Install from PyPI
pip install genomehouse Documentation / v1.2.0
Documentation for working minds.
Everything you need to move from sequence-level exploration to reproducible genomic workflows.
Installation
Install the latest release from the Python Package Index or work from source.
Install from source
git clone https://github.com/GenomeHouse/GenomeHouse-1.1.git
cd GenomeHouse-1.1
pip install -e .Requirements
The core package is intentionally lightweight. Add optional dependencies as your analysis grows.
Python requirements
Python ≥ 3.8
NumPy ≥ 1.19.0
Pandas ≥ 1.3.0
Optional dependencies
Matplotlib for visualization
Scikit-learn for ML features
Seaborn for advanced plots
Quick start
Import the modules you need and compose them in a normal Python workflow.
Basic workflow
from genomehouse import sequence_tools, genomic_parsers
seq = "ATGCGTAC"
print(sequence_tools.gc_content(seq))
for header, sequence in genomic_parsers.parse_fasta("example.fasta"):
print(header, sequence)sequence_tools module
Composable primitives for common bioinformatics analysis tasks.
gc_content(sequence)
Calculate the GC content percentage of a DNA sequence.
sequence_tools.gc_content("ATGCGCGCTA")reverse_complement(sequence)
Generate the reverse complement of a DNA sequence.
sequence_tools.reverse_complement("ATGCGT")find_motifs(sequence, motif)
Find all occurrences of a motif pattern in a sequence.
sequence_tools.find_motifs("ATGCGCGCTA", "GC")translate(sequence, frame=0)
Translate DNA sequence to amino acid sequence.
sequence_tools.translate("ATGAAATAG")genomic_parsers module
Composable primitives for common bioinformatics analysis tasks.
parse_fasta(filename)
Parse FASTA files and yield header-sequence pairs.
for header, sequence in genomic_parsers.parse_fasta("sequences.fasta")parse_fastq(filename)
Parse FASTQ files with quality scores.
for record in genomic_parsers.parse_fastq("reads.fastq")parse_vcf(filename)
Parse VCF variant files.
for variant in genomic_parsers.parse_vcf("variants.vcf")ml_tools module
Composable primitives for common bioinformatics analysis tasks.
extract_features(sequences, method="kmer")
Extract numerical features from biological sequences.
features = ml_tools.extract_features(sequences, method="kmer")SequenceClassifier()
Machine learning classifier for biological sequences.
classifier.fit(features, labels)
predictions = classifier.predict(new_features)Basic analysis
A complete sequence analysis workflow using the core sequence_tools API.
Analyze a sequence
from genomehouse import sequence_tools
dna_sequence = "ATGAAACGCATTAGCACCACCATTACCACCACCATCACCATTACCACAGGTAACGGTGCGGGCTGA"
gc_content = sequence_tools.gc_content(dna_sequence)
protein = sequence_tools.translate(dna_sequence)
print(f"GC content: {gc_content:.1f}%")
print(f"Protein: {protein}")Troubleshooting
Common issues
ImportError: check that the package is installed in the active environment.
FileNotFoundError: verify the path and working directory.
Memory concerns: use streaming parsers for large files.
Contributing
Contributions, tests, and documentation improvements are welcome.
Development setup
git clone https://github.com/GenomeHouse/GenomeHouse-1.1.git
cd GenomeHouse-1.1
pip install -e .[dev]
pytest tests/