def setattributes(self, args): self.hk = HouseKeeping() self.s = Stats_And_Summary() if args.subparser_name == 'graft': commands = ExternalProgramSuite([ 'orfm', 'nhmmer', 'hmmsearch', 'mfqe', 'pplacer', 'ktImportText', 'diamond' ]) self.hk.set_attributes(self.args) self.hk.set_euk_hmm(self.args) if args.euk_check: self.args.search_hmm_files.append(self.args.euk_hmm_file) self.ss = SequenceSearcher( self.args.search_hmm_files, (None if self.args.search_only else self.args.aln_hmm_file)) self.sequence_pair_list = self.hk.parameter_checks(args) if hasattr(args, 'reference_package'): self.p = Pplacer(self.args.reference_package) elif self.args.subparser_name == "create": commands = ExternalProgramSuite( ['taxit', 'FastTreeMP', 'hmmalign', 'mafft']) self.create = Create(commands)
def _get_hmm_from_alignment(self, alignment, hmm_filename, output_alignment_filename): '''Return a HMM file and alignment of sequences to that HMM Parameters ---------- alignment: str path to aligned proteins hmm_filename: str write the hmm to this file path output_alignment_filename: str write the output alignment to this file path Returns ------- Return the pipeline type of the HMM. ''' logging.info("Building HMM from alignment") with tempfile.NamedTemporaryFile(suffix='.fasta', prefix='graftm', mode='w') as tempaln: cmd = "hmmbuild -O /dev/stdout -o /dev/stderr '%s' '%s'" % ( hmm_filename, alignment) output = extern.run(cmd) SeqIO.write(SeqIO.parse(StringIO(output), 'stockholm'), tempaln, 'fasta') tempaln.flush() ptype, _ = self._pipe_type(hmm_filename) SequenceSearcher(hmm_filename).alignment_correcter( [tempaln.name], output_alignment_filename) return ptype
def test_merg_aln(self): forward_reads = '''>no_overlap -------CGTATGCAACCTACCTT--------------------------------------- >overlap_all_match -------CGTATGCAACCTACCTT--------------------------------------- >overlap_mismatch_in_reverse -------CGTATGCAACCTACCTT--------------------------------------- >overlap_mismatch_in_forward -------CGTATGCAACCTTCCTT--------------------------------------- >complete_overlap_all_match -------CGTATGCAACCTACCTT--------------------------------------- >complete_overlap_mismatch -------CGTATGCATCCTACCTT---------------------------------------''' reverse_reads = '''>no_overlap ---------------------------------------CGTATGCAACCTACCTT------- >overlap_all_match ----------------CCTACCTTCAACCTACCTT---------------------------- >overlap_mismatch_in_reverse ----------------CCTTCCTTCAACCTACCTT---------------------------- >overlap_mismatch_in_forward ----------------CCTACCTTCAACCTACCTT---------------------------- >complete_overlap_all_match -------CGTATGCAACCTACCTT--------------------------------------- >complete_overlap_mismatch -------CGTTTGCAAGCTACCTT---------------------------------------''' expected_aln = '''>no_overlap -------CGTATGCAACCTACCTT---------------CGTATGCAACCTACCTT------- >overlap_all_match -------CGTATGCAACCTACCTTCAACCTACCTT---------------------------- >overlap_mismatch_in_reverse -------CGTATGCAACCTACCTTCAACCTACCTT---------------------------- >overlap_mismatch_in_forward -------CGTATGCAACCTTCCTTCAACCTACCTT---------------------------- >complete_overlap_all_match -------CGTATGCAACCTACCTT--------------------------------------- >complete_overlap_mismatch -------CGTATGCATCCTACCTT---------------------------------------'''.split() with tempfile.NamedTemporaryFile(suffix='_forward.fa') as forward_file: with tempfile.NamedTemporaryFile( suffix='_reverse.fa') as reverse_file: with tempfile.NamedTemporaryFile(suffix='.fa') as output_file: forward_file.write(forward_reads.encode()) reverse_file.write(reverse_reads.encode()) forward_file.flush() reverse_file.flush() SequenceSearcher(None).merge_forev_aln([forward_file.name], [reverse_file.name], [output_file.name]) count = 0 for line in open(output_file.name): self.assertEqual(expected_aln[count], line.strip()) count += 1 with open(output_file.name) as f: self.assertEqual(count, len(f.readlines()))
def _align_sequences_to_hmm(self, hmm_file, sequences_file, output_alignment_file): '''Align sequences to an HMM, and write an alignment of these proteins after cleanup so that they can be used for tree-making Parameters ---------- sequences_file: str path to file of unaligned protein sequences hmm_file: str path to hmm file output_alignment_file: str write alignment to this file Returns ------- nothing ''' ss = SequenceSearcher(hmm_file) with tempfile.NamedTemporaryFile(prefix='graftm', suffix='.aln.fasta') as tempalign: ss.hmmalign_sequences(hmm_file, sequences_file, tempalign.name) ss.alignment_correcter([tempalign.name], output_alignment_file)
