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"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"#!/usr/bin/env python\n",
"\n",
"import re\n",
"import sys\n",
"import string\n",
"import argparse\n",
"import operator\n",
"import pandas as pd\n",
"import xgboost as xgb\n",
"import numpy as np\n",
"\n",
"VERSION='0.1.0'\n",
"\n",
"parser = argparse.ArgumentParser(description=\"\"\"\n",
"\n",
"DESCRIPTION\n",
"\n",
"EXAMPLE:\n",
"\n",
" \"\"\", formatter_class= argparse.RawTextHelpFormatter)\n",
"\n",
"parser.add_argument('--fasta', '-f',\n",
" type= str,\n",
" help='''Input fasta file to search. Use '-' to read the file from stdin.\n",
" \n",
" ''',default='./seq.fasta',\n",
" required= True)\n",
"\n",
"parser.add_argument('--classifier',\n",
" required= False,\n",
" default= 'G4Boost_classifier.json',\n",
" help='''Use specified classifier (G4Boost_classifier.sav)\n",
" ''')\n",
"parser.add_argument('--regressor',\n",
" required= False,\n",
" default= 'G4Boost_regressor.json',\n",
" help='''Use specified classifier (G4Boost_regressor.sav)\n",
" ''')\n",
"parser.add_argument('--maxloop', '-N',\n",
" type= int,\n",
" required= False,\n",
" default= 12,\n",
" help='''Maximum length of the loop. Default is to report up to 12nt.\n",
" ''')\n",
"parser.add_argument('--minloop', '-n',\n",
" type= int,\n",
" required= False,\n",
" default= 1,\n",
" help='''Minimum length of the loop. Default is to report up to 1nt.\n",
" ''')\n",
"parser.add_argument('--maxG', '-G',\n",
" type= int,\n",
" required= False,\n",
" default= 7,\n",
" help='''Maximum number of consecutive G bases within a G-stem. Default is to report up to 7 Gs.\n",
" ''')\n",
"parser.add_argument('--minG', '-g',\n",
" type= int,\n",
" required= False,\n",
" default= 2,\n",
" help='''Maximum number of consecutive G bases within a G-stem. Default is to report up to 1 Gs.\n",
" ''')\n",
"parser.add_argument('--loops', '-l',\n",
" type= int,\n",
" required= False,\n",
" default= 11,\n",
" help='''Maximum number of flexible loops separating the G-stems. Default is to report up to 11 Gs.\n",
" ''')\n",
"\n",
"parser.add_argument('--noreverse',\n",
" action= 'store_true',\n",
" help='''Do not search the reverse complement of the input fasta.\n",
" ''')\n",
"\n",
"parser.add_argument('--quiet', '-q',\n",
" action= 'store_true',\n",
" help='''Do not print progress report (i.e. sequence names as they are scanned). \n",
" ''')\n",
"\n",
"parser.add_argument('--version', '-v', action='version', version='%(prog)s ' + VERSION)\n",
"args = parser.parse_args()\n",
"\n",
"\n",
"\" ------------------------------[ Functions ]--------------------------------- \"\n",
"\n",
"def sort_table(table, cols):\n",
" for col in reversed(cols):\n",
" table = sorted(table, key=operator.itemgetter(col))\n",
" return(table)\n",
"\n",
"\n",
"def chrom_name(header):\n",
" if not header.startswith('>'):\n",
"# raise Exception('FASTA header does not start with \">\":\\n%s' % header)\n",
" return 'noID'\n",
" chr= re.sub('^>\\s*', '', header)\n",
" chr= re.sub('\\s.*', '', chr)\n",
" return chr\n",
"\n",
"def revcomp(seq):\n",
" complement = {'A': 'T', 'C': 'G', 'G': 'C', 'T': 'A', 'U': 'A', 'N': 'N'}\n",
" return \"\".join(complement.get(base, base) for base in reversed(seq))\n",
"\n",
"\n",
"def findall(seq, search):\n",
" count=-1\n",
" loc= 0\n",
" newloc=0\n",
" while newloc > -1:\n",
" newloc=seq[loc:].find(search)\n",
