. These technologies have revolutionized the field of genomics, transcriptomics, and proteomics and have been widely used in cancer research, leading to advances in clinical diagnosis and treatment. Improvements in the NGS technologies enabled millions of fragments to be sequenced simultaneously in a time- and cost-effective manner and resulted in large amount of genomic data which require efficient analysis methods. Analysis of the genomic data requires both efficient computer resources and bioinformatics approaches. This chapter details a comprehensive computational approach and analysis steps for genomic data analysis.

Computational Biology↗

PubMed2025

GBRAP: A Comprehensive Database and Tool for Exploring Genomic Diversity Across All Domains of Life.

Evolutionary studies require extensive examination of genomic information across all domains of life. Despite the availability of a large number of genomes through GenBank, the effective visualization or comparison of the information they contain is challenging due to many reasons, including their size. We introduce genome-based retrieval and analysis parser, a comprehensive software tool to analyze genome files, and an online database housing an extensive collection of carefully curated, high-quality genome statistics for all the organisms available in the RefSeq database of National Center for Biotechnology Information. Users can either directly search, or select from precategorized groups, the organisms of their choice and retrieve data, and the output is generated as tables containing more than 200 columns of useful genomic information (base counts, GC content, Shannon entropy, codon usage, etc.) separately calculated for different genomic elements (e.g. coding sequences, introns, transfer RNA, ribosomal RNA, noncoding RNA, etc.). The data are independently displayed (if applicable) for each chromosomal, mitochondrial, plastid, or plasmid sequence. All the data can be visualized on the database or downloaded as comma-separated value or Excel files. The genome-based retrieval and analysis parser database is free to access without any registration and is publicly available at http://tacclab.org/gbrap/.

Software↗

PubMed2026

fRagmentomics: an R package for integrating cell-free DNA fragment features with mutational status to support liquid biopsy interpretation.

SUMMARY: Liquid biopsy offers a non-invasive approach to study tumor-derived genetic material circulating in plasma. Beyond genetic alterations, the fragmentomic features of cell-free DNA-such as fragment size, genomic position, and end-motifs-provide valuable insights into the biological and clinical context of DNA release. fRagmentomics is a user-friendly R package designed to characterize cfDNA fragments overlapping one or multiple small mutations of any type, starting from an aligned sequencing file (BAM). It supports multiple mutation input formats, accommodates one-based and zero-based genomic conventions, resolves mutation representation ambiguities, and accepts any reference file in FASTA format. For each fragment overlapping a mutation of interest, fRagmentomics outputs fragment-level features including its fragment size, end-motifs, and mutational status, along with additional fragment-level or read-level information. The package implements an indel-aware and optionally soft-clip-preserving fragment size computation that improves accuracy over conventional size estimates based solely on aligned positions. AVAILABILITY AND IMPLEMENTATION: fRagmentomics is licensed under GNU General Public License v3.0 and available at https://github.com/ElsaB-Lab/fRagmentomics, https://anaconda.org/elsab-lab/r-fragmentomics and https://bioconductor.org/packages/fRagmentomics, with documentation and a tutorial. CONTACT: yoann.pradat@gustaveroussy.fr, elsa.bernard@gustaveroussy.fr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Software↗

PubMed2026

BriGHT: transcriptome-regularized multimodal neuroimaging for brain disorder prediction.

MOTIVATION: Hypergraph-based models for brain disorder prediction mainly adopt imaging-derived hypergraphs as propagation backbones. However, the entanglement of topology construction and feature propagation leaves regional representations weakly constrained by underlying biological organization, making them vulnerable to subject-specific variation and noise, particularly in heterogeneous multimodal settings. RESULTS: We present BriGHT, a Brain transcriptome-reGularized Hypergraph framework for mulTimodal disorder prediction. BriGHT employs a transcriptome-derived structural reference as a soft anchoring prior to regularize neuroimaging ROI embeddings, stabilizing representation geometry while preserving disease-relevant subject-specific variation. BriGHT further incorporates a reliability-aware fusion module to estimate subject-specific modality reliability from prediction confidence, cross-modal consistency, and decision certainty, enabling adaptive integration under heterogeneous modality quality. Experiments on three neuroimaging cohorts (ADNI, ADHD-200, REST-meta-MDD) and four modalities (VBM, fMRI, FDG, AV45) demonstrate that BriGHT consistently outperforms competing graph/hypergraph learning methods across six brain disorder prediction tasks. Perturbation analyses show that BriGHT benefits from the spatial correspondence between transcriptomic modules and imaging ROIs, rather than from arbitrary hypergraph regularization alone. Ablation and meta-analytic interpretability analyses support the contribution of transcriptomic anchoring and adaptive fusion to robust and biologically meaningful brain disorder prediction. AVAILABILITY: The software is publicly available at: https://github.com/Yaolab-fantastic/BriGHT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Journal Article↗

PubMed2026

'PePApipe': A complete bioinformatics analysis pipeline for African Swine Fever Virus genome.

