python运行hhsearch二进制命令的包装器类
hhsearch 是 HMM-HMM（Hidden Markov Model to Hidden Markov Model）比对方法的一部分，属于 HMMER 软件套件。它用于进行蛋白质序列的高效比对，特别适用于检测远缘同源性。
以下是 hhsearch 的一些主要特点和用途：
-
HMM-HMM比对：
hhsearch使用隐藏马尔可夫模型（HMM）来表示蛋白质家族的模型。与传统的序列-序列比对方法不同，HMM-HMM比对考虑了氨基酸残基的多序列信息，使得在比对中能够更好地捕捉蛋白质家族的模式和结构。 -
检测远缘同源性：
hhsearch的一个主要优势是其能够检测到相对远离的同源关系。它在比对中引入了更多的信息，从而提高了对远缘同源蛋白的发现能力。 -
灵敏度和特异性：
hhsearch的设计旨在在维持高灵敏度的同时，减少假阳性的比对。这使得它在寻找结构和功能相似性时更为可靠。 -
数据库搜索： 用户可以使用
hhsearch在大型蛋白质数据库中搜索与给定蛋白质序列相似的蛋白质。
"""Library to run HHsearch from Python."""
import glob
import os
import subprocess
from typing import Sequence, Optional, List, Iterable
from absl import logging
import contextlib
import tempfile
import dataclasses
import contextlib
import time
import shutil
import re
@contextlib.contextmanager
def timing(msg: str):
logging.info('Started %s', msg)
tic = time.time()
yield
toc = time.time()
logging.info('Finished %s in %.3f seconds', msg, toc - tic)
@dataclasses.dataclass(frozen=True)
class TemplateHit:
"""Class representing a template hit."""
index: int
name: str
aligned_cols: int
sum_probs: Optional[float]
query: str
hit_sequence: str
indices_query: List[int]
indices_hit: List[int]
@contextlib.contextmanager
def tmpdir_manager(base_dir: Optional[str] = None):
"""Context manager that deletes a temporary directory on exit."""
tmpdir = tempfile.mkdtemp(dir=base_dir)
try:
yield tmpdir
finally:
shutil.rmtree(tmpdir, ignore_errors=True)
def parse_hhr(hhr_string: str) -> Sequence[TemplateHit]:
"""Parses the content of an entire HHR file."""
lines = hhr_string.splitlines()
# Each .hhr file starts with a results table, then has a sequence of hit
# "paragraphs", each paragraph starting with a line 'No '. We
# iterate through each paragraph to parse each hit.
block_starts = [i for i, line in enumerate(lines) if line.startswith('No ')]
hits = []
if block_starts:
block_starts.append(len(lines)) # Add the end of the final block.
for i in range(len(block_starts) - 1):
hits.append(_parse_hhr_hit(lines[block_starts[i]:block_starts[i + 1]]))
return hits
def _parse_hhr_hit(detailed_lines: Sequence[str]) -> TemplateHit:
"""Parses the detailed HMM HMM comparison section for a single Hit.
This works on .hhr files generated from both HHBlits and HHSearch.
Args:
detailed_lines: A list of lines from a single comparison section between 2
sequences (which each have their own HMM's)
Returns:
A dictionary with the information from that detailed comparison section
Raises:
RuntimeError: If a certain line cannot be processed
"""
# Parse first 2 lines.
number_of_hit = int(detailed_lines[0].split()[-1])
name_hit = detailed_lines[1][1:]
# Parse the summary line.
pattern = (
'Probab=(.*)[\t ]*E-value=(.*)[\t ]*Score=(.*)[\t ]*Aligned_cols=(.*)[\t'
' ]*Identities=(.*)%[\t ]*Similarity=(.*)[\t ]*Sum_probs=(.*)[\t '
']*Template_Neff=(.*)')
match = re.match(pattern, detailed_lines[2])
if match is None:
raise RuntimeError(
'Could not parse section: %s. Expected this: \n%s to contain summary.' %
(detailed_lines, detailed_lines[2]))
(_, _, _, aligned_cols, _, _, sum_probs, _) = [float(x)
for x in match.groups()]
# The next section reads the detailed comparisons. These are in a 'human
# readable' format which has a fixed length. The strategy employed is to
# assume that each block starts with the query sequence line, and to parse
# that with a regexp in order to deduce the fixed length used for that block.
query = ''
hit_sequence = ''
indices_query = []
indices_hit = []
length_block = None
for line in detailed_lines[3:]:
# Parse the query sequence line
if (line.startswith('Q ') and not line.startswith('Q ss_dssp') and
not line.startswith('Q ss_pred') and
not line.startswith('Q Consensus')):
# Thus the first 17 characters must be 'Q ', and we can parse
# everything after that.
# start sequence end total_sequence_length
patt = r'[\t ]*([0-9]*) ([A-Z-]*)[\t ]*([0-9]*) \([0-9]*\)'
groups = _get_hhr_line_regex_groups(patt, line[17:])
# Get the length of the parsed block using the start and finish indices,
# and ensure it is the same as the actual block length.
start = int(groups[0]) - 1 # Make index zero based.
