Python Phylo.write方法代碼示例

# 需要導入模塊: from Bio import Phylo [as 別名]
# 或者: from Bio.Phylo import write [as 別名]
def save_tree_with_new_names(tree_in, tree_out, name_subs):
    print()
    print('Writing Microreact tree to: {}'.format(tree_out))
    tree_format = None
    for try_tree_format in ['newick', 'nexus', 'nexml', 'phyloxml', 'cdao']:
        try:
            Phylo.read(tree_in, try_tree_format)
            tree_format = try_tree_format
            break
        except ValueError:
            pass
    if tree_format is None:
        sys.exit('Error: could not read input tree')

    tree = Phylo.read(tree_in, tree_format)
    for node in tree.get_terminals():
        name = str(node.name)
        try:
            node.name = name_subs[name]
        except IndexError:
            sys.exit('Error: sample name in tree not in Kleborate data: ' + name)
    Phylo.write(tree, tree_out, 'newick') 

# 需要導入模塊: from Bio import Phylo [as 別名]
# 或者: from Bio.Phylo import write [as 別名]
def polytomies_midpointRooting(infileName, outfileName, clusterID):
    # use ete2 to solve polytomies and midpoint rooting
    from ete2 import Tree
    newickString=open(infileName, 'rb').readline().rstrip()
    tree = Tree(newickString,format=1);
    tree.resolve_polytomy(recursive=True)
    try:
        tree.set_outgroup( tree.get_midpoint_outgroup() )
    except:
        pass
        #print clusterID, ' can not conduct midpoint rooting'
    tree.ladderize()

    ## adding the missing node.name
    #for ind, node in enumerate(tree.traverse("postorder")):
    for ind, node in enumerate(tree.iter_descendants("postorder")):
        if node.name=='': node.name='%s%s'%('NODE_0',ind);

    with open('./%s'%outfileName, 'wb') as outfile:
        outfile.write(tree.write(format=1)) 

# 需要導入模塊: from Bio import Phylo [as 別名]
# 或者: from Bio.Phylo import write [as 別名]
def make_nj_tree(dist_mat, dArgs, aSampleNames):
    '''
    Uses Biopython.Phylo to make a neighbour joining tree from a distance matrix

    Parameters
    ----------
    dist_mat: dict
        distance matrix as a dict of dicts
        distance_a_to_b = dist_mat[a][b]
    dArgs: dict
        input argument dictionary
    aSampleNames: list
        list of sample names

    Returns
    -------
    returns 0
    also writes tree file to to dArgs['tree'] in newick format
    '''

    aSimpleMatrix = []
    for i, sample_1 in enumerate(aSampleNames):
        mat_line = []
        for j, sample_2 in enumerate(aSampleNames):
            if j < i:
                mat_line.append(dist_mat[sample_1][sample_2])
            elif j == i:
                mat_line.append(0)
            else:
                pass
        aSimpleMatrix.append(mat_line)

    oDistMat = TreeConstruction._DistanceMatrix(aSampleNames, aSimpleMatrix)
    constructor = TreeConstruction.DistanceTreeConstructor()
    oTree = constructor.nj(oDistMat)
    Phylo.write(oTree, dArgs['tree'], 'newick')
    logging.info("Tree file written.")