def _get_hmm_from_alignment(self, alignment, hmm_filename, output_alignment_filename): '''Return a HMM file and alignment of sequences to that HMM Parameters ---------- alignment: str path to aligned proteins hmm_filename: str write the hmm to this file path output_alignment_filename: str write the output alignment to this file path Returns ------- Return the pipeline type of the HMM. ''' logging.info("Building HMM from alignment") with tempfile.NamedTemporaryFile(suffix='.fasta', prefix='graftm') as tempaln: cmd = "hmmbuild -O /dev/stdout -o /dev/stderr '%s' '%s'" % ( hmm_filename, alignment) process = subprocess.Popen(["bash", "-c", cmd], stdout=subprocess.PIPE, stderr=subprocess.PIPE) output, error = process.communicate() logging.debug("Got STDERR from hmmbuild: %s" % error) if process.returncode != 0: logging.error( "hmmbuild exitstatus was non-zero, likely indicating an error of " "some description") logging.error("Got STDERR from hmmbuild: %s" % error) SeqIO.write(SeqIO.parse(StringIO(output), 'stockholm'), tempaln, 'fasta') tempaln.flush() ptype, _ = self._pipe_type(hmm_filename) SequenceSearcher(hmm_filename).alignment_correcter( [tempaln.name], output_alignment_filename) return ptype
def generate_expand_search_database_from_contigs(self, contig_files, output_database_file, search_method): '''Given a collection of search_hmm_files, search the contigs in contig_files, and generate an HMM from the resulting hits, outputting it as output_database_file. Parameters ---------- contig_files: list of str list of files to search output_database_file: str path to output file search_method: str "diamond" or "hmmsearch", to specify search method to use and what type of database to build. Returns ------- True if genes were recovered, else False''' ss = SequenceSearcher(self.search_hmm_files) seqio = SequenceIO() if search_method == self.DIAMOND_SEARCH_METHOD: if self.diamond_database == None or self.unaligned_sequence_database == None: logging.warning( "Cannot expand_search continue with no diamond database or unaligned sequences." ) return False with tempfile.NamedTemporaryFile( prefix='graftm_expand_search_orfs') as orfs: logging.info("Finding expand_search hits in provided contigs..") for contig_file in contig_files: logging.debug("Finding expand_search hits in %s.." % contig_file) unpack = UnpackRawReads(contig_file) with tempfile.NamedTemporaryFile(prefix='graftm_expand_search') as \ hit_reads_orfs_fasta: # search and extract matching ORFs with tempfile.NamedTemporaryFile(prefix='graftm_expand_search2') as \ hmmsearch_output_table: with tempfile.NamedTemporaryFile(prefix='graftm_expand_search3') as \ hit_reads_fasta: ss.search_and_extract_orfs_matching_protein_database(\ unpack, search_method, self.maximum_range, self.threads, self.evalue, self.min_orf_length, None, (self.diamond_database if self.diamond_database else None), hmmsearch_output_table.name, hit_reads_fasta.name, hit_reads_orfs_fasta.name) # Append to the file shutil.copyfileobj(open(hit_reads_orfs_fasta.name), orfs) # Now have a fasta file of ORFs. # Check to make sure the file is not zero-length orfs.flush() with tempfile.NamedTemporaryFile( prefix="graftm_expand_search_aln") as aln: if search_method == self.HMM_SEARCH_METHOD: # Check that there is more than one sequence to align. if len( seqio.read_fasta_file(orfs.name) ) <= 1: # Just to build on this, you need to check if there is > 1 hit # otherwise mafft will fail to align, causing a crash when hmmbuild is # run on an empty file. logging.warn( "Failed to find two or more matching ORFs in the expand_search contigs" ) return False # Run mafft to align them cmd = "mafft --auto %s >%s" % (orfs.name, aln.name) logging.info("Aligning expand_search hits..") extern.run(cmd) # Run hmmbuild to create an HMM cmd = "hmmbuild --amino %s %s >/dev/null" % ( output_database_file, aln.name) logging.info("Building HMM from expand_search hits..") extern.run(cmd) elif search_method == self.DIAMOND_SEARCH_METHOD: # Concatenate database with existing database with tempfile.NamedTemporaryFile( prefix="concatenated_database") as databasefile: for f in [orfs.name, self.unaligned_sequence_database]: for line in open(f): databasefile.write(line) databasefile.flush() # Run diamond make to create a diamond database cmd = "diamond makedb --in '%s' -d '%s'" % ( databasefile.name, output_database_file) logging.info( "Building a diamond database from expand_search hits.." ) extern.run(cmd) else: raise Exception("Search method not recognised: %s" % search_method) return False return True
