claude-scientific-skills/scientific-packages/biopython/references/core_modules.md

BioPython Core Modules Reference

This document provides detailed information about BioPython's core modules and their capabilities.

Sequence Handling

Bio.Seq - Sequence Objects

Seq objects are BioPython's fundamental data structure for biological sequences, providing biological methods on top of string-like behavior.

Creation:

from Bio.Seq import Seq
my_seq = Seq("AGTACACTGGT")

Key Operations:

• String methods: find(), count(), count_overlap() (for overlapping patterns)
• Complement/Reverse complement: Returns complementary sequences
• Transcription: DNA → RNA (T → U)
• Back transcription: RNA → DNA
• Translation: DNA/RNA → protein with customizable genetic codes and stop codon handling

Use Cases:

• DNA/RNA sequence manipulation
• Converting between nucleic acid types
• Protein translation from coding sequences
• Sequence searching and pattern counting

Bio.SeqRecord - Sequence Metadata

SeqRecord wraps Seq objects with metadata like ID, description, and features.

Attributes:

• seq: The sequence itself (Seq object)
• id: Unique identifier
• name: Short name
• description: Longer description
• features: List of SeqFeature objects
• annotations: Dictionary of additional information
• letter_annotations: Per-letter annotations (e.g., quality scores)

Bio.SeqFeature - Sequence Annotations

Manages sequence annotations and features such as genes, promoters, and coding regions.

Common Features:

• Gene locations
• CDS (coding sequences)
• Promoters and regulatory elements
• Exons and introns
• Protein domains

File Input/Output

Bio.SeqIO - Sequence File I/O

Unified interface for reading and writing sequence files in multiple formats.

Supported Formats:

• FASTA/FASTQ: Standard sequence formats
• GenBank/EMBL: Feature-rich annotation formats
• Clustal/Stockholm/PHYLIP: Alignment formats
• ABI/SFF: Trace and flowgram data
• Swiss-Prot/PIR: Protein databases
• PDB: Protein structure files

Key Functions:

SeqIO.parse() - Iterator for reading multiple records:

from Bio import SeqIO
for record in SeqIO.parse("file.fasta", "fasta"):
    print(record.id, len(record.seq))

SeqIO.read() - Read single record:

record = SeqIO.read("file.fasta", "fasta")

SeqIO.write() - Write sequences:

SeqIO.write(sequences, "output.fasta", "fasta")

SeqIO.convert() - Direct format conversion:

count = SeqIO.convert("input.gb", "genbank", "output.fasta", "fasta")

SeqIO.index() - Memory-efficient random access for large files:

record_dict = SeqIO.index("large_file.fasta", "fasta")
sequence = record_dict["seq_id"]

SeqIO.to_dict() - Load all records into dictionary (memory-based):

record_dict = SeqIO.to_dict(SeqIO.parse("file.fasta", "fasta"))

Common Patterns:

• Format conversion between FASTA, GenBank, FASTQ
• Filtering sequences by length, ID, or content
• Extracting subsequences
• Batch processing large files with iterators

Bio.AlignIO - Multiple Sequence Alignment I/O

Handles multiple sequence alignment files.

Key Functions:

• write(): Save alignments
• parse(): Read multiple alignments
• read(): Read single alignment
• convert(): Convert between formats

Supported Formats:

• Clustal
• PHYLIP (sequential and interleaved)
• Stockholm
• NEXUS
• FASTA (aligned)
• MAF (Multiple Alignment Format)

Sequence Alignment

Bio.Align - Alignment Tools

PairwiseAligner - High-performance pairwise alignment:

from Bio import Align
aligner = Align.PairwiseAligner()
aligner.mode = 'global'  # or 'local'
aligner.match_score = 2
aligner.mismatch_score = -1
aligner.gap_score = -2.5
alignments = aligner.align(seq1, seq2)

CodonAligner - Codon-aware alignment

MultipleSeqAlignment - Container for MSA with column access

Bio.pairwise2 (Legacy)

Legacy pairwise alignment module with functions like align.globalxx(), align.localxx().

Sequence Analysis Utilities

Bio.SeqUtils - Sequence Analysis

Collection of utility functions:

CheckSum - Calculate sequence checksums (CRC32, CRC64, GCG)

MeltingTemp - DNA melting temperature calculations:

• Nearest-neighbor method
• Wallace rule
• GC content method

IsoelectricPoint - Protein pI calculation

ProtParam - Protein analysis:

• Molecular weight
• Aromaticity
• Instability index
• Secondary structure fractions

GC/GC_skew - Calculate GC content and GC skew for sequence windows

Bio.Data.CodonTable - Genetic Codes

Access to NCBI genetic code tables:

from Bio.Data import CodonTable
standard_table = CodonTable.unambiguous_dna_by_id[1]
print(standard_table.forward_table)  # codon to amino acid
print(standard_table.back_table)     # amino acid to codons
print(standard_table.start_codons)
print(standard_table.stop_codons)

Available codes:

• Standard code (1)
• Vertebrate mitochondrial (2)
• Yeast mitochondrial (3)
• And many more organism-specific codes

Sequence Motifs and Patterns

Bio.motifs - Sequence Motif Analysis

Tools for working with sequence motifs:

Position Weight Matrices (PWM):

• Create PWM from aligned sequences
• Calculate information content
• Search sequences for motif matches
• Generate consensus sequences

Position Specific Scoring Matrices (PSSM):

• Convert PWM to PSSM
• Score sequences against motifs
• Determine significance thresholds

Supported Formats:

• JASPAR
• TRANSFAC
• MEME
• AlignAce

Bio.Restriction - Restriction Enzymes

Comprehensive restriction enzyme database and analysis:

Capabilities:

• Search for restriction sites
• Predict digestion products
• Analyze restriction maps
• Access enzyme properties (recognition site, cut positions, isoschizomers)

Example usage:

from Bio import Restriction
from Bio.Seq import Seq

seq = Seq("GAATTC...")
enzyme = Restriction.EcoRI
results = enzyme.search(seq)