Bioinformatics & Genomics
Open Access
Open
Continuous
20% acceptance rate
Peer Review
Open
OA Type
Gold OA
Acceptance
20%
Time to Decision
5 weeks
Frequency
Continuous
APC
2,500 USD
Impact Factor (2023)
5.3
CiteScore (2023)
7.4
About This Journal
Bioinformatics & Genomics (B&G) is a fully open-access journal publishing high-impact research at the convergence of computational science, molecular biology, and genomic medicine. The journal covers genome sequencing, transcriptomics, proteomics, metabolomics, systems biology, and development of novel bioinformatics tools. B&G is committed to open science: all published software must be open-source and datasets must be publicly deposited.
Aims & Scope
B&G welcomes original research, methods papers, software articles, and reviews:
• Genome sequencing, assembly, and annotation (short-read, long-read, single-molecule)
• Comparative genomics, population genomics, and evolutionary analysis
• Transcriptomics: RNA-seq, single-cell RNA-seq, spatial transcriptomics
• Proteomics and post-translational modification analysis
• Metabolomics and multi-omics data integration
• Structural bioinformatics and protein structure prediction
• Machine learning and deep learning in genomics
• Clinical genomics: variant interpretation, polygenic risk scores, pharmacogenomics
• Microbiome and metagenomics research
Reproducibility is a core criterion; all analyses must be reproducible from published code and data.
• Genome sequencing, assembly, and annotation (short-read, long-read, single-molecule)
• Comparative genomics, population genomics, and evolutionary analysis
• Transcriptomics: RNA-seq, single-cell RNA-seq, spatial transcriptomics
• Proteomics and post-translational modification analysis
• Metabolomics and multi-omics data integration
• Structural bioinformatics and protein structure prediction
• Machine learning and deep learning in genomics
• Clinical genomics: variant interpretation, polygenic risk scores, pharmacogenomics
• Microbiome and metagenomics research
Reproducibility is a core criterion; all analyses must be reproducible from published code and data.