class Run: PIPELINE_AA = "P" PIPELINE_NT = "D" _MIN_VERBOSITY_FOR_ART = 3 # with 2 then, only errors are printed PPLACER_TAXONOMIC_ASSIGNMENT = 'pplacer' DIAMOND_TAXONOMIC_ASSIGNMENT = 'diamond' MIN_ALIGNED_FILTER_FOR_NUCLEOTIDE_PACKAGES = 95 MIN_ALIGNED_FILTER_FOR_AMINO_ACID_PACKAGES = 30 DEFAULT_MAX_SAMPLES_FOR_KRONA = 100 NO_ORFS_EXITSTATUS = 128 def __init__(self, args): self.args = args self.setattributes(self.args) def setattributes(self, args): self.hk = HouseKeeping() self.s = Stats_And_Summary() if args.subparser_name == 'graft': commands = ExternalProgramSuite([ 'orfm', 'nhmmer', 'hmmsearch', 'mfqe', 'pplacer', 'ktImportText', 'diamond' ]) self.hk.set_attributes(self.args) self.hk.set_euk_hmm(self.args) if args.euk_check: self.args.search_hmm_files.append(self.args.euk_hmm_file) self.ss = SequenceSearcher( self.args.search_hmm_files, (None if self.args.search_only else self.args.aln_hmm_file)) self.sequence_pair_list = self.hk.parameter_checks(args) if hasattr(args, 'reference_package'): self.p = Pplacer(self.args.reference_package) elif self.args.subparser_name == "create": commands = ExternalProgramSuite( ['taxit', 'FastTreeMP', 'hmmalign', 'mafft']) self.create = Create(commands) def summarise(self, base_list, trusted_placements, reverse_pipe, times, hit_read_count_list, max_samples_for_krona): ''' summarise - write summary information to file, including otu table, biom file, krona plot, and timing information Parameters ---------- base_list : array list of each of the files processed by graftm, with the path and and suffixed removed trusted_placements : dict dictionary of placements with entry as the key, a taxonomy string as the value reverse_pipe : bool True = run reverse pipe, False = run normal pipeline times : array list of the recorded times for each step in the pipeline in the format: [search_step_time, alignment_step_time, placement_step_time] hit_read_count_list : array list containing sublists, one for each file run through the GraftM pipeline, each two entries, the first being the number of putative eukaryotic reads (when searching 16S), the second being the number of hits aligned and placed in the tree. max_samples_for_krona: int If the number of files processed is greater than this number, then do not generate a krona diagram. Returns ------- ''' # Summary steps. placements_list = [] for base in base_list: # First assign the hash that contains all of the trusted placements # to a variable to it can be passed to otu_builder, to be written # to a file. :) placements = trusted_placements[base] self.s.readTax( placements, GraftMFiles(base, self.args.output_directory, False).read_tax_output_path(base)) placements_list.append(placements) #Generate coverage table #logging.info('Building coverage table for %s' % base) #self.s.coverage_of_hmm(self.args.aln_hmm_file, # self.gmf.summary_table_output_path(base), # self.gmf.coverage_table_path(base), # summary_dict[base]['read_length']) logging.info('Writing summary table') with open(self.gmf.combined_summary_table_output_path(), 'w') as f: self.s.write_tabular_otu_table(base_list, placements_list, f) logging.info('Writing biom file') with biom_open(self.gmf.combined_biom_output_path(), 'w') as f: biom_successful = self.s.write_biom(base_list, placements_list, f) if not biom_successful: os.remove(self.gmf.combined_biom_output_path()) logging.info('Building summary krona plot') if len(base_list) > max_samples_for_krona: logging.warn( "Skipping creation of Krona diagram since there are too many input files. The maximum can be overridden using --max_samples_for_krona" ) else: self.s.write_krona_plot(base_list, placements_list, self.gmf.krona_output_path()) # Basic statistics placed_reads = [len(trusted_placements[base]) for base in base_list] self.s.build_basic_statistics(times, hit_read_count_list, placed_reads, \ base_list, self.gmf.basic_stats_path()) # Delete unnecessary files logging.info('Cleaning up') for base in base_list: directions = ['forward', 'reverse'] if reverse_pipe: for i in range(0, 2): self.gmf = GraftMFiles(base, self.args.output_directory, directions[i]) self.hk.delete([ self.gmf.for_aln_path(base), self.gmf.rev_aln_path(base), self.gmf.conv_output_rev_path(base), self.gmf.conv_output_for_path(base), self.gmf.euk_free_path(base), self.gmf.euk_contam_path(base), self.gmf.readnames_output_path(base), self.gmf.sto_output_path(base), self.gmf.orf_titles_output_path(base), self.gmf.orf_output_path(base), self.gmf.output_for_path(base), self.gmf.output_rev_path(base) ]) else: self.gmf = GraftMFiles(base, self.args.output_directory, False) self.hk.delete([ self.gmf.for_aln_path(base), self.gmf.rev_aln_path(base), self.gmf.conv_output_rev_path(base), self.gmf.conv_output_for_path(base), self.gmf.euk_free_path(base), self.gmf.euk_contam_path(base), self.gmf.readnames_output_path(base), self.gmf.sto_output_path(base), self.gmf.orf_titles_output_path(base), self.gmf.orf_output_path(base), self.gmf.output_for_path(base), self.gmf.output_rev_path(base) ]) logging.info('Done, thanks for using graftM!\n') def graft(self): # The Graft pipeline: # Searches for reads using hmmer, and places them in phylogenetic # trees to derive a community structure. if self.args.graftm_package: gpkg = GraftMPackage.acquire(self.args.graftm_package) else: gpkg = None REVERSE_PIPE = (True if self.args.reverse else False) INTERLEAVED = (True if self.args.interleaved else False) base_list = [] seqs_list = [] search_results = [] hit_read_count_list = [] db_search_results = [] if gpkg: maximum_range = gpkg.maximum_range() if self.args.search_diamond_file: self.args.search_method = self.hk.DIAMOND_SEARCH_METHOD diamond_db = self.args.search_diamond_file[0] else: diamond_db = gpkg.diamond_database_path() if self.args.search_method == self.hk.DIAMOND_SEARCH_METHOD: if not diamond_db: logging.error( "%s search method selected, but no diamond database specified. \ Please either provide a gpkg to the --graftm_package flag, or a diamond \ database to the --search_diamond_file flag." % self.args.search_method) raise Exception() else: # Get the maximum range, if none exists, make one from the HMM profile if self.args.maximum_range: maximum_range = self.args.maximum_range else: if self.args.search_method == self.hk.HMMSEARCH_SEARCH_METHOD: if not self.args.search_only: maximum_range = self.hk.get_maximum_range( self.args.aln_hmm_file) else: logging.debug( "Running search only pipeline. maximum_range not configured." ) maximum_range = None else: logging.warning( 'Cannot determine maximum range when using %s pipeline and with no GraftM package specified' % self.args.search_method) logging.warning( 'Setting maximum_range to None (linked hits will not be detected)' ) maximum_range = None if self.args.search_diamond_file: diamond_db = self.args.search_diamond_file else: if self.args.search_method == self.hk.HMMSEARCH_SEARCH_METHOD: diamond_db = None else: logging.error( "%s search method selected, but no gpkg or diamond database selected" % self.args.search_method) if self.args.assignment_method == Run.DIAMOND_TAXONOMIC_ASSIGNMENT: if self.args.reverse: logging.warn( "--reverse reads specified with --assignment_method diamond. Reverse reads will be ignored." ) self.args.reverse = None # If merge reads is specified, check that there are reverse reads to merge with if self.args.merge_reads and not hasattr(self.args, 'reverse'): raise Exception("Programming error") # Set the output directory if not specified and create that directory logging.debug('Creating working directory: %s' % self.args.output_directory) self.hk.make_working_directory(self.args.output_directory, self.args.force) # Set pipeline and evalue by checking HMM format if self.args.search_only: if self.args.search_method == self.hk.HMMSEARCH_SEARCH_METHOD: hmm_type, hmm_tc = self.hk.setpipe( self.args.search_hmm_files[0]) logging.debug("HMM type: %s Trusted Cutoff: %s" % (hmm_type, hmm_tc)) else: hmm_type, hmm_tc = self.hk.setpipe(self.args.aln_hmm_file) logging.debug("HMM type: %s Trusted Cutoff: %s" % (hmm_type, hmm_tc)) if self.args.search_method == self.hk.HMMSEARCH_SEARCH_METHOD: setattr(self.args, 'type', hmm_type) if hmm_tc: setattr(self.args, 'evalue', '--cut_tc') else: setattr(self.args, 'type', self.PIPELINE_AA) if self.args.filter_minimum is not None: filter_minimum = self.args.filter_minimum else: if self.args.type == self.PIPELINE_NT: filter_minimum = Run.MIN_ALIGNED_FILTER_FOR_NUCLEOTIDE_PACKAGES else: filter_minimum = Run.MIN_ALIGNED_FILTER_FOR_AMINO_ACID_PACKAGES # Generate expand_search database if required if self.args.expand_search_contigs: if self.args.graftm_package: pkg = GraftMPackage.acquire(self.args.graftm_package) else: pkg = None boots = ExpandSearcher(search_hmm_files=self.args.search_hmm_files, maximum_range=self.args.maximum_range, threads=self.args.threads, evalue=self.args.evalue, min_orf_length=self.args.min_orf_length, graftm_package=pkg) # this