Bio Fasta Database Curator

FASTA Database Curator

Overview

Automate the curation and standardization of biological sequence databases. This skill handles the tedious work of processing FASTA/FAA files, ensuring consistent header formats, removing duplicates, and preparing databases for downstream analysis.

Supplementary version-grounded tool notes: tools.md.

Key Capabilities:

• Header format standardization (pipe separators, prefixes)
• Duplicate detection and removal (by sequence or ID)
• Format conversion (GenBank → FASTA, multi-line → single-line)
• Database merging with conflict resolution
• Statistics generation (counts, lengths, taxonomy, GC content)
• Validation (no whitespace in headers, proper formatting)
• Taxonomy label extraction and standardization

When to Use This Skill

Use this skill when:

• User needs to standardize sequence headers
• User wants to merge multiple FASTA files
• User needs to remove duplicate sequences
• User is preparing a database for HMM/BLAST/MMseqs2
• User wants database statistics and quality metrics
• User needs to convert between sequence formats

Header Format Standards

Recommended Format

Use pipe-separated fields with consistent prefixes:

>PREFIX|ACCESSION|DESCRIPTION
SEQUENCE...

Examples:

>VP|Mavirus_MCP|Major capsid protein [Virophage]
>PLV|NC_021333_1|Polinton-like virus hypothetical protein
>NCLDV|YP_009173877.1|DNA polymerase [Marseilleviridae]

Common Transformations

# Remove whitespace from headers
old: ">VP_MCP Mavirus major capsid protein"
new: ">VP_MCP|Mavirus_major_capsid_protein"

# Add taxonomy prefix
old: ">NC_021333.1 hypothetical protein"
new: ">PLV|NC_021333.1|hypothetical_protein"

# Standardize separators
old: ">seq1 [organism=Virus] protein"
new: ">seq1|Virus|protein"

Quick Reference

Instructions

Use scripts/curate_fasta.py for routine FASTA curation. It parses raw headers before any library can truncate them, uses SHA-256 sequence digests, refuses empty inputs and existing outputs, and writes both a header mapping and a JSON deduplication report. Keep the snippets below for custom transformations only.

Step 1: Analyze Input Database

First, understand what you're working with:

# Count sequences
grep -c "^>" database.fasta

# Sample headers (first 20)
grep "^>" database.fasta | head -20

# Check for problematic characters
grep "^>" database.fasta | grep -E "[\t ]" | head -10

# Sequence length distribution
awk '/^>/ {if (seq) print length(seq); seq=""} !/^>/ {seq=seq$0} END {print length(seq)}' database.fasta | sort -n | uniq -c

Step 2: Curate with the Bundled Script

The script standardizes headers, merges multiple inputs, deduplicates, and writes the mapping and report in one pass:

uv run --no-project python scripts/curate_fasta.py \
  input1.fasta input2.fasta \
  --output curated.fasta \
  --mapping header_mapping.tsv \
  --report dedup_report.json \
  --prefix REF \
  --deduplicate both        # id | sequence | both

Write custom Biopython transformations only when a rule falls outside the script's flags, and keep the original-to-new ID mapping in that case too.

Step 3: Generate Statistics and Validate

Use SeqKit (versions and more commands in tools.md) for statistics, then verify parseability, alphabet, and the prefix distribution in one pass:

seqkit stats -a curated.fasta               # counts, length distribution, sequence type
seqkit grep -nrp " " curated.fasta | head   # must return nothing: no whitespace in headers
uv run --with biopython python3 - <<'EOF'   # parse end-to-end, flag invalid residues, count prefixes
from Bio import SeqIO
from collections import Counter
valid = set("ACDEFGHIKLMNPQRSTVWYXBZJUO*-")  # adjust to ACGTUN*- for nucleotide databases
prefixes, bad = Counter(), []
n = 0
for rec in SeqIO.parse("curated.fasta", "fasta"):
    n += 1
    prefixes[rec.id.split("|")[0] if "|" in rec.id else "none"] += 1
    extra = set(str(rec.seq).upper()) - valid
    if extra:
        bad.append((rec.id, "".join(sorted(extra))))
print(f"records: {n}")
print("prefix counts:", dict(prefixes))
print("invalid residues:", bad if bad else "none")
EOF