    return 0

# -------------------------------------------------------------------------------------------------- 

# 需要導入模塊: from Bio import Phylo [as 別名]
# 或者: from Bio.Phylo import write [as 別名]
def register_arguments(parser):
    parser.add_argument('--alignment', '-a', help="alignment in fasta or VCF format")
    parser.add_argument('--tree', '-t', required=True, help="prebuilt Newick")
    parser.add_argument('--metadata', type=str, help="tsv/csv table with meta data for sequences")
    parser.add_argument('--output-tree', type=str, help='file name to write tree to')
    parser.add_argument('--output-node-data', type=str, help='file name to write branch lengths as node data')
    parser.add_argument('--timetree', action="store_true", help="produce timetree using treetime")
    parser.add_argument('--coalescent', help="coalescent time scale in units of inverse clock rate (float), optimize as scalar ('opt'), or skyline ('skyline')")
    parser.add_argument('--gen-per-year', default=50, type=float, help="number of generations per year, relevant for skyline output('skyline')")
    parser.add_argument('--clock-rate', type=float, help="fixed clock rate")
    parser.add_argument('--clock-std-dev', type=float, help="standard deviation of the fixed clock_rate estimate")
    parser.add_argument('--root', nargs="+", default='best', help="rooting mechanism ('best', least-squares', 'min_dev', 'oldest') "
                                "OR node to root by OR two nodes indicating a monophyletic group to root by. "
                                "Run treetime -h for definitions of rooting methods.")
    parser.add_argument('--keep-root', action="store_true", help="do not reroot the tree; use it as-is. "
                                "Overrides anything specified by --root.")
    parser.add_argument('--covariance', dest='covariance', action='store_true', help="Account for covariation when estimating "
                                "rates and/or rerooting. "
                                "Use --no-covariance to turn off.")
    parser.add_argument('--no-covariance', dest='covariance', action='store_false')  #If you set help here, it displays 'default: True' - which is confusing!
    parser.add_argument('--keep-polytomies', action='store_true', help='Do not attempt to resolve polytomies')
    parser.add_argument('--precision', type=int, choices=[0,1,2,3], help="precision used by TreeTime to determine the number of grid points that are used for the evaluation of the branch length interpolation objects. Values range from 0 (rough) to 3 (ultra fine) and default to 'auto'.")
    parser.add_argument('--date-format', default="%Y-%m-%d", help="date format")
    parser.add_argument('--date-confidence', action="store_true", help="calculate confidence intervals for node dates")
    parser.add_argument('--date-inference', default='joint', choices=["joint", "marginal"],
                                help="assign internal nodes to their marginally most likely dates, not jointly most likely")
    parser.add_argument('--branch-length-inference', default='auto', choices = ['auto', 'joint', 'marginal', 'input'],
                                help='branch length mode of treetime to use')
    parser.add_argument('--clock-filter-iqd', type=float, help='clock-filter: remove tips that deviate more than n_iqd '
                                'interquartile ranges from the root-to-tip vs time regression')
    parser.add_argument('--vcf-reference', type=str, help='fasta file of the sequence the VCF was mapped to')
    parser.add_argument('--year-bounds', type=int, nargs='+', help='specify min or max & min prediction bounds for samples with XX in year')
    parser.add_argument('--divergence-units', type=str, choices=['mutations', 'mutations-per-site'],
                        default='mutations-per-site', help='Units in which sequence divergences is exported.')
    parser.add_argument('--seed', type=int, help='seed for random number generation')
    parser.set_defaults(covariance=True) 

# 需要導入模塊: from Bio import Phylo [as 別名]
# 或者: from Bio.Phylo import write [as 別名]
def register_arguments_beast(subparsers):
    """
    Arguments available to `augur import beast`
    """
    beast_parser = subparsers.add_parser('beast', help="Import beast analysis")
    beast_parser.add_argument("--beast", help=SUPPRESS, default=True) # used to disambiguate subcommands
    beast_parser.add_argument('--mcc', required=True, help="BEAST MCC tree")
    beast_parser.add_argument('--most-recent-tip-date', default=0, type=float, help='Numeric date of most recent tip in tree (--tip-date-regex, --tip-date-format and --tip-date-delimeter are ignored if this is set)')
    beast_parser.add_argument('--tip-date-regex', default=r'[0-9]{4}(\-[0-9]{2})*(\-[0-9]{2})*$', type=str, help='regex to extract dates from tip names')
    beast_parser.add_argument('--tip-date-format', default="%Y-%m-%d", type=str, help='Format of date (if extracted by regex)')
    beast_parser.add_argument('--tip-date-delimeter', default="-", type=str, help='delimeter used in tip-date-format. Used to match partial dates.')
    beast_parser.add_argument('--verbose', action="store_true", help="Display verbose output. Only useful for debugging.")
    beast_parser.add_argument('--recursion-limit', default=False, type=int, help="Set a custom recursion limit (dangerous!)")
    beast_parser.add_argument('--output-tree', required=True, type=str, help='file name to write tree to')
    beast_parser.add_argument('--output-node-data', required=True, type=str, help='file name to write (temporal) branch lengths & BEAST traits as node data') 

# 需要導入模塊: from Bio import Phylo [as 別名]
# 或者: from Bio.Phylo import write [as 別名]
def run_beast(args):
    '''
    BEAST MCC tree to newick and node-data JSON for further augur processing / export
    '''
    verbose = args.verbose
    print("importing from BEAST MCC tree", args.mcc)

    if args.recursion_limit:
        print("Setting recursion limit to %d"%(args.recursion_limit))
        sys.setrecursionlimit(args.recursion_limit)

    # node data is the dict that will be exported as json
    node_data = {
        'comment': "Imported from a BEAST MCC tree using `augur import beast`",
        'mcc_file': args.mcc
    }

    tree = parse_nexus(tree_path=args.mcc, verbose=args.verbose)
    summarise_parsed_traits(tree)
    # Phylo.draw_ascii(tree)
    # instantiate treetime for the sole reason to name internal nodes (!)
    # note that tt.tree = tree, and this is modified in-place by this function
    tt = TreeAnc(tree=tree, aln=fake_alignment(tree), ref=None, gtr='JC69', verbose=1)