African Swine Fever Virus (ASFV) is of high concern in porcine livestock across the world due to both the high mortality rates and the trade restrictions imposed on affected regions. The viral genome is large and complex, and genomic analysis is essential for tracing its origin and evolution. Although several bioinformatics tools exist for genome assembly and analysis, no single platform integrates all necessary steps in an accessible and systematic way. In this study the authors developed 'PePApipe', a custom-built, user-friendly pipeline that enables rapid, complete, and efficient ASFV genome analysis. It is specifically designed for laboratory professionals with limited bioinformatics experience, requiring only basic command-line knowledge. Starting from raw sequencing data, PePApipe integrates thirteen software tools into one automated workflow, covering quality control and pre-processing of raw reads, de novo genome assembly and variant calling. Programmed in Python, it can be executed locally through bash scripts, or using a Slurm protocol for batch processing of multiple samples. The main outputs are the ASFV consensus genome sequence and a file listing its putative variants compared to the selected reference genome. PePApipe classifies generated files into structured folders and produces intermediate files that can be used as inputs for further or parallel analyses; users can also enable or disable specific steps in each particular case. This pipeline is adaptable and complementary to downstream steps such as viral genome annotation or genome visualization. By consolidating all stages of viral genome analysis into a single automated workflow, PePApipe reduces the likelihood of user error, and enhances reproducibility and efficiency. This user-friendly pipeline facilitates the transition from sequencing to assembly and downstream analysis of viral genomes, ensuring a fast and reliable response to molecular analysis demands. Finally, the pipeline can be easily adapted to the study of other viral species, expanding its application in infectious diseases surveillance.

African Swine Fever Virus↗

PubMed2025

polars-bio-fast, scalable, and out-of-core operations on large genomic interval datasets.

MOTIVATION: Genomic studies very often rely on computationally intensive analyses of relationships between features, which are typically represented as intervals along a 1D coordinate system (such as positions on a chromosome). In this context, the Python programming language is extensively used for manipulating and analyzing data stored in a tabular form of rows and columns, called a DataFrame. Pandas is the most widely used Python DataFrame package and has been criticized for inefficiencies and scalability issues, which its modern alternative-Polars-aims to address with a native backend written in the Rust programming language. RESULTS: polars-bio is a Python library that enables fast, parallel and out-of-core operations on large genomic interval datasets. Its main components are implemented in Rust, using the Apache DataFusion query engine and Apache Arrow for efficient data representation. It is compatible with Polars and Pandas DataFrame formats. In a real-world comparison (107 versus 1.2×106 intervals), our library runs overlap queries 6.5×, nearest queries 15.5×, count_overlaps queries 38×, and coverage queries 15× faster than Bioframe. On equally sized synthetic sets (107 versus 107), the corresponding speedups are 1.6×, 5.5×, 6×, and 6×. In streaming mode, on real and synthetic interval pairs, our implementation uses 90× and 15× less memory for overlap, 4.5× and 6.5× less for nearest, 60× and 12× less for count_overlaps, and 34× and 7× less for coverage than Bioframe. Multi-threaded benchmarks show good scalability characteristics. To the best of our knowledge, polars-bio is the most efficient single-node library for genomic interval DataFrames in Python. AVAILABILITY AND IMPLEMENTATION: polars-bio is an open-source Python package distributed under the Apache License available for major platforms, including Linux, macOS, and Windows in the PyPI registry. The online documentation is https://biodatageeks.org/polars-bio/ and the source code is available on GitHub: https://github.com/biodatageeks/polars-bio and Zenodo: https://doi.org/10.5281/zenodo.16374290. are available at Bioinformatics online.

Software↗

PubMed2026

Large-scale benchmarking of prokaryotic annotation tools across thousands of species.

