#-*- coding:utf-8 -*- # import sys, os, shutil, re, logging, subprocess, string, io, argparse, bisect, concurrent, gzip, zipfile, tarfile, json, pickle, time, datetime, random, math, copy, itertools, functools, collections, multiprocessing, threading, queue, signal, inspect, warnings, distutils.spawn import sys import os import pickle import re import torch import random import gzip from os.path import exists, join, getsize, isfile, isdir, abspath, basename from typing import Dict, Union, Optional, List, Tuple, Mapping import numpy as np import pandas as pd from tqdm.auto import trange, tqdm from concurrent.futures import ThreadPoolExecutor, as_completed from typing import Dict, Union, Optional, List, Tuple, Mapping import datasets def get_md5(aa_str): """ Calculate MD5 values for protein sequence """ import hashlib assert isinstance(aa_str, str), aa_str aa_str = aa_str.upper() return hashlib.md5(aa_str.encode('utf-8')).hexdigest() def load_fasta(seqFn, rem_tVersion=False, load_annotation=False, full_line_as_id=False): """ seqFn -- Fasta file or input handle (with readline implementation) rem_tVersion -- Remove version information. ENST000000022311.2 => ENST000000022311 load_annotation -- Load sequence annotation full_line_as_id -- Use the full head line (starts with >) as sequence ID. Can not be specified simutanouly with load_annotation Return: {tid1: seq1, ...} if load_annotation==False {tid1: seq1, ...},{tid1: annot1, ...} if load_annotation==True """ if load_annotation and full_line_as_id: raise RuntimeError("Error: load_annotation and full_line_as_id can not be specified simutanouly") if rem_tVersion and full_line_as_id: raise RuntimeError("Error: rem_tVersion and full_line_as_id can not be specified simutanouly") fasta = {} annotation = {} cur_tid = '' cur_seq = '' if isinstance(seqFn, str): IN = open(seqFn) elif hasattr(seqFn, 'readline'): IN = seqFn else: raise RuntimeError(f"Expected seqFn: {type(seqFn)}") for line in IN: if line[0] == '>': if cur_tid != '': fasta[cur_tid] = re.sub(r"\s", "", cur_seq) cur_seq = '' data = line[1:-1].split(None, 1) cur_tid = line[1:-1] if full_line_as_id else data[0] annotation[cur_tid] = data[1] if len(data)==2 else "" if rem_tVersion and '.' in cur_tid: cur_tid = ".".join(cur_tid.split(".")[:-1]) elif cur_tid != '': cur_seq += line.rstrip() if isinstance(seqFn, str): IN.close() if cur_seq != '': fasta[cur_tid] = re.sub(r"\s", "", cur_seq) if load_annotation: return fasta, annotation else: return fasta def load_msa_txt(file_or_stream, load_id=False, load_annot=False, sort=False): """ Read msa txt file Parmeters -------------- file_or_stream: file or stream to read (with read method) load_id: read identity and return Return -------------- msa: list of msa sequences, the first sequence in msa is the query sequence id_arr: Identity of msa sequences annotations: Annotations of msa sequences """ msa = [] id_arr = [] annotations = [] if hasattr(file_or_stream, 'read'): lines = file_or_stream.read().strip().split('\n') elif file_or_stream.endswith('.gz'): with gzip.open(file_or_stream) as IN: lines = IN.read().decode().strip().split('\n') else: with open(file_or_stream) as IN: lines = IN.read().strip().split('\n') # lines = open(file_or_stream).read().strip().split('\n') for idx,line in enumerate(lines): data = line.strip().split() if idx == 0: assert len(data) == 1, f"Expect 1 element for the 1st line, but got {data} in {file_or_stream}" q_seq = data[0] else: if len(data) >= 2: id_arr.append( float(data[1]) ) else: assert len(q_seq) == len(data[0]) id_ = round(np.mean([ r1==r2 for r1,r2 in zip(q_seq, data[0]) ]), 3) id_arr.append(id_) msa.append( data[0] ) if len(data) >= 3: annot = " ".join(data[2:]) annotations.append( annot ) else: annotations.append(None) id_arr = np.array(id_arr, dtype=np.float64) if sort: id_order = np.argsort(id_arr)[::-1] msa = [ msa[i] for i in id_order ] id_arr = id_arr[id_order] annotations = [ annotations[i] for i in id_order ] msa = [q_seq] + msa outputs = [ msa ] if load_id: outputs.append( id_arr ) if load_annot: outputs.append( annotations ) if len(outputs) == 1: return outputs[0] return outputs # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """ """ # You can copy an official description _DESCRIPTION = """ Fold class prediction is a scientific classification task that assigns protein sequences to one of 1,195 known folds. The primary application of this task lies in the identification of novel remote homologs among proteins of interest, such as emerging antibiotic-resistant genes and industrial enzymes. The study of protein fold holds great significance in fields like proteomics and structural biology, as it facilitates the analysis of folding patterns, leading to the discovery of remote homologies and advancements in disease research. """ _HOMEPAGE = "https://huggingface.co/datasets/genbio-ai/fold_prediction_rag" _LICENSE = "Apache license 2.0" class DownStreamConfig(datasets.BuilderConfig): """BuilderConfig for downstream taks dataset.""" def __init__(self, *args, **kwargs): """BuilderConfig downstream tasks dataset. Args: **kwargs: keyword arguments forwarded to super. """ super().__init__(*args, name=f"downstream", **kwargs) class DownStreamTasks(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.1.0") BUILDER_CONFIG_CLASS = DownStreamConfig BUILDER_CONFIGS = [ DownStreamConfig() ] DEFAULT_CONFIG_NAME = None def _info(self): features = datasets.Features( { "seq": datasets.Value("string"), "label": datasets.Value("int32"), "msa": datasets.Sequence(datasets.Value("string")), "str_emb": datasets.Array2D(shape=(None, 384), dtype='float32'), } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # This defines the different columns of the dataset and their types features=features, # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators( self, dl_manager: datasets.DownloadManager ) -> List[datasets.SplitGenerator]: # breakpoint() train_parquet_file = dl_manager.download(f"data/train-00000-of-00001.parquet") valid_parquet_file = dl_manager.download(f"data/valid-00000-of-00001.parquet") test_parquet_file = dl_manager.download(f"data/test-00000-of-00001.parquet") msa_path = dl_manager.download_and_extract(f"msa.tar") str_file = dl_manager.download(f"md5_to_str.fasta") codebook_file = dl_manager.download(f"codebook.pt") assert os.path.exists(join(msa_path, 'msa')) msa_path = join(msa_path, 'msa') return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "parquet_file": train_parquet_file, "msa_path": msa_path, "str_file": str_file, "codebook_file": codebook_file } ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={ "parquet_file": valid_parquet_file, "msa_path": msa_path, "str_file": str_file, "codebook_file": codebook_file } ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "parquet_file": test_parquet_file, "msa_path": msa_path, "str_file": str_file, "codebook_file": codebook_file } ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, parquet_file, msa_path, str_file, codebook_file): dataset = datasets.Dataset.from_parquet(parquet_file) md5_to_str = load_fasta(str_file) codebook = torch.load(codebook_file, 'cpu', weights_only=True).numpy() for key, item in enumerate(dataset): seq = item['seq'] label = item['label'] md5_val = get_md5(seq) if md5_val not in md5_to_str or md5_to_str[md5_val] == "": str_emb = np.zeros([len(seq), 384], dtype=np.float32) else: str_toks = np.array([ int(x) for x in md5_to_str[md5_val].split('-')]) str_emb = codebook[str_toks] msa = load_msa_txt(join(msa_path, md5_val+'.txt.gz')) assert len(msa[0]) == len(seq), f"Error: {len(msa[0])} != {len(seq)}" assert len(msa[0]) == str_emb.shape[0], f"Error: {len(msa[0])} != {str_emb.shape[0]}" # breakpoint() yield key, { "seq": seq, "label": label, "msa": msa, "str_emb": str_emb } def _as_dataset( self, split: Optional[datasets.Split] = None, **kwargs ) -> datasets.Dataset: dataset = super()._as_dataset(split=split, **kwargs) dataset.set_format( type="numpy", columns=["str_emb"], output_all_columns=True ) return dataset