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miRvestigator.py
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#################################################################
# @Program: miRvestigator.py #
# @Version: 1 #
# @Author: Chris Plaisier #
# @Sponsored by: #
# Nitin Baliga, ISB #
# Institute for Systems Biology #
# 1441 North 34th Street #
# Seattle, Washington 98103-8904 #
# (216) 732-2139 #
# @Also Sponsored by: #
# Luxembourg Systems Biology Grant #
# #
# If this program is used in your analysis please mention who #
# built it. Thanks. :-) #
# #
# Copyrighted by Chris Plaisier 8/12/2009 #
#################################################################
from copy import deepcopy
from sys import stdout
import os, cPickle
# A class designed to compute and hold the information from analyzing
# miRNA seeds against motifs from 3' UTRs.
#
# Variables:
# miRNAver - version of miRBase.org used.
# miRNAs - list of miRNAs where each entry is unique, and names are appened for overlapping seeds.
# permKMers - list of possible Kmers given the seed length.
#
#
# Functions:
# addSorted(curList,newItem) - adds an entry into miRNAScores[pssm] so that the list is sorted in the end. Ties are possible and not handled at this level.
# allKmers(length) - creates a list of all possible Kmers given a specific length and alphabet.
# setMiRNAs(seedStart,seedEnd) - gets miRNAs from miRBase.org from the latest release.
# getTopHit(pssm.getName()) - gets top hit for a PSSM.
# getScoreList(pssm.getName()) - returns scores for all miRNA seeds for given PSSM, sorted of course.
#
class miRvestigator:
# Initialize and start the run
def __init__(self,pssms,seqs3pUTR,seedModel=[6,7,8], minor=True, p5=True, p3=True, textOut=True, wobble=True, wobbleCut=0.25):
print '\nmiRvestigator analysis started...'
self.pssms = pssms
self.miRNAs = self.setMiRNAs(0,8,minor,p5,p3)
# Trim sequences down
self.miRNAs_6mer_1 = self.trimSeqs(deepcopy(self.miRNAs),0,6)
self.miRNAs_6mer_2 = self.trimSeqs(deepcopy(self.miRNAs),1,7)
self.miRNAs_6mer_3 = self.trimSeqs(deepcopy(self.miRNAs),2,8)
self.miRNAs_7mer_m8 = self.trimSeqs(deepcopy(self.miRNAs),1,8)
self.miRNAs_7mer_a1 = self.trimSeqs(deepcopy(self.miRNAs),0,7)
self.miRNAs_8mer = self.trimSeqs(deepcopy(self.miRNAs),0,8)
p3utrSeqs = 'X'.join(seqs3pUTR)
dirName = 'miRNA'
if not os.path.exists(dirName):
os.mkdir(dirName)
if 6 in seedModel:
print 'Screening out 6mers not present in 3\' UTRs...'
if not os.path.exists('miRNA/permKMers_6mers.pkl'):
permKMers_6mer = self.allKmers(6)
tmpKMers = []
for i in permKMers_6mer:
if not p3utrSeqs.find(i)==-1:
tmpKMers.append(i)
self.permKMers_6mer = tmpKMers
pklFile = open('miRNA/permKMers_6mers.pkl','wb')
cPickle.dump(self.permKMers_6mer,pklFile)
else:
pklFile = open('miRNA/permKMers_6mers.pkl','rb')
self.permKMers_6mer = cPickle.load(pklFile)
pklFile.close()
if 7 in seedModel:
print 'Screening out 7mers not present in 3\' UTRs...'
if not os.path.exists('miRNA/permKMers_7mers.pkl'):
permKMers_7mer = self.allKmers(7)
tmpKMers = []
for i in permKMers_7mer:
if not p3utrSeqs.find(i)==-1:
tmpKMers.append(i)
self.permKMers_7mer = tmpKMers
pklFile = open('miRNA/permKMers_7mers.pkl','wb')
cPickle.dump(self.permKMers_7mer,pklFile)
else:
pklFile = open('miRNA/permKMers_7mers.pkl','rb')
self.permKMers_7mer = cPickle.load(pklFile)
pklFile.close()
if 8 in seedModel:
print 'Screening out 8mers not present in 3\' UTRs...'