is a hack, it should really use GraftMFiles but that class isn't currently flexible enough new_database = (os.path.join(self.args.output_directory, "expand_search.hmm") \ if self.args.search_method == self.hk.HMMSEARCH_SEARCH_METHOD \ else os.path.join(self.args.output_directory, "expand_search") ) if boots.generate_expand_search_database_from_contigs( self.args.expand_search_contigs, new_database, self.args.search_method): if self.args.search_method == self.hk.HMMSEARCH_SEARCH_METHOD: self.ss.search_hmm.append(new_database) else: diamond_db = new_database first_search_method = self.args.search_method if self.args.decoy_database: decoy_filter = DecoyFilter( Diamond(diamond_db, threads=self.args.threads), Diamond(self.args.decoy_database, threads=self.args.threads)) doing_decoy_search = True elif self.args.search_method == self.hk.HMMSEARCH_AND_DIAMOND_SEARCH_METHOD: decoy_filter = DecoyFilter( Diamond(diamond_db, threads=self.args.threads)) doing_decoy_search = True first_search_method = self.hk.HMMSEARCH_SEARCH_METHOD else: doing_decoy_search = False # For each pair (or single file passed to GraftM) logging.debug('Working with %i file(s)' % len(self.sequence_pair_list)) for pair in self.sequence_pair_list: # Guess the sequence file type, if not already specified to GraftM unpack = UnpackRawReads(pair[0], self.args.input_sequence_type, INTERLEAVED) # Set the basename, and make an entry to the summary table. base = unpack.basename() pair_direction = ['forward', 'reverse'] logging.info("Working on %s" % base) # Make the working base subdirectory self.hk.make_working_directory( os.path.join(self.args.output_directory, base), self.args.force) # for each of the paired end read files for read_file in pair: unpack = UnpackRawReads(read_file, self.args.input_sequence_type, INTERLEAVED) if read_file is None: # placeholder for interleaved (second file is None) continue if not os.path.isfile(read_file): # Check file exists logging.info('%s does not exist! Skipping this file..' % read_file) continue # Set the output file_name if len(pair) == 2: direction = 'interleaved' if pair[1] is None \ else pair_direction.pop(0) logging.info("Working on %s reads" % direction) self.gmf = GraftMFiles(base, self.args.output_directory, direction) self.hk.make_working_directory( os.path.join(self.args.output_directory, base, direction), self.args.force) else: direction = False self.gmf = GraftMFiles(base, self.args.output_directory, direction) if self.args.type == self.PIPELINE_AA: logging.debug("Running protein pipeline") try: search_time, ( result, complement_information) = self.ss.aa_db_search( self.gmf, base, unpack, first_search_method, maximum_range, self.args.threads, self.args.evalue, self.args.min_orf_length, self.args.restrict_read_length, diamond_db, self.args.diamond_performance_parameters, ) except NoInputSequencesException as e: logging.error( "No sufficiently long open reading frames were found, indicating" " either the input sequences are too short or the min orf length" " cutoff is too high. Cannot continue sorry. Alternatively, there" " is something amiss with the installation of OrfM. The specific" " command that failed was: %s" % e.command) exit(Run.NO_ORFS_EXITSTATUS) # Or the DNA pipeline elif self.args.type == self.PIPELINE_NT: logging.debug("Running nucleotide pipeline") search_time, ( result, complement_information) = self.ss.nt_db_search( self.gmf, base, unpack, self.args.euk_check, self.args.search_method, maximum_range, self.args.threads, self.args.evalue) reads_detected = True if not result.hit_fasta() or os.path.getsize( result.hit_fasta()) == 0: logging.info('No reads found in %s' % base) reads_detected = False if self.args.search_only: db_search_results.append(result) base_list.append(base) continue # Filter out decoys if specified if reads_detected and doing_decoy_search: with tempfile.NamedTemporaryFile(prefix="graftm_decoy", suffix='.fa') as f: tmpname = f.name any_remaining = decoy_filter.filter( result.hit_fasta(), tmpname) if any_remaining: shutil.move(tmpname, result.hit_fasta()) else: # No hits remain after decoy filtering. os.remove(result.hit_fasta()) continue if self.args.assignment_method == Run.PPLACER_TAXONOMIC_ASSIGNMENT: logging.info( 'aligning reads to reference package database') hit_aligned_reads = self.gmf.aligned_fasta_output_path( base) if reads_detected: aln_time, aln_result = self.ss.align( result.hit_fasta(), hit_aligned_reads, complement_information, self.args.type, filter_minimum) else: aln_time = 'n/a' if not os.path.exists( hit_aligned_reads ): # If all were filtered out, or there just was none.. with open(hit_aligned_reads, 'w') as f: pass # just touch the file, nothing else seqs_list.append(hit_aligned_reads) db_search_results.append(result) base_list.append(base) search_results.append(result.search_result) hit_read_count_list.append(result.hit_count) # Write summary table srchtw = SearchTableWriter() srchtw.build_search_otu_table( [x.search_objects for x in db_search_results], base_list, self.gmf.search_otu_table()) if self.args.search_only: logging.info( 'Stopping before alignment and taxonomic assignment phase\n') exit(0) if self.args.merge_reads: # not run when diamond is the assignment mode- enforced by argparse grokking logging.debug("Running merge reads output") if self.args.interleaved: fwd_seqs = seqs_list rev_seqs = [] else: base_list = base_list[0::2] fwd_seqs = seqs_list[0::2] rev_seqs = seqs_list[1::2] merged_output=[GraftMFiles(base, self.args.output_directory, False).aligned_fasta_output_path(base) \ for base in base_list] logging.debug("merged reads to %s", merged_output) self.ss.merge_forev_aln(fwd_seqs, rev_seqs, merged_output) seqs_list = merged_output REVERSE_PIPE = False elif REVERSE_PIPE: base_list = base_list[0::2] # Leave the pipeline if search only was specified if self.args.search_and_align_only: logging.info('Stopping before taxonomic assignment phase\n') exit(0) elif not any(base_list): logging.error( 'No hits in any of the provided files. Cannot continue with no reads to assign taxonomy to.\n' ) exit(0) self.gmf = GraftMFiles('', self.args.output_directory, False) if self.args.assignment_method == Run.PPLACER_TAXONOMIC_ASSIGNMENT: clusterer = Clusterer() # Classification steps seqs_list = clusterer.cluster(seqs_list, REVERSE_PIPE) logging.info("Placing reads into phylogenetic tree") taxonomic_assignment_time, assignments = self.p.place( REVERSE_PIPE, seqs_list, self.args.resolve_placements, self.gmf, self.args, result.slash_endings, gpkg.taxtastic_taxonomy_path(), clusterer) assignments = clusterer.uncluster_annotations( assignments, REVERSE_PIPE) elif self.args.assignment_method == Run.DIAMOND_TAXONOMIC_ASSIGNMENT: logging.info("Assigning taxonomy with diamond") taxonomic_assignment_time, assignments = self._assign_taxonomy_with_diamond(\ base_list, db_search_results, gpkg, self.gmf, self.args.diamond_performance_parameters) aln_time = 'n/a' else: raise Exception("Unexpected assignment method encountered: %s" % self.args.placement_method) self.summarise(base_list, assignments, REVERSE_PIPE, [search_time, aln_time, taxonomic_assignment_time], hit_read_count_list, self.args.max_samples_for_krona) @T.timeit def _assign_taxonomy_with_diamond(self, base_list, db_search_results, graftm_package, graftm_files, diamond_performance_parameters): '''Run diamond to assign taxonomy Parameters ---------- base_list: list of str list of sequence block names db_search_results: list of DBSearchResult the result of running hmmsearches graftm_package: GraftMPackage object Diamond is run against this database graftm_files: GraftMFiles object Result files are written here diamond_performance_parameters : str extra args for DIAMOND Returns ------- list of 1. time taken for assignment 2. assignments i.e. dict of base_list entry to dict of read names to to taxonomies, or None if there was no hit detected. ''' runner = Diamond(graftm_package.diamond_database_path(), self.args.threads, self.args.evalue) taxonomy_definition = Getaxnseq().read_taxtastic_taxonomy_and_seqinfo\ (open(graftm_package.taxtastic_taxonomy_path()), open(graftm_package.taxtastic_seqinfo_path())) results = {} # For each of the search results, for i, search_result in enumerate(db_search_results): if search_result.hit_fasta() is None: sequence_id_to_taxonomy = {} else: sequence_id_to_hit = {} # Run diamond logging.debug("Running diamond on %s" % search_result.hit_fasta()) diamond_result = runner.run( search_result.hit_fasta(), UnpackRawReads.PROTEIN_SEQUENCE_TYPE, daa_file_basename=graftm_files. diamond_assignment_output_basename(base_list[i]), extra_args=diamond_performance_parameters) for res in diamond_result.each([ SequenceSearchResult.QUERY_ID_FIELD, SequenceSearchResult.HIT_ID_FIELD ]): if res[0] in sequence_id_to_hit: # do not accept duplicates if sequence_id_to_hit[res[0]] != res[1]: raise Exception( "Diamond unexpectedly gave two hits for a single query sequence for %s" % res[0]) else: sequence_id_to_hit[res[0]] = res[1] # Extract taxonomy of the best hit, and add in the no hits sequence_id_to_taxonomy = {} for seqio in SequenceIO().read_fasta_file( search_result.hit_fasta()): name = seqio.name if name in sequence_id_to_hit: # Add Root; to be in line with pplacer assignment method sequence_id_to_taxonomy[name] = [ 'Root' ] + taxonomy_definition[sequence_id_to_hit[name]] else: # picked up in the initial search (by hmmsearch, say), but diamond misses it sequence_id_to_taxonomy[name] = ['Root'] results[base_list[i]] = sequence_id_to_taxonomy return results def main(self): if self.args.subparser_name == 'graft': if self.args.verbosity >= self._MIN_VERBOSITY_FOR_ART: print(''' GRAFT Joel Boyd, Ben Woodcroft __/__ ______| _- - _ ________| |_____/ - - - | |____/_ - _ >>>> - >>>> ____| - _- - - | ______ - _ |_____| - |______ ''') self.graft() elif self.args.subparser_name == 'create': if self.args.verbosity >= self._MIN_VERBOSITY_FOR_ART: print(''' CREATE Joel Boyd, Ben Woodcroft / >a / ------------- / >b | | -------- >>> | GPKG | >c |________| ---------- ''') if self.args.dereplication_level < 0: logging.error( "Invalid dereplication level selected! please enter a positive integer" ) exit(1) else: if not self.args.sequences: if not self.args.alignment and not self.args.rerooted_annotated_tree \ and not self.args.rerooted_tree: logging.error( "Some sort of sequence data must be provided to run graftM create" ) exit(1) if self.args.taxonomy: if self.args.rerooted_annotated_tree: logging.error( "--taxonomy is incompatible with --rerooted_annotated_tree" ) exit(1) if self.args.taxtastic_taxonomy or self.args.taxtastic_seqinfo: logging.error( "--taxtastic_taxonomy and --taxtastic_seqinfo are incompatible with --taxonomy" ) exit(1) elif self.args.rerooted_annotated_tree: if self.args.taxtastic_taxonomy or self.args.taxtastic_seqinfo: logging.error( "--taxtastic_taxonomy and --taxtastic_seqinfo are incompatible with --rerooted_annotated_tree" ) exit(1) else: if not self.args.taxtastic_taxonomy or not self.args.taxtastic_seqinfo: logging.error( "--taxonomy, --rerooted_annotated_tree or --taxtastic_taxonomy/--taxtastic_seqinfo is required" ) exit(1) if bool(self.args.taxtastic_taxonomy) ^ bool( self.args.taxtastic_seqinfo): logging.error( "Both or neither of --taxtastic_taxonomy and --taxtastic_seqinfo must be defined" ) exit(1) if self.args.alignment and self.args.hmm: logging.warn( "Using both --alignment and --hmm is rarely useful, but proceding on the assumption you understand." ) if len([ _f for _f in [ self.args.rerooted_tree, self.args.rerooted_annotated_tree, self.args.tree ] if _f ]) > 1: logging.error("Only 1 input tree can be specified") exit(1) self.create.main( dereplication_level=self.args.dereplication_level, sequences=self.args.sequences, alignment=self.args.alignment, taxonomy=self.args.taxonomy, rerooted_tree=self.args.rerooted_tree, unrooted_tree=self.args.tree, tree_log=self.args.tree_log, prefix=self.args.output, rerooted_annotated_tree=self.args.rerooted_annotated_tree, min_aligned_percent=float(self.args.min_aligned_percent) / 100, taxtastic_taxonomy=self.args.taxtastic_taxonomy, taxtastic_seqinfo=self.args.taxtastic_seqinfo, hmm=self.args.hmm, search_hmm_files=self.args.search_hmm_files, force=self.args.force, threads=self.args.threads) elif self.args.subparser_name == 'update': logging.info( "GraftM package %s specified to update with sequences in %s" % (self.args.graftm_package, self.args.sequences)) if self.args.regenerate_diamond_db: gpkg = GraftMPackage.acquire(self.args.graftm_package) logging.info("Regenerating diamond DB..") gpkg.create_diamond_db() logging.info("Diamond database regenerated.") return elif not self.args.sequences: logging.error( "--sequences is required unless regenerating the diamond DB" ) exit(1) if not self.args.output: if self.args.graftm_package.endswith(".gpkg"): self.args.output = self.args.graftm_package.replace( ".gpkg", "-updated.gpkg") else: self.args.output = self.args.graftm_package + '-update.gpkg' Update( ExternalProgramSuite([ 'taxit', 'FastTreeMP', 'hmmalign', 'mafft' ])).update(input_sequence_path=self.args.sequences, input_taxonomy_path=self.args.taxonomy, input_graftm_package_path=self.args.graftm_package, output_graftm_package_path=self.args.output) elif self.args.subparser_name == 'expand_search': args = self.args if not args.graftm_package and not args.search_hmm_files: logging.error( "expand_search mode requires either --graftm_package or --search_hmm_files" ) exit(1) if