--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/commons/core/seq/AlignedBioseqDB.py	Fri Jan 18 04:54:14 2013 -0500
@@ -0,0 +1,396 @@
+# Copyright INRA (Institut National de la Recherche Agronomique)
+# http://www.inra.fr
+# http://urgi.versailles.inra.fr
+#
+# This software is governed by the CeCILL license under French law and
+# abiding by the rules of distribution of free software.  You can  use, 
+# modify and/ or redistribute the software under the terms of the CeCILL
+# license as circulated by CEA, CNRS and INRIA at the following URL
+# "http://www.cecill.info". 
+#
+# As a counterpart to the access to the source code and  rights to copy,
+# modify and redistribute granted by the license, users are provided only
+# with a limited warranty  and the software's author,  the holder of the
+# economic rights,  and the successive licensors  have only  limited
+# liability. 
+#
+# In this respect, the user's attention is drawn to the risks associated
+# with loading,  using,  modifying and/or developing or reproducing the
+# software by the user in light of its specific status of free software,
+# that may mean  that it is complicated to manipulate,  and  that  also
+# therefore means  that it is reserved for developers  and  experienced
+# professionals having in-depth computer knowledge. Users are therefore
+# encouraged to load and test the software's suitability as regards their
+# requirements in conditions enabling the security of their systems and/or 
+# data to be ensured and,  more generally, to use and operate it in the 
+# same conditions as regards security. 
+#
+# The fact that you are presently reading this means that you have had
+# knowledge of the CeCILL license and that you accept its terms.
+
+
+import sys
+from commons.core.seq.BioseqDB import BioseqDB
+from commons.core.seq.Bioseq import Bioseq
+from commons.core.coord.Align import Align
+from commons.core.coord.Range import Range
+from commons.core.stat.Stat import Stat
+from math import log
+
+
+## Multiple Sequence Alignment Representation   
+#   
+#
+class AlignedBioseqDB( BioseqDB ):
+    
+    def __init__( self, name="" ):
+        BioseqDB.__init__( self, name )
+        seqLength = self.getLength()
+        if self.getSize() > 1:
+            for bs in self.db[1:]:
+                if bs.getLength() != seqLength:
+                    print "ERROR: aligned sequences have different length"
+                    
+                    
+    ## Get length of the alignment
+    # 
+    # @return length
+    # @warning name before migration was 'length'
+    #
+    def getLength( self ):
+        length = 0
+        if self.db != []:
+            length = self.db[0].getLength()
+        return length
+    
+    
+    ## Get the true length of a given sequence (without gaps)
+    #
+    # @param header string header of the sequence to analyze
+    # @return length integer
+    # @warning  name before migration was 'true_length'
+    #
+    def getSeqLengthWithoutGaps( self, header ):
+        bs = self.fetch( header )
+        count = 0
+        for pos in xrange(0,len(bs.sequence)):
+            if bs.sequence[pos] != "-":
+                count += 1
+        return count
+    
+    
+    ## Record the occurrences of symbols (A, T, G, C, N, -, ...) at each site
+    #
+    # @return: list of dico whose keys are symbols and values are their occurrences
+    #
+    def getListOccPerSite( self ):
+        lOccPerSite = []   # list of dictionaries, one per position on the sequence
+        n = 0    # nb of sequences parsed from the input file
+        firstSeq = True
+
+        # for each sequence in the bank
+        for bs in self.db:
+            if bs.sequence == None:
+                break
+            n += 1
+
+            # if it is the first to be parsed, create a dico at each site
+            if firstSeq:
+                for i in xrange(0,len(bs.sequence)):
+                    lOccPerSite.append( {} )
+                firstSeq = False
+
+            # for each site, add its nucleotide
+            for i in xrange(0,len(bs.sequence)):
+                nuc = bs.sequence[i].upper()
+                if lOccPerSite[i].has_key( nuc ):
+                    lOccPerSite[i][nuc] += 1
+                else:
+                    lOccPerSite[i][nuc] = 1
+
+        return lOccPerSite
+    
+    #TODO: review minNbNt !!! It should be at least 2 nucleotides to build a consensus...
