Biological Sequence Analysis

⚠️ TOP-OF-MIND RULE: Trimmomatic "reads completely discarded" = F + R + 2*D, summed across samples

When a question asks about Trimmomatic "reads completely discarded" / "reads thrown out" / "reads not in any output", do NOT report the Dropped field alone. Dropped counts PAIRS where both mates failed; each pair = 2 individual reads. Plus the Forward-only and Reverse-only buckets also discard one read per pair.

reads_discarded = sum over samples of (Forward_Only + Reverse_Only + 2 * Dropped)

❌ WRONG: sum(Dropped per sample) — typically reports ~thousands, GT is 100×+ higher

✅ RIGHT: sum(F + R + 2*D per sample)

Full counter table is in the FASTQ section below.

RULE ZERO — Check for pre-computed results FIRST

Before following any instruction below, scan the data folder for:

• *_executed.ipynb → read with tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}' and cite its cell outputs as the authoritative answer
• Pre-computed result files (CSV/TSV with names like *results*, *deseq*, *enrich*, *stats*, *_simplified.csv) → read directly and report the requested value
• Canonical analysis scripts (analysis.R, run_*.py, find_*.R, *.Rmd) → execute as-is and read the output

Only follow this skill's re-analysis recipe below if none of the above exist. Re-running from raw data produces different numbers than the published answer and is much slower (often 5-10× turn count).

Retrieve, annotate, and compare biological sequences from NCBI, Ensembl, and UniProt. Covers nucleotide search, sequence fetching, gene summaries, ortholog discovery, and protein sequence extraction.

FASTQ QC, Trimmomatic, and read alignment (when raw reads are present)

When the data folder has *.fastq files and the question involves Trimmomatic, BWA, samtools, FastQC, or coverage depth, this skill is the entry point — but the actual work is shell-level (no specific ToolUniverse data tool).

Trimmomatic PE — counting "completely discarded" reads

Trimmomatic PE classifies each input pair as:

• Both Surviving (B): R1 and R2 both pass → kept as paired
• Forward Only Surviving (F): R1 passes, R2 dropped → R1 kept as singleton, R2 fully discarded
• Reverse Only Surviving (R): R2 passes, R1 dropped → R2 kept as singleton, R1 fully discarded
• Dropped (D): both fail → BOTH R1 and R2 fully discarded

Counter selection — read the question carefully. CRITICAL: "READS completely discarded" ≠ "PAIRS dropped". The Trimmomatic Dropped count counts PAIRS (each = 2 individual reads). When the question asks about reads (not pairs), translate every counter to per-read terms:

Trimmomatic's stderr summary gives Input Read Pairs: N Both Surviving: B (b%) Forward Only Surviving: F (f%) Reverse Only Surviving: R (r%) Dropped: D (d%). Always sum across ALL input sample pairs (e.g., SRR1 + SRR2 + ...).

DO NOT report just D as "reads completely discarded" — that's pair count, not read count, and is off by ~100×. The "Forward Only" R2 mate IS discarded; the "Reverse Only" R1 mate IS discarded; "Dropped" pairs lose BOTH reads.

Coverage depth (samtools depth / mosdepth)

For "average coverage depth", run samtools depth -a alignment.bam | awk '{sum+=$3; n++} END {print sum/n}' — the -a flag includes positions with zero coverage (otherwise the average is biased upward). For per-chromosome coverage, group by $1.

When to Use

• "Get the mRNA sequence for BRCA1"
• "Search NCBI for E. coli K-12 complete genome"
• "Find orthologs of TP53 across species"
• "Fetch the protein sequence for UniProt P04637"
• "Get the CDS sequence for Ensembl transcript ENST00000269305"

Workflow

Input -> Phase 1: Gene ID resolution -> Phase 2: Nucleotide retrieval
      -> Phase 3: Protein sequences -> Phase 4: Orthologs -> Output

Phase 1: Gene Identification and Summary

NCBIGene_search: term (string REQUIRED, format "TP53[Symbol] AND Homo sapiens[Organism]"), retmax (int, default 10). Returns {status, data: {esearchresult: {idlist: ["7157"]}}}.

