Source code for gecco.refine

"""Algorithm to smooth contiguous BGC predictions into single regions.

import itertools
import functools
import operator
import typing
from typing import List, Mapping, Optional, Tuple, Iterator

import numpy
from Bio.SeqRecord import SeqRecord

from .model import Cluster, Domain, Gene, Protein, Strand

__all__ = ["BIO_PFAMS", "GeneGrouper", "ClusterRefiner"]

# fmt: off
# `set` of `str`: A set of domains from Pfam considered 'biosynthetic' by AntiSMASH.
BIO_PFAMS = frozenset({
    "PF00109", "PF02801", "PF08659", "PF00378", "PF08541", "PF08545",
    "PF02803", "PF00108", "PF02706", "PF03364", "PF08990", "PF00501",
    "PF00668", "PF08415", "PF00975", "PF03061", "PF00432", "PF00494",
    "PF03936", "PF01397", "PF00432", "PF04275", "PF00348", "PF02401",
    "PF04551", "PF00368", "PF00534", "PF00535", "PF02922", "PF01041",
    "PF00128", "PF00908", "PF02719", "PF04321", "PF01943", "PF02806",
    "PF02350", "PF02397", "PF04932", "PF01075", "PF00953", "PF01050",
    "PF03033", "PF01501", "PF05159", "PF04101", "PF02563", "PF08437",
    "PF02585", "PF01721", "PF02052", "PF02674", "PF03515", "PF04369",
    "PF08109", "PF08129", "PF09221", "PF09683", "PF10439", "PF11420",
    "PF11632", "PF11758", "PF12173", "PF04738", "PF04737", "PF04604",
    "PF05147", "PF08109", "PF08129", "PF08130", "PF00155", "PF00202",
    "PF00702", "PF06339", "PF04183", "PF10331", "PF03756", "PF00106",
    "PF01370", "PF00107", "PF08240", "PF00441", "PF02770", "PF02771",
    "PF08028", "PF01408", "PF02894", "PF00984", "PF00725", "PF03720",
    "PF03721", "PF07993", "PF02737", "PF00903", "PF00037", "PF04055",
    "PF00171", "PF00067", "PF01266", "PF01118", "PF02668", "PF00248",
    "PF01494", "PF01593", "PF03992", "PF00355", "PF01243", "PF00384",
    "PF01488", "PF00857", "PF04879", "PF08241", "PF08242", "PF00698",
    "PF00483", "PF00561", "PF00583", "PF01636", "PF01039", "PF00288",
    "PF00289", "PF02786", "PF01757", "PF02785", "PF02409", "PF01553",
    "PF02348", "PF00891", "PF01596", "PF04820", "PF02522", "PF08484",

class GeneGrouper:
    """A callable to group genes under or over a BGC probability threshold.

    Use with a list of genes in combination with `itertools.groupby`.

    def __init__(self, threshold: float):  # noqa: D102, D107
        self.in_cluster = False
        self.threshold = threshold

    def __call__(self, gene: Gene) -> bool:  # noqa: D102
        if gene.average_probability is not None:
            self.in_cluster = gene.average_probability > self.threshold
        return self.in_cluster

[docs]class ClusterRefiner: """A post-processor to extract contiguous BGCs from CRF predictions. """
[docs] def __init__( self, threshold: float = 0.3, criterion: str = "gecco", n_cds: int = 5, n_biopfams: int = 5, average_threshold: float = 0.6, ) -> None: """Create a new `ClusterRefiner` instance. Arguments: threshold (`float`): The probability threshold to use to consider a protein to be part of a BGC region. criterion (`str`): The criterion to use when checking for BGC validity. See `gecco.bgc.BGC.is_valid` documentation for allowed values and expected behaviours. n_cds (`int`): The minimum number of CDS a gene cluster must contain to be considered valid. If ``criterion`` is ``gecco``, then this is the minimum number of **annotated** CDS. n_biopfams (`int`): The minimum number of biosynthetic Pfam domains a gene cluster must contain to be considered valid (*only when the criterion is* ``antismash``). average_threshold (`int`): The average probability threshold to use to consider a BGC valid (*only when the criterion is* ``antismash``). """ self.threshold = threshold self.criterion = criterion self.n_cds = n_cds self.n_biopfams = n_biopfams self.average_threshold = average_threshold
[docs] def iter_clusters(self, genes: List[Gene]) -> Iterator[Cluster]: """Find all clusters in a table of CRF predictions. Arguments: genes (`list` of `~gecco.model.Gene`): A list of genes with probability annotations estimated by `~gecco.crf.ClusterCRF`. Yields: `gecco.model.Cluster`: Valid clusters found in the input with respect to the postprocessing criterion given at initialisation. """ unfiltered_clusters = map(self._trim_cluster, self._iter_clusters(genes)) return filter(self._validate_cluster, unfiltered_clusters)
def _validate_cluster(self, cluster: Cluster) -> bool: """Check a cluster validity depending on the postprocessing criterion. """ if self.criterion == "gecco": annotated = [ g for g in cluster.genes if ] cds_crit = len(annotated) >= self.n_cds return cds_crit elif self.criterion == "antismash": domains = { for gene in cluster.genes for d in} p_crit = numpy.mean([g.average_probability for g in cluster.genes]) >= self.average_threshold bio_crit = len(domains & BIO_PFAMS) >= self.n_biopfams cds_crit = len(cluster.genes) >= self.n_cds return p_crit and bio_crit and cds_crit else: raise ValueError(f"unknown BGC filtering criterion: {self.criterion}") def _trim_cluster(self, cluster: Cluster) -> Cluster: """Remove unannotated proteins from the cluster edges. """ while cluster.genes and not cluster.genes[0] cluster.genes.pop(0) while cluster.genes and not cluster.genes[-1] cluster.genes.pop() return cluster def _iter_clusters( self, genes: List[Gene], ) -> Iterator[Cluster]: """Iterate over contiguous BGC segments from a list of genes. """ grouper = GeneGrouper(self.threshold) key = operator.attrgetter("") # iterate over the genes grouped by sequence ids for seq_id, sequence in itertools.groupby(sorted(genes, key=key), key=key): # sort genes within the same sequence by coordinates seqsort = sorted(sequence, key=operator.attrgetter("start", "end")) # group contiguous genes if they are over or under the threshold groups = itertools.groupby(seqsort, key=grouper) # filter out regions that are not identified to be clusters bgcs = (genes for in_bgc, genes in groups if in_bgc) for i, bgc in enumerate(bgcs): yield Cluster(id=f"{seq_id}_cluster_{i+1}", genes=list(bgc))