    # extract date information from the tree
    root_date_offset, most_recent_tip = calc_tree_dates(tree, args.most_recent_tip_date, args.tip_date_regex, args.tip_date_format, args.tip_date_delimeter)
    compute_entropies_for_discrete_traits(tree)

    node_data['nodes'] = collect_node_data(tree, root_date_offset, most_recent_tip)

    tree_success = Phylo.write(tree, args.output_tree, 'newick', format_branch_length='%1.8f')
    json_success = write_json(node_data, args.output_node_data)

    print_what_to_do_next(nodes=node_data['nodes'], mcc_path=args.mcc, tree_path=args.output_tree, node_data_path=args.output_node_data) 

# 需要導入模塊: from Bio import Phylo [as 別名]
# 或者: from Bio.Phylo import write [as 別名]
def print_save_plot_skyline(tt, n_std=2.0, screen=True, save='', plot=''):
    if plot:
        import matplotlib.pyplot as plt

    skyline, conf = tt.merger_model.skyline_inferred(gen=50, confidence=n_std)
    if save: fh = open(save, 'w', encoding='utf-8')
    header1 = "Skyline assuming 50 gen/year and approximate confidence bounds (+/- %f standard deviations of the LH)\n"%n_std
    header2 = "date \tN_e \tlower \tupper"
    if screen: print('\t'+header1+'\t'+header2)
    if save: fh.write("#"+ header1+'#'+header2+'\n')
    for (x,y, y1, y2) in zip(skyline.x, skyline.y, conf[0], conf[1]):
        if screen: print("\t%1.1f\t%1.1f\t%1.1f\t%1.1f"%(x,y, y1, y2))
        if save: fh.write("%1.1f\t%1.1f\t%1.1f\t%1.1f\n"%(x,y, y1, y2))

    if save:
        print("\n --- written skyline to %s\n"%save)
        fh.close()

    if plot:
        plt.figure()
        plt.fill_between(skyline.x, conf[0], conf[1], color=(0.8, 0.8, 0.8))
        plt.plot(skyline.x, skyline.y, label='maximum likelihood skyline')
        plt.yscale('log')
        plt.legend()
        plt.ticklabel_format(axis='x',useOffset=False)
        plt.savefig(plot) 

# 需要導入模塊: from Bio import Phylo [as 別名]
# 或者: from Bio.Phylo import write [as 別名]
def tree_inference(aln_fname, tree_fname, tmp_dir=None,
                   methods = ['iqtree', 'fasttree', 'raxml'], **kwargs):
    import os,shutil
    from Bio import Phylo
    if not os.path.isfile(aln_fname):
        print("alignment file does not exist")

    cwd = os.getcwd()
    if tmp_dir:
        if not os.path.isdir(tmp_dir):
            try:
                os.makedirs(tmp_dir)
            except OSError as e:
                print("Cannot create run_dir",e)
        aln_fname_base = os.path.basename(aln_fname)
        shutil.copyfile(aln_fname,os.path.join(tmp_dir, aln_fname_base))
        aln_fname = aln_fname_base
        os.chdir(tmp_dir)

    for method in methods:
        T = None
        try:
            if method.lower()=='iqtree':
                T = build_newick_iqtree(aln_fname)
            elif method.lower()=='fasttree':
                T = build_newick_fasttree(aln_fname, nuc=True)
            elif method.lower()=='raxml':
                T = build_newick_raxml(aln_fname)
            else:
                print("Method not supported",method)
            if T:
                break
        except:
            continue
    os.chdir(cwd)
    if T is None:
        print("tree building failed. tried", ", ".join(methods), "but none worked")
    else:
        Phylo.write(T, tree_fname, 'newick') 

# 需要導入模塊: from Bio import Phylo [as 別名]
# 或者: from Bio.Phylo import write [as 別名]
def build_newick_raxml(aln_fname, nthreads=2, raxml_bin="raxml", **kwargs):
    import shutil,os
    print("Building tree with raxml")
    from Bio import Phylo, AlignIO
    AlignIO.write(AlignIO.read(aln_fname, 'fasta'),"temp.phyx", "phylip-relaxed")
    cmd = raxml_bin + " -f d -T " + str(nthreads) + " -m GTRCAT -c 25 -p 235813 -n tre -s temp.phyx"
    os.system(cmd)
    return Phylo.read('RAxML_bestTree.tre', "newick") 

# 需要導入模塊: from Bio import Phylo [as 別名]
# 或者: from Bio.Phylo import write [as 別名]
def main():
    args = get_arguments()
    name_subs = name_substitution(args.kleborate_in)
    check_for_unique_names(name_subs)
    save_tree_with_new_names(args.tree_in, args.tree_out, name_subs)
    autocolour_columns = get_autocolour_columns(args.kleborate_in)

    csv_lines = []
    with open(args.kleborate_in, 'rt') as kleborate_results:
        original_header, new_header = None, None
        for line in kleborate_results:
            line = line.rstrip('\n')
            if original_header is None:
                original_header = line.split('\t')
                new_