BACKGROUND: Genome annotation is an important step in deriving functional meaning from prokaryotic sequencing data, yet systematic evaluations guiding tool selection are lacking. We present the first large-scale investigation of four prominent open-source annotation tools (Prokka, Bakta, EggNOG-mapper, and PGAP) across 156,033 diverse genomes. This includes Escherichia coli strains for baseline performance, thousands of archaea and bacteria genomes, as well as frameshifted and metagenome-assembled genomes. RESULTS: Bakta excels in annotating high-quality bacterial genomes, while PGAP was better for archaeal genomes and challenging bacterial assemblies, including metagenome-assembled, fragmented, or contaminated samples. For Gene Ontology annotation, PGAP consistently provides broader term coverage, whereas EggNOG-mapper offers more terms per feature. CONCLUSIONS: Our findings highlight tool-specific strengths crucial for selecting optimal solutions based on genome quality, taxonomy, and origin (e.g. MAGs). This study provides an evidence-based guide for users and informs future tool development.

Molecular Sequence Annotation↗

PubMed2026

ProteoParc: A Reference Protein Database Builder for Ancient and Nonmodel Organisms.

Over the past few years, the increasing interest in analyzing the proteome of extinct and nonmodel organisms has generated a new field of research expanding the scope of proteomics. The lack of curated databases and/or molecular data from these organisms forces researchers to manually search in different public repositories for related protein sequences, either for MS/MS peptide identification or ZooMS marker annotation. This can lead to format incongruences and hinder reproducibility between studies. To address this issue, we introduce ProteoParc, a user-friendly software that builds reference databases by systematically downloading and processing protein sequences from the most widely used public repositories. The pipeline's output is a nonredundant protein database, formatted in a way to be interpreted by typical peptide identification software. Moreover, the user can adjust the database dimension and composition by applying different criteria to include only a certain number of genes or species. Thus, ProteoParc is an easy and fast, custom-made bioinformatic tool useful for future paleoproteomics analysis in ancient samples related to understudied organisms.

Databases, Protein↗

PubMed2024

SPOP expression is associated with tumor-infiltrating lymphocytes in pancreatic cancer.

BACKGROUND: Speckle Type POZ Protein (SPOP), despite its tumor type-dependent role in tumorigenesis, primarily as a tumor suppressor gene is associated with a variety of different cancers. However, its function in pancreatic cancer remains uncertain. METHODS: SPOP expression and the association between its expression and patient prognosis and immune function were evaluated using The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), The Tumor Immune Estimation Resource 2.0 (TIMER2.0) database, cBioportal, and various bioinformatic databases. Enrichment analysis of SPOP and the association between SPOP expression with clinical stage and grade were analyzed using the R software package. Then immunohistochemistry (IHC) was used to estimate the correlation between SPOP and tumor-infiltrating lymphocytes (TILs) in patients with pancreatic cancer. RESULTS: As part of our study, we assessed that SPOP was anomalously expressed in kinds of cancers, associated with clinical stage and outcomes. Meanwhile, SPOP also played a crucial role in the tumor microenvironment (TME). The expression level of SPOP was significantly correlated to tumor-infiltrating immune cells (TICs) in pancreatic cancer. CONCLUSIONS: Our study uncovered the potential corrections in SPOP with TICs, suggesting that SPOP may act as a biomarker for immunotherapy in pancreatic cancer.

Humans↗

PubMed2025

Bioinformatic analyses and validated experiments reveal an aging hallmark gene set and protective miR of coronary artery disease.

To investigate how aging hallmarks exert roles in the age-related disease of coronary artery disease (CAD). R software and the GEO2R online tool identified differentially expressed genes (DEGs) and differentially expressed microRNAs (DEMis) in CAD microarray datasets from the Gene Expression Omnibus. Genes common to target genes of DEMis, DEGs, and an aging gene list from Human Aging Genomic Resources were then identified and analyzed for protein-protein interactions and functional and pathway enrichment. An miR-mRNA network was constructed using Cytoscape. Receiver operating characteristic curve analysis assessed the diagnostic utility of DEMis in CAD. The expression of two DEMis from a CAD cohort was employed to validate the findings. An aging hallmark gene set, comprising 18 genes, was delineated, with the hub gene TP53 established through protein-protein interaction and microRNA-mRNA networks. Within the microRNA-mRNA network, two DEMis (hsa-miR-423-5p and hsa-miR-564) potentially regulated TP53, rendering them potential CAD biomarkers, as indicated by their area under the curves (AUC) surpassing 0.6. Validation experiments corroborated an AUC of 0.7002 for hsa-miR-423-5p and 0.7261 for hsa-miR-564, highlighting its protective association with CAD. Combining hsa-miR-423-5p, hsa-miR-564, total cholesterol (TC), high-density lipoprotein-cholesterol (HDL-C), low-density lipoprotein-cholesterol (LDL-C), white blood cells (WBC) achieved an area under the receiver operating characteristics curve of 0.783. A CAD-associated gene set was identified, with TP53 as the central hub. Hsa-miR-564 emerged as a potential protective factor against CAD.

