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prophyle_assignment.py
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prophyle_assignment.py
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#! /usr/bin/env python3
"""ProPhyle assignment algorithm (reference implementation).
Example: ./prophyle/prophyle_index/prophyle_index query -k 5 -u -b _index_test/index.fa tests/simulation_bacteria.1000.fq |./prophyle/prophyle_assignment.py -m h1 -f sam _index_test/tree.nw 5 -
Author: Karel Brinda <kbrinda@hsph.harvard.edu>
License: MIT
Todo:
- test with CRAM
"""
import argparse
#import bitarray
import collections
import functools
import itertools
import os
import sys
sys.path.append(os.path.dirname(__file__))
import prophylelib as pro
import version
###############################################################################################
###############################################################################################
CONFIG = {
# this should be longer than any possible read, because of CRAM (non-tested yet)
'FAKE_CONTIG_LENGTH': 42424242,
# print diagnostics messages
'DIAGNOSTICS': False,
# sort nodes alphabetically when reporting assignments
'SORT_NODES': False,
}
###############################################################################################
###############################################################################################
from bitarray import bitarray as _bitarray
class bitarray(_bitarray):
def __hash__(self):
return self.tobytes().__hash__()
class Assignment:
"""Class for handling a single read.
Args:
output_fo (file): Output file object.
tree_index (TreeIndex): Tree index.
kmer_lca (bool): Simulate LCA on the k-mer level.
tie_lca (bool): If a tie (multiple winning nodes), compute LCA.
annotate (bool): If taxonomic info present in the tree, annotate the assignments using SAM tags.
Attributes:
output_fo (file): Output file object.
tree_index (TreeIndex): Tree index.
k (int): k-mer size.
kmer_lca (bool): Simulate LCA on the k-mer level.
tie_lca (bool): If a tie (multiple winning nodes), compute LCA.
annotate (bool): If taxonomic info present in the tree, annotate the assignments using SAM tags.
krakline_parser (KraklineParser): Parser of kraklines.
hitmasks_dict (dict): Hit masks
covmasks_dict (dict): Cov masks.
ass_dict (dict): Assignment dictionary.
max_nodenames (list of str): List of nodenames of winners.
max_val (int/float): Maximal value of the measure.
"""
def __init__(self, output_fo, tree_index, kmer_lca=False, tie_lca=False, annotate=False):
self.output_fo = output_fo
self.tree_index = tree_index
self.k = self.tree_index.k
self.kmer_lca = kmer_lca
self.annotate = annotate
self.tie_lca = tie_lca
self.krakline_parser = KraklineParser()
self.hitmasks_dict = {}
self.covmasks_dict = {}
self.ass_dict = {}
self.max_nodenames = []
self.max_val = 0
def process_read(self, krakline, form, measure):
"""Process one Kraken-like line.
Args:
krakline (str): Kraken-like line.
form (str): Expected output format (sam/kraken).
measure (str): Measure (h1/c1/h2/c2).
"""
self.krakline_parser.parse_krakline(krakline)
if CONFIG['DIAGNOSTICS']:
self.krakline_parser.diagnostics()
self.blocks_to_masks(self.krakline_parser.kmer_blocks, self.kmer_lca)
if CONFIG['DIAGNOSTICS']:
self.diagnostics()
self.compute_assignments()
if CONFIG['DIAGNOSTICS']:
self.diagnostics()
self.select_best_assignments(measure)
if CONFIG['DIAGNOSTICS']:
self.diagnostics()
self.print_selected_assignments(form)
def blocks_to_masks(self, kmer_blocks, kmer_lca):
"""Extract hit and coverage masks from krakline blocks (without propagation) and store them in self.{hit,cov}masks_dict.
Args:
kmer_blocks (list): List of assigned k-mers, i.e., list of (list of node_names, count).
kmer_lca (bool): Simulate k-mer LCA on the k-mer level.
"""
readlen = sum([x[1] for x in kmer_blocks])
hitmask_len = readlen
covmask_len = readlen + self.k - 1
# zero masks
hitmask_empty = self.bitarray_block(hitmask_len, 0, 0)
covmask_empty = self.bitarray_block(covmask_len, 0, 0)
# fast copying (bitarray trick)
hitmasks_dict = collections.defaultdict(lambda: bitarray(hitmask_empty))
covmasks_dict = collections.defaultdict(lambda: bitarray(covmask_empty))
pos = 0
for (node_names, count) in kmer_blocks:
# Kraken output format: 0 and A have special meanings, no blocks
if node_names != ["0"] and node_names != ["A"]:
hitmask_1block = self.bitarray_block(hitmask_len, count, pos)
covmask_1block = self.bitarray_block(covmask_len, count + self.k - 1, pos)
if kmer_lca:
node_names = [self.tree_index.lca(*node_names)]
for node_name in node_names:
hitmasks_dict[node_name] |= hitmask_1block
covmasks_dict[node_name] |= covmask_1block
pos += count
self.hitmasks_dict = hitmasks_dict
self.covmasks_dict = covmasks_dict
def compute_assignments(self):
"""Compute assignments & characteristics.