delta_query = groups[1]
end = int(groups[2])
num_insertions = len([x for x in delta_query if x == '-'])
length_block = end - start + num_insertions
assert length_block == len(delta_query)
# Update the query sequence and indices list.
query += delta_query
_update_hhr_residue_indices_list(delta_query, start, indices_query)
elif line.startswith('T '):
# Parse the hit sequence.
if (not line.startswith('T ss_dssp') and
not line.startswith('T ss_pred') and
not line.startswith('T Consensus')):
# Thus the first 17 characters must be 'T ', and we can
# parse everything after that.
# start sequence end total_sequence_length
patt = r'[\t ]*([0-9]*) ([A-Z-]*)[\t ]*[0-9]* \([0-9]*\)'
groups = _get_hhr_line_regex_groups(patt, line[17:])
start = int(groups[0]) - 1 # Make index zero based.
delta_hit_sequence = groups[1]
assert length_block == len(delta_hit_sequence)
# Update the hit sequence and indices list.
hit_sequence += delta_hit_sequence
_update_hhr_residue_indices_list(delta_hit_sequence, start, indices_hit)
return TemplateHit(
index=number_of_hit,
name=name_hit,
aligned_cols=int(aligned_cols),
sum_probs=sum_probs,
query=query,
hit_sequence=hit_sequence,
indices_query=indices_query,
indices_hit=indices_hit,
)
def _get_hhr_line_regex_groups(
regex_pattern: str, line: str) -> Sequence[Optional[str]]:
match = re.match(regex_pattern, line)
if match is None:
raise RuntimeError(f'Could not parse query line {line}')
return match.groups()
def _update_hhr_residue_indices_list(
sequence: str, start_index: int, indices_list: List[int]):
"""Computes the relative indices for each residue with respect to the original sequence."""
counter = start_index
for symbol in sequence:
if symbol == '-':
indices_list.append(-1)
else:
indices_list.append(counter)
counter += 1
class HHSearch:
"""Python wrapper of the HHsearch binary."""
def __init__(self,
*,
binary_path: str,
databases: Sequence[str],
maxseq: int = 1_000_000):
"""Initializes the Python HHsearch wrapper.
Args:
binary_path: The path to the HHsearch executable.
databases: A sequence of HHsearch database paths. This should be the
common prefix for the database files (i.e. up to but not including
_hhm.ffindex etc.)
maxseq: The maximum number of rows in an input alignment. Note that this
parameter is only supported in HHBlits version 3.1 and higher.
Raises:
RuntimeError: If HHsearch binary not found within the path.
"""
self.binary_path = binary_path
self.databases = databases
self.maxseq = maxseq
#for database_path in self.databases:
# if not glob.glob(database_path + '_*'):
# logging.error('Could not find HHsearch database %s', database_path)
# raise ValueError(f'Could not find HHsearch database {database_path}')
@property
def output_format(self) -> str:
return 'hhr'
@property
def input_format(self) -> str:
return 'a3m'
def query(self, a3m: str) -> str:
"""Queries the database using HHsearch using a given a3m."""
with tmpdir_manager() as query_tmp_dir:
input_path = os.path.join(query_tmp_dir, 'query.a3m')
hhr_path = os.path.join(query_tmp_dir, 'output.hhr')
with open(input_path, 'w') as f:
f.write(a3m)
db_cmd = []
for db_path in self.databases:
db_cmd.append('-d')
db_cmd.append(db_path)
cmd = [self.binary_path,
'-i', input_path,
'-o', hhr_path,
'-maxseq', str(self.maxseq)
] + db_cmd
print("cmd:",cmd)
logging.info('Launching subprocess "%s"', ' '.join(cmd))
process = subprocess.Popen(
cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
with timing('HHsearch query'):
stdout, stderr = process.communicate()
retcode = process.wait()
if retcode:
# Stderr is truncated to prevent proto size errors in Beam.
raise RuntimeError(
'HHSearch failed:\nstdout:\n%s\n\nstderr:\n%s\n' % (
stdout.decode('utf-8'), stderr[:100_000].decode('utf-8')))
with open(hhr_path) as f:
hhr = f.read()
return hhr
def get_template_hits(self,
output_string: str,
input_sequence: str) -> Sequence[TemplateHit]:
"""Gets parsed template hits from the raw string output by the tool."""
