What is the Bioinformatics JIF?

The JIF is 5.4 (2024 JCR). See the full year-by-year trend and category ranking in our Bioinformatics JIF analysis.

What is the acceptance rate of Bioinformatics?

Manusights currently treats ~40-50% as an estimate for Bioinformatics, not an official journal-published rate. One common reason papers fail is algorithm development without biological validation or application.

How long does Bioinformatics take to review?

Bioinformatics typically takes ~60-90 days median, but timing varies by editor, field, and reviewer availability. Expect an editorial screen first, then manuscript preparation, then peer review.

All Journal Guides

Journal Guide

Publishing in Bioinformatics: Fit, Timeline & Submission Guide

Q: Is Bioinformatics a good journal?

Bioinformatics is a strong target if present computational approach revealing biological insight. It is a weaker fit if pure algorithmic papers without demonstration on biological data have limited Bioinformatics impact.

Q: Is Bioinformatics peer reviewed?

Yes, Bioinformatics is fully peer reviewed. All submissions undergo rigorous peer review by 2-3 bioinformatics experts assess algorithm design, validation approach, and practical utility. Reviewers evaluate code availability and reproducibility. First decision 60-90 days..

Computational methods for understanding biology from sequences to systems

Should you submit here?

Submit if present computational approach revealing biological insight. Be careful if pure algorithmic papers without demonstration on biological data have limited Bioinformatics impact.

IF 5.4 · ~40-50% accepted · ~60-90 days median

Best fit if

Present computational approach revealing biological insight

Not ideal if

Pure algorithmic papers without demonstration on biological data have limited Bioinformatics impact

Also compare

RNA

5.4

Impact Factor (2024)

~40-50%

Acceptance Rate

~60-90 days median

Time to First Decision

Submission guide

Bioinformatics Submission Guide: Scope, Format & Editor Priorities

Bioinformatics submission guide covering computational fit, biological value, submission setup, and what editors screen before review.

Journal assessment

Is Bioinformatics a Good Journal? Impact Factor, Comparison, and Fit Verdict

Bioinformatics fit verdict with key metrics, comparison to Nature Methods and Genome Biology, and practical guidance for computational biology tool and method authors.

Desk rejection

How to Avoid Desk Rejection at Bioinformatics

How to avoid desk rejection at Bioinformatics by proving real biological utility, credible benchmarking, and usable computation.

What Bioinformatics Publishes

Bioinformatics published by Oxford University Press is the premier journal for computational biology and bioinformatics. With JIF 5.4 and Q1-Q2 ranking in Biology & Biotechnology/Computer Science, Bioinformatics emphasizes computational methods enabling biological discovery. The journal publishes research on sequence analysis, structural prediction, systems biology, and computational analysis of biological data. Critically: Bioinformatics values practical methods with biological relevance. Pure algorithmic papers without biological application are less competitive. The journal seeks papers showing how computation solves biological problems.

Sequence analysis: alignment, annotation, homology searching
Protein structure: prediction, fold recognition, structure-function relationships
Gene expression: analysis, regulatory prediction, RNA-seq methods
Systems biology: network analysis, pathway modeling, integration
Machine learning in biology: neural networks, classification, prediction
Population genomics: variant analysis, evolutionary inference
Structural bioinformatics: protein modeling, docking, simulation
Data integration: multi-omics analysis, knowledge bases

Editor Insight

“Bioinformatics publishes computational methods advancing biological understanding. We seek practical methods with rigorous validation on real biological data and clear biological relevance. The best papers show how computation enables biological discovery.”

What Bioinformatics Editors Look For

Novel computational method with demonstrated biological application

Present computational approach revealing biological insight. Novel algorithm? Better prediction accuracy? Faster analysis? Show how method enables new biological understanding or solves practical problem.

Rigorous method validation with appropriate benchmarks

Validate methods against gold-standard datasets and compare with existing approaches. Show performance metrics: accuracy, sensitivity, specificity, computational efficiency. Sloppy validation unacceptable.

Real biological datasets demonstrating practical applicability

Test methods on actual biological data: genome sequences, expression datasets, protein structures. Validation on toy problems insufficient. Real-world data proves practical utility.

Biological interpretation of computational results

Don't just report predictions - explain biological meaning. What do results reveal about biology? How do computational findings advance understanding? Connect computation to biology.

Accessibility and reproducibility of computational method

Provide source code, make tools available, document parameters. Reproducibility and accessibility critical for computational work. Inaccessible methods limit impact.

Why Papers Get Rejected

These patterns appear repeatedly in manuscripts that don't make it past Bioinformatics's editorial review:

Algorithm development without biological validation or application

Pure algorithmic papers without demonstration on biological data have limited Bioinformatics impact. Show how method solves biological problem or enables biological discovery.

Method validation only on small toy datasets

Validation on hand-curated small datasets is insufficient. Use realistic biological datasets (real genomes, actual expression data, known protein structures). Large-scale validation essential.

No comparison with existing methods or established baselines

Show how your method compares with state-of-the-art approaches. Performance improvement should be quantified and statistically significant.

Lack of biological interpretation of computational results

Papers reporting predictions without explaining biological meaning are less impactful. What do results reveal about biology? How do findings advance understanding?

Inaccessible code or insufficient documentation for reproduction

Computational work must be reproducible. Provide source code, document parameters, enable others to apply method. Inaccessible methods limit impact.