Compute and their characteristics from hitmasks and store
them in self.ass_dict.
"""
nodenames = self.hitmasks_dict.keys()
self.ass_dict = {nodename: self.evaluate_single_assignment(nodename) for nodename in nodenames}
def evaluate_single_assignment(self, nodename):
"""Evaluate a single assignment.
Args:
nodename (str): Name of the node for which we will compute characteristics.
Returns:
assignment (dict): Assignment dictionary.
"""
#################################
# A) Start with the current masks
#################################
hitmask = bitarray(self.hitmasks_dict[nodename])
covmask = bitarray(self.covmasks_dict[nodename])
node = self.tree_index.nodename_to_node[nodename]
##########################
# B) Update from ancestors
##########################
ancestors = self.tree_index.nodename_to_upnodenames[nodename] & self.hitmasks_dict.keys()
for anc_nodename in ancestors:
hitmask |= self.hitmasks_dict[anc_nodename]
covmask |= self.covmasks_dict[anc_nodename]
##############################
# C) Calculate characteristics
##############################
hit = hitmask.count()
cov = covmask.count()
readlen = self.krakline_parser.readlen
assignment = {
# 1. hit count
'hitmask': hitmask,
#'hitcigar': self.cigar_from_bitmask(hitmask),
'h1': [hit],
'hf': [hit / (readlen - self.k + 1)],
'h2': [hit / self.tree_index.nodename_to_kmercount[nodename]],
# 2. coverage
'covmask': covmask,
#'covcigar': self.cigar_from_bitmask(covmask),
'c1': [cov],
'cf': [cov / readlen],
'c2': [cov / self.tree_index.nodename_to_kmercount[nodename]],
# 3. other values
'ln': readlen,
}
return assignment
def select_best_assignments(self, measure):
"""Find the best assignments and save it to self.max_nodenames (max value: self.max_val).
Args:
measure (str): Measure (h1/c1/h2/c2).
"""
self.max_val = 0
self.max_nodenames = []
for nodename in self.ass_dict:
ass = self.ass_dict[nodename]
if ass[measure][0] > self.max_val:
self.max_val = ass[measure][0]
self.max_nodenames = [nodename]
elif ass[measure][0] == self.max_val:
self.max_nodenames.append(nodename)
if CONFIG['SORT_NODES']:
self.max_nodenames.sort()
def make_lca_from_winners(self):
"""Create LCA from winners.
Assemble the characteristics of the LCA from the characteristics
of the winning node. Output mutliple tag measures (e.g., h1 or c1).
"""
# all characteristics are already computed
if len(self.max_nodenames) == 1:
return
first_winner = self.ass_dict[max_nodenames[0]]
lca = self.tree_index.lca(winners)
ass = {
'covmask': None,
'covcigar': None,
'hitmask': None,
'hitcigar': None,
'ln': self.krakline_parser.readlen,
}
for tag in ['h1', 'hf', 'h2', 'c1', 'cf', 'c2']:
ass[tag] = [self.ass_dict[nodename][tag] for nodename in self.max_nodenames],
self.max_nodenames = [lca]
self.ass_dict[lca] = ass
asg['covmask'] = None
asg['hitmask'] = None
def print_selected_assignments(self, form):
"""Print the best assignments.
Args:
form (str): Expected output format (sam/kraken).
"""
if form == "sam":
tag_is = len(self.max_nodenames)
for tag_ii, nodename in enumerate(self.max_nodenames, 1):
ass = self.ass_dict[nodename]
# compute cigar
if ass['covmask'] is None:
ass['covcigar'] = None
else:
ass['covcigar'] = self.cigar_from_bitmask(ass['covmask'])
if ass['hitmask'] is None:
ass['hitcigar'] = None
else:
ass['hitcigar'] = self.cigar_from_bitmask(ass['hitmask'])
suffix_parts = ["ii:i:{}".format(tag_ii), "is:i:{}".format(tag_is)]
if self.annotate:
suffix_parts.append(self.tree_index.nodename_to_samannot[nodename])
self.print_sam_line(nodename, "\t".join([""] + suffix_parts))
elif form == "kraken":
self.print_kraken_line(*self.max_nodenames)
@staticmethod
@functools.lru_cache(maxsize=5)
def cigar_from_bitmask(bitmask):
"""Create a CIGAR from a binary mask.