del input_sequence # Used by hmmseach but not needed for hhsearch.
return parse_hhr(output_string)
def convert_stockholm_to_a3m (stockholm_format: str,
max_sequences: Optional[int] = None,
remove_first_row_gaps: bool = True) -> str:
"""Converts MSA in Stockholm format to the A3M format."""
descriptions = {}
sequences = {}
reached_max_sequences = False
for line in stockholm_format.splitlines():
reached_max_sequences = max_sequences and len(sequences) >= max_sequences
if line.strip() and not line.startswith(('#', '//')):
# Ignore blank lines, markup and end symbols - remainder are alignment
# sequence parts.
seqname, aligned_seq = line.split(maxsplit=1)
if seqname not in sequences:
if reached_max_sequences:
continue
sequences[seqname] = ''
sequences[seqname] += aligned_seq
for line in stockholm_format.splitlines():
if line[:4] == '#=GS':
# Description row - example format is:
# #=GS UniRef90_Q9H5Z4/4-78 DE [subseq from] cDNA: FLJ22755 ...
columns = line.split(maxsplit=3)
seqname, feature = columns[1:3]
value = columns[3] if len(columns) == 4 else ''
if feature != 'DE':
continue
if reached_max_sequences and seqname not in sequences:
continue
descriptions[seqname] = value
if len(descriptions) == len(sequences):
break
# Convert sto format to a3m line by line
a3m_sequences = {}
if remove_first_row_gaps:
# query_sequence is assumed to be the first sequence
query_sequence = next(iter(sequences.values()))
query_non_gaps = [res != '-' for res in query_sequence]
for seqname, sto_sequence in sequences.items():
# Dots are optional in a3m format and are commonly removed.
out_sequence = sto_sequence.replace('.', '')
if remove_first_row_gaps:
out_sequence = ''.join(
_convert_sto_seq_to_a3m(query_non_gaps, out_sequence))
a3m_sequences[seqname] = out_sequence
fasta_chunks = (f">{k} {descriptions.get(k, '')}\n{a3m_sequences[k]}"
for k in a3m_sequences)
return '\n'.join(fasta_chunks) + '\n' # Include terminating newline
def _convert_sto_seq_to_a3m(
query_non_gaps: Sequence[bool], sto_seq: str) -> Iterable[str]:
for is_query_res_non_gap, sequence_res in zip(query_non_gaps, sto_seq):
if is_query_res_non_gap:
yield sequence_res
elif sequence_res != '-':
yield sequence_res.lower()
if __name__ == "__main__":
### 1. 准备输入数据
## 输入序列先通过Jackhmmer多次迭代从uniref90，MGnify数据库搜索同源序列，输出的多序列比对文件（如globins4.sto），转化为a3m格式后，再通过hhsearch从pdb数据库中找到同源序列
input_fasta_file = '/home/zheng/test/Q94K49.fasta'
## input_sequence
with open(input_fasta_file) as f:
input_sequence = f.read()
test_templates_sto_file = "/home/zheng/test/Q94K49_aln.sto"
with open(test_templates_sto_file) as f:
test_templates_sto = f.read()
## sto格式转a3m格式（）
test_templates_a3m = convert_stockholm_to_a3m(test_templates_sto)
hhsearch_binary_path = "/home/zheng/software/hhsuite-3.3.0-SSE2-Linux/bin/hhsearch"
### 2.类实例化
# scop70_1.75文件名前缀
scop70_database_path = "/home/zheng/database/scop70_1.75_hhsuite3/scop70_1.75"
pdb70_database_path = "/home/zheng/database/pdb70_from_mmcif_latest/pdb70"
#hhsuite数据库下载地址：https://wwwuser.gwdg.de/~compbiol/data/hhsuite/databases/hhsuite_dbs/
## 单一数据库
#template_searcher = HHSearch(binary_path = hhsearch_binary_path,
# databases = [scop70_database_path])
## 多个数据库
database_lst = [scop70_database_path, pdb70_database_path]
template_searcher = HHSearch(binary_path = hhsearch_binary_path,
databases = database_lst)
### 3. 同源序列搜索
## 搜索结果返回.hhr文件字符串
templates_result = template_searcher.query(test_templates_a3m)
print(templates_result)
## pdb序列信息列表
template_hits = template_searcher.get_template_hits(
output_string=templates_result, input_sequence=input_sequence)
print(template_hits)