Agent skill

bio-sequence-similarity

Find homologous sequences using iterative BLAST (PSI-BLAST), profile HMMs (HMMER), and reciprocal best hit analysis. Use when identifying orthologs, distant homologs, or protein family members where standard BLAST is not sensitive enough.

majiayu000534★ · 1 repos on radarProfile →
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill sequence-similarity --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 7 KB
Bundled scripts: none
Path: skills/ai-ml/sequence-similarity/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Sequence Similarity Searches Advanced methods for finding homologous sequences beyond standard BLAST. ## PSI-BLAST (Position-Specific Iterated BLAST) Builds a position-specific scoring matrix (PSSM) through iterations to find distant homologs. ### Basic PSI-BLAST ```bash psiblast -query protein.fasta -db nr -out results.txt -num_iterations 3 ``` ### Save PSSM for Reuse ```bash psiblast -query protein.fasta -db nr \ -out results.txt \ -out_pssm pssm.asn \ -out_ascii_pssm pssm.txt \ -num_iterations 5 ``` ### Use Existing PSSM ```bash psiblast -in_pssm pssm.asn -db nr -out results.txt ``` ### Output Format ```bash psiblast -query protein.fasta -db nr \ -out results.txt \ -outfmt 6 \ -num_iterations 3 \ -inclusion_ethresh 0.001 ``` ### Key Parameters ```bash psiblast -query protein.fasta -db nr \ -num_iterations 5 \ -inclusion_ethresh 0.001 \ -evalue 0.01 \ -num_threads 8 \ -out results.txt ``` ### PSI-BLAST Parameters | Parameter | Default | Description | |-----------|---------|-------------| | -num_iterations | 1 | Number of iterations | | -inclusion_ethresh | 0.002 | E-value for PSSM inclusion | | -evalue | 10 | E-value threshold for reporting | | -num_threads | 1 | CPU threads |

What's inside
Steps it walks through
  1. PSI-BLAST (Position-Specific Iterated BLAST)
  2. Basic PSI-BLAST
  3. Save PSSM for Reuse
  4. Use Existing PSSM
  5. Output Format
  6. Key Parameters
  7. PSI-BLAST Parameters
  8. HMMER for Profile Searches
  9. Search with Single Sequence
  10. Build Profile from Alignment
  11. Search Database with Profile
  12. Download Pfam Profiles
  13. Scan Sequence Against Pfam
  14. Parse HMMER Output
Ships with 1 file
  • metadata.json
Commands it runs
psiblast -query protein.fasta -db nr -out results.txt -num_iterations 3
psiblast -query protein.fasta -db nr \
psiblast -in_pssm pssm.asn -db nr -out results.txt
jackhmmer -o results.txt -A aligned.sto --cpu 8 query.fasta database.fasta
hmmbuild profile.hmm alignment.sto
hmmsearch -o results.txt --tblout hits.tbl profile.hmm database.fasta
hmmsearch -o results.txt --domtblout domains.tbl profile.hmm database.fasta
wget https://ftp.ebi.ac.uk/pub/databases/Pfam/current_release/Pfam-A.hmm.gz
gunzip Pfam-A.hmm.gz
hmmpress Pfam-A.hmm
More from claude-skill-registry
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About this skill
What does the bio-sequence-similarity skill do?

Find homologous sequences using iterative BLAST (PSI-BLAST), profile HMMs (HMMER), and reciprocal best hit analysis. Use when identifying orthologs, distant homologs, or protein family members where standard BLAST is not sensitive enough.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill sequence-similarity --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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