Version Compatibility

Reference examples tested with: BioPython 1.83+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show biopython then help(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.

Read Sequences

Read biological sequence data from files using Biopython's Bio.SeqIO module.

"Read sequences from a file" → Parse file into a collection of SeqRecord objects with IDs, sequences, and annotations accessible.

• Python: SeqIO.parse() or SeqIO.read() (BioPython)
• R: readDNAStringSet() or readAAStringSet() (Biostrings)

Required Import

#### Core import

from Bio import SeqIO

Core Functions

SeqIO.parse() - Multiple Records

Use for files with one or more sequences. Returns an iterator of SeqRecord objects.

for record in SeqIO.parse('sequences.fasta', 'fasta'):
    print(record.id, len(record.seq))

Important: Always specify the format explicitly as the second argument.

SeqIO.read() - Single Record

Use when file contains exactly one sequence. Raises error if zero or multiple records.

record = SeqIO.read('single.fasta', 'fasta')

SeqIO.to_dict() - Load All Into Memory

Use for random access by record ID. Loads entire file into memory.

records = SeqIO.to_dict(SeqIO.parse('sequences.fasta', 'fasta'))
seq = records['sequence_id'].seq

SeqIO.index() - Large File Random Access

Use for large files when random access is needed without loading everything into memory.

records = SeqIO.index('large.fasta', 'fasta')
seq = records['sequence_id'].seq
records.close()

SeqIO.index_db() - SQLite-Backed Indexing

Use for very large files or multiple files. Creates persistent SQLite index.

# Create index (first time - parses file)
records = SeqIO.index_db('index.sqlite', 'large.fasta', 'fasta')
seq = records['sequence_id'].seq
records.close()

# Reuse existing index (instant load)
records = SeqIO.index_db('index.sqlite')

# Index multiple files together
records = SeqIO.index_db('combined.sqlite', ['file1.fasta', 'file2.fasta'], 'fasta')

Advantages over index():

• Persistent index survives program restarts
• Can index multiple files as one database
• Lower memory for extremely large files
• SQLite file can be shared across processes

High-Performance Parsing

For maximum throughput on large files, use low-level parsers (3-6x faster than SeqIO.parse):

SimpleFastaParser

Goal: Parse large FASTA files at maximum speed without SeqRecord overhead.

Approach: Use low-level tuple-based parser returning (title, sequence) strings.

Reference (BioPython 1.83+):

from Bio.SeqIO.FastaIO import SimpleFastaParser

with open('large.fasta') as handle:
    for title, sequence in SimpleFastaParser(handle):
        if len(sequence) > 1000:
            print(title.split()[0])  # First word is usually ID

Returns (title, sequence) tuples as strings (no SeqRecord overhead).

FastqGeneralIterator

Goal: Parse large FASTQ files at maximum speed.

Approach: Use low-level tuple-based parser returning (title, sequence, quality_string) strings.

Reference (BioPython 1.83+):

from Bio.SeqIO.QualityIO import FastqGeneralIterator

with open('reads.fastq') as handle:
    for title, sequence, quality in FastqGeneralIterator(handle):
        avg_qual = sum(ord(c) - 33 for c in quality) / len(quality)

Returns (title, sequence, quality_string) tuples.

Common Formats

Specialized Formats

Reading ABI Trace Files

# Read Sanger sequencing trace with quality
record = SeqIO.read('sample.ab1', 'abi')
print(f'Sequence: {record.seq}')
qualities = record.letter_annotations['phred_quality']

# Auto-trim low quality ends
record_trimmed = SeqIO.read('sample.ab1', 'abi-trim')

Reading 454/Ion Torrent SFF

for record in SeqIO.parse('reads.sff', 'sff'):
    print(record.id, len(record.seq))

# With trimming applied
for record in SeqIO.parse('reads.sff', 'sff-trim'):
    print(record.id, len(record.seq))

Reading PDB Sequences

# Get sequences from SEQRES records
for record in SeqIO.parse('structure.pdb', 'pdb-seqres'):
    print(f'Chain {record.id}: {record.seq}')

# Get sequences from ATOM coordinates
for record in SeqIO.parse('structure.pdb', 'pdb-atom'):
    print(f'Chain {record.id}: {record.seq}')

Alignment Formats (Read-Only)

SeqRecord Object Attributes

After parsing, each record has these key attributes:

record.id          # Sequence identifier (string)
record.name        # Sequence name (string)
record.description # Full description line (string)
record.seq         # Sequence data (Seq object)
record.features    # List of SeqFeature objects (GenBank/EMBL)
record.annotations # Dictionary of annotations
record.letter_annotations  # Per-letter annotations (quality scores)
record.dbxrefs     # Database cross-references

Code Patterns

Collect All Sequences Into a List

records = list(SeqIO.parse('sequences.fasta', 'fasta'))

Count Records Without Loading All

count = sum(1 for _ in SeqIO.parse('sequences.fasta', 'fasta'))

Fast Count (FASTA only)

from Bio.SeqIO.FastaIO import SimpleFastaParser
with open('sequences.fasta') as f:
    count = sum(1 for _ in SimpleFastaParser(f))

Get Sequence IDs Only

ids = [record.id for record in SeqIO.parse('sequences.fasta', 'fasta')]

Read GenBank with Features

for record in SeqIO.parse('sequence.gb', 'genbank'):
    for feature in record.features:
        if feature.type == 'CDS':
            print(feature.qualifiers.get('product', ['Unknown'])[0])
            cds_seq = feature.extract(record.seq)  # Get feature sequence

Access FASTQ Quality Scores

for record in SeqIO.parse('reads.fastq', 'fastq'):
    qualities = record.letter_annotations['phred_quality']
    avg_quality = sum(qualities) / len(qualities)

Read From File Handle

with open('sequences.fasta', 'r') as handle:
    for record in SeqIO.parse(handle, 'fasta'):
        print(record.id)

Custom ID Function for Indexing

def get_accession(identifier):
    return identifier.split('.')[0]  # Remove version

records = SeqIO.index('sequences.fasta', 'fasta', key_function=get_accession)

Common Errors

Decision Tree

Need to read sequences?
├── Single record in file?
│   └── Use SeqIO.read()
├── Multiple records?
│   ├── Need all in memory at once?
│   │   └── Use list(SeqIO.parse()) or SeqIO.to_dict()
│   ├── Process one at a time (memory efficient)?
│   │   └── Use SeqIO.parse() iterator
│   ├── Large file, need random access by ID?
│   │   ├── Single session? → Use SeqIO.index()
│   │   └── Persistent/multi-file? → Use SeqIO.index_db()
│   └── Maximum throughput needed?
│       └── Use SimpleFastaParser or FastqGeneralIterator
├── Sanger sequencing trace?
│   └── Use 'abi' or 'abi-trim' format
├── 454/Ion Torrent data?
│   └── Use 'sff' or 'sff-trim' format
└── Protein from structure?
    └── Use 'pdb-seqres' or 'pdb-atom' format

Related Skills

• write-sequences - Write parsed sequences to new files
• filter-sequences - Filter sequences by criteria after reading
• format-conversion - Convert between formats
• compressed-files - Read gzip/bzip2/BGZF compressed sequence files
• sequence-manipulation/seq-objects - Work with parsed SeqRecord objects
• database-access - Fetch sequences from NCBI instead of local files
• alignment-files - For SAM/BAM/CRAM alignment files, use samtools/pysam