aeon
Aeon is a scikit-learn compatible Python toolkit for time series machine learning. It provides state-of-the-art algorithms for classificatio…
Maintainer FreedomIntelligence · Last updated April 1, 2026
Calculate alignment statistics including sequence identity, conservation scores, substitution matrices, and similarity metrics. Use when comparing alignment quality, measuring sequence divergence, and analyzing evolutionary patterns.
Original source
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-alignment-msa-statistics
Skill Snapshot
Source Doc
Goal: Load modules for alignment I/O, substitution scoring, and statistical calculations.
Approach: Import AlignIO for reading alignments, Counter for column analysis, numpy for matrix operations, and math for entropy calculations.
"Calculate percent identity" → Compute the fraction of identical aligned residues between sequence pairs.
Goal: Measure sequence similarity as percent identity for individual pairs or across all sequences in an alignment.
Approach: Count matching non-gap positions divided by total aligned positions; optionally compute a full N-by-N identity matrix.
Goal: Quantify per-column and overall alignment conservation to identify conserved and variable regions.
Approach: Calculate the fraction of the most common residue at each column, optionally ignoring gaps, and smooth with a sliding window.
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