Hi everyone,

I previously posted about an early MVP of a bioinspired genomic pipeline called E.C.O. — Entérico Codificador Orgánico.

The original version was focused mainly on reading FASTA files, validating DNA sequences, calculating GC/N content, and detecting simple regulatory motifs such as TATA box, CAAT box, GC box, polyA signals, and homopolymer runs using regex patterns.

The repository has evolved since then, so I would appreciate updated feedback from a bioinformatics perspective.

Repository: https://github.com/CristianGormaz/ECO-genoma-pipeline

Current scope

E.C.O. is now an educational, bioinformatics-oriented, experimental pipeline that treats genomic inputs as “information packages” processed through staged layers inspired by biological digestion and the enteric nervous system:

• ingestion: input FASTA / genomic-like data;
• validation: sequence quality and allowed symbols;
• filtering: ambiguous bases and invalid records;
• transformation: motif and feature extraction;
• absorption: accepted/usable signal;
• rejection/discard: invalid or unsafe input;
• feedback: internal reporting and regression checks;
• state routing: experimental internal state classification for pipeline behavior.

The project does not interpret patients, does not provide diagnosis, and does not make clinical claims. It is currently intended as an experimental and educational architecture for learning, testing, and documenting bioinspired data-processing ideas.

What is implemented now

At the current stage, the project includes:

• FASTA sequence parsing and validation;
• GC/N content calculation;
• basic motif scanning;
• regression tests;
• command-line validation scripts;
• reports for accepted/rejected/absorbed inputs;
• experimental S.N.E.-E.C.O. state routing;
• observability/reporting scripts;
• safety notes clarifying that this is not a clinical tool.

The latest validation suite is currently passing, and the project has moved beyond the first small motif-scanning module into a more structured pipeline architecture.

Feedback requested

I would appreciate feedback on these points:

1. Is the current first validation layer reasonable for an educational genomic pipeline?
2. Which additional sequence-level checks would be useful before moving toward promoter/enhancer testing?
3. What public datasets would be appropriate for testing promoter-like or regulatory regions in a non-clinical educational context?
4. Should GC content continue to ignore ambiguous N bases, or should both corrected and raw GC metrics be reported?
5. Which motifs or feature checks would be useful to add next without overclaiming biological interpretation?
6. How would you recommend structuring benchmark examples so the project remains reproducible and scientifically modest?
7. Are there existing lightweight tools or conventions I should compare against before extending the motif scanner?

Important limitation

This project is not intended to classify disease, diagnose biological samples, or infer clinical meaning. I am trying to keep the scope limited to reproducible educational bioinformatics, basic sequence processing, and transparent experimental reporting.

Any suggestions on validation, motif selection, public datasets, benchmarking, or project structure would be very helpful.

Thanks!