Format conversion
Skill BioTender-max/awesome-bio-agent-skills/skills/bioskills/format-conversion
Convert between sequence file formats (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO. Use when changing file formats or preparing data for different tools.From its SKILL.md
npx -y skills add BioTender-max/awesome-bio-agent-skills --skill format-conversionAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
SKILL.md
5.8 KB, ~1.4k tokens by cl100k_base, as published. Nobody here has run it
Version Compatibility
Reference examples tested with: BioPython 1.83+, samtools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Format Conversion
"Convert this file to a different format" → Read records in one format, optionally add missing annotations, and write in the target format.
- Python:
SeqIO.convert()for direct conversion, orSeqIO.parse()+SeqIO.write()when modifications are needed (BioPython) - CLI:
seqkit seq(SeqKit) for FASTA/FASTQ;samtools viewfor SAM/BAM/CRAM
Convert sequence files between formats using Biopython's Bio.SeqIO module.
Required Import
from Bio import SeqIO
Core Function
SeqIO.convert() - Direct Conversion
Convert between formats in a single call. Most efficient method.
count = SeqIO.convert('input.gb', 'genbank', 'output.fasta', 'fasta')
print(f'Converted {count} records')
Parameters:
in_file- Input filename or handlein_format- Input format stringout_file- Output filename or handleout_format- Output format string
Returns: Number of records converted
Common Conversions
| From | To | Notes |
|---|---|---|
| GenBank | FASTA | Loses annotations, keeps sequence |
| FASTA | GenBank | Need to add molecule_type |
| FASTQ | FASTA | Loses quality scores |
| FASTA | FASTQ | Need to add quality scores |
| GenBank | EMBL | Usually works directly |
| Stockholm | FASTA | Alignment to sequences |
Code Patterns
Simple Conversion
SeqIO.convert('input.gb', 'genbank', 'output.fasta', 'fasta')
GenBank to FASTA
SeqIO.convert('sequence.gb', 'genbank', 'sequence.fasta', 'fasta')
FASTQ to FASTA (drop quality)
SeqIO.convert('reads.fastq', 'fastq', 'reads.fasta', 'fasta')
FASTA to GenBank (requires molecule_type)
Goal: Convert FASTA to GenBank format, which requires molecule_type annotation.
Approach: Stream records through a generator that injects the missing annotation, then write.
Reference (BioPython 1.83+):
records = SeqIO.parse('input.fasta', 'fasta')
def add_molecule_type(records):
for record in records:
record.annotations['molecule_type'] = 'DNA'
yield record
SeqIO.write(add_molecule_type(records), 'output.gb', 'genbank')
FASTA to FASTQ (add dummy quality)
Goal: Convert FASTA to FASTQ by assigning uniform placeholder quality scores.
Approach: Stream records through a generator that adds phred_quality to each, then write as FASTQ.
Reference (BioPython 1.83+):
def add_quality(records, quality=30):
for record in records:
record.letter_annotations['phred_quality'] = [quality] * len(record.seq)
yield record
records = SeqIO.parse('input.fasta', 'fasta')
SeqIO.write(add_quality(records), 'output.fastq', 'fastq')
Batch Convert Multiple Files
Goal: Convert all files of one format in a directory to another format.
Approach: Glob for input files, apply SeqIO.convert() to each, and report per-file counts.
Reference (BioPython 1.83+):
from pathlib import Path
for gb_file in Path('.').glob('*.gb'):
fasta_file = gb_file.with_suffix('.fasta')
count = SeqIO.convert(str(gb_file), 'genbank', str(fasta_file), 'fasta')
print(f'{gb_file.name}: {count} records')
Convert with Modifications
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
def uppercase_record(rec):
return SeqRecord(rec.seq.upper(), id=rec.id, description=rec.description)
records = SeqIO.parse('input.fasta', 'fasta')
modified = (uppercase_record(rec) for rec in records)
SeqIO.write(modified, 'output.fasta', 'fasta')
Alignment Format Conversion
from Bio import AlignIO
AlignIO.convert('alignment.sto', 'stockholm', 'alignment.phy', 'phylip')
Format Compatibility Matrix
Can convert directly (no modifications needed):
- GenBank <-> EMBL
- FASTA -> any format (may need annotations added)
- Any format -> FASTA (always works, may lose data)
- FASTQ -> FASTA
Requires adding data:
- FASTA -> FASTQ (need quality scores)
- FASTA -> GenBank (need molecule_type)
May lose data:
- GenBank -> FASTA (loses features, annotations)
- FASTQ -> FASTA (loses quality scores)
- Any rich format -> FASTA
Common Errors
| Error | Cause | Solution |
|---|---|---|
ValueError: missing molecule_type | FASTA to GenBank | Add molecule_type annotation |
ValueError: missing quality scores | FASTA to FASTQ | Add phred_quality to letter_annotations |
KeyError: 'phred_quality' | Wrong FASTQ variant | Try 'fastq-sanger', 'fastq-illumina' |
Decision Tree
Converting formats?
├── Simple conversion (no data changes)?
│ └── Use SeqIO.convert() directly
├── Need to add annotations?
│ └── Parse, modify records, then write
├── Need to transform sequences?
│ └── Parse, apply transformation, then write
└── Multiple files?
└── Loop with SeqIO.convert() or batch generator
Related Skills
- read-sequences - Parse sequences for custom conversion logic
- write-sequences - Write converted sequences with modifications
- batch-processing - Convert multiple files at once
- compressed-files - Handle compressed input/output during conversion
- alignment-files - For SAM/BAM/CRAM conversion, use samtools view
What ships with it: 2 files
2.7 KB alongside SKILL.md, 1 of them executable
examples/
- convert_format.pyruns1.6 KB
- usage-guide.md1.1 KB
Gives 0 of the 12 instructions most product growth skills give in ~1.4k tokens
Counted across 728 of the 1,010 authors here whose files we hold, read 2026-08-07
- Read product marketing context before asking questionsin 24 of 728, across 18 files
- Define the ideal customer profilein 21 of 728, across 3 files
- Document a rollback plan before deploymentin 21 of 728, across 12 files
- Analyze the codebase to understand the productin 19 of 728, across 1 file
- Ask clarifying questions about the value propositionin 19 of 728, across 1 file
- Search for companies matching the criteriain 19 of 728, across 1 file
- Look for signals of immediate needin 19 of 728, across 1 file
- Assign a fit score from one to tenin 19 of 728, across 1 file
- Identify the target decision-maker rolein 19 of 728, across 1 file
- Suggest a personalized contact strategyin 19 of 728, across 1 file
- Provide conversation starters for outreachin 19 of 728, across 1 file
- Format results in a scannable markdown templatein 19 of 728, across 1 file
Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.