How do you create efficient bioinformatics pipelines?

I am leading an aspect of my project where I would be building a bioinformatics pipeline from scratch. Major factors I am keeping in mind are the computational tools I would be utilizing, what integrated development environment (IDE), what the data input would be, what algorithms would fit, and what kind of output I am envisioning. I strongly believe these are key things to consider before setting out!

Start by clearly defining your objectives and data requirements. This clarity helps in designing a precise and purposeful pipeline. Choose the right tools and software optimized for bioinformatics. Tools like Galaxy for workflow management and Bioconductor for data analysis are popular. Focus on automating repetitive tasks with scripting languages like Python and statistical analysis with R. Ensure scalability to handle increasing data volumes efficiently. Regularly update and maintain your pipeline, incorporating the latest software and methodologies. Test thoroughly for accuracy and reliability. Document your pipeline well for reproducibility and future modifications.

In bioinformatics, there's a wide array of tools, each with its own strengths and weaknesses. You'll find general-purpose tools, great for various tasks, alongside specialized ones for niche data types or functions. some tools are straightforward, ideal for beginners, while others require advanced skills and dependencies. They also range from open-source, offering community support, to proprietary, which may have unique features but at a cost. When selecting, consider your needs, budget, and ease of use. For a robust approach, combine tools that are compatible and work well together. Trending tools include Python and R for data analysis, BLAST for sequence alignment, and Galaxy for workflow management.

To tackle the challenge of varied data formats in bioinformatics pipelines: Standardize data formats wherever possible. Consistency in formats reduces errors and streamlines processes. Use specific formats for different data types: FASTA or FASTQ for sequences, GFF or BED for annotations, VCF for variants, and CSV or TSV for tables. Employ converters or parsers to bridge format differences. This ensures compatibility between different tools and stages of your pipeline. Document your data formats meticulously. Clear documentation prevents confusion and aids in future data handling. Adhere to best practices in file naming and organization. This enhances clarity and facilitates easier data management.

You can use make to define rules that specify which files are computed from which other files and how. When running make, if any input files changed, the output files are recomputed. Make rules can be executed in parallel.

Logging is key for debugging. While print statements are helpful, there is always the risk of leaving them in production code. Good logging, such as keeping track of timestamps, modules, and as much state as possible is great for debugging before release and also for identifying future faults if requirements silently change.

If your pipeline involves downloading data from external sources that you have no control over (e.g. via REST HTTP APIs), you should handle these external interactions separately. Store the inputs and outputs of these interactions to ensure you can reproduce old results even when these services go down or change.