The methods section is the most frequently criticized part of a scientific manuscript. Reviewers complain it’s incomplete. Editors flag it for missing details. Other scientists try to reproduce your work and can’t. And yet, most researchers spend proportionally less time on methods than on any other part of the paper — because it feels like the part that “just describes what you did.”
That framing is the problem. A well-written methods section is not a lab notebook entry translated into passive voice. It’s a precise, reproducible protocol that a trained scientist in your field could follow without contacting you. Getting it right serves everyone: it protects your work from reproducibility criticism, makes peer review go faster, and — practically — keeps your paper out of the revise-and-resubmit cycle for missing details.
This guide covers the methods section for life science manuscripts broadly: wet lab experiments (cell biology, immunology, biochemistry, molecular biology) and computational analyses (bioinformatics, genomics, statistics). The principles are the same; the specific details differ.
What a Methods Section Must Do
Before getting into structure, understand the job. A methods section must:
- Enable reproduction. Someone with appropriate expertise should be able to replicate your experiment using your methods section alone, without contacting you or consulting supplementary materials for critical steps.
- Justify your analytical choices. Why did you use a particular statistical test? Why that sample size? Methods isn’t just what you did — it’s implicitly why those choices were appropriate.
- Provide enough detail for critique. Reviewers need to evaluate whether your methods were sound. Vague descriptions prevent them from assessing this — and vague descriptions that make it through review invite post-publication criticism.
General Structure
Most journals use a variant of this structure:
- Study design (or experimental design overview)
- Materials (reagents, antibodies, cell lines, software, datasets)
- Experimental procedures (the actual protocols)
- Data analysis and statistics
- Ethical approvals (for human subjects, animal work)
Some journals separate materials into a table or supplementary section. Check the author guidelines before drafting.
Writing Wet Lab Methods
Cell lines and culture conditions
State the cell line name, source (ATCC, DSMZ, a collaborator’s lab), passage range used, and culture conditions. Don’t write “HeLa cells were cultured in DMEM.” Write:
HeLa cells (ATCC CCL-2) were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM; Gibco, #11965092) supplemented with 10% heat-inactivated fetal bovine serum (FBS; Sigma-Aldrich, F2442) and 1% penicillin-streptomycin (Gibco, #15140122) at 37°C with 5% CO2. Cells were used between passages 5 and 20. Mycoplasma testing was performed monthly using a PCR-based detection kit (LookOut Mycoplasma PCR Detection Kit, Sigma-Aldrich).
The catalog numbers matter. Reagents change formulations. Reviewers and readers need to know exactly what you used.
Antibodies
Antibodies deserve their own table in many manuscripts — especially immunology papers with large panels. At minimum, specify: target antigen, clone name, host species, manufacturer, catalog number, and dilution used. Don’t say “anti-CD3 antibody” — say “anti-human CD3 (clone OKT3, BioLegend, #317301, 1:200).” Different anti-CD3 clones behave differently. This is not pedantry; it’s the difference between a reproducible and non-reproducible experiment.
Protocols and procedures
Write procedures in chronological order at the level of detail a graduate student with general lab skills could follow. Some checkpoints:
- Incubation times AND temperatures (don’t omit either)
- Centrifugation speed AND duration AND temperature (room temp vs. 4°C often matters)
- Buffer compositions (if you made it yourself, give the recipe; if commercial, catalog number)
- Number of washes and wash volume
- Instrument settings that affect results (laser power, detector gain, temperature programs)
A common failure is writing procedures at the level of a methods section from a 1990s paper, where space constraints forced brevity. Modern online-first journals have supplementary sections — there is no excuse for omitting critical details. If the main methods section has a word limit, move detailed protocols to a supplementary methods section but make sure they are there.
Controls
Positive and negative controls should be mentioned explicitly. “Western blot was performed using standard protocols” is not acceptable if the standard protocol varies meaningfully across labs. The controls you included should be named.
Sample sizes and replication
State explicitly: how many biological replicates, how many technical replicates, and what “n” refers to. “n = 3” means three things at least: three independent experiments, three wells on the same plate, or three mice. Make clear which. Reviewers will ask if you don’t.
Writing Computational and Bioinformatics Methods
Computational methods have a reproducibility problem that, if anything, is worse than wet lab methods. The specific challenge is that software versions, reference genome versions, and parameter choices all affect results — and papers frequently omit all three.
Software and versions
Every tool must be cited with its version number. Not “reads were aligned using STAR” but “reads were aligned using STAR v2.7.11 (Dobin et al., 2013, Bioinformatics) with default parameters.” This matters because STAR’s default parameters and behavior have changed across major versions.