if not os.path.exists('miRNA/permKMers_8mers.pkl'):
permKMers_8mer = self.allKmers(8)
tmpKMers = []
for i in permKMers_8mer:
if not p3utrSeqs.find(i)==-1:
tmpKMers.append(i)
self.permKMers_8mer = tmpKMers
pklFile = open('miRNA/permKMers_8mers.pkl','wb')
cPickle.dump(self.permKMers_8mer,pklFile)
else:
pklFile = open('miRNA/permKMers_8mers.pkl','rb')
self.permKMers_8mer = cPickle.load(pklFile)
pklFile.close()
print 'Done.\n'
miRNAScores = {}
cur = 1
# Building HMM Model
for pssm in pssms:
print 'Building HMM model for '+str(pssm.getConsensusMotif())+'...'
miRNAScores[pssm.getName()] = []
# Then setup the HMM
## States ##
## and Starting Probabilities ##
# NM1 = no match 1
# NM2 = no match 2
# PSSMi = PSSM at spot i
maxPSSMi = len(pssm.getMatrix())
states = ['NM1', 'NM2']
sp = {'NM1': float(1)/float(maxPSSMi+1), 'NM2': 0}
# Add the PSSM states
for i in range(maxPSSMi):
states += ['PSSM'+str(i)]
sp['PSSM'+str(i)] = float(1)/float(maxPSSMi+1)
if wobble==True:
states += ['WOBBLE'+str(i)]
sp['WOBBLE'+str(i)] = 0
## Transition probabilities
tp = {}
# NM1
nm1_2_nm1 = 0.01
tp['NM1'] = { 'NM1': nm1_2_nm1, 'NM2': 0 }
leftOver1 = float(1-nm1_2_nm1)/float(maxPSSMi)
for i in range(maxPSSMi):
tp['NM1']['PSSM'+str(i)] = leftOver1
if wobble==True:
tp['NM1']['WOBBLE'+str(i)] = 0 # Don't start a seed with a wobble
# NM2
tp['NM2'] = { 'NM1': 0, 'NM2': 1 }
for i in range(maxPSSMi):
tp['NM2']['PSSM'+str(i)] = 0
if wobble==True:
tp['NM2']['WOBBLE'+str(i)] = 0
# PSSMis
for i in range(maxPSSMi):
tp['PSSM'+str(i)] = { 'NM1': 0, 'NM2': 0.01 }
if wobble==True:
tp['WOBBLE'+str(i)] = { 'NM1': 0, 'NM2': 0.01 }
if i==(maxPSSMi-1):
tp['PSSM'+str(i)]['NM2'] = 1
if wobble==True:
tp['WOBBLE'+str(i)]['NM2'] = 1
for j in range(maxPSSMi):
if j == i+1:
if wobble==True:
# Allow wobbly matches if T is >= wobbleCut
if float(pssm.getMatrix()[i+1][2])>=float(wobbleCut) or float(pssm.getMatrix()[i+1][3])>=float(wobbleCut):
tp['PSSM'+str(i)]['PSSM'+str(j)] = 0.80
tp['PSSM'+str(i)]['WOBBLE'+str(j)] = 0.19
# Otherwise don't allow wobbly matches
else:
tp['PSSM'+str(i)]['PSSM'+str(j)] = 0.99
tp['PSSM'+str(i)]['WOBBLE'+str(j)] = 0
tp['WOBBLE'+str(i)]['PSSM'+str(j)] = 1
tp['WOBBLE'+str(i)]['WOBBLE'+str(j)] = 0
else:
tp['PSSM'+str(i)]['PSSM'+str(j)] = 0.99
