def blastanalysescbgjunction( gsg, prevCBG, nextCBG, omit_cbg_orfs=False, omit_non_cbg_orfs=False, extra_blastp_params=CBG_JUNCTION_BLAST2PACBPCOL_EXTRA_BLASTP_PARAMS, omsr_2_mask_aa_length_correction=CBG_JUNCTION_BLAST2PACBPCOL_OMSR_2_AA_MASK, verbose=False): """ """ ############################################################ if verbose: stw = StopWatch('blastanalysescbgjunction') stw.start() ############################################################ orfs = {} if not omit_cbg_orfs: # gather Orfs from prevCBG and nextCBG for org, orflist, in prevCBG.get_orfs_of_graph().iteritems(): orf = orflist[0] orfs[(org, orf.id)] = orf for org, orflist, in nextCBG.get_orfs_of_graph().iteritems(): orf = orflist[0] orfs[(org, orf.id)] = orf ############################################################ if verbose: print stw.lap(), "orfs (1):", len(orfs) print _format_orf_nodes_to_string(orfs.keys()) ############################################################ # create masked fasta database in a dict fastadbmfa = parseFasta( create_hmmdb_for_neighbouring_cbgs( gsg.input, prevCBG, nextCBG, omsr_2_mask_aa_length_correction=omsr_2_mask_aa_length_correction, ).split("\n")) ############################################################ if verbose: print stw.lap(), "fasta db (1):", len(fastadbmfa) ############################################################ # remove ORFs that do not belong to prevCBG and nextCBG, # or that DO belong to prevCBG and nextCBG, or neither fastaheaders = fastadbmfa.keys() for header in fastaheaders: org, orfid = header.split("_orf_") orfid = int(orfid) node = (org, orfid) # check for the omit_non_cbg_orfs criterion add_orf = False if omit_non_cbg_orfs: if node not in orfs: del (fastadbmfa[header]) else: add_orf = True # check for the omit_cbg_orfs criterion if omit_cbg_orfs and node in orfs: del (fastadbmfa[header]) if add_orf: # get this Orf and add to orfs orfs[node] = gsg.input[org]['orfs'].get_orf_by_id(orfid) ############################################################ if verbose: print stw.lap(), "fasta db (2):", len(fastadbmfa) print _format_fastadbmfa_nodes_to_string(fastadbmfa.keys()) ############################################################ ############################################################ if verbose: print stw.lap(), "orfs (2):", len(orfs) print _format_orf_nodes_to_string(orfs.keys()) ############################################################ # no query/sbjct range left at all if not fastadbmfa: return [] # check if all organisms are still covered orgSet = Set([k.split("_orf_")[0] for k in fastadbmfa.keys()]) if orgSet.symmetric_difference(gsg.organism_set()): return [] # create !single! fasta database fastadbname = prevCBG.barcode() + "_" + nextCBG.barcode() + ".mfa" writeMultiFasta(fastadbmfa, fastadbname) formatdb(fname=fastadbname) # remap the identifiers of the orf objects i.o.t.... multifastas = {} blastdbs = {} pacbpcol = PacbpCollectionGraph() dpcpacbpcol = PacbpCollectionGraph() # ``deepcopied`` variant for pacbps ############################################################ if verbose: print stw.lap(), "blastp starting" ############################################################ for orgQ, orgS in prevCBG.pairwisecrosscombinations_organism(): for nodeQ, orfQ in orfs.iteritems(): # only blast the (masked) Orfs of orgQ if prevCBG.organism_by_node(nodeQ) != orgQ: continue # get the masked protein sequence of this orfObj header = orgQ + "_orf_" + str(orfQ.id) # check if key exists in fastadbmfa. In a case where # an Orf is masked out completely, it is absent here! if not fastadbmfa.has_key(header): continue protseq = fastadbmfa[orgQ + "_orf_" + str(orfQ.id)] # run blast_seqs2db blastrec = blastall_seq2db(orfQ.id, protseq, fastadbname, extra_blastp_params=extra_blastp_params) # omit empty blast records if len(blastrec.alignments) == 0: continue for alignment in blastrec.alignments: # get sbjct Org and Orf identifiers _orgS, _orfSid = alignment.title.replace(">", "").split("_orf_") if _orgS != orgS: continue nodeS = (_orgS, int(_orfSid)) orfS = orfs[nodeS] # take only the *best* HSP (highest scoring first one) hsp = alignment.hsps[0] # correct to absolute positions hsp.query_start = hsp.query_start + orfQ.protein_startPY hsp.sbjct_start = hsp.sbjct_start + orfS.protein_startPY # initialize the PacbP pacbporf = pacb.conversion.pacbp2pacbporf( pacb.PacbP(blastp_hsp=hsp), orfQ, orfS) ################################################################ if verbose: print pacbporf, orgQ, orgS, orfQ print pacbporf.query print pacbporf.match print pacbporf.sbjct ################################################################ # create nodes; ( Organism Identifier, Orf Identifier ) nodeQ = (orgQ, orfQ.id) nodeS = (orgS, orfS.id) uqkey = pacbporf.construct_unique_key(nodeQ, nodeS) if not nodeQ in pacbpcol.get_nodes(): pacbpcol.add_node(nodeQ) if not nodeS in pacbpcol.get_nodes(): pacbpcol.add_node(nodeS) pacbpcol.add_edge(nodeQ, nodeS, wt=pacbporf.bitscore) # store to dpcpacbpcol -> pacbpcol is broken in pieces lateron! dpcpacbpcol.pacbps[(uqkey, nodeQ, nodeS)] = pacbporf ############################################################ if verbose: print stw.lap(), "blastp done" ############################################################ # file cleanup _file_cleanup(multifastas.values()) _file_cleanup(["formatdb.log"]) _file_cleanup([fname + ".