In short
- Free, open-source pipelines such as Bactopia and nf-core/bacass can take bacterial reads through QC, assembly and annotation reproducibly.
- Running them well needs a Linux environment, workflow software, up-to-date databases and time to troubleshoot.
- Outsourcing makes sense when you need interpretation and publication-ready results rather than output files, or when you have no bioinformatics support.
- Whichever route you take, record tool versions, database versions and parameters so the analysis can be reproduced.
The do-it-yourself options
Several open-source pipelines automate bacterial genome analysis. Bactopia, built with the Nextflow workflow system, prepares species-specific datasets, runs quality control and assembly, and offers add-on tools for pangenome analysis, average nucleotide identity, 16S extraction and taxonomic classification. Its authors demonstrated it on 1,664 public genomes, and it scales from a single genome to thousands [1].
nf-core is a community collection of peer-reviewed pipelines that follow shared standards for testing, documentation and portability [2]. It includes nf-core/bacass for bacterial genome assembly. nf-core pipelines run on Nextflow, which was designed to make computational workflows reproducible across laptops, clusters and the cloud [3]. Snakemake is a popular alternative workflow manager that focuses on analyses that are reproducible, adaptable and transparent [4].
What running a pipeline yourself involves
| Requirement | What it means in practice |
|---|---|
| Computing | A Linux workstation, an institutional cluster or cloud credits, with enough memory for assembly |
| Software | A workflow manager plus Docker, Singularity or Conda to install the tools |
| Databases | AMR, virulence and taxonomy databases that need downloading and regular updates |
| Skills | Comfort with the command line, configuration files and reading error logs |
| Interpretation | Pipelines produce files and tables, not conclusions. Someone still has to judge what the results mean |
When each route makes sense
Running a pipeline yourself makes sense when:
- you have, or want to build, in-house bioinformatics skills
- you sequence many genomes regularly and the setup effort pays off
- you need full control over every parameter for method development
Outsourcing makes sense when:
- you have a few genomes and a deadline, such as a thesis chapter, grant report or manuscript revision
- nobody in the lab has time to set up and maintain pipelines and databases
- you need interpretation, figures and a written report as well as raw outputs
- you want an independent check of an analysis you have already run
Questions to ask any analysis provider
- Which tools and versions do you use for each step?
- Which databases do you use, and when were they last updated?
- Which identity and coverage thresholds do you apply to gene hits?
- What happens if my data fail quality control?
- How is my data stored, kept confidential and deleted after the project?
- What is the turnaround, and what exactly will I receive?
- Will someone explain the results and answer questions afterwards?
How BacSeq Analytics works
BacSeq Analytics runs reproducible Snakemake pipelines built on established tools, including fastp, SPAdes, Prokka, RAST, FastANI, ResFinder, antiSMASH and dbCAN2. A highlight report is delivered within 72 hours, and comprehensive reports within 5–7 days. Every report states the methods used and includes scientific interpretation, not just output files. Compare the packages or upload your data to get started.
References
- Petit RA III, Read TD. Bactopia: a flexible pipeline for complete analysis of bacterial genomes. mSystems. 2020;5(4):e00190-20. doi:10.1128/mSystems.00190-20
- Ewels PA, Peltzer A, Fillinger S, et al. The nf-core framework for community-curated bioinformatics pipelines. Nat Biotechnol. 2020;38:276–278. doi:10.1038/s41587-020-0439-x
- Di Tommaso P, Chatzou M, Floden EW, et al. Nextflow enables reproducible computational workflows. Nat Biotechnol. 2017;35:316–319. doi:10.1038/nbt.3820
- Mölder F, Jablonski KP, Letcher B, et al. Sustainable data analysis with Snakemake. F1000Res. 2021;10:33. doi:10.12688/f1000research.29032.3