else:
tp['PSSM'+str(i)]['PSSM'+str(j)] = 0
if wobble==True:
tp['PSSM'+str(i)]['WOBBLE'+str(j)] = 0
tp['WOBBLE'+str(i)]['PSSM'+str(j)] = 0
tp['WOBBLE'+str(i)]['WOBBLE'+str(j)] = 0
## Emission probabilities
ep = {}
# NM1
ep['NM1'] = { 'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25 }
# S1 - None
# S2 - None
# NM2
ep['NM2'] = { 'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25 }
# PSSMis
for i in range(maxPSSMi):
ep['PSSM'+str(i)] = { 'A': pssm.getMatrix()[i][0], 'C': pssm.getMatrix()[i][1], 'G': pssm.getMatrix()[i][2], 'T': pssm.getMatrix()[i][3] }
# If motif has both G and U probability greater than wobblecut or random (0.25)
if wobble==True:
if float(pssm.getMatrix()[i][2])>=float(wobbleCut) and float(pssm.getMatrix()[i][3])>=float(wobbleCut):
ep['WOBBLE'+str(i)] = { 'A': 0.5, 'C': 0.5, 'G': 0, 'T': 0 }
# If motif has G greater than wobblecut or random (0.25)
elif float(pssm.getMatrix()[i][2])>=float(wobbleCut):
ep['WOBBLE'+str(i)] = { 'A': 1, 'C': 0, 'G': 0, 'T': 0 }
# If motif has U greater than wobblecut or random (0.25)
elif float(pssm.getMatrix()[i][3])>=float(wobbleCut):
ep['WOBBLE'+str(i)] = { 'A': 0, 'C': 1, 'G': 0, 'T': 0 }
# Otherwise be random (0.25 x 4)
else:
ep['WOBBLE'+str(i)] = { 'A': 0.25, 'C': 0.25, 'G': 0.25, 'T': 0.25 }
print 'Done.\n'
# Calculate the distribution of p-values for the PSSM
if 6 in seedModel:
print 'Computing background distribution for 6mer ('+str(len(self.permKMers_6mer))+')...'
totPs_6mer = []
vitPs_6mer = []
for kMer in self.permKMers_6mer:
permVit = self.forwardViterbi(list(kMer), states, sp, tp, ep)
totPs_6mer.append(permVit[0])
vitPs_6mer.append(permVit[2])
self.totPs_6mer = totPs_6mer
self.vitPs_6mer = vitPs_6mer
if 7 in seedModel and len(pssm.getMatrix())>=7:
print 'Computing background distribution for 7mer ('+str(len(self.permKMers_7mer))+')...'
totPs_7mer = []
vitPs_7mer = []
for kMer in self.permKMers_7mer:
permVit = self.forwardViterbi(list(kMer), states, sp, tp, ep)
totPs_7mer.append(permVit[0])
vitPs_7mer.append(permVit[2])
self.totPs_7mer = totPs_7mer
self.vitPs_7mer = vitPs_7mer
if 8 in seedModel and len(pssm.getMatrix())>=8:
print 'Computing background distribution for 8mer ('+str(len(self.permKMers_8mer))+')...'
