Python for Bioinformatics Domain¶

What you'll learn¶

• Working with DNA/RNA/protein sequences
• Basic sequence operations (tested, pure Python)
• File formats (FASTA/FASTQ) and BioPython
• Sequence alignment and genomics concepts
• The ecosystem

Bioinformatics applies computation to biology — analyzing DNA, proteins, and genomes. Python is the field's dominant language. The basic sequence operations here are run-verified with pure Python; higher-level work uses BioPython (documented API).

Sequences are just strings (tested)¶

Sequences are just strings in Python for Bioinformatics — what it is and when to use it.

DNA is a string over the alphabet A, C, G, T. Many core operations are pure string manipulation — runnable:

def gc_content(dna: str) -> float:
    """Fraction of G and C bases — a basic genomic measure."""
    gc = sum(1 for base in dna if base in "GC")
    return gc / len(dna)

def complement(dna: str) -> str:
    """The complementary strand (A<->T, C<->G)."""
    pairs = {"A": "T", "T": "A", "C": "G", "G": "C"}
    return "".join(pairs[b] for b in dna)

def reverse_complement(dna: str) -> str:
    return complement(dna)[::-1]

seq = "GATTACA"
print("GC content:", round(gc_content(seq), 3))
print("complement:", complement(seq))
print("reverse complement:", reverse_complement(seq))

Output:

GC content: 0.286
complement: CTAATGT
reverse complement: TGTAATC

The reverse complement matters biologically — DNA's two strands run in opposite directions, so the reverse complement is what the other strand reads. All of this is dict lookups and slicing; the biology is in interpreting the results, not the code.

Transcription (DNA → RNA) is just replacing T with U:

def transcribe(dna: str) -> str:
    return dna.replace("T", "U")

print(transcribe("GATTACA"))    # -> GAUUACA

FASTA/FASTQ and BioPython¶

Read standard sequence formats and manipulate them with BioPython.

Real sequence data comes in formats like FASTA (sequences) and FASTQ (sequences + quality scores). BioPython parses these and provides the field's toolkit:

from Bio import SeqIO          # pip install biopython
from Bio.Seq import Seq

# Parse a FASTA file
for record in SeqIO.parse("sequences.fasta", "fasta"):
    print(record.id, len(record.seq))
    print(record.seq.reverse_complement())

# BioPython Seq objects know biology
dna = Seq("GATTACA")
protein = dna.translate()      # DNA -> amino acids

Bigger concepts¶

Alignment, phylogenetics, and variant analysis at a glance.

• Sequence alignment — finding how two sequences match up (mutations, insertions, deletions). Algorithms like Needleman-Wunsch (global) and Smith-Waterman (local) use dynamic programming (see the Algorithms section). Tools: BioPython, BLAST.
• Phylogenetics — building evolutionary trees from sequence similarity.
• Genomic pipelines — processing raw sequencer output through alignment, variant calling, and annotation, often orchestrated as a data pipeline.

The ecosystem¶

BioPython, scikit-bio, and specialized tools.

Practice exercises¶

1. Write a function that counts each base (A/C/G/T) in a sequence using collections.Counter.
2. Find the most common 3-base codon in a sequence.
3. Implement transcription and a simple codon → amino acid lookup for a few codons.
4. Detect whether a sequence is a palindrome under reverse-complement (a restriction site property).
5. Explain why reverse complement is biologically meaningful, referencing DNA's two strands.