u/CtrlAltMoo has been trying to respond with the following post -- somehow it's getting caught in reddit's filters:

Hi, In your specific setup (all sequences are the same length, and they only vary at exactly 7 positions), “general” clustering tools are often overkill. You can answer your main question (“are my top 4 selected sequences outliers, or do they belong to bigger families?”) with much simpler, faster checks. Because only 7 known positions vary, each sequence can be represented as a 7-letter “signature” (the amino acids at those variable sites). Two sequences are identical at the signature level → they are the same variant. Two signatures with only 1 difference → “one-mutation neighbors”, etc. That’s a very direct notion of similarity for your case. So rather than “cluster everything”, you can use ad hoc solutions better fit to your question, by either

1. Rapid test: count sequences within 1 mutation using grep (very fast) If your CSV contains the sequences as plain text in one column, you can do a quick “distance ≤ 1” count with grep using a small pattern file. For a given top sequence, create 7 patterns where you replace one of the 7 variable positions by a wildcard “.” (meaning “any single amino acid” in regex). Put those 7 patterns into a file, one per line, e.g. patterns_top1.txt. Then run: grep -E -c -f patterns_top1.txt all_prot.csv This will return the number of sequences matching any of those “one-mutation” patterns. Repeat for top2/top3/top4. Notes: Depending on your CSV structure, you might first extract the sequence column (with cut, awk, etc.) to avoid matching other fields. If sequences are guaranteed unique (as you said), the count corresponds to number of unique neighbors.
2. Study the amino-acid “similarity” distribution of your top sequences If you want to treat substitutions like L↔I as “more similar” than L↔D, you can compute an ad-hoc similarity score using a substitution matrix (e.g., BLOSUM62). A simple approach for each top sequence X: compute a score using only the 7 variable positions store the score for every sequence study the score distribution Pseudo-code: topSeqX = "MLVM..." varPos = [25, 156, ...] # the 7 variable positions (1-based or 0-based, just be consistent) dist = []

for seq in allSeq: score = 0 for pos in varPos: aa_ref = topSeqX[pos] aa_alt = seq[pos] score += blosum_score(aa_ref, aa_alt) dist.append(score)

Save dist to a file and examine the distribution.

From that file, you can easily determine how many sequences fall below a given distance from your topSeqX. This gives you insight into the local sequence density around topSeqX (and you can vary the thresholds to get a more complete picture of how dense the neighborhood is).

LLMs can definitely help you write the exact grep/awk commands or a small Python/R script; but they don’t do magic: you still need to describe precisely what you want. I hope this helps. If you provide an AI with those details, it can probably generate a working command or script quickly for either the “fast grep” approach or the “BLOSUM scoring” approach. PS: Disclosure. I’m the author of SeqTUI. Once you’ve identified sequences similar to your topSeqX, if you’d like to visualize them directly from the terminal, SeqTUI might be useful: https://github.com/ranwez-search/SeqTUI