NCBIGene_get_summary: id (string REQUIRED, e.g., "7157"). Returns {status, data: {result: {"7157": {name, description, summary, chromosome, maplocation, genomicinfo, mim}}}}. Result is keyed by gene ID string.

NCBIDatasets_get_gene_by_symbol: symbol (string REQUIRED, e.g., "BRCA1"), taxon (string, e.g., "human"). Returns gene ID, description, location, cross-references.

NCBIDatasets_get_gene: gene_id (string REQUIRED, e.g., "7157"). Returns comprehensive gene info.

Phase 2: Nucleotide Sequence Search and Retrieval

NCBI_search_nucleotide: query (free-form), organism (string), gene (string), strain (string), keywords (string), seq_type ("complete_genome"/"mRNA"/"refseq"), limit (int, default 20). Returns {status, data: {uids: [...], accessions: [...]}}.

NCBI_fetch_accessions: uids (array REQUIRED, e.g., ["545778205"]). Returns {status, data: ["U00096.3"], count: 1}.

NCBI_get_sequence: accession (string REQUIRED, e.g., "NM_007294"), format ("fasta"/"gb"/"embl"). Returns {status, data: "FASTA string...", accession, format, length}.

EnsemblSeq_get_region_sequence: region (string REQUIRED, "chr:start-end", e.g., "17:7668421-7668520"), species (default "homo_sapiens"). Returns {status, data: {sequence, sequence_length}}.

ensembl_get_sequence: id (string REQUIRED, Ensembl ID), type ("genomic"/"cds"/"cdna"/"protein"), multiple_sequences (bool). Returns sequence data.

Gotchas:

• NCBI_search_nucleotide returns UIDs, not accessions. Use NCBI_fetch_accessions to convert.
• NCBI_fetch_accessions requires uids (NOT accessions).
• ensembl_get_sequence with gene ID (ENSG) + type != "genomic" requires multiple_sequences=true. Use transcript IDs (ENST) for specific sequences.

Recipe: Get mRNA for a human gene

1. NCBI_search_nucleotide(organism="Homo sapiens", gene="BRCA1", seq_type="mRNA", limit=5)
2. NCBI_fetch_accessions(uids=[first_uid]) -> accession
3. NCBI_get_sequence(accession="NM_007294", format="fasta")

Phase 3: Protein Sequence Retrieval

UniProt_get_sequence_by_accession: accession (string REQUIRED, e.g., "P04637"). Returns {result: "MEEPQSDP..."}. Note: response key is result, NOT data.

EnsemblSeq_get_id_sequence: ensembl_id (string REQUIRED, e.g., "ENSP00000269305"), type ("protein"/"cdna"/"cds"). Returns {status, data: {ensembl_id, molecule, sequence, sequence_length}}.

UniProt_get_entry_by_accession: accession (string REQUIRED). Full protein annotation.

Gotchas:

• UniProt_get_sequence_by_accession returns {result: "..."}, not {status, data}.
• For Ensembl protein seqs, use ENSP IDs. For cDNA/CDS, use ENST IDs.
• To find UniProt accession from gene: use NCBIDatasets_get_gene_by_symbol (has cross-refs).

Phase 4: Ortholog and Comparative Analysis

NCBIDatasets_get_orthologs: gene_id (string REQUIRED, NCBI Gene ID e.g., "7157"), page_size (int, default 20, max 100). Returns {status, data: [{gene_id, symbol, description, taxname, common_name, chromosomes}]}.

NCBIProtein_get_summary: id (string REQUIRED, GI number or accession). Returns protein title, organism, length.

Gotcha: NCBIDatasets_get_orthologs requires NCBI Gene ID (numeric string), not gene symbol or Ensembl ID. Resolve via Phase 1 first.

Recipe: Compare orthologs

1. NCBIGene_search(term="TP53[Symbol] AND Homo sapiens[Organism]") -> "7157"
2. NCBIDatasets_get_orthologs(gene_id="7157", page_size=10) -> mouse Trp53, rat Tp53, etc.