totPs_8mer = []
vitPs_8mer = []
for kMer in self.permKMers_8mer:
permVit = self.forwardViterbi(list(kMer), states, sp, tp, ep)
totPs_8mer.append(permVit[0])
vitPs_8mer.append(permVit[2])
self.totPs_8mer = totPs_8mer
self.vitPs_8mer = vitPs_8mer
print 'Done.\n'
## Do viterbi for each miRNA and store data
print 'Starting miRNA detection for '+str(pssm.getConsensusMotif())+':'
if textOut==True:
writeMeOut = {}
dirName = 'miRNA'
if not os.path.exists(dirName):
os.mkdir(dirName)
outFile = open(dirName+'/'+str(pssm.getName())+'.csv','w')
outFile.write('miRNAname,miRNAseed,AlignStart,AlignStop,AlignLength,AlignmentModel,MotifAlign (5\'->3\'),Align,SeedAlign(3\'->5\'),P(Total),P-valueTotal,P(Viterbi),P-valueViterbi') # Header
out2File = open(dirName+'/'+str(pssm.getName())+'_all.csv','w')
headNum = 0
if 6 in seedModel:
headNum += 3
if 7 in seedModel and len(pssm.getMatrix())>=7:
headNum += 2
if 8 in seedModel and len(pssm.getMatrix())>=8:
headNum += 1
out2File.write(','.join(['miRNAname,miRNAseed,AlignStart,AlignStop,AlignLength,AlignmentModel,MotifAlign (5\'->3\'),Align,SeedAlign(3\'->5\'),P(Total),P-valueTotal,P(Viterbi),P-valueViterbi']*headNum)) # header
for miRNA in self.miRNAs_6mer_1:
# Now decide which complementary seed is the best fit for each miRNA (take the max vitP)
forVit = None
compModel = None
seed = None
tmpPs = None
writeMe = ''
if 6 in seedModel:
# Compute probabilities
forVit_6mer_1 = self.forwardViterbi(list(self.miRNAs_6mer_1[miRNA]), states, sp, tp, ep)
forVit_6mer_2 = self.forwardViterbi(list(self.miRNAs_6mer_2[miRNA]), states, sp, tp, ep)
forVit_6mer_3 = self.forwardViterbi(list(self.miRNAs_6mer_3[miRNA]), states, sp, tp, ep)
if textOut==True:
writeMe += ','.join(self.outData([pssm, pssm.getConsensusMotif(), miRNA, self.reverseComplement(self.miRNAs_6mer_1[miRNA]), deepcopy(forVit_6mer_1)], self.getPValue(forVit_6mer_1[0],totPs_6mer), self.getPValue(forVit_6mer_1[2],vitPs_6mer), '6mer_1', self.miRNAs_8mer[miRNA]))
writeMe += ','+','.join(self.outData([pssm, pssm.getConsensusMotif(), miRNA, self.reverseComplement(self.miRNAs_6mer_2[miRNA]), deepcopy(forVit_6mer_2)], self.getPValue(forVit_6mer_2[0],totPs_6mer), self.getPValue(forVit_6mer_2[2],vitPs_6mer), '6mer_2', self.miRNAs_8mer[miRNA]))
writeMe += ','+','.join(self.outData([pssm, pssm.getConsensusMotif(), miRNA, self.reverseComplement(self.miRNAs_6mer_3[miRNA]), deepcopy(forVit_6mer_3)], self.getPValue(forVit_6mer_3[0],totPs_6mer), self.getPValue(forVit_6mer_3[2],vitPs_6mer), '6mer_3', self.miRNAs_8mer[miRNA]))
# Default set 6mer_1 as the best
forVit = forVit_6mer_1
compModel = '6mer_1'
seed = self.miRNAs_6mer_1[miRNA]
tmpPs = [totPs_6mer,vitPs_6mer]
# See if the others are better
if self.getPValue(forVit_6mer_2[2],vitPs_6mer) <= self.getPValue(forVit[2],tmpPs[1]):
forVit = forVit_6mer_2
compModel = '6mer_2'
seed = self.miRNAs_6mer_2[miRNA]
tmpPs = [totPs_6mer,vitPs_6mer]
if self.getPValue(forVit_6mer_3[2],vitPs_6mer) <= self.getPValue(forVit[2],tmpPs[1]):
forVit = forVit_6mer_3
compModel = '6mer_3'
seed = self.miRNAs_6mer_3[miRNA]
tmpPs = [totPs_6mer,vitPs_6mer]
if 7 in seedModel and len(pssm.getMatrix())>=7:
# Compute probabilities
forVit_7mer_m8 = self.forwardViterbi(list(self.miRNAs_7mer_m8[miRNA]), states, sp, tp, ep)
forVit_7mer_a1 = self.forwardViterbi(list(self.miRNAs_7mer_a1[miRNA]), states, sp, tp, ep)
if textOut==True:
if 6 in seedModel:
writeMe += ','
writeMe += ','.join(self.outData([pssm, pssm.getConsensusMotif(), miRNA, self.reverseComplement(self.miRNAs_7mer_m8[miRNA]), deepcopy(forVit_7mer_m8)], self.getPValue(forVit_7mer_m8[0],totPs_7mer), self.getPValue(forVit_7mer_m8[2],vitPs_7mer), '7mer_m8', self.miRNAs_8mer[miRNA]))
writeMe += ',' + ','.join(self.outData([pssm, pssm.getConsensusMotif(), miRNA, self.reverseComplement(self.miRNAs_7mer_a1[miRNA]), deepcopy(forVit_7mer_a1)], self.getPValue(forVit_7mer_a1[0],totPs_7mer), self.getPValue(forVit_7mer_a1[2],vitPs_7mer), '7mer_a1', self.miRNAs_8mer[miRNA]))
# If not yet set then default it
if forVit==None:
forVit = forVit_7mer_m8
compModel = '7mer_m8'
seed = self.miRNAs_7mer_m8[miRNA]
tmpPs = [totPs_7mer,vitPs_7mer]
# Or see if it is better
elif self.getPValue(forVit_7mer_m8[2],vitPs_7mer) <= self.getPValue(forVit[2],tmpPs[1]):
forVit = forVit_7mer_m8
compModel = '7mer-m8'
seed = self.miRNAs_7mer_m8[miRNA]
tmpPs = [totPs_7mer,vitPs_7mer]
# See if they are better
if self.getPValue(forVit_7mer_a1[2],vitPs_7mer) <= self.getPValue(forVit[2],tmpPs[1]):
forVit = forVit_7mer_a1
compModel = '7mer-a1'
seed = self.miRNAs_7mer_a1[miRNA]
tmpPs = [totPs_7mer,vitPs_7mer]
if 8 in seedModel and len(pssm.getMatrix())>=8:
# Compute probabilities
forVit_8mer = self.forwardViterbi(list(self.miRNAs_8mer[miRNA]), states, sp, tp, ep)
if textOut==True:
if 6 in seedModel or 7 in seedModel:
writeMe += ','
writeMe += ','.join(self.outData([pssm, pssm.getConsensusMotif(), miRNA, self.reverseComplement(self.miRNAs_8mer[miRNA]), deepcopy(forVit_8mer)], self.getPValue(forVit_8mer[0],totPs_8mer), self.getPValue(forVit_8mer[2],vitPs_8mer), '8mer', self.miRNAs_8mer[miRNA]))
# If not yet set then default it
if forVit==None:
forVit = forVit_8mer
compModel = '8mer'
seed = self.miRNAs_8mer[miRNA]
tmpPs = [totPs_8mer,vitPs_8mer]
# Or see if it is better
elif self.getPValue(forVit_8mer[2],vitPs_8mer) <= self.getPValue(forVit[2],tmpPs[1]):
forVit = forVit_8mer
compModel = '8mer'
seed = self.miRNAs_8mer[miRNA]
tmpPs = [totPs_8mer,vitPs_8mer]
# PSSM.name, PSSM.consesusMotif, miRNA.name, miRNA.seed, state.path, totP, totPValue, vitP, vitPValue
newScore = {'miRNA.name':miRNA, 'miRNA.seed':seed, 'statePath':deepcopy(forVit[1]), 'totP':forVit[0], 'totPValue':self.getPValue(forVit[0],tmpPs[0]), 'vitP':forVit[2], 'vitPValue':self.getPValue(forVit[2],tmpPs[1]), 'model':compModel}
miRNAScores[pssm.getName()] = self.addSorted(miRNAScores[pssm.getName()],newScore)
if textOut==True:
out2File.write('\n'+writeMe)
writeMeOut[miRNA] = ','.join(self.outData([pssm, pssm.getConsensusMotif(), miRNA, self.reverseComplement(seed), forVit], self.getPValue(forVit[0],tmpPs[0]), self.getPValue(forVit[2],tmpPs[1]), compModel, self.miRNAs_8mer[miRNA]))
if textOut==True:
outFile.write('\n'+'\n'.join([writeMeOut[i['miRNA.name']] for i in miRNAScores[pssm.getName()]]))
outFile.close()
out2File.close()
#if cur%10==0:
# stdout.write(str(cur))
#else:
# stdout.write('.')
#stdout.flush()
print 'Done.\n'
cur += 1
print 'miRvestigator analysis completed.\n'
self.miRNAScores = miRNAScores
def getScore(self, forVit, vitPs):
return (float(forVit[0])/float(forVit[2]))*float(self.getPValue(forVit[2],vitPs))
# Generates all possible sequences with letters to the length of depth.
def allKmers(self,depth,letters=['A','C','G','T'],seqs=[''],curdepth=0):
newseqs = []
for seq in seqs:
for letter in letters:
newseqs.append(seq + letter)
if depth > curdepth:
return(self.allKmers(depth,letters,newseqs,curdepth + 1))
else:
return(seqs)
# Get the miRNAs to compare against
def setMiRNAs(self,seedStart,seedEnd, minor=True, p5=True, p3=True):
if not os.path.exists('mature.fa.gz'):
print '\nDownloading miRNA seeds from miRBase.org...'
# Grab down the latest miRNA data from mirbase.org:
# ftp://mirbase.org/pub/mirbase/CURRENT/mature.fa.gz
from ftplib import FTP
ftp1 = FTP('mirbase.org')
ftp1.login()
ftp1.cwd('/pub/mirbase/CURRENT/')
# Get the miRBase.org version number for reference.
self.miRNAver = (ftp1.pwd().split('/'))[-1]
outFile = open('mature.fa.gz','wb')
ftp1.retrbinary('RETR mature.fa.gz',outFile.write)
outFile.close()
ftp1.quit()
print 'Done.\n'
else:
print '\nUsing already downloaded miRNA seeds.\n'
# Read in miRNAs: miRNAs are labeled by the hsa-* names and grabbing 2-8bp
### Could merge these as they come in so that don't do redundant, and also so that the labels are together
import gzip
miRNAFile = gzip.open('mature.fa.gz','r')
miRNAs = {}
while 1:
miRNALine = miRNAFile.readline()
seqLine = miRNAFile.readline()
if not miRNALine:
break
# Get the miRNA name
curMiRNA = (miRNALine.lstrip('>').split(' '))[0]
if (curMiRNA.split('-'))[0]=='hsa':
if (minor==True or curMiRNA.find('*')==-1) and (p5==True or curMiRNA.find('-5p')==-1) and (p3==True or curMiRNA.find('-3p')==-1):
# Now grab out the 2-8bp and do reverse complement on it
miRNAs[curMiRNA] = self.reverseComplement((seqLine.strip())[seedStart:seedEnd])
miRNAFile.close()
# How many distinct kMers in miRNAs
miRNAuniq = {}
for miRNA in miRNAs:
if not miRNAs[miRNA] in miRNAuniq:
miRNAuniq[miRNAs[miRNA]] = [miRNA]
else:
miRNAuniq[miRNAs[miRNA]].append(miRNA)
# Then merge them
miRNAsMerged = {}
for seed in miRNAuniq:
tmpName = '_'.join(miRNAuniq[seed])
miRNAsMerged[tmpName] = seed
return miRNAsMerged
def trimSeqs(self, miRNAs, start, stop):
tmp = {}
for miRNA in miRNAs:
tmp[miRNA] = miRNAs[miRNA][start:stop]
#print tmp[miRNA]
return tmp
# Complement
def complement(self,seq):
complement = {'A':'T', 'T':'A', 'C':'G', 'G':'C', 'N':'N', 'U':'A'}
complseq = [complement[base] for base in seq]
return complseq
# Reverse complement
def reverseComplement(self,seq):
seq = list(seq)
seq.reverse()
return ''.join(self.complement(seq))
# Reverse complement
def reverseMe(self,seq):
seq = list(seq)
seq.reverse()
return ''.join(seq)
# Modified from From wikipedia to do both forward calculation and viterbi
def forwardViterbi(self, obs, states, start_p, trans_p, emit_p):
T = {}
for state in states:
## prob. V. path V. prob.
T[state] = (start_p[state], [state], start_p[state])
for output in obs:
U = {}
for next_state in states:
total = float(0)
argmax = None
valmax = float(0)
for source_state in states:
(prob, v_path, v_prob) = T[source_state]
p = emit_p[source_state][output] * trans_p[source_state][next_state]
prob *= p
v_prob *= p
total += prob
if v_prob > valmax:
argmax = v_path + [next_state]
valmax = v_prob
U[next_state] = (total, argmax, valmax)
T = U
## apply sum/max to the final states:
total = float(0)
argmax = None
valmax = float(0)
for state in states:
(prob, v_path, v_prob) = T[state]
total += prob
if v_prob > valmax:
argmax = v_path
valmax = v_prob
return (total, argmax, valmax)
# Decide whether to add and add at correct position
# Strucuture of elements:
# [pssm, getConsensus(pssms[pssm]), miRNA, miRNAs[miRNA], forVit]
def addSorted(self,all,new):
inserted = 0
if len(all)>0:
for i in range(len(all)):
if new['vitPValue']<all[i]['vitPValue']:
all.insert(i,new)
inserted = 1
break
if inserted==0:
all.append(new)
else:
all.append(new)
return all
# Get p-value from a probability and a distribution
def getPValue(self,prob,dist):
a = float(len([i for i in dist if i>=prob]))
b = float(len(dist))
if not a==0:
return a/b
else:
return 0
# Get score list for a PSSM
def getScoreList(self,pssmName):
return self.miRNAScores[pssmName]
# Get top hit(s) for a PSSM.
# Returns either one or more miRNAs based on whether a clear winner or a tie.
def getTopHit(self,pssmName):
scoreList = self.getScoreList(pssmName)
if scoreList[0]['vitPValue']<scoreList[1]['vitPValue']:
return [scoreList[0]]
else:
retMe = []
i = 0
while scoreList[0]['vitPValue']==scoreList[i]['vitPValue']:
retMe.append(scoreList[i])
i += 1
return retMe
# Get the scores for a PSSM.
# Returns all miRNAs which conatin the miRNA name.
def getmiRNAHit(self,pssmName,miRNAname):
scoreList = self.getScoreList(pssmName)
retMe = []
for i in range(len(scoreList)):
if not scoreList[i]['miRNA.name'].find(miRNAname)==-1:
scoreList[i]['rank'] = i
retMe.append(scoreList[i])
return retMe
# Strucuture of elements:
# [pssm, getConsensus(pssms[pssm]), miRNA, miRNAs[miRNA], forVit]
# <tr><td>miRNA Name</td><td>Alignment Start<sup>*</sup></td><td>Alignment Stop<sup>*</sup></td><td>Alignment Length</td><td>Motif (Length)</td><td>Alignment Type</td><td>Alignment</td><td>P(Alignment)</td></tr>
def outHtml(self, outMe):
writeMe = ''
# miRNA name
writeMe += '<tr align=\'center\' valign=\'center\'><td>'+str(outMe[2])+'</td><td>'+str(outMe[3])+'</td>'
alignment = outMe[4][1] # Grab the alignment
alignment.pop() # Get rid of the extra state which is added by the forwardViterbi function
start = 1
if alignment[0]=='NM1':
for i in alignment:
if i=='NM1':
start += 1
# Alignment
seedAlign = ''
seed = list(outMe[3])
motifAlign = ''
motif = list(outMe[1])
alignMe = alignment
aligned = ''
lenMatch = 0
# First get them zero'd to the same point
if start>1:
for i in range(start-1):
seedAlign += seed.pop(0)
aligned += ' '
motifAlign += '-'
alignMe.pop(0)
if not alignMe[0]=='PSSM0' and not alignMe[0]=='WOBBLE0':