Args:
mask (list): Bitmask.
Return:
cigar (str): Cigar string (X/= ops).
"""
c = []
bitmask = map(bool, bitmask)
runs = itertools.groupby(bitmask)
for run in runs:
c.append(str(len(list(run[1]))))
c.append('=' if run[0] else 'X')
return "".join(c)
def print_sam_line(self, node_name, suffix):
"""Print a single SAM record.
Args:
node_name (str): Node name. None if unassigned.
suffix (str): Suffix with additional tags.
"""
tags = []
qname = self.krakline_parser.readname
if node_name is not None:
flag = 0
mapq = "60"
pos = "1"
cigar = self.ass_dict[node_name]['covcigar']
else:
flag = 4
node_name = None
pos = None
mapq = None
cigar = None
columns = [
qname, # QNAME
str(flag), # FLAG
node_name if node_name else "*", # RNAME
pos if pos else "0", # POS
mapq if mapq else "0", # MAPQ
cigar if cigar else "*", # CIGAR
"*", # RNEXT
"0", # PNEXT
"0", # TLEN
self.krakline_parser.seq if self.krakline_parser.seq else "*", # SEQ
self.krakline_parser.qual if self.krakline_parser.qual else "*", # QUAL
]
if node_name is not None:
asg = self.ass_dict[node_name]
for tag, datatype in [
('h1', 'i'),
('h2', 'f'),
('hf', 'f'),
('c1', 'i'),
('c2', 'f'),
('cf', 'f'),
]:
for val in asg[tag]:
columns.append("{}:{}:{}".format(tag, datatype, val))
if asg['hitcigar']:
columns.append("hc:Z:{}".format(asg['hitcigar']))
print("\t".join(columns), suffix, file=self.output_fo, sep="")
def print_sam_header(self):
"""Print SAM headers.
"""
print("@HD", "VN:1.5", "SO:unsorted", sep="\t", file=self.output_fo)
print("@PG", "PN:prophyle", "ID:prophyle", "VN:{}".format(version.VERSION), sep="\t", file=self.output_fo)
for node in self.tree_index.tree.traverse("postorder"):
try:
ur = "\tUR:{}".format(node.fastapath)
except:
ur = ""
try:
sp = "\tSP:{}".format(node.sci_name)
except:
sp = ""
try:
as_ = "\tAS:{}".format(node.gi)
except:
as_ = ""
if node.name != '':
print(
"@SQ\tSN:{rname}\tLN:{rlen}{as_}{ur}{sp}".format(
rname=node.name,
rlen=CONFIG['FAKE_CONTIG_LENGTH'],
as_=as_,
ur=ur,
sp=sp,
), file=self.output_fo
)
def print_kraken_line(self, *nodenames):
"""Print a single record in the Kraken-like format.
Args:
*nodenames (list of str): Node names of assignments to report.
"""
if len(nodenames) == 0:
stat = "U"
krak_ass = "0"
else:
stat = "C"
krak_ass = ",".join(nodenames)
# recompute krakenmers
if self.kmer_lca:
nodenames_lca_seq = []
for [nodenames, count] in self.krakline_parser.kmer_blocks:
if len(nodenames) == 1:
nodename = nodenames[0]
#if nodename == "A" or nodename == "0":
# pass
#else:
# pass
else:
nodename = self.tree_index.lca(*nodenames)
nodenames_lca_seq.extend(count * [nodename])
c = []
runs = itertools.groupby(nodenames_lca_seq)
for run in runs:
c.append("{}:{}".format(str(run[0]), len(list(run[1]))))
krakmers = " ".join(c)
else:
krakmers = self.krakline_parser.krakmers
columns = [stat, self.krakline_parser.readname, krak_ass, str(self.krakline_parser.readlen), krakmers]
print("\t".join(columns), file=self.output_fo)
@staticmethod
def bitarray_block(alen, blen, pos):
"""Create a bitarray containing a block of one's.
Args:
alen (int): Array length.
blen (int): Block length (within the array).
pos (int): Position of the block (0-based).
Return:
bitarray (bitarray)
"""
return bitarray(pos * "0" + blen * "1" + (alen - pos - blen) * "0")
def diagnostics(self):
"""Print debug messages.