*" for fname in blastdbs.values()]) # check if all Organism/Gene identifiers are covered in PacbPs if not pacbpcol.organism_set_size() == gsg.organism_set_size(): return [] # ``deepcopy`` PacbPcollection pacbpcol to dpcpacbpcol # In dpcpacbpcol the actual PacbPORFs are stores & kept, # whereas pacbpcol itself is splitted in CBGs (which # function does not yet (!?) take the actual pacbps into account) dpcpacbpcol.add_nodes(pacbpcol.get_nodes()) for (uqkey, nodeQ, nodeS) in dpcpacbpcol.pacbps.keys(): (bitscore, length, orfQid, orfSid) = uqkey dpcpacbpcol.add_edge(nodeQ, nodeS, wt=bitscore) ################################################################ if verbose: print pacbpcol print "PCG bitscores:", print[p.bitscore for p in dpcpacbpcol.pacbps.values()] print "PCG nodes:", dpcpacbpcol.get_ordered_nodes() ################################################################ #### do some transformations on the pacbpcol ####pacbpcol.remove_low_connectivity_nodes(min_connectivity=gsg.EXACT_SG_NODE_COUNT-1) ####splittedCBGs = pacbpcol.find_fully_connected_subgraphs( #### edges=gsg.node_count()-1 , max_missing_edges=0 ) ##### convert to list of CBGs and do some transformations ####cbgList = ListOfCodingBlockGraphs(splittedCBGs,input={},crossdata={}) ####cbgList.remove_all_but_complete_cbgs() ####cbgList.remove_cbgs_with_lt_nodes(gsg.EXACT_SG_NODE_COUNT) ####cbgList.harvest_pacbps_from_pacbpcollection(dpcpacbpcol) ####cbgList.remove_cbgs_without_omsr() ####cbgList.update_edge_weights_by_minimal_spanning_range() ####cbgList.order_list_by_attribute(order_by='total_weight',reversed=True) min_connectivity = max([1, gsg.EXACT_SG_NODE_COUNT - 1 - 2]) pacbpcol.remove_low_connectivity_nodes(min_connectivity=min_connectivity) max_missing_edges = gsg.EXACT_SG_NODE_COUNT - 3 splittedCBGs = pacbpcol.find_fully_connected_subgraphs( edges=gsg.node_count() - 1, max_missing_edges=max_missing_edges) # convert to list of CBGs and do some transformations cbgList = ListOfCodingBlockGraphs(splittedCBGs, input={}, crossdata={}) cbgList.remove_all_but_cbgs() cbgList.harvest_pacbps_from_pacbpcollection(dpcpacbpcol) cbgList.make_pacbps_for_missing_edges() cbgList.remove_all_but_complete_cbgs() cbgList.remove_cbgs_with_lt_nodes(gsg.EXACT_SG_NODE_COUNT) cbgList.remove_cbgs_without_omsr() cbgList.update_edge_weights_by_minimal_spanning_range() cbgList.order_list_by_attribute(order_by='total_weight', reversed=True) # and create_cache() for these CBGs for cbg in cbgList: cbg.create_cache() #################################################################### if verbose: print stw.lap(), "CBGs created", len(cbgList) for newcbg in cbgList: print "new:", newcbg #################################################################### # return list with CBGs return cbgList.codingblockgraphs
####cbgList.remove_cbgs_with_lt_nodes(gsg.EXACT_SG_NODE_COUNT) ####cbgList.harvest_pacbps_from_pacbpcollection(dpcpacbpcol) ####cbgList.remove_cbgs_without_omsr() ####cbgList.update_edge_weights_by_minimal_spanning_range() ####cbgList.order_list_by_attribute(order_by='total_weight',reversed=True) min_connectivity = max([1,gsg.EXACT_SG_NODE_COUNT-1-2]) pacbpcol.remove_low_connectivity_nodes(min_connectivity=min_connectivity) max_missing_edges = gsg.EXACT_SG_NODE_COUNT - 3 splittedCBGs = pacbpcol.find_fully_connected_subgraphs( edges=gsg.node_count()-1 , max_missing_edges=max_missing_edges ) # convert to list of CBGs and do some transformations cbgList = ListOfCodingBlockGraphs(splittedCBGs,input={},crossdata={}) cbgList.remove_all_but_cbgs() cbgList.harvest_pacbps_from_pacbpcollection(dpcpacbpcol) cbgList.make_pacbps_for_missing_edges() cbgList.remove_all_but_complete_cbgs() cbgList.remove_cbgs_with_lt_nodes(gsg.EXACT_SG_NODE_COUNT) cbgList.remove_cbgs_without_omsr() cbgList.update_edge_weights_by_minimal_spanning_range() cbgList.order_list_by_attribute(order_by='total_weight',reversed=True) # and create_cache() for these CBGs for cbg in cbgList: cbg.create_cache() #################################################################### if verbose: print stw.lap(), "CBGs created", len(cbgList) for newcbg in cbgList: print "new:", newcbg ####################################################################