Humans↗

PubMed2026

The Gabriella Miller Kids First Data Resource for genomic research in pediatric cancer and congenital anomalies.

Nine-year-old brain tumor patient Gabriella Miller challenged members of Congress to "stop talking and start doing" when providing federal funding for research into cures for pediatric cancer and congenital anomalies. Though she ultimately lost her life to that cancer, her advocacy efforts resulted in the 2014 Gabriella Miller Kids First Research Act, launching the Gabriella Miller Kids First Pediatric Research Program at the National Institutes of Health (NIH). The overarching goal of the Gabriella Miller Kids First Pediatric Research Program is to help researchers uncover new insights into the biology of childhood cancer and congenital anomalies. Following the signing of the Gabriella Miller Kids First Research Act 2.0 in January 2025, the program has been extended at NIH through 2028 to advance the groundwork laid in the program's first ten years. The Gabriella Miller Kids First Data Resource Center has since honored her legacy by building a comprehensive data resource for genomic research into pediatric conditions. Data from more than 30,000 participants annotated with demographic and clinical information related to their diagnoses have been released for secondary research and analysis using the center's web-based platforms. This paper analyzes the outcomes of the initiative and highlights breakthroughs made by the larger research community resulting from the availability of this data resource. We explore the future expansion of the data resource to include new modalities and tools for supporting life-saving research for children like Gabriella Miller.

Humans↗

PubMed2026

VisPan: real-time visualisation of multiplex amplicon-based sequencing panels for rapid syndromic surveillance and pathogen detection.

MOTIVATION: Infectious diseases persist as a major global public health challenge. Diverse factors, including climate change, globalization, deforestation, human-animal interactions, lifestyle choices, and various biological factors, can contribute to their emergence and reemergence. Rapid detection and characterization of (re)emerging pathogens are therefore critical for effective outbreak management and for enhancing our understanding of epidemics by monitoring the transmission, spread, evolution, and genomics of pathogens. In this context, next-generation sequencing technologies (NGS), particularly long-read platforms such as Oxford Nanopore Technologies (ONT), have opened new avenues for real-time pathogen monitoring. However, the bioinformatics bottleneck remains a challenge, emphasizing the need for efficient, accessible, and user-friendly analysis tools. RESULTS: Here, we present a tool adapted from the RAMPART software that enables real-time data visualisation of multiplex PCR syndromic panels combined with Oxford Nanopore sequencing. This real-time analysis enables rapid pathogen detection, from raw data acquisition to taxonomic assignment, within minutes. The interface offers dynamic visual tracking of the sequencing run and amplicon coverage, facilitating immediate insights during diagnostic workflows. Validation experiments confirmed the system's reliability, accurately identifying all pathogens present in complex clinical or environmental samples. This tool provides an integrated, user-friendly solution for genomic pathogen surveillance in field or clinical settings.

Software↗

PubMed2026

scSNViz: visualization and analysis of cell-specific expressed SNVs.

MOTIVATION: Accurately characterizing expressed genetic variation at the single-cell level is essential for understanding transcriptional heterogeneity, allelic regulation, and mutational dynamics within complex tissues. However, few tools enable comprehensive visualization and quantitative analysis of expressed variants across individual cells. RESULTS: scSNViz is an R package for the exploration, quantification, and visualization of expressed single-nucleotide variants (SNVs) from cell-barcoded single-cell RNA sequencing (scRNA-seq) data. The software supports estimation of variant allele fractions, clustering of SNV expression profiles, and 2D and 3D visualization of individual SNVs or user-defined SNV groups. Beyond visualization, scSNViz facilitates investigation of cell-, cluster-, or lineage-specific variant expression patterns, as well as allelic dynamics including imprinting, random allele inactivation, and transcriptional bursting. It interoperates seamlessly with established single-cell frameworks-Seurat for clustering, Slingshot for trajectory inference, scType for cell-type annotation, and CopyKat for copy-number profiling-enabling integrative multi-omic analyses of expressed variation. AVAILABILITY AND IMPLEMENTATION: scSNViz is implemented in R and freely available at https://github.com/HorvathLab/scSNViz (DOI: 10.5281/zenodo.17307516). The package includes comprehensive documentation and example workflows designed for users with limited bioinformatics experience.

Software↗

PubMed2026

Practicing Data Science in Interactive Notebooks.

