bio-fasta-database-curator · diff
git:20260711.5ae6f6f to git:20260712.3675171
18 added, 1 removed. Audit A to A.
---
name: bio-fasta-database-curator
description: Curate and validate FASTA or FAA databases. Use when standardizing headers, merging references, deduplicating sequences, converting GenBank files, or preparing BLAST, MMseqs2, and HMM inputs.
---
# 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](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
```python
# 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
| Task | Action |
|------|--------|
| Inspect database | Count records, sample headers, check whitespace and length distribution before changing anything. |
| Standardize headers | Define deterministic transformation rules and preserve original-to-new ID mapping. |
| Merge or deduplicate | Decide whether duplicates are removed by ID, sequence, or both, then report what changed. |
| Validate output | Re-count records, verify FASTA syntax, and write database statistics. |
+ | Run the bundled curator | `uv run --no-project python scripts/curate_fasta.py input.fasta --output curated.fasta --prefix REF --deduplicate both` |
## 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:
```bash
# 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: Define Transformation Rules
Based on the source database, define rules:
```python
# Example transformation rules
rules = {
"header_separator": "|", # Use pipe as field separator
"prefix": "VP", # Add prefix to all sequences
"remove_whitespace": True, # Replace spaces with underscores
"fields_to_keep": ["accession", "description"],
"taxonomy_source": "description", # Extract taxonomy from description
}
```
### Step 3: Process Sequences
```python
from Bio import SeqIO
+ import hashlib
import re
def standardize_header(header: str, rules: dict) -> str:
"""Standardize a FASTA header according to rules."""
# Remove > prefix
header = header.lstrip(">")
# Split on common separators
parts = re.split(r'[\s\|]+', header, maxsplit=2)
# Apply prefix
if rules.get("prefix"):
parts[0] = f"{rules['prefix']}|{parts[0]}"
# Remove whitespace from all parts
if rules.get("remove_whitespace"):
parts = [p.replace(" ", "_") for p in parts]
# Rejoin with standard separator
sep = rules.get("header_separator", "|")
return sep.join(parts)
def process_database(input_path: str, output_path: str, rules: dict):
"""Process a FASTA database with standardization rules."""
with open(output_path, 'w') as out:
for record in SeqIO.parse(input_path, "fasta"):
new_id = standardize_header(record.description, rules)
out.write(f">{new_id}\n{str(record.seq)}\n")
```
### Step 4: Remove Duplicates
```python
def deduplicate_by_sequence(input_path: str, output_path: str):
"""Remove sequences with identical sequences, keeping first occurrence."""
seen_seqs = set()
with open(output_path, 'w') as out:
for record in SeqIO.parse(input_path, "fasta"):
- seq_hash = hash(str(record.seq).upper())
+ seq_hash = hashlib.sha256(str(record.seq).upper().encode("ascii")).hexdigest()
if seq_hash not in seen_seqs:
seen_seqs.add(seq_hash)
out.write(f">{record.description}\n{str(record.seq)}\n")
return len(seen_seqs)
def deduplicate_by_id(input_path: str, output_path: str):
"""Remove sequences with duplicate IDs, keeping first occurrence."""
seen_ids = set()
with open(output_path, 'w') as out:
for record in SeqIO.parse(input_path, "fasta"):
if record.id not in seen_ids:
seen_ids.add(record.id)
out.write(f">{record.description}\n{str(record.seq)}\n")
return len(seen_ids)
```
### Step 5: Merge Databases
```python
def merge_databases(input_paths: list, output_path: str,
deduplicate: bool = True):
"""Merge multiple FASTA files into one."""
all_records = []
seen_ids = set()
for path in input_paths:
for record in SeqIO.parse(path, "fasta"):
if deduplicate and record.id in seen_ids:
continue
seen_ids.add(record.id)
all_records.append(record)
with open(output_path, 'w') as out:
for record in all_records:
out.write(f">{record.description}\n{str(record.seq)}\n")
return len(all_records)
```
### Step 6: Generate Statistics
```python
def generate_stats(input_path: str) -> dict:
"""Generate comprehensive database statistics."""
