Crate gecco 

GECCO: Gene Cluster prediction with Conditional Random Fields.

This crate provides both a CLI tool and a library API for identifying putative Biosynthetic Gene Clusters (BGCs) in genomic and metagenomic data.

Library usage

The easiest way to use GECCO as a library is through the Gecco struct:

use gecco::Gecco;
use gecco::orf::SeqRecord;

let pipeline = Gecco::builder()
    .data_dir("gecco_data")
    .threshold(0.8)
    .build()
    .unwrap();

let records = vec![SeqRecord {
    id: "contig_1".into(),
    seq: "ATGCCC...".into(),
}];

let results = pipeline.scan(&records).unwrap();
for cluster in &results.clusters {
    println!("{}: {} genes", cluster.id, cluster.genes.len());
}

For finer control, individual pipeline stages are also available as public methods on Gecco, or through the lower-level modules directly.

Re-exports

pub use model::Cluster;

pub use model::Domain;

pub use model::Gene;

pub use model::Protein;

pub use orf::SeqRecord;

pub use pipeline::Gecco;

pub use pipeline::GeccoBuilder;

pub use pipeline::GeccoResults;

Modules

cli

CLI commands for GECCO.

crf

Gene cluster prediction using a conditional random field.

data_dir

Resolve the GECCO data directory containing HMM, CRF model, and InterPro files.

hmmer

Compatibility wrapper for HMMER profiles and search results.

interpro

Simple data classes to expose embedded InterPro data.

io

I/O utilities: TSV tables, compression, FASTA/GenBank.

model

Data layer types for gene cluster detection.

orf

Generic protocol for ORF detection in DNA sequences.

output

pipeline

High-level pipeline API for library consumers.

refine

Algorithm to smooth contiguous gene cluster predictions into single regions.

sklearn_rf

Evaluator and small trainer for scikit-learn-shaped random forests.

types

Supervised classifier to predict the type of a gene cluster.

util

Shared utility helpers, mirroring the small grab-bag in gecco/_meta.py.