Aims & Scope
Bioinformatics Methods and Applications is an open access, peer-reviewed journal dedicated to advancing bioinformatics by disseminating innovative methodologies and promoting reproducible data analysis practices.
The journal provides a platform for the bioinformatics community to share cutting-edge computational techniques, practical tools, and robust analytical workflows that improve the interpretation of complex biological data. The journal publishes original manuscripts presenting algorithms, computational pipelines, statistical models, or software tools that significantly enhance the analysis, interpretation, or visualization of biological data.
The journal also accepts methodologically rigorous reports of bioinformatics analyses that complement published biological studies or describe analyses of unpublished datasets. For published studies, these reports provide a more detailed and comprehensive account of the bioinformatics data analysis than is typically included in the original publication. These articles must enable accurate reproduction of results by providing complete workflows, including software versions, parameter settings, and data availability. This focus on transparency and reusability promotes reliability and fosters best practices in bioinformatics research.
The journal welcomes contributions across the broad spectrum of bioinformatics, including but not limited to genomics, transcriptomics, proteomics, metabolomics, structural bioinformatics, systems biology, computational biology, and AI/machine learning. By fostering a culture of methodological innovation and reproducibility, Bioinformatics Methods and Applications supports the development of reliable, scalable, and impactful bioinformatics research. The journal is published quarterly online by Scilight Press.
The journal invites contributions spanning the full range of bioinformatics and computational biology, including but not limited to:
- Genomics and Genome Informatics – Variant calling, genome assembly, annotation, comparative genomics, and population genomics.
- Transcriptomics and Gene Expression Analysis – Bulk and single-cell RNA-seq, spatial transcriptomics, alternative splicing, transcript quantification, gene co-expression networks.
- Epigenomics – DNA methylation, histone modifications, chromatin accessibility, and 3D genome architecture.
- Gene Regulation and Regulatory Genomics – Identification and analysis of transcription factor binding sites, enhancer–promoter interactions, regulatory element annotation, and integrative approaches to understanding gene regulatory networks.
- Proteomics and Protein Informatics – Mass spectrometry data analysis, protein identification and quantification, post-translational modifications, protein–protein interaction networks.
- Metabolomics and Lipidomics – Computational tools for spectral analysis, metabolite identification, pathway mapping, and data integration.
- Microbiome and Metagenomics – Microbial community profiling, taxonomic classification, functional annotation, and microbiome–host interactions.
- Structural Bioinformatics – Protein structure prediction, modeling, docking, dynamics, and structure-based drug design.
- Systems Biology and Network Analysis – Pathway modeling, gene regulatory networks, metabolic network simulations, and multi-omics data integration.
- Evolutionary and Phylogenetic Bioinformatics – Phylogenetic tree reconstruction, molecular evolution, ancestral sequence inference, and comparative analysis.
- Biomedical and Clinical Informatics – Computational approaches for precision medicine, biomarker discovery, electronic health records (EHR), and integrative analysis of clinical and omics data.
- AI and Machine Learning in Bioinformatics – Applications of deep learning, large language models, graph neural networks, and interpretable AI in biological research.
- Data Standards, Ontologies, and FAIR Principles – Development of community standards, metadata models, and practices that enhance data interoperability, accessibility, and reusability.
- High-Performance and Scalable Computing – Workflow management, cloud computing, GPU acceleration, and tools for handling large-scale or streaming biological data.