Phase 5: Domain Architecture and Homology

InterPro_get_entries_for_protein: accession (UniProt ID). Returns InterPro domain/family/superfamily entries with positions.

Pfam_get_protein_annotations: accession (UniProt ID). Returns Pfam domain hits with exact residue coordinates and E-values.

BLAST_protein_search: sequence (amino acid string), database (default "swissprot"), limit. Returns homologs with alignment scores, identity, E-values.

EnsemblCompara_get_orthologues: gene (gene symbol, e.g., "CFTR"), species (e.g., "human"). User-friendly alternative to NCBIDatasets_get_orthologs — accepts gene symbols directly.

Phase 6: Variant and Clinical Context

EnsemblVEP_annotate_hgvs: hgvs_notation (e.g., "NM_000492.4:c.1521_1523del"). Returns consequence, protein impact, genomic coordinates.

ClinVar_search_variants: gene (gene symbol). Returns variant count and IDs for clinical significance lookup.

PubMed_search_articles: query, limit. Literature context for gene/variant findings.

Tool Parameter Quick Reference

Fallbacks

• Gene not found -> try NCBIDatasets_get_gene_by_symbol with explicit taxon
• No accessions from search -> broaden query (remove strain/seq_type filters)
• Ensembl error for gene+CDS -> use transcript ID (ENST) or set multiple_sequences=true
• UniProt accession unknown -> NCBIDatasets_get_gene or UniProt_search for cross-refs
• Ortholog search empty -> verify gene_id is numeric NCBI Gene ID

Sequence Analysis Reasoning (CRITICAL)

LOOK UP DON'T GUESS -- always fetch sequences, coordinates, and domain boundaries from databases. Do not reconstruct them from memory.

When to Use Which Tool

Reading Frame Selection Strategy

When translating a DNA sequence to protein:

1. Do NOT guess the reading frame -- preferred: use DNA_translate_reading_frames tool; fallback: translate_dna.py which tries all 3 frames automatically
2. The correct frame is the one with the LONGEST open reading frame (no premature stops)
3. If the sequence starts with ATG, frame 1 is likely correct -- but verify
4. If all 3 frames have early stop codons, the sequence may be: (a) non-coding, (b) reversed, or (c) contains sequencing errors. Try reverse complement first.

Protein Domain Interpretation

When asked about protein function or structure:

1. Get domain architecture first: InterPro_get_entries_for_protein returns all annotated domains with positions
2. Domain families indicate function: Kinase domain = phosphorylation activity; SH2 domain = phosphotyrosine binding; zinc finger = DNA binding
3. Variants in conserved domains are more likely pathogenic than those in linker regions
4. LOOK UP domain boundaries from the database -- do not estimate positions from memory

Reasoning for Protein Feature Questions

When asked "how many X residues in region Y of protein Z":

1. Identify the correct protein — Gene names are ambiguous. GABAA has many subunits (GABRA1, GABRB2, GABRR1...). Read the question carefully for the specific subunit. Use proteins_api_search with gene name + "human" to find the right accession.

2. Find the region boundaries — Use proteins_api_get_features with the accession to get annotated domains (TRANSMEM, DOMAIN, REGION). Don't guess positions — get them from the database.

3. Count residues in the region — Fetch the sequence, extract the region, count. WRITE Python code for this — don't try to count manually.

   • Residue Counting Strategy: python3 skills/tooluniverse-sequence-analysis/scripts/sequence_tools.py --type count_region --accession P24046 --start 318 --end 440 --residue C
   • For residue counting questions, ALWAYS use the script or sequence[start:end].count('C'). Do NOT estimate or count from memory.

4. Account for multimers — READ THE QUESTION for "homomeric", "pentamer", "tetramer", "dimer". If the question asks about a homomeric receptor (e.g., "homomeric GABAAρ1"), every subunit is identical. Count the residues in ONE subunit, then multiply:

   • Homomeric pentamer (most ligand-gated ion channels like GABAA ρ1): × 5
   • Homotetramer (many ion channels): × 4
   • Homodimer: × 2 If the question says "in the TM3-TM4 linker domains" (plural), it means across all subunits in the complex.

