Parse FASTA and GenBank with Biotite, read GC content, run pairwise and multiple alignments over a substitution matrix, and write a consensus FASTA.

Sequence analysis lets you parse FASTA and GenBank files, run pairwise and multiple alignments, and summarize consensus and conservation. Your substitution matrix, gap penalty, and how you normalize bases beforehand are what most affect the alignments you get.

Where this code runs

You cannot open a shell on the platform yourself. The scripts on this page are plain Biotite Python and shell: ask Ivy in chat to run them in the chat's code workspace, which ships Biotite, or run them on your own machine.

Parse common formats

from biotite.sequence.io import load_sequence

fasta = load_sequence("inputs/sequences/demo.fasta", format="fasta")
genbank = load_sequence("inputs/sequences/demo.gb", format="genbank")

seq = fasta.get_sequence()
print("Sequence length:", len(seq))
print("GC%:", seq.get_gc_content()*100)

Pairwise alignment

from biotite.sequence.align import (
    align_optimal,
    SubstitutionMatrix,
    PairwiseAlignment
)
from biotite.sequence import NucleotideSequence

ref = NucleotideSequence("ATGCGTAGCATGACTG")
query = NucleotideSequence("ATGCGTAGAATGACTA")

matrix = SubstitutionMatrix.std_nucleotide_matrix()
alignments = align_optimal(query, ref, matrix, gap_penalty=-8)

best: PairwiseAlignment = alignments[0]
print(best)
print("Score:", best.score)

Multiple sequence alignment summary

Use Biotite to generate consensus and per-position conservation:

from biotite.sequence import NucleotideSequence
from biotite.sequence.align import SubstitutionMatrix, align_multiple

seqs = [
    NucleotideSequence("ATGCGTAGCATGACTG"),
    NucleotideSequence("ATGCGTAGAATGACTA"),
    NucleotideSequence("ATGCGTAGCATGACCG"),
]

matrix = SubstitutionMatrix.std_nucleotide_matrix()
msa = align_multiple(seqs, matrix)

consensus = msa.get_consensus()
print("Consensus:", consensus)
print("Conservation scores:", msa.get_conservation())

Basic quality checks

Validate file encodings before ingestion; prefer UTF-8.
For large batches, stream FASTA/GenBank files to avoid loading everything into memory.
Normalize ambiguous bases and upper/lowercase before running alignments.

Export results

from biotite.sequence.io.fasta import FastaFile

fasta_out = FastaFile()
fasta_out["consensus"] = consensus
with open("outputs/consensus.fasta", "w", encoding="utf-8") as handle:
    fasta_out.write(handle)

Sequence analysis guide

Parse common formats

Pairwise alignment

Multiple sequence alignment summary

Basic quality checks

Export results

On this page