pySCA

5-30 minutesPaperCodeProduction Ready

Statistical coupling analysis of protein sequence alignments

Readiness

Readiness: Production Ready
Smoke: passed_real_slurm_runner
Artifacts: verified
Production: production_pass_pysca_process_core
Storage: <5GB
Strategy: shared python312_slim.sif + palm_py312_site + staged pySCA source
Next: Production Ready for scoped pySCA MSA preprocessing and core SCA matrix workflow. Optional hardening: expose uploaded MSA controls and larger curated family benchmarks.

SOP main-thread triage cloned upstream ranganathanlab/pySCA revision d848ee4d1d7c5270a87e0fdc7362b7016b3dbe02 at /media/nik/seagate_nik/bio_server/tools/pysca/source; source about 32M. pySCA is a small Python SCA toolkit with scripts sca-process-msa and sca-core; dependencies include biopython, numpy, scipy, matplotlib. No Slurm benchmark was run in this batch because a validated pySCA env and small alignment workflow were not yet prepared. 2026-07-09T00:56+08:00 SOP production validation: promoted from Production Blocked to Production Ready. Applied a minimal backend compatibility patch to staged ranganathanlab/pySCA source d848ee4d so scaProcessMSA_py3_big.py initializes headers when filterSeq keeps the reference sequence normally. Validated through Ubuntu runner API job 3b9f3afc904a / Slurm job 578 using shared python312_slim.sif plus palm_py312_site dependencies. The benchmark ran sca-process-msa and sca-core on a 20-sequence, 34-position alignment with refindex 0, permissive sequence filtering, and 2 randomization trials. Run completed in 3.903 s and produced pysca_alignment.db.gz with Csca shape 34x34 and simMat shape 20x20. Artifacts verified: summary.json, results.csv, alignment.fasta, alignment_processed.fasta, pysca_alignment.db.gz, pysca process/core stdout/stderr logs, slurm.out, slurm.err.