多序列比对（Multiple Sequence Alignment，MSA）是对多个生物序列进行对齐的过程，以揭示它们之间的共同模式和结构。在生物信息学中，有多种文件格式用于存储多序列比对的结果，其中包括Stockholm (.sto) 和 A3M (.a3m) 格式。

注释信息： Stockholm文件通常包含更多的注释信息，提供关于序列和比对的额外信息。相比之下，A3M文件主要关注于序列本身。

应用领域： Stockholm文件广泛用于多种生物信息学工具，如HMMER。A3M文件通常用于蛋白质结构预测和深度学习模型的训练，如Alphafold。

from typing import Sequence, Iterable, Optionaldef _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_reselif sequence_res != '-':yield sequence_res.lower()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 = Falsefor line in stockholm_format.splitlines():reached_max_sequences = max_sequences and len(sequences) >= max_sequencesif 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:continuesequences[seqname] = ''sequences[seqname] += aligned_seqfor 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':continueif reached_max_sequences and seqname not in sequences:continuedescriptions[seqname] = valueif len(descriptions) == len(sequences):break# Convert sto format to a3m line by linea3m_sequences = {}if remove_first_row_gaps:# query_sequence is assumed to be the first sequencequery_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_sequencefasta_chunks = (f">{k} {descriptions.get(k, '')}\n{a3m_sequences[k]}"for k in a3m_sequences)return '\n'.join(fasta_chunks) + '\n'  # Include terminating newlinewith open("test_aln.sto") as f:sto_str = f.read()print(sto_str)
print("====")
a3m_str = convert_stockholm_to_a3m(sto_str)
print(a3m_str)