The Jupyter Notebook is a platform for interactive computing that displays code and results in the same browser, making it valuable for teaching, prototyping, data analysis, and collaboration. Its explicit and transparent structure greatly reproducibility while its backend server supports flexible deployment. In the past few years, Jupyter notebooks and similar tools have become increasingly popular. In this chapter, we will review key aspects of data analysis in a cloud environment and demonstrate common tasks for analyzing metabolomics data using template notebooks. This is an accompaniment to the basic bioinformatics tools and essential data science toolkit introduced in the first edition.

Software↗

PubMed2024

ONCOLINER: A new solution for monitoring, improving, and harmonizing somatic variant calling across genomic oncology centers.

The characterization of somatic genomic variation associated with the biology of tumors is fundamental for cancer research and personalized medicine, as it guides the reliability and impact of cancer studies and genomic-based decisions in clinical oncology. However, the quality and scope of tumor genome analysis across cancer research centers and hospitals are currently highly heterogeneous, limiting the consistency of tumor diagnoses across hospitals and the possibilities of data sharing and data integration across studies. With the aim of providing users with actionable and personalized recommendations for the overall enhancement and harmonization of somatic variant identification across research and clinical environments, we have developed ONCOLINER. Using specifically designed mosaic and tumorized genomes for the analysis of recall and precision across somatic SNVs, insertions or deletions (indels), and structural variants (SVs), we demonstrate that ONCOLINER is capable of improving and harmonizing genome analysis across three state-of-the-art variant discovery pipelines in genomic oncology.

Humans↗

PubMed2025

CountASAP: a lightweight, easy to use python package for processing ASAPseq data.

BACKGROUND: Declining sequencing costs coupled with the increasing availability of easy-to-use kits for the isolation of DNA and RNA transcripts from single cells have driven a rapid proliferation of studies centered around genomic and transcriptomic data. Simultaneously, a wealth of new techniques have been developed that utilize single cell technologies to interrogate a broad range of cell-biological processes. One recently developed technique, transposase-accessible chromatin with sequencing (ATAC) with select antigen profiling by sequencing (ASAPseq), provides a combination of chromatin accessibility assessments with measurements of cell-surface marker expression levels. While software exists for the characterization of these datasets, there currently exists no tool explicitly designed to reformat ASAP surface marker FASTQ data into a count matrix which can then be used for these downstream analyses. RESULTS: To address this lack of a dedicated tool for ASAPseq data processing, we created CountASAP, an easy-to-use Python package purposefully designed to transform FASTQ files from ASAP experiments into count matrices compatible with commonly-used downstream bioinformatic analysis packages. CountASAP takes advantage of the independence of the relevant data structures to perform fully parallelized matches of each sequenced read to user-supplied input ASAP oligos and unique cell-identifier sequences. We directly compare the performance and user-friendliness of CountASAP to existing tools using similarly-structured data from a more common sequencing experiment: cellular indexing of transcriptomes and epitopes by sequencing (CITEseq). Further benchmarking against existing tools helps to identify proper defaults for CountASAP and assess the agreement of outputs from all tested software. A final test using a novel ASAPseq dataset provides evidence that CountASAP can generate biologically meaningful results that correlate well with paired chromatin accessibility data. CONCLUSIONS: CountASAP shows good agreement with existing, well-tested data processing tools in the analysis of similarly-structured benchmarking data. CountASAP runs efficiently on a standard laptop, has user-friendly documentation, a one-step installation, and represents the first and only tool designed specifically for the processing of ASAPseq data.

Software↗

PubMed2025

Analysis of differentially expressed genes in schizophrenia based on bioinformatics and corresponding mRNA expression levels.

OBJECTIVE: This study aimed to use bioinformatics analysis to identify differentially expressed genes (DEGs) involved in the pathogenesis of schizophrenia and validate their mRNA expression levels through real-time quantitative PCR (qPCR). MATERIAL/METHODS: Datasets from the publicly available Gene Expression Omnibus (GEO) database were analyzed using R software to identify DEGs. Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, were conducted. A protein-protein interaction (PPI) network was constructed using Cytoscape software to identify key genes with notable expression changes. The expression levels of these key genes were subsequently validated in schizophrenia patients using qPCR to assess potential susceptibility genes. RESULTS: In total, 813 DEGs were identified, with six key genes highlighted through GO analysis and PPI network screening. Among these, HDAC1, UBA52, and FYN demonstrated statistically significant differences in mRNA expression between schizophrenia patients and healthy controls (P < 0.05). CONCLUSIONS: This study identified several DEGs potentially linked to the pathogenesis of schizophrenia, suggesting that HDAC1, UBA52, and FYN could serve as candidate susceptibility genes and diagnostic biomarkers. These findings provide new insights and directions for future schizophrenia research.

Humans↗