from collections import Counter
stats = {
"total_sequences": 0,
"total_residues": 0,
"lengths": [],
"prefixes": Counter(),
"gc_content": [], # For nucleotide
}
for record in SeqIO.parse(input_path, "fasta"):
stats["total_sequences"] += 1
seq_len = len(record.seq)
stats["total_residues"] += seq_len
stats["lengths"].append(seq_len)
# Extract prefix
prefix = record.id.split("|")[0] if "|" in record.id else "none"
stats["prefixes"][prefix] += 1
# GC content for nucleotides
seq_upper = str(record.seq).upper()
if set(seq_upper) <= set("ATGCN"):
gc = (seq_upper.count("G") + seq_upper.count("C")) / len(seq_upper)
stats["gc_content"].append(gc)
# Calculate summary statistics
lengths = stats["lengths"]
+ if not lengths:
+ raise ValueError("database contains no FASTA records")
stats["min_length"] = min(lengths)
stats["max_length"] = max(lengths)
stats["mean_length"] = sum(lengths) / len(lengths)
stats["median_length"] = sorted(lengths)[len(lengths)//2]
return stats
def format_stats_report(stats: dict, db_name: str) -> str:
"""Format statistics as markdown report."""
report = f"""# Database Statistics: {db_name}
## Summary
- **Total sequences:** {stats['total_sequences']:,}
- **Total residues:** {stats['total_residues']:,}
## Sequence Lengths
- **Minimum:** {stats['min_length']:,}
- **Maximum:** {stats['max_length']:,}
- **Mean:** {stats['mean_length']:.1f}
- **Median:** {stats['median_length']:,}
## Prefix Distribution
| Prefix | Count | Percentage |
|--------|-------|------------|
"""
total = stats['total_sequences']
for prefix, count in stats['prefixes'].most_common():
pct = count / total * 100
report += f"| {prefix} | {count:,} | {pct:.1f}% |\n"
return report
```
### Step 7: Validate Database
```python
def validate_database(input_path: str) -> list:
"""Validate database and return list of issues."""
issues = []
for i, record in enumerate(SeqIO.parse(input_path, "fasta"), 1):
# Check for whitespace in header
if " " in record.id or "\t" in record.id:
issues.append(f"Line {i}: Whitespace in ID '{record.id}'")
# Check for empty sequences
if len(record.seq) == 0:
issues.append(f"Line {i}: Empty sequence for '{record.id}'")
# Check for invalid characters (protein)
valid_aa = set("ACDEFGHIKLMNPQRSTVWXY*-")
invalid = set(str(record.seq).upper()) - valid_aa
if invalid:
issues.append(f"Line {i}: Invalid characters {invalid} in '{record.id}'")
# Check header format
if "|" not in record.description:
issues.append(f"Line {i}: Non-standard header format for '{record.id}'")
return issues
```
## 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
```python
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
```bash
# Using awk
awk '/^>/ {if (seq) print seq; print; seq=""} !/^>/ {seq=seq$0} END {print seq}' multi.fasta > single.fasta
```
### Extract CDS from GenBank
```python
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
### 1. Always Backup Originals
```bash
cp original.fasta original.fasta.bak
```
### 2. Validate Before Processing
Check header formats and sequence content before bulk operations.
### 3. Use Consistent Prefixes
Define a taxonomy prefix scheme and stick to it:
- `VP_` for virophages
- `PLV_` for polinton-like viruses
- `NCLDV_` for NCLDVs
- `MIRUS_` for Mirus viruses
### 4. Document Transformations
Keep a log of all transformations applied to the database.
### 5. Generate Statistics After Processing
Always verify the output database matches expectations.
## Examples
+
+ ```bash
+ uv run --no-project python 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