+    ## Make a consensus from the MSA
+    #
+    # @param minNbNt: minimum nb of nucleotides to edit a consensus
+    # @param minPropNt: minimum proportion for the major nucleotide to be used, otherwise add 'N' (default=0.0)
+    # @param verbose: level of information sent to stdout (default=0/1)
+    # @return: consensus
+    #
+    def getConsensus( self, minNbNt, minPropNt=0.0, verbose=0 , isHeaderSAtannot=False):
+
+        maxPropN = 0.40  # discard consensus if more than 40% of N's
+
+        nbInSeq = self.getSize()
+        if verbose > 0:
+            print "nb of aligned sequences: %i" % ( nbInSeq ); sys.stdout.flush()
+        if nbInSeq < 2:
+            print "ERROR: can't make a consensus with less than 2 sequences"
+            sys.exit(1)
+        if minNbNt >= nbInSeq:
+            minNbNt = nbInSeq - 1
+            print "minNbNt=%i" % ( minNbNt )
+        if minPropNt >= 1.0:
+            print "ERROR: minPropNt=%.2f should be a proportion (below 1.0)" % ( minPropNt )
+            sys.exit(1)
+
+        lOccPerSite = self.getListOccPerSite()
+        nbSites = len(lOccPerSite)
+        if verbose > 0:
+            print "nb of sites: %i" % ( nbSites ); sys.stdout.flush()
+
+        seqConsensus = ""
+
+        # for each site (i.e. each column of the MSA)
+        nbRmvColumns = 0
+        countSites = 0
+        for dNt2Occ in lOccPerSite:
+            countSites += 1
+            if verbose > 1:
+                print "site %s / %i" % ( str(countSites).zfill( len(str(nbSites)) ),
+                                         nbSites )
+                sys.stdout.flush()
+            occMaxNt = 0   # occurrences of the predominant nucleotide at this site
+            lBestNt = []
+            nbNt = 0   # total nb of A, T, G and C (no gap)
+
+            # for each distinct symbol at this site (A, T, G, C, N, -,...)
+            for j in dNt2Occ.keys():
+                if j != "-":
+                    nbNt += dNt2Occ[j]
+                    if verbose > 1:
+                        print "%s: %i" % ( j, dNt2Occ[j] )
+                    if dNt2Occ[j] > occMaxNt:
+                        occMaxNt = dNt2Occ[j]
+                        lBestNt = [ j ]
+                    elif dNt2Occ[j] == occMaxNt:
+                        lBestNt.append( j )
+            if nbNt == 0:   # some MSA programs can remove some sequences (e.g. Muscle after Recon) or when using Refalign (non-alignable TE fragments put together via a refseq)
+                nbRmvColumns += 1
+
+            if len( lBestNt ) >= 1:
+                bestNt = lBestNt[0]
+            
+            # if the predominant nucleotide occurs in less than x% of the sequences, put a "N"
+            if minPropNt > 0.0 and nbNt != 0 and float(occMaxNt)/float(nbNt) < minPropNt:
+                bestNt = "N"
+
+            if int(nbNt) >= int(minNbNt):
+                seqConsensus += bestNt
+                if verbose > 1:
+                    print "-> %s" % ( bestNt )
+
+        if nbRmvColumns:
+            if nbRmvColumns == 1:
+                print "WARNING: 1 site was removed (%.2f%%)" % (nbRmvColumns / float(nbSites) * 100)
+            else:
+                print "WARNING: %i sites were removed (%.2f%%)" % ( nbRmvColumns, nbRmvColumns / float(nbSites) * 100 )
+            sys.stdout.flush()
+            if seqConsensus == "":
+                print "WARNING: no consensus can be built (no sequence left)"
+                return
+
+        propN = seqConsensus.count("N") / float(len(seqConsensus))
+        if propN >= maxPropN:
+            print "WARNING: no consensus can be built (%i%% of N's >= %i%%)" % ( propN * 100, maxPropN * 100 )
+            return