Input Requirements

• One or more FASTA, FAA, FNA, FFN, or GenBank files.
• Desired header convention, prefix policy, and duplicate-removal rule.
• Taxonomy labels or accession metadata when headers need biological grouping.
• Downstream tool constraints, such as BLAST, DIAMOND, MMseqs2, HMMER, or pyhmmer header behavior.

Output

• Curated FASTA/FAA database with stable identifiers.
• Header mapping table from original IDs to curated IDs.
• Deduplication report with retained and removed records.
• Summary statistics for record count, length distribution, sequence alphabet, prefix/taxonomy counts, and GC content when nucleotide sequences are used.
• Validation notes documenting any skipped transformations or unresolved IDs.

Quality Gates

• Every output header is unique and contains no whitespace.
• Original-to-curated ID mapping is written before destructive transformations.
• Duplicate policy is explicit: by ID, by sequence, or by both.
• FASTA parser can read the curated database end-to-end.
• Record counts before and after curation match the deduplication and filtering report.

Format Conversions

GenBank to FASTA

from Bio import SeqIO

def genbank_to_fasta(input_gb: str, output_fasta: str):
    """Convert GenBank format to FASTA."""
    records = SeqIO.parse(input_gb, "genbank")
    count = SeqIO.write(records, output_fasta, "fasta")
    return count

Multi-line to Single-line FASTA

seqkit seq -w 0 multi.fasta > single.fasta

Extract CDS from GenBank

def extract_cds_proteins(input_gb: str, output_faa: str):
    """Extract CDS translations from GenBank file."""
    with open(output_faa, 'w') as out:
        for record in SeqIO.parse(input_gb, "genbank"):
            for feature in record.features:
                if feature.type == "CDS":
                    if "translation" in feature.qualifiers:
                        protein = feature.qualifiers["translation"][0]
                        locus = feature.qualifiers.get("locus_tag", ["unknown"])[0]
                        product = feature.qualifiers.get("product", ["unknown"])[0]
                        out.write(f">{locus}|{product}\n{protein}\n")

Best Practices

• Define a taxonomy prefix scheme and stick to it (e.g. VP| virophages, PLV| polinton-like viruses, NCLDV| NCLDVs, MIRUS| Mirus viruses).
• Keep the header mapping and deduplication report with the database so every transformation stays auditable.
• Re-run statistics after processing and compare against the pre-curation counts.

Examples

uv run --script skills/bio-fasta-database-curator/scripts/curate_fasta.py \
  fixtures/mixed-headers.fasta \
  --output curated.fasta \
  --prefix REF \
  --deduplicate both

User: "Standardize the headers in virophage_raw.fasta and remove duplicates"

1. Analyze input:
   - 1,869 sequences
   - Headers have spaces and inconsistent formats
   - Some duplicate accessions

2. Define rules:
   - Add VP| prefix
   - Replace spaces with underscores
   - Use pipe separator

3. Process and deduplicate:
   - Standardized 1,869 headers
   - Removed 23 duplicates
   - Final: 1,846 unique sequences

4. Validate output:
   - No whitespace in headers OK
   - All sequences non-empty OK
   - Consistent format OK

5. Generate stats report

Troubleshooting

Whitespace in Headers

Problem: BLAST/MMseqs2 truncate at first whitespace Solution: Replace spaces with underscores or pipes

Duplicate IDs

Problem: Same accession from different sources Solution: Add source prefix to disambiguate

Invalid Characters

Problem: Non-standard amino acid codes Solution: Replace with X or remove sequences

Mixed Case Sequences

Problem: Inconsistent case in sequences Solution: Standardize to uppercase