
Biopython Bio
Bioinformatics operations via Biopython.
Install
npx skills add beita6969/ScienceClaw --skill biopython-bio -g -a claude-code -y- Repository
- beita6969/ScienceClaw1k
- Category
- Engineering
- Updated
- 5d ago
- License
- MIT
- Context cost
- Lightabout 782 tokens
- Runs scripts
- No, instructions only
What it does
Bioinformatics operations via Biopython. Use when: user asks about DNA/protein sequences, BLAST, or PDB structures. NOT for: clinical genomics or variant calling pipelines.
Biopython Bio
Bioinformatics operations using Biopython.
When to Use
- Reading/writing sequence files (FASTA, GenBank)
- Running BLAST searches (local or remote NCBI)
- Sequence alignment and manipulation
- Parsing PDB protein structures
- Phylogenetic tree construction
- Querying NCBI Entrez databases
When NOT to Use
- Clinical genomics or variant calling (use GATK, bcftools)
- RNA-seq differential expression (use DESeq2, edgeR)
- Genome assembly (use SPAdes, Canu)
- Molecular dynamics simulations (use GROMACS, OpenMM)
Sequence Reading and Writing
from Bio import SeqIO
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
for record in SeqIO.parse('sequences.fasta', 'fasta'):
print(f"{record.id}: {len(record.seq)} bp")
# Write FASTA
records = [SeqRecord(Seq('ATGCGATCGATCG'), id='seq1', description='example')]
SeqIO.write(records, 'output.fasta', 'fasta')
Sequence Manipulation
from Bio.Seq import Seq
from Bio.SeqUtils import gc_fraction, molecular_weight
dna = Seq('ATGCGATCGATCGATCG')
rev_comp = dna.reverse_complement()
protein = dna.translate()
gc = gc_fraction(dna)
mw = molecular_weight(dna, seq_type='DNA')
BLAST Searches
from Bio.Blast import NCBIWWW, NCBIXML
result_handle = NCBIWWW.qblast('blastn', 'nt', 'ATGCGATCGATCGATCG')
for record in NCBIXML.parse(result_handle):
for aln in record.alignments:
for hsp in aln.hsps:
if hsp.expect < 1e-10:
print(f"{aln.title[:60]}, E={hsp.expect}")
Pairwise Alignment
from Bio import Align
aligner = Align.PairwiseAligner()
aligner.mode = 'global'
aligner.match_score = 2
aligner.mismatch_score = -1
best = aligner.align('ATCGATCGATCG', 'ATCAATCAATCG')[0]
print(best, f"Score: {best.score}")
PDB Structure Parsing
from Bio.PDB import PDBParser, PDBList