Bundled Computation Scripts

Never manually count residues, compute GC%, or write reverse-complement logic inline. Run these scripts instead — they are tested and handle edge cases.

biology_facts.py — Biology reference lookup

Script: skills/tooluniverse-sequence-analysis/scripts/biology_facts.py

Use this script to look up commonly-confused biology facts instead of relying on memory. It covers receptor types, ion channel stoichiometry, neurotransmitters, immune cell markers, and gene naming confusions.

python3 skills/tooluniverse-sequence-analysis/scripts/biology_facts.py --type receptor --name "GABAA"
python3 skills/tooluniverse-sequence-analysis/scripts/biology_facts.py --type ion_channel --name "NMDA"
python3 skills/tooluniverse-sequence-analysis/scripts/biology_facts.py --type gene_confusion --name "GABRA1"
python3 skills/tooluniverse-sequence-analysis/scripts/biology_facts.py --type receptor  # list all entries

Types: receptor (stoichiometry, pharmacology), ion_channel (subunit arrangement), neurotransmitter (synthesis, receptors), immune_cell (markers, lineage), gene_confusion (commonly mixed-up genes like GABRA1 vs GABRR1).

Mandatory use: any question about receptor type/stoichiometry, immune cell markers, or gene name disambiguation.

amino_acids.py — Codon table, amino acid properties, wobble pairing

Script: skills/tooluniverse-sequence-analysis/scripts/amino_acids.py

Use this script for any question about the genetic code, codon degeneracy, amino acid chemistry, codon usage bias, or tRNA wobble pairing. All outputs are JSON.

python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type codon_table
python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type amino_acid --name "Cysteine"
python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type amino_acid --code C
python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type amino_acid --code TRP
python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type amino_acid            # list all 20
python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type count_codons --sequence "ATGCCCAAATTT..."
python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type wobble --anticodon "GAU"
python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type wobble --anticodon "IAU"

Modes:

When to use (mandatory):

• Any question about how many codons encode a given amino acid (degeneracy)
• Any question about rare vs. common codons for protein expression optimisation
• Any question about tRNA anticodon recognition / wobble base pairing
• Any question about amino acid physical-chemical properties (MW, pKa, hydrophobicity, polarity, charge)
• Any question about the names of stop codons (Amber/Ochre/Opal)
• Before manually stating codon degeneracy — verify with codon_table

Wobble rules: I pairs U/C/A (3 codons); G pairs U/C; U pairs A/G; C pairs G only; A pairs U only (rare). Use --type wobble --anticodon "GAU" to verify.

Amino acid lookup: accepts full name (--name "Cysteine"), 1-letter (--code C), or 3-letter (--code CYS).

Codon-Anticodon Matching Reasoning (CRITICAL for tRNA problems)

When solving "which codons does this tRNA recognize" or "which tRNA reads this codon":

1. Anticodon is written 3'->5' but conventionally listed 5'->3'. The FIRST position of the anticodon (5' end) is the WOBBLE position and pairs with the THIRD position of the codon (3' end).
2. Anticodon-codon pairing is ANTIPARALLEL: anticodon 5'-X-Y-Z-3' pairs with codon 3'-X'-Y'-Z'-5' (i.e., codon 5'-Z'-Y'-X'-3').
3. Wobble position rules (anticodon 5' base -> codon 3' base it can pair with):
   • C -> G only (1 codon)
   • A -> U only (1 codon; rare in bacteria, common in mitochondria)
   • U -> A or G (2 codons)
   • G -> C or U (2 codons)
   • I (inosine, deaminated A) -> U, C, or A (3 codons)
4. Minimum tRNA set: Because I reads 3 bases and G/U each read 2, a 4-codon family (e.g., GCN = Ala) needs only 2 tRNAs: one with I at wobble position (reads 3 of 4 codons) and one with C or U at wobble (reads the remaining 1-2).
5. ALWAYS use the script: python3 skills/tooluniverse-sequence-analysis/scripts/amino_acids.py --type wobble --anticodon "IAU" to verify rather than reasoning from memory.

translate_dna.py — DNA to protein translation

Preferred: use DNA_translate_reading_frames tool (via MCP/SDK) with sequence parameter. Fallback: run translate_dna.py directly.

python3 skills/tooluniverse-sequence-analysis/scripts/translate_dna.py "ATGCCC..."