args.graftm_package: pkg = GraftMPackage.acquire(args.graftm_package) else: pkg = None expandsearcher = ExpandSearcher( search_hmm_files=args.search_hmm_files, maximum_range=args.maximum_range, threads=args.threads, evalue=args.evalue, min_orf_length=args.min_orf_length, graftm_package=pkg) expandsearcher.generate_expand_search_database_from_contigs( args.contigs, args.output_hmm, search_method=ExpandSearcher.HMM_SEARCH_METHOD) elif self.args.subparser_name == 'tree': if self.args.graftm_package: # shim in the paths from the graftm package, not overwriting # any of the provided paths. gpkg = GraftMPackage.acquire(self.args.graftm_package) if not self.args.rooted_tree: self.args.rooted_tree = gpkg.reference_package_tree_path() if not self.args.input_greengenes_taxonomy: if not self.args.input_taxtastic_seqinfo: self.args.input_taxtastic_seqinfo = gpkg.taxtastic_seqinfo_path( ) if not self.args.input_taxtastic_taxonomy: self.args.input_taxtastic_taxonomy = gpkg.taxtastic_taxonomy_path( ) if self.args.rooted_tree: if self.args.unrooted_tree: logging.error( "Both a rooted tree and an un-rooted tree were provided, so it's unclear what you are asking GraftM to do. \ If you're unsure see graftM tree -h") exit(1) elif self.args.reference_tree: logging.error( "Both a rooted tree and reference tree were provided, so it's unclear what you are asking GraftM to do. \ If you're unsure see graftM tree -h") exit(1) if not self.args.decorate: logging.error( "It seems a rooted tree has been provided, but --decorate has not been specified so it is unclear what you are asking graftM to do." ) exit(1) dec = Decorator(tree_path=self.args.rooted_tree) elif self.args.unrooted_tree and self.args.reference_tree: logging.debug( "Using provided reference tree %s to reroot %s" % (self.args.reference_tree, self.args.unrooted_tree)) dec = Decorator(reference_tree_path=self.args.reference_tree, tree_path=self.args.unrooted_tree) else: logging.error( "Some tree(s) must be provided, either a rooted tree or both an unrooted tree and a reference tree" ) exit(1) if self.args.output_taxonomy is None and self.args.output_tree is None: logging.error( "Either an output tree or taxonomy must be provided") exit(1) if self.args.input_greengenes_taxonomy: if self.args.input_taxtastic_seqinfo or self.args.input_taxtastic_taxonomy: logging.error( "Both taxtastic and greengenes taxonomy were provided, so its unclear what taxonomy you want graftM to decorate with" ) exit(1) logging.debug("Using input GreenGenes style taxonomy file") dec.main(self.args.input_greengenes_taxonomy, self.args.output_tree, self.args.output_taxonomy, self.args.no_unique_tax, self.args.decorate, None) elif self.args.input_taxtastic_seqinfo and self.args.input_taxtastic_taxonomy: logging.debug("Using input taxtastic style taxonomy/seqinfo") dec.main(self.args.input_taxtastic_taxonomy, self.args.output_tree, self.args.output_taxonomy, self.args.no_unique_tax, self.args.decorate, self.args.input_taxtastic_seqinfo) else: logging.error( "Either a taxtastic taxonomy or seqinfo file was provided. GraftM cannot continue without both." ) exit(1) elif self.args.subparser_name == 'archive': # Back slashes in the ASCII art are escaped. if self.args.verbosity >= self._MIN_VERBOSITY_FOR_ART: print(""" ARCHIVE Joel Boyd, Ben Woodcroft ____.----. ____.----' \\ \\ \\ \\ \\ \\ \\ \\ ____.----'`--.__ \\___.----' | `--.____ /`-._ | __.-' \\ / `-._ ___.---' \\ / `-.____.---' \\ +------+ / / | \\ \\ |`. |`. / / | \\ _.--' <===> | `+--+---+ `-. / | \\ __.--' | | | | `-._ / | \\ __.--' | | | | | | `-./ | \\_.-' | +---+--+ | | | | `. | `. | | | | `+------+ | | | | | | | | | | | | | | | `-. | _.-' `-. | __..--' `-. | __.-' `-|__.--' """) if self.args.create: if self.args.extract: logging.error( "Please specify whether to either create or export a GraftM package" ) exit(1) if not self.args.graftm_package: logging.error( "Creating a GraftM package archive requires an package to be specified" ) exit(1) if not self.args.archive: logging.error( "Creating a GraftM package archive requires an output archive path to be specified" ) exit(1) archive = Archive() archive.create(self.args.graftm_package, self.args.archive, force=self.args.force) elif self.args.extract: archive = Archive() archive.extract(self.args.archive, self.args.graftm_package, force=self.args.force) else: logging.error( "Please specify whether to either create or export a GraftM package" ) exit(1) else: raise Exception("Unexpected subparser name %s" % self.args.subparser_name)