" loc=loc+newloc+1\n",
" count+=1\n",
" return count\n",
"\n",
"def initialize_dataFrame():\n",
" header=[\"seq\", \"seq_length\", \"g4motif\", 'maxgbase', 'maxgstem', \"length\", \"maxlbase\", \"minlbase\", \"G\", \"C\", \"GG\", \"CC\"]\n",
" data_dict={}\n",
" for h in header:\n",
" data_dict[h]=[]\n",
" return data_dict\n",
"\n",
"def topology(reg, seq):\n",
" split_seq=re.split(reg, seq)\n",
" if len(split_seq[-1])==0: gstem_base=split_seq[-2]\n",
" else: gstem_base=split_seq[-1]\n",
" g=len(gstem_base)\n",
" loops=[len(sp_seq)-g for sp_seq in split_seq]\n",
" loops=[lbase for lbase in loops if lbase>0]\n",
" maxlbase=max(loops)\n",
" minlbase=min(loops)\n",
" test=gstem_base\n",
" for sp_seq in split_seq:\n",
" if len(sp_seq)>g:\n",
" test+=sp_seq[g:].lower()\n",
" test+=gstem_base\n",
" return [test, len(test), len(loops)+1, g, maxlbase, minlbase]\n",
"\n",
"def update_dataFrame(features, reg, seq, ref):\n",
" [test, length, maxgstem, maxgbase, maxlbase, minlbase] = topology(reg, seq)\n",
" features['g4motif'].append(test)\n",
" features['length'].append(length)\n",
" features['seq_length'].append(len(ref))\n",
" features['maxgstem'].append(maxgstem)\n",
" features['maxgbase'].append(maxgbase)\n",
" features['maxlbase'].append(maxlbase)\n",
" features['minlbase'].append(minlbase)\n",
" features['G'].append(int(findall(ref,'G')*100/len(ref)))\n",
" features['GG'].append(int(findall(ref,'GG')*100/len(ref)))\n",
" features['C'].append(int(findall(ref,'C')*100/len(ref)))\n",
" features['CC'].append(int(findall(ref,'CC')*100/len(ref)))\n",
" return features\n",
"\n",
"\n",
"def findmotifs(reg, seq, start):\n",
" gquad_list=[]\n",
" for m in re.finditer(reg, seq):\n",
" seq= m.group(0)\n",
" quad_id= chrom + '_' + str(m.start()+start) + '_' + str(m.end()+start)\n",
" gquad_list.append([chrom, m.start()+start, m.end()+start, quad_id, len(m.group(0)), '+', seq])\n",
" return gquad_list\n",
"# -----------------------------------------------------------------------------\n",
"\n",
"\n",
"if args.fasta != '-':\n",
" ref_seq_fh= open(args.fasta)\n",
" output= args.fasta+'.gff'\n",
"else:\n",
" ref_seq_fh= sys.stdin\n",
" output='G4Boost_quadruplexes.gff'\n",
"\n",
"# ref_seq=[]\n",
"# line= ref_seq_fh.readline()\n",
"# if chrom != 'noID': line= ref_seq_fh.readline()\n",
"# else: chrom = line.strip()\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"\n",
"import re\n",
"import sys\n",
"import string\n",
"import argparse\n",
"import operator\n",
"import pandas as pd\n",
"import xgboost as xgb\n",
"import numpy as np\n",
"\n",
"def predict(seqs,):\n",
" gquad_list= []\n",
"# eof= False\n",
"\n",
"\n",
"\n",
"#if args.fasta != '-': output= args.fasta+'.gff'\n",
"#else: output = 'G4Boost_quadruplexes.gff'\n",
"#out=open(output, 'w')\n",
"\n",
"\n",
" sys.stderr.write('Starting stability prediction!\\n\\n')\n",
" regressor = xgb.XGBRegressor()\n",
" classifier = xgb.XGBClassifier()\n",
" regressor.load_model(args.regressor)\n",
" classifier.load_model(args.classifier)\n",
" preds = []\n",
" all_features = []\n",
" for i in range(len(seqs)):\n",
" chrom = str(i)\n",
" gb=range(args.minG, args.maxG+1)[::-1]\n",
" gs=range(3, args.loops+1)[::-1]\n",
" longest = (args.maxG + args.maxloop) * args.loops + args.maxG\n",
" features=initialize_dataFrame()\n",
" ref_seq = seqs[i]\n",
" # ref_seq= ''.join(ref_seq)\n",
" ref_seq=ref_seq.upper().replace('U', 'T')\n",