Provide the version for: alignment tools, quantification tools, downstream analysis packages (DESeq2, Seurat, etc.), programming language (R 4.3.1, Python 3.11.2), and any key packages with non-obvious version sensitivity.
Reference genomes and annotation
State the genome assembly and gene annotation version. “Reads were aligned to the human genome” is incomplete. “Reads were aligned to GRCh38/hg38 (Ensembl release 109) using…” is correct. Reference genome choice and annotation release affect quantification results. This information must be in the methods section, not buried in a supplementary table.
Parameters
For most tools, “default parameters” is acceptable when that’s true — but only when genuinely true. If you changed any parameter from default, state what you changed and why. Common areas where researchers forget to report deviations: minimum cell counts for single-cell filtering, fold-change and FDR cutoffs for differential expression, the number of variable features used for PCA, clustering resolution parameters.
Statistical analysis
The statistics section is where methods sections most commonly fail peer review. Required elements:
- Statistical tests used, with justification if non-obvious (why a Mann-Whitney U instead of a t-test? say so)
- Normality assumptions — were they tested? How?
- Multiple testing correction method (Benjamini-Hochberg, Bonferroni, etc.) and threshold used
- What “n” represents (biological replicates, patients, cells — be explicit)
- What error bars represent (SEM, SD, 95% CI — always label in figure legends and define in methods)
- What software was used for statistics and visualization (R, GraphPad Prism with version, Python/scipy)
A sentence like “Statistical analysis was performed using GraphPad Prism” is not a statistical methods section. Say what tests were run. Say what threshold was used for significance. If you used p < 0.05 as your threshold, say so. If you corrected for multiple comparisons, say how.
Tense, Voice, and Style
Methods are written in past tense (you did these things; they happened in the past). They’re typically written in passive voice, though many journals now allow active voice and it can actually improve clarity: “We aligned reads to GRCh38 using STAR v2.7.11” reads more cleanly than “Reads were aligned to GRCh38 using STAR v2.7.11.”
Check your target journal’s style guide on this. Nature journals prefer third person. Some PLOS journals allow first person. Use what the journal allows, but be consistent throughout.
What Reviewers Check (and What Gets You Rejected)
Based on common review criticisms, these are the most frequent methods section failures:
Missing reagent details. “Anti-GAPDH antibody” without catalog number, clone, or dilution. Every antibody needs this information.
Unclear sample sizes. “n = 5” without clarifying whether that’s five independent experiments, five mice, or five wells. This alone will generate a reviewer comment.
No statistical justification. Running a t-test on data that violates normality assumptions without acknowledging it. Using parametric tests without stating that assumptions were checked.
Outdated or missing software versions. “Analyzed with DESeq2” without a version number. This is especially critical for papers that reviewers may need to assess for methods robustness.
Reproducibility gaps. Anything where the reader would need to contact you to reproduce the experiment is a gap. Go through your own methods and ask: “Could I do this without asking anyone?” Be honest.
A Practical Checklist
Before submitting, run through this for every methods section:
For wet lab procedures:
- Cell lines: source, passage range, culture conditions, mycoplasma testing
- All antibodies: clone, host, manufacturer, catalog number, dilution
- All reagents: manufacturer and catalog number for anything non-standard
- Centrifugation: speed, time, temperature
- Incubations: time and temperature
- Controls named explicitly
- Biological vs. technical replicates defined
For computational methods:
- Software versions for every tool
- Reference genome assembly and annotation release
- Parameters that deviate from defaults named and justified
- Statistical tests named with justification
- Multiple testing correction method named
- What “n” represents defined clearly
- Error bars defined
Next Steps
A strong methods section doesn’t happen on the first draft. Write it as you go — update it with exact details while the experiment is still running, before memory compresses what you actually did into what you think you did. The version in your lab notebook entry, with catalog numbers still in front of you, is far more accurate than the version written six months after the experiment.
For the broader picture of how a methods section fits into a complete manuscript, read our guide to writing a scientific manuscript from scratch — it covers structure, argument flow, and journal selection in addition to individual sections.
Two books that cover methods writing in more depth than any course can: The Scientist’s Guide to Writing by Stephen Heard has a strong chapter specifically on methods sections and is the most readable book-length treatment of scientific writing available. Scientific Writing and Communication by Angelika Hofmann takes a more comprehensive textbook approach and covers methods in the context of the full manuscript.