Aims and scope

BMC Bioinformatics is an open access, peer-reviewed journal that considers articles describing novel computational algorithms and software, models and tools, including statistical methods, machine learning and artificial intelligence, for the modelling and analysis of all kinds of biological data, as well as systems biology.

BMC Bioinformatics welcomes manuscripts in the following broad areas of research:

    Analysis and modelling of complex systems
    Comparative genomics 
    Data visualization
    Imaging, and image analysis
    Knowledge-based analysis
    Machine learning and artificial intelligence in bioinformatics
    Network analysis
    Proteomics
    Sequence analysis
    Structural analysis
    Transcriptome analysis

As a BMC Series journal, BMC Bioinformatics does not make editorial decisions based on the perceived interest or potential impact of a study. Manuscripts are considered for publication if they are scientifically valid. For research articles, this includes having a clearly defined and sound research question, appropriate methodology and analysis, and adherence to community-agreed standards relevant to the field.

Special Issue on Bioinformatics platforms for the study and detection of anti-microbial resistance
Submission Date: 2026-02-17

BMC Bioinformatics is welcoming submissions to our Collection on Bioinformatics platforms for the study and detection of antimicrobial resistance.

Antimicrobial resistance (AMR) poses a significant threat to global health, necessitating advanced tools to detect and analyze resistance mechanisms. Bioinformatics platforms can help researchers identify AMR determinants within genomic and metagenomic data, providing insights into the spread and evolution of resistance genes. These platforms are important for public health surveillance, guiding the development of targeted interventions and informing policy decisions.

This Collection welcomes submissions on the development of new computational and/or statistical approaches for the study of antimicrobial resistance. We encourage contributions that highlight innovative methods for detecting resistance genes and integrating genomic data with phenotypic information.

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer-review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.

Special Issue on Cell tracking
Submission Date: 2026-03-23

BMC Bioinformatics is welcoming submissions to our Collection on Cell Tracking.

Cell tracking is a technique used to monitor and analyze the movement and behavior of cells over time, allowing the study of cellular behaviors, dynamics, and interactions within various biological contexts. Advanced bioinformatics tools play a vital role in analyzing cell tracking data. They help identify cell movement patterns and understand their biological implications. These tools are particularly relevant when processing large datasets and when investigating cell cycles.

This Collection welcomes submissions on the development of new computational and/or statistical approaches for cell tracking. We encourage contributions detailing methods for detecting and characterizing cell movements to better understand cell migration and behavior.

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer-review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.

Special Issue on Extracellular vesicle research
Submission Date: 2026-03-30

BMC Bioinformatics is welcoming submissions to our Collection on Extracellular vesicles research.

BMC Bioinformatics is welcoming submissions to our Collection on Extracellular vesicles research. Extracellular vesicles (EVs) are are small lipid bilayer-delimited particles released by cells that play crucial roles in intercellular communication and various physiological processes. The study of EVs has gained significant attention due to their potential as biomarkers for disease diagnosis, therapeutic targets and drug delivery systems. Advanced bioinformatics tools are essential for analyzing EV data, identifying EV-associated molecules, and understanding their biological functions.

This Collection welcomes submissions on the development of new computational and/or statistical approaches for the study of extracellular vesicles. We encourage contributions that highlight innovative methods for detecting and characterizing EVs and elucidating the molecular mechanisms underlying EV biogenesis and function.

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer-review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.

Special Issue on Epigenomics
Submission Date: 2026-04-16

BMC Bioinformatics is welcoming submissions to our Collection on the development of computational approaches in the study of epigenomics.

Epigenomics is the study of the complete set of epigenetic modifications across the genome and how these changes influence gene activity without altering the underlying DNA sequence. These modifications include DNA methylation and histone modifications, all of which influence gene activity. By mapping and understanding these modifications across the entire genome, epigenomic research provides insight into how cells respond to developmental cues and environmental signals, and how disruptions in these processes can lead to disease.

This Collection welcomes submissions on the development of new computational and/or statistical approaches for the study of epigenomics. We particularly encourage work that bridges computational innovation with meaningful biological interpretation. To promote cross-disciplinary impact, submissions should be clearly explained and accessible to both computational data scientists and biologists. Topics of interest include, but are not limited to, approaches for detecting epigenomic markers, analyzing chromatin accessibility, mapping histone modifications, and integrating epigenomic data with transcriptomic and phenotypic information.

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer-review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.

Special Issue on Predictive toxicology
Submission Date: 2026-08-14

BMC Bioinformatics is welcoming submissions to our Collection on Predictive toxicology.

Predictive toxicology investigates the harmful effects of chemical substances using models and data-driven methods, often aiming to decrease dependence on traditional animal testing, such as mammals, for assessing health risks. Developments in this field support New Approach Methodologies (NAMs) for evaluating chemical safety and regulation. NAMs refer to any methods that enhance safety assessments while avoiding animal testing. Specifically, predictive toxicology employs computational techniques with a mechanistic understanding of toxicity to estimate risks to human health and the environment.

Recent advances have highlighted the use of various technologies that generate data valuable for in silico toxicity prediction, including omics, in vitro screening, high-throughput phenotyping, organoids, and alternative in vivo models. These innovations, combined with comparative biology and insights from other disciplines (e.g., genetics, evolution), refine hazard and risk assessment methods, facilitating a more precise evaluation of chemical safety and ultimately improving health outcomes.

This Collection welcomes submissions on the development of new computational and/or statistical approaches for predictive toxicology.

All manuscripts submitted to this journal, including those submitted to collections and special issues, are assessed in line with our editorial policies and the journal’s peer-review process. Reviewers and editors are required to declare competing interests and can be excluded from the peer review process if a competing interest exists.