for i in range(int(alignMe[0][4])):
seedAlign += '-'
aligned += ' '
motifAlign += motif.pop(0)
# Then add the parts that align
while 1:
if len(alignMe)==0 or alignMe[0]=='NM2':
break
seedAlign += seed.pop(0)
if alignMe[0][0]=='P':
aligned += '|'
elif alignMe[0][0]=='W':
aligned += ':'
lenMatch += 1
motifAlign += motif.pop(0)
alignMe.pop(0)
# Then do the ending
if len(alignMe)>0:
for i in alignMe:
seedAlign += seed[0]
seed = seed[1:]
aligned += ' '
motifAlign += '-'
alignMe = []
if len(motif)>0 and len(alignMe)==0:
for i in motif:
seedAlign += '-'
aligned += ' '
motifAlign += i
writeMe += '<td>'+str(start)+'</td><td>'+str(start+lenMatch-1)+'</td><td>'+str(lenMatch)+'</td><td><font face="Courier New"><pre>Seed '+str(seedAlign)+'\n '+str(aligned)+'\nMotif '+str(motifAlign)+'</pre></font></td>'
# P(Alignment)
writeMe += '<td>'+str(outMe[4][0])+'</td><td>'+str(float(len([i for i in self.totPs if i>=outMe[4][0]]))/float(len(self.totPs)))+'</td><td>'+str(outMe[4][2])+'</td><td>'+str(float(len([i for i in self.vitPs if i>=outMe[4][2]]))/float(len(self.vitPs)))+'</td></tr>'
return writeMe
# Decide whether to add and add at correct position
# Strucuture of elements:
# input = [pssm, getConsensus(pssms[pssm]), miRNA, miRNAs[miRNA], forVit], totPs, vitPs (Ps are permuted probabilities)
# output = [miRNAname,miRNAseed,AlignStart,AlignStop,AlignLength,SeedAlign,Align,MotifAlign,P(Total),P-valueTotal,P(Viterbi),P-valueViterbi]
def outData(self,outMe,totP,vitP,compModel,fullSeed):
#print outMe[4][1]
output = []
# miRNA name
output += [outMe[2],self.reverseComplement(fullSeed)]
alignment = outMe[4][1] # Grab the alignment
alignment.pop() # Get rid of the extra state which is added by the forwardViterbi function
start = 1
if alignment[0]=='NM1':
for i in alignment:
if i=='NM1':
start += 1
# Alignment
seedAlign = ''
seed = list(self.reverseMe(outMe[3]))
motifAlign = ''
motif = list(outMe[1])
alignMe = alignment
aligned = ''
lenMatch = 0
# First get them zero'd to the same point
if start>1:
for i in range(start-1):
seedAlign += seed.pop(0)
aligned += '_'
motifAlign += '-'
alignMe.pop(0)
if len(alignMe)>0 and not alignMe[0]=='PSSM0' and not alignMe[0]=='WOBBLE0':
if alignMe[0][0]=='P':
upTo = int(alignMe[0][4])
elif alignMe[0][0]=='W':
upTo = int(alignMe[0][6])
for i in range(upTo):
seedAlign += '-'
aligned += '_'
motifAlign += motif.pop(0)
# Then add the parts that align
while 1:
if len(alignMe)==0 or alignMe[0]=='NM2':
break
seedAlign += seed.pop(0)
if alignMe[0][0]=='P':
aligned += '|'
elif alignMe[0][0]=='W':
aligned += ':'
lenMatch += 1
motifAlign += motif.pop(0)
alignMe.pop(0)
# Then do the ending
if len(alignMe)>0:
for i in alignMe:
seedAlign += seed[0]
seed = seed[1:]
aligned += '_'
motifAlign += '-'
alignMe = []
if len(motif)>0 and len(alignMe)==0:
for i in motif:
seedAlign += '-'
aligned += '_'
motifAlign += i
output += [start,start+lenMatch-1,lenMatch,compModel,"'"+motifAlign,"'"+aligned,"'"+seedAlign]
# P(Alignment)
output += [outMe[4][0],totP,outMe[4][2],vitP]
return [str(i) for i in output]