+        elif propN >= maxPropN * 0.5:
+            print "WARNING: %i%% of N's" % ( propN * 100 )
+
+        consensus = Bioseq()
+        consensus.sequence = seqConsensus
+        if isHeaderSAtannot:
+            header = self.db[0].header
+            pyramid = header.split("Gr")[1].split("Cl")[0]
+            pile = header.split("Cl")[1].split(" ")[0]
+            consensus.header = "consensus=%s length=%i nbAlign=%i pile=%s pyramid=%s" % (self.name, len(seqConsensus), self.getSize(), pile, pyramid)
+        else:
+            consensus.header = "consensus=%s length=%i nbAlign=%i" % ( self.name, len(seqConsensus), self.getSize() )
+
+        if verbose > 0:
+       
+            statEntropy = self.getEntropy( verbose - 1 )
+            print "entropy: %s" % ( statEntropy.stringQuantiles() )
+            sys.stdout.flush()
+
+        return consensus
+    
+    
+    ## Get the entropy of the whole multiple alignment (only for A, T, G and C)
+    #
+    # @param verbose level of verbosity
+    #
+    # @return statistics about the entropy of the MSA
+    #
+    def getEntropy( self, verbose=0 ):
+
+        stats = Stat()
+
+        # get the occurrences of symbols at each site
+        lOccPerSite = self.getListOccPerSite()
+
+        countSite = 0
+
+        # for each site
+        for dSymbol2Occ in lOccPerSite:
+            countSite += 1
+
+            # count the number of nucleotides (A, T, G and C, doesn't count gap '-')
+            nbNt = 0
+            dATGC2Occ = {}
+            for base in ["A","T","G","C"]:
+                dATGC2Occ[ base ] = 0.0
+            for nt in dSymbol2Occ.keys():
+                if nt != "-":
+                    nbNt += dSymbol2Occ[ nt ]
+                    checkedNt = self.getATGCNFromIUPAC( nt )
+                    if checkedNt in ["A","T","G","C"] and dSymbol2Occ.has_key( checkedNt ):
+                        dATGC2Occ[ checkedNt ] += 1 * dSymbol2Occ[ checkedNt ]
+                    else:   # for 'N'
+                        if dSymbol2Occ.has_key( checkedNt ):
+                            dATGC2Occ[ "A" ] += 0.25 * dSymbol2Occ[ checkedNt ]
+                            dATGC2Occ[ "T" ] += 0.25 * dSymbol2Occ[ checkedNt ]
+                            dATGC2Occ[ "G" ] += 0.25 * dSymbol2Occ[ checkedNt ]
+                            dATGC2Occ[ "C" ] += 0.25 * dSymbol2Occ[ checkedNt ]
+            if verbose > 2:
+                for base in dATGC2Occ.keys():
+                    print "%s: %i" % ( base, dATGC2Occ[ base ] )
+
+            # compute the entropy for the site
+            entropySite = 0.0
+            for nt in dATGC2Occ.keys():
+                entropySite += self.computeEntropy( dATGC2Occ[ nt ], nbNt )
+            if verbose > 1:
+                print "site %i (%i nt): entropy = %.3f" % ( countSite, nbNt, entropySite )
+            stats.add( entropySite )
+
+        return stats
+    
+    
+    ## Get A, T, G, C or N from an IUPAC letter
+    #  IUPAC = ['A','T','G','C','U','R','Y','M','K','W','S','B','D','H','V','N']
+    #
+    # @return A, T, G, C or N
+    #
+    def getATGCNFromIUPAC( self, nt ):
+        iBs = Bioseq()
+        return iBs.getATGCNFromIUPAC( nt )
+    
+    
+    ## Compute the entropy based on the occurrences of a certain nucleotide and the total number of nucleotides
+    #
+    def computeEntropy( self, nbOcc, nbNt ):
+        if nbOcc == 0.0:
+            return 0.0
+        else:
+            freq = nbOcc / float(nbNt)
+            return - freq * log(freq) / log(2) 
+        
+        
+    ## Save the multiple alignment as a matrix with '0' if gap, '1' otherwise
+    #
+    def saveAsBinaryMatrix( self, outFile ):
+        outFileHandler = open( outFile, "w" )