" rev_ref_seq=revcomp(ref_seq)\n",
" seqlen= len(ref_seq)\n",
" for g in gb:\n",
" for s in gs:\n",
" gstem_base=''\n",
" for i in range(g): gstem_base+=\"G\"\n",
" reg=\"\"\n",
" for i in range(s): reg+='([gG]{%d}\\w{%d,%d})' % (g , args.minloop, args.maxloop)\n",
" reg+='([gG]{%d})' % (g)\n",
" for m in re.finditer(reg, ref_seq):\n",
" seq= m.group(0)\n",
" start=m.start()\n",
" end=m.end()\n",
" if len(ref_seq) > longest: ref = seq\n",
" else: ref = ref_seq\n",
" quad_id= chrom + '_' + str(m.start()) + '_' + str(m.end())\n",
" gquad_list.append([chrom, start, end, quad_id, len(seq), '+', seq])\n",
" if seq not in features['g4motif']:\n",
" features = update_dataFrame(features, reg, seq, ref)\n",
" features['seq'].append(chrom)\n",
" temp=''\n",
" for i in range(start,end): temp+='N'\n",
" ref_seq=ref_seq[:start]+temp+ref_seq[end:]\n",
" if args.noreverse is False:\n",
" for m in re.finditer(reg, rev_ref_seq):\n",
" seq= m.group(0)\n",
" start=m.start()\n",
" end=m.end()\n",
" if len(rev_ref_seq) > longest: ref = seq\n",
" else: ref = rev_ref_seq\n",
" quad_id= chrom + '_' + str(m.start()) + '_' + str(m.end())\n",
" gquad_list.append([chrom, seqlen-end, seqlen-start, quad_id, len(seq), '-', seq])\n",
" if seq not in features['g4motif']:\n",
" features = update_dataFrame(features, reg, seq, ref)\n",
" features['seq'].append(chrom)\n",
" temp=''\n",
" for i in range(start,end): temp+='N'\n",
" rev_ref_seq=rev_ref_seq[:start]+temp+rev_ref_seq[end:]\n",
" gquad_sorted= sort_table(gquad_list, (1,2,3))\n",
" gquad_list= []\n",
" for xline in gquad_sorted:\n",
" xline= '\\t'.join([str(x) for x in xline])\n",
" with open(output, 'a') as out: out.write(xline+'\\n')\n",
"\n",
"\n",
" #---------------\n",
"\n",
"\n",
"\n",
"\n",
" selected=[\"seq_length\", \"length\", \"maxgstem\" ,\"maxgbase\", \"maxlbase\", \"minlbase\", \"G\", \"C\", \"GG\", \"CC\"]\n",
" # print(features)\n",
" # del features['G-quartet']\n",
" # del features['loops']\n",
" features=pd.DataFrame.from_dict(features)\n",
" X_test = features[selected]\n",
"\n",
" X_test.columns = [\"length\", \"len\", \"maxgstem\", \"maxgbase\", \"maxlbase\", \"minlbase\", \"G\", \"C\", \"GG\", \"CC\"]\n",
"\n",
" # print(X_test)\n",
"\n",
"\n",
" # X_test=xgb.DMatrix(X_test)\n",
" g4_pred=classifier.predict(X_test)\n",
" if len(g4_pred) < 1:\n",
" preds.append(0)\n",
" else:\n",
" g4_pred_proba=classifier.predict_proba(X_test)[:, 1]\n",
" mfe_pred = regressor.predict(X_test)\n",
" if np.max(g4_pred_proba) < 0.5:\n",
" preds.append(0)\n",
" else:\n",
" preds.append(1)\n",
" # features['g4_pred']=g4_pred\n",
" # features['g4_prob']=g4_pred_proba\n",
" # features['mfe_pred']=mfe_pred\n",
" # features['maxgstem']=[l-1 for l in features['maxgstem']]\n",
"\n",
" # if args.fasta != '-': output= args.fasta+'.g4scores.csv'\n",
" # else: output = 'G4Boost_quadruplexes.g4.csv'\n",
" # features.to_csv(output,sep='\\t',index=False)\n",
"\n",
"\n",
" # print(float(np.sum(preds))/len(preds))\n",
" return preds"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from Bio import SeqIO\n",
"import pandas as pd\n",
"import sys\n",
"\n",
"# read generated\n",
"lines = []\n",
"for record in SeqIO.parse('/data4/sina/UTR/MEME/1024_10000_init.fasta','fasta'):\n",
" lines.append(str(record.seq))\n",
"\n",
"mut_inits = []\n",
"for i in range(len(lines)):\n",
" mut_inits.append(lines[i].replace('\\n','')[:50])\n",
