CLI bioinformatics tools
Run genomics and metagenomics workflows with aligners, assemblers, taxonomic profilers, gene predictors, format utilities, and related command-line pipelines.
Bioinformatics execution service
Defined analyses, run cleanly, documented clearly.
We execute clearly scoped bioinformatics and scientific data-processing workflows when local setup, many files, large datasets, or reproducible reporting become the bottleneck.
Scope firstFixed quote before work starts
ReproducibleTools, versions, and parameters noted
Data-awareSensitive data discussed before transfer
Best fit Defined tasks, standard tools, multi-file processing, report generation.
Not a fit Clinical diagnosis, open-ended research, or interpretation-only requests.
Workflows
Useful when the analysis is bigger than a browser tool.
Public tools are ideal for quick checks. Custom analysis is for full datasets, repeated runs, custom parameters, and deliverables you can hand to a collaborator.
Data quality control
Assess sequencing files, tabular outputs, sample metadata, and intermediate results with concise summaries and flags for files needing attention.
Result formatting
Turn tool outputs into clean tables, reports, summaries, figures, or structured files ready for downstream statistics, plotting, and sharing.
Sequence processing
Large FASTA/FASTQ filtering, splitting, conversion, ID extraction, summary metrics, and batch processing.
Scripting and automation
Run, adapt, and document shell, Python, and R scripts for batch processing, reproducible analysis steps, file conversion, and custom summaries.
Deliverables
Outputs that are useful after the run.
We focus on execution and structured results. When relevant, deliverables include the commands, versions, parameters, and notes needed to understand how the outputs were produced.
Result files and merged tables
Tool names, versions, and key parameters
Short notes on scope and limitations
Logs or summaries when useful for reproducibility
Process
A controlled path from request to results.
The first step is always scope. Do not send sensitive, clinical, personal, or regulated data before we agree on the task and transfer method.
Send task details01
Describe the task
Tell us the data type, number of files, approximate size, desired output, and tools or parameters if known.
02
Confirm scope and quote
We check feasibility, define deliverables, and agree on fixed pricing before files are transferred.
03
Run the workflow
Analyses are executed with documented tools, parameters, and data-handling expectations.
04
Return outputs
You receive the agreed result files, tables, reports, and execution notes.
Example requests
Concrete tasks work best.
Run Kraken2 and Bracken on 30 FASTQ files and return abundance tables.
Aggregate FastQC outputs and flag samples that need trimming or re-checking.
Filter large FASTA files by length and split accepted records into batches.
Merge tool reports into one CSV table with sample IDs as columns.
Start with scope
Have a defined analysis to run?
Send the task, input format, approximate size, number of files, and expected output.