+        for bs in self.db:
+            string = "%s" % ( bs.header )
+            for nt in bs.sequence:
+                if nt != "-":
+                    string += "\t%i" % ( 1 )
+                else:
+                    string += "\t%i" % ( 0 )
+            outFileHandler.write( "%s\n" % ( string ) )
+        outFileHandler.close()
+        
+        
+    ## Return a list of Align instances corresponding to the aligned regions (without gaps)
+    #
+    # @param query string header of the sequence considered as query
+    # @param subject string header of the sequence considered as subject
+    #
+    def getAlignList( self, query, subject ):
+        lAligns = []
+        alignQ = self.fetch( query ).sequence
+        alignS = self.fetch( subject ).sequence
+        createNewAlign = True
+        indexAlign = 0
+        indexQ = 0
+        indexS = 0
+        while indexAlign < len(alignQ):
+            if alignQ[ indexAlign ] != "-" and alignS[ indexAlign ] != "-":
+                indexQ += 1
+                indexS += 1
+                if createNewAlign:
+                    iAlign = Align( Range( query, indexQ, indexQ ),
+                                    Range( subject, indexS, indexS ),
+                                    0,
+                                    int( alignQ[ indexAlign ] == alignS[ indexAlign ] ),
+                                    int( alignQ[ indexAlign ] == alignS[ indexAlign ] ) )
+                    lAligns.append( iAlign )
+                    createNewAlign = False
+                else:
+                    lAligns[-1].range_query.end += 1
+                    lAligns[-1].range_subject.end += 1
+                    lAligns[-1].score += int( alignQ[ indexAlign ] == alignS[ indexAlign ] )
+                    lAligns[-1].identity += int( alignQ[ indexAlign ] == alignS[ indexAlign ] )
+            else:
+                if not createNewAlign:
+                    lAligns[-1].identity = 100 * lAligns[-1].identity / lAligns[-1].getLengthOnQuery()
+                    createNewAlign = True
+                if alignQ[ indexAlign ] != "-":
+                    indexQ += 1
+                elif alignS[ indexAlign ] != "-":
+                    indexS += 1
+            indexAlign += 1
+        if not createNewAlign:
+            lAligns[-1].identity = 100 * lAligns[-1].identity / lAligns[-1].getLengthOnQuery()
+        return lAligns
+    
+    
+    def removeGaps(self):
+        for iBs in self.db:
+            iBs.removeSymbol( "-" )
+    
+    ## Compute mean per cent identity for MSA. 
+    # First sequence in MSA is considered as reference sequence. 
+    #
+    #        
+    def computeMeanPcentIdentity(self):
+        seqRef = self.db[0]
+        sumPcentIdentity = 0
+
+        for seq in self.db[1:]:
+            pcentIdentity = self._computePcentIdentityBetweenSeqRefAndCurrentSeq(seqRef, seq) 
+            sumPcentIdentity = sumPcentIdentity + pcentIdentity
+        
+        nbSeq = len(self.db[1:])
+        meanPcentIdentity = round (sumPcentIdentity/nbSeq)
+        
+        return meanPcentIdentity
+
+    def _computePcentIdentityBetweenSeqRefAndCurrentSeq(self, seqRef, seq):
+            indexOnSeqRef = 0
+            sumIdentity = 0
+            for nuclSeq in seq.sequence:
+                nuclRef = seqRef.sequence[indexOnSeqRef]
+            
+                if nuclRef != "-" and nuclRef == nuclSeq:
+                    sumIdentity = sumIdentity + 1
+                indexOnSeqRef = indexOnSeqRef + 1   
+            
+            return float(sumIdentity) / float(seqRef.getLength()) * 100       
+
+ 
+
+
+    
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+