"\n",
"\n",
"# read optimus\n",
"optimus = []\n",
"for record in SeqIO.parse('/data4/sina/UTR/MEME/optimus_fasta.fasta','fasta'):\n",
" optimus.append(str(record.seq))\n",
"\n",
"\n",
"# read generated\n",
"inits = []\n",
"for record in SeqIO.parse('/data4/sina/UTR/MEME/1024_10000_init.fasta','fasta'):\n",
" inits.append(str(record.seq))\n",
"\n",
"opts = []\n",
"for record in SeqIO.parse('/data4/sina/UTR/MEME/1024_10000_opt.fasta','fasta'):\n",
" opts.append(str(record.seq))\n",
"\n",
"\n",
"# read natural:\n",
"df = pd.read_csv('./../UTRGAN/data/utrdb2.csv')\n",
"df = df['seq'].to_numpy()\n",
"nats = []\n",
"for i in range(len(df)):\n",
" if len(df[i])< 129 and len(df[i]) > 64:\n",
" nats.append(df[i].replace('T','U'))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Starting stability prediction!\n",
"\n",
"/var/anaconda3/envs/tf25/lib/python3.10/site-packages/xgboost/core.py:160: UserWarning: [12:44:31] WARNING: /croot/xgboost-split_1713972711803/work/cpp_src/src/learner.cc:873: Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3.\n",
" warnings.warn(smsg, UserWarning)\n",
"/var/anaconda3/envs/tf25/lib/python3.10/site-packages/xgboost/core.py:160: UserWarning: [12:44:32] WARNING: /croot/xgboost-split_1713972711803/work/cpp_src/src/learner.cc:873: Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3.\n",
" warnings.warn(smsg, UserWarning)\n",
"Starting stability prediction!\n",
"\n",
"/var/anaconda3/envs/tf25/lib/python3.10/site-packages/xgboost/core.py:160: UserWarning: [12:54:47] WARNING: /croot/xgboost-split_1713972711803/work/cpp_src/src/learner.cc:873: Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3.\n",
" warnings.warn(smsg, UserWarning)\n",
"Starting stability prediction!\n",
"\n",
"/var/anaconda3/envs/tf25/lib/python3.10/site-packages/xgboost/core.py:160: UserWarning: [12:55:05] WARNING: /croot/xgboost-split_1713972711803/work/cpp_src/src/learner.cc:873: Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3.\n",
" warnings.warn(smsg, UserWarning)\n",
"Starting stability prediction!\n",
"\n",
"/var/anaconda3/envs/tf25/lib/python3.10/site-packages/xgboost/core.py:160: UserWarning: [12:55:24] WARNING: /croot/xgboost-split_1713972711803/work/cpp_src/src/learner.cc:873: Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3.\n",
" warnings.warn(smsg, UserWarning)\n",
"Starting stability prediction!\n",
"\n",
"/var/anaconda3/envs/tf25/lib/python3.10/site-packages/xgboost/core.py:160: UserWarning: [12:55:33] WARNING: /croot/xgboost-split_1713972711803/work/cpp_src/src/learner.cc:873: Found JSON model saved before XGBoost 1.6, please save the model using current version again. The support for old JSON model will be discontinued in XGBoost 2.3.\n",
" warnings.warn(smsg, UserWarning)\n"
]
}
],
"source": [
"nat_pred = predict(nats)\n",
"init_pred = predict(inits)\n",
"opt_pred = predict(opts)\n",
"optimus_pred = predict(optimus)\n",
"mut_init_pred = predict(mut_inits)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"236.76727819548873\n",
"209\n",
"199\n",
"16\n",
"162\n"
]
}
],
"source": [
"print(np.sum(nat_pred)/len(nat_pred)*1024.)\n",
"print(np.sum(init_pred))\n",
"print(np.sum(opt_pred))\n",
"print(np.sum(optimus_pred))\n",
"print(np.sum(mut_init_pred))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "tf25",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.11"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
|