Tries all 3 reading frames, picks longest ORF automatically.

sequence_tools.py — Residue counting, GC content, reverse complement, stats

Script: skills/tooluniverse-sequence-analysis/scripts/sequence_tools.py

Preferred: Use ToolUniverse tools (via MCP/SDK) instead of the script:

• Sequence_count_residues tool -- Count residues in a sequence or region. Fallback: sequence_tools.py --type count_residues or --type count_region
• Sequence_gc_content tool -- GC% of DNA. Fallback: sequence_tools.py --type gc_content
• Sequence_reverse_complement tool -- DNA reverse complement. Fallback: sequence_tools.py --type reverse_complement
• Sequence_stats tool -- Auto-detect type, length, MW. Fallback: sequence_tools.py --type stats

Fallback script modes (use --type):

• count_residues: Count residue in full sequence. --sequence "ACDE..." --residue C
• count_region: Count in region (1-based inclusive). --sequence "MAC..." --start 5 --end 20 --residue C OR --accession P24046 --start 318 --end 440 --residue C (fetches from UniProt live)
• gc_content: GC% of DNA. --sequence "ATGCGATCG"
• reverse_complement: DNA reverse complement. --sequence "ATGCGATCG"
• stats: Auto-detect DNA/RNA/Protein, compute length, MW for protein. --sequence "ATGCG..."

ALWAYS use count_region --accession when the user gives a UniProt accession + region -- do not count manually.

Interpretation Framework

Sequence Quality Assessment

Synthesis Questions

1. Is this the correct sequence? (verify organism, gene symbol, isoform)
2. Is it the canonical isoform? (RefSeq MANE Select or UniProt canonical)
3. How well-annotated is it? (SwissProt > TrEMBL > GenBank predicted)
4. Are there known variants? (ClinVar pathogenic variants in this sequence)

Answer Formatting (CRITICAL)

TRIM YOUR ANSWER: If the question asks "what protein", answer with JUST the protein name. Do not add parenthetical abbreviations, descriptions, or qualifications. Example: answer "Glucose-6-phosphate 1-dehydrogenase", NOT "Glucose-6-phosphate 1-dehydrogenase (G6PD, EC 1.1.1.49)". When identifying a protein from a sequence, use BLAST/UniProt and report the top hit name exactly as it appears in the database — no embellishment.

Peptide & Foldamer Structure

• Alpha-peptide helices: alpha-helix (3.6 res/turn, i->i+4 H-bonds), 3_10-helix (3 res/turn, i->i+3), pi-helix (4.4 res/turn, i->i+5).
• Beta-peptide helices: named by H-bond ring size. 14-helix (i->i+2, 14-membered rings), 12-helix, 10-helix, 8-helix.
• Beta-amino acid ring size determines helix type: 4-membered cyclic constraint -> 10-helix; 5-membered (e.g., ACPC) -> 12-helix; 6-membered (e.g., ACHC) -> 14-helix. Acyclic beta3-residues default to 14-helix.
• Mixed alpha/beta foldamers (1:1 alternation): form 11-helix (i->i+3, 11-atom rings) or 14/15-helix (i->i+4, alternating 14- and 15-atom rings). Longer sequences prefer the 14/15-helix.
• Key rule: the number in the helix name = number of atoms in the hydrogen-bonded ring.
• Cyclic beta-amino acids (ACPC, ACHC) constrain backbone torsion angles, favoring specific helix types over acyclic residues.

Limitations

• ensembl_get_sequence gene IDs + non-genomic type need multiple_sequences=true
• NCBIDatasets_get_orthologs requires NCBI Gene ID (not symbol); UniProt returns canonical isoform only