python解析.hhr文件

"HHR" 文件格式通常与HMMER（Hidden Markov Model for Molecular Evolution）软件相关联，该软件用于执行隐马尔可夫模型的序列比对和搜索。HMMER用于生物信息学中的蛋白质和核酸序列比对任务，例如蛋白质家族分类、序列相似性搜索等。HHR 文件是HMMER比对结果的一种输出格式，它包含了与序列比对相关的信息。

 hhr文件格式介绍

import dataclasses
import re
from typing import List, Optional, Sequence
 
@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]
 
        
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
 
        
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=(.*)')
    
    # "()":标记一个子表达式的开始和结束位置. 
    # '.':匹配除换行符 \n 之外的任何单字符
    # '*': 匹配前面的子表达式零次或多次。
    # '[]':匹配其中列举的字符，可以是很多单个，也可以范围
    # '\t': 换行符
    
    pattern = (
        'Probab=(.*)[\t ]*E-value=(.*)[\t ]*Score=(.*)[\t ]*Aligned_cols=(.*)[\t'
        ' ]*Identities=(.*)%[\t ]*Similarity=(.*)[\t ]*Sum_probs=(.*)')
    
    # Probab=100.00  E-value=1.5e-142  Score=1017.19  Aligned_cols=366  Identities=56%  Similarity=1.021  Sum_probs=364.0
    match = re.match(pattern, detailed_lines[2])
    # 示例detailed_lines[2]：Probab=91.65  E-value=0.12  Score=40.00  Aligned_cols=62  Identities=16%  Similarity=0.149  Sum_probs=42.0
    # print(detailed_lines[2])
    # print(match)
    
    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]*) \([0-9]*\)'
            # pattern示例：306 TATYDFKMADLQQVAPEATVRRFLQGRRNLAKVCALLRGYLLPGAPADL  355 (431)
            groups = _get_hhr_line_regex_groups(patt, line[17:])
            
            #print("groups")
            #print(groups)
            
            # 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 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
 
 
## query.hhr文件路径 path/to/hh-suite/data/query.hhr
with open("query.hhr") as f:
    hhr_str = f.read()
 
hits = parse_hhr(hhr_str)
print(len(hits))
print(hits)

参考：

python正则表达式