Does your manuscript avoid these patterns?

The Free Readiness Scan reads your full manuscript against Bioinformatics's criteria and flags the specific issues most likely to cause rejection.

Run Free Readiness Scan →

Insider Tips from Bioinformatics Authors

Machine learning methods for biological prediction increasingly competitive

Deep learning, neural networks, and advanced ML for structure prediction, expression analysis, or variant interpretation increasingly valued in bioinformatics.

Single-cell and multi-omics analysis methods gaining prominence

Computational methods for single-cell RNA-seq analysis, spatial transcriptomics, or integrated multi-omics analysis increasingly competitive as technologies expand.

Methods enabling large-scale data analysis are highly valued

Scalable algorithms handling genome-scale datasets or population-level analysis provide practical value. Computational efficiency matters for real-world application.

Biological discovery examples strengthen method papers

Papers showing specific biological insights enabled by the method (disease associations, functional predictions, evolutionary insights) more impactful than methods alone.

Open-source tools with user base are highly cited

Bioinformatics methods widely adopted by research community receive high citations. Tools like BLAST, STAR, DESeq2 exemplify impactful software.

The Bioinformatics Submission Process

Manuscript preparation

Prep

5,000-8,000 words with 5-7 figures. Include method description, validation on gold-standard and real biological datasets, performance comparison with existing methods, biological examples demonstrating utility, and computational efficiency assessment. Supporting: source code, detailed algorithms, test datasets.

Submission via Oxford Academic

Day 0

Submit at https://academic.oup.com/bioinformatics/. Required: manuscript emphasizing method novelty and biological utility, figures showing method performance and biological applications, code availability.

Editorial assessment

1-2 weeks

Editor assesses method novelty, validation rigor, and biological relevance. Papers lacking real data validation or biological application face lower priority. Moderate desk rejection ~20-30%.

Peer review

60-90 days

2-3 bioinformatics experts assess algorithm design, validation approach, and practical utility. Reviewers evaluate code availability and reproducibility. First decision 60-90 days.

Revision and publication

Revision: 2-4 weeks

Revisions often request additional validation, clearer documentation, or biological application examples. Publication 1-3 weeks after acceptance (fast OUP publication).

Bioinformatics by the Numbers

2024 Impact Factor	4.4
5-Year Impact Factor	4.7
Acceptance rate	~40-50%
Desk rejection rate	~20-30%
Median first decision	~75 days
Open access option	~£1,500
Publisher	Oxford University Press
Founded	1997

Before you submit

Bioinformatics accepts a small fraction of submissions. Make your attempt count.

Start with the Free Readiness Scan. Unlock the Full AI Diagnostic for $29. If you need deeper scientific feedback, choose Expert Review. The full report is calibrated to Bioinformatics.

Run Free Readiness Scan See a sample report

Article Types

Original Paper

5,000-8,000 words

Novel computational method with biological application

Review

7,000-12,000 words

Bioinformatics method review (usually invited)

Applications Note

2,000-3,000 words

Software tool or database description

Landmark Bioinformatics Papers

Papers that defined fields and changed science:

BLAST sequence alignment (Altschul et al., 1990) - foundational sequence search tool
Hidden Markov Models for biology (1990s) - probabilistic frameworks for sequences
Gene expression clustering and analysis (2000s) - microarray analysis foundations
RNA-seq analysis methods (2010s) - transcriptome analysis approaches
Deep learning for protein structure (AlphaFold, 2020) - revolutionized structure prediction

Preparing a Bioinformatics Submission?

Get pre-submission feedback from reviewers who've published in Bioinformatics and know exactly what editors look for.

Run Free Readiness Scan

Need expert depth? See Expert Review Options

NDA-protected

Confidential

Primary Fields

Sequence AnalysisStructure PredictionGene Expression AnalysisMachine Learning in BiologySystems BiologyGenome Analysis

More Guides for This Journal

Ready to submit to Bioinformatics?

A desk rejection costs months. Get expert feedback before you submit, from scientists who know exactly what Bioinformatics editors look for.

Avoid Desk Rejection

Get expert pre-submission review before you submit to Bioinformatics. 3-7 day turnaround.

Manuscript Rejected?

Expert revision help to strengthen your manuscript and resubmit with confidence.

Reviewer Response Help

Get expert guidance crafting your response to Bioinformatics reviewers.

Reference library

Compare Bioinformatics with the broader publishing context

This journal guide is the best starting point for Bioinformatics. The reference library covers the surrounding questions authors usually ask next: whether the package is ready, what drives desk rejection, how neighboring journals compare, and what the submission constraints look like across the field.

Open the reference library →

Checklist system / operational asset

Elite Submission Checklist

A flagship pre-submission checklist that turns journal-fit, desk-reject, and package-quality lessons into one operational final-pass audit.

Flagship report / decision support

Desk Rejection Report

A canonical desk-rejection report that organizes the most common editorial failure modes, what they look like, and how to prevent them.

Dataset / reference hub

Journal Intelligence Dataset

A canonical journal dataset that combines selectivity posture, review timing, submission requirements, and Manusights fit signals in one citeable reference asset.

Dataset / reference guide

Peer Review Timelines by Journal

Reference-grade journal timeline data that authors, labs, and writing centers can cite when discussing realistic review timing.