"""
print("---------------------", file=sys.stderr)
print("Alignment diagnostics", file=sys.stderr)
print("---------------------", file=sys.stderr)
print("Alignment.hitmasks_dict: ", dict(self.hitmasks_dict), file=sys.stderr)
print("Alignment.covmasks_dict: ", dict(self.covmasks_dict), file=sys.stderr)
print("Alignment.ass_dict: ", dict(self.ass_dict), file=sys.stderr)
print("Alignment.max_nodenames: ", self.max_nodenames, file=sys.stderr)
print("Alignment.max_val: ", self.max_val, file=sys.stderr)
###############################################################################################
###############################################################################################
class TreeIndex:
"""Class for an indexed Phylogenetic tree.
Args:
tree_newick_fn (str): Filename of the phylogenetic tree.
k (int): K-mer size.
Attributes:
tree (ete3.Tree): Minimal subtree of the original phylogenetic tree.
k (int): K-mer size.
nodename_to_node (dict): node name => node.
nodename_to_samannot (dict): node name => string to append in SAM.
nodename_to_upnodenames (dict): node name => set of node names of ancestors.
nodename_to_kmercount (dict): nname => number of k-mers (full set).
"""
def __init__(self, tree_newick_fn, k):
tree = pro.load_nhx_tree(tree_newick_fn)
self.tree = pro.minimal_subtree(tree)
self.k = k
self.nodename_to_node = {}
self.nodename_to_kmercount = {}
self.nodename_to_samannot = {}
self.nodename_to_upnodenames = collections.defaultdict(lambda: set())
for node in self.tree.traverse("postorder"):
nodename = node.name
self.nodename_to_node[nodename] = node
self.nodename_to_kmercount[nodename] = int(node.kmers_full)
# annotations
tags_parts = []
try:
tags_parts.append("gi:Z:{}".format(node.gi))
except AttributeError:
pass
try:
tags_parts.append("sn:Z:{}".format(node.sci_name))
except AttributeError:
pass
try:
tags_parts.append("ra:Z:{}".format(node.rank))
except AttributeError:
pass
self.nodename_to_samannot[nodename] = "\t".join(tags_parts)
# set of upper nodes
while node.up:
node = node.up
self.nodename_to_upnodenames[nodename].add(node.name)
def lca(self, *node_names):
"""Return LCA for a given list of nodes.
*node_names (list of str): List of node names.
Returns:
str: Name of the LCA.
"""
assert len(node_names) > 0
if len(node_names) == 1:
return node_names[0]
nodes = list(map(lambda x: self.nodename_to_node[x], node_names))
lca = nodes[0].get_common_ancestor(nodes)
if lca.is_root() and len(lca.children) == 1:
lca = lca.children[0]
assert lca.name != "" #, [x.name for x in lca.children]
return lca.name
def diagnostics(self):
"""Print debug messages.
"""
print("---------------------", file=sys.stderr)
print("TreeIndex diagnostics", file=sys.stderr)
print("---------------------", file=sys.stderr)
print("TreeIndex.k: ", self.k, file=sys.stderr)
print("TreeIndex.nodename_to_node: ", self.nodename_to_node, file=sys.stderr)
print("TreeIndex.nodename_to_samannot: ", self.nodename_to_samannot, file=sys.stderr)
print("TreeIndex.nodename_to_upnodenames: ", self.nodename_to_upnodenames, file=sys.stderr)
print("TreeIndex.nodename_to_kmercount: ", self.nodename_to_kmercount, file=sys.stderr)
###############################################################################################
###############################################################################################
class KraklineParser():
"""Class for parsing Kraken-like input into a structure.
Attributes:
krakline (str): Original krakline.
readname (str): Name of the read.
raedlen (str): Length of the read.
seq (str): Sequence of nucleotides. None if unknown.
qual (str): Sequence of qualities. None if unknown.
kmer_blocks (list of (list of str, int)): Assigned k-mer blocks, list of (nodenames, count).
"""
def __init__(self):
self.krakline = None
self.readname = None
self.readlen = None
self.seq = None
self.qual = None
self.kmer_blocks = []
def parse_krakline(self, krakline):
"""Load a krakline to the current object.
Args:
krakline (str): Kraken-like line.
"""
self.krakline = krakline
parts = krakline.strip().split("\t")
self.readname, _, readlen, self.krakmers = parts[1:5]
self.readlen = int(readlen)
if len(parts) == 7:
self.seq = parts[5]
self.qual = parts[6]
else:
self.seq = None
self.qual = None
# list of (count,list of nodes)
self.kmer_blocks = []
for block in self.krakmers.split(" "):
(ids, count) = block.split(":")
count = int(count)
nodenames = ids.split(",")
self.kmer_blocks.append((nodenames, count))
def check_consistency(self, k):
"""Check consistency of the fields loaded from the krakline.
Args:
k (int): k-mer length.
Returns:
bool: Consistent.
"""
if self.qlen < k:
return True
block_len_sum = sum([x[1] for x in self.kmer_blocks])
if not self.readlen == block_len_sum + k - 1:
return False
if self.seq is not None and len(self.seq) != self.readlen():
return False
if self.qual is not None and len(self.qual) != self.readlen():
return False
return True
def diagnostics(self):
"""Print debug messages.
"""
print("--------------------------", file=sys.stderr)
print("KraklineParser diagnostics", file=sys.stderr)
print("--------------------------", file=sys.stderr)
#print("KraklineParser.krakline: ", self.krakline.strip(), file=sys.stderr)
print("KraklineParser.readname: ", self.readname, file=sys.stderr)
print("KraklineParser.krakmers: ", self.krakmers, file=sys.stderr)
print("KraklineParser.readlen: ", self.readlen, file=sys.stderr)
print("KraklineParser.seq: ", self.seq, file=sys.stderr)
print("KraklineParser.qual: ", self.qual, file=sys.stderr)
print("KraklineParser.kmer_blocks: ", self.kmer_blocks, file=sys.stderr)
###############################################################################################
###############################################################################################
def assign_all_reads(
tree_fn,
inp_fo,
kmer_lca,
tie_lca,
form,
k,
measure,
annotate,
):
assert form in ["kraken", "sam"]
assert k > 1
tree_index = TreeIndex(
tree_newick_fn=tree_fn,
k=k,
)
if CONFIG['DIAGNOSTICS']:
tree_index.diagnostics()
assignment = Assignment(
output_fo=sys.stdout,
tree_index=tree_index,
kmer_lca=kmer_lca,
tie_lca=tie_lca,
annotate=annotate,
)
if form == "sam":
assignment.print_sam_header()
for krakline in inp_fo:
assignment.process_read(krakline, form=form, measure=measure)
def parse_args():
parser = argparse.ArgumentParser(description='Implementation of assignment algorithm')
parser.add_argument(
'tree_fn',
type=str,
metavar='<tree.nhx>',
help='phylogenetic tree (Newick/NHX)',
)
parser.add_argument(
'k',
type=int,
metavar='<k>',
help='k-mer length',
)
parser.add_argument(
'input_file',
type=argparse.FileType('r'),
metavar='<assignments.txt>',
help='assignments in generalized Kraken format',
)
parser.add_argument(
'-f',
choices=['kraken', 'sam'],
default='sam',
dest='format',
help='format of output [sam]',
)
parser.add_argument(
'-m',
choices=['h1', 'c1', 'c2', 'h2'],
default='h1',
dest='measure',
help='measure: h1=hit count, c1=coverage, h2=norm.hit count, c2=norm.coverage [h1]',
)
parser.add_argument(
'-A',
action='store_true',
dest='annotate',
help='annotate assignments',
)
parser.add_argument(
'-L',
action='store_true',
dest='tie_lca',
help='use LCA when tie (multiple assignments with the same score)',
)
parser.add_argument(
'-X',
action='store_true',
dest='kmer_lca',
help='use LCA for k-mers (multiple hits of a k-mer)',
)
parser.add_argument(
'-c',
dest='config',
metavar='STR',
nargs='*',
type=str,
default=[],
help='configuration (a JSON dictionary)',
)
args = parser.parse_args()
return args
def main():
args = parse_args()
global CONFIG
prophyle_conf_string = pro.load_prophyle_conf(CONFIG, args.config)
try:
assign_all_reads(
tree_fn=args.tree_fn,
inp_fo=args.input_file,
form=args.format,
k=args.k,
measure=args.measure,
annotate=args.annotate,
tie_lca=args.tie_lca,
kmer_lca=args.kmer_lca,
)
# Karel: I don't remember why I was considering also IOError here
# except (BrokenPipeError, IOError):
except BrokenPipeError:
# pipe error (e.g., when head is used)
sys.stderr.close()
sys.stdout.close()
exit(0)
except KeyboardInterrupt:
pro.message("Error: Keyboard interrupt")
pro.close_log()
exit(1)
finally:
try:
sys.stdout.flush()
except BrokenPipeError:
pass
finally:
try:
sys.stderr.flush()
except:
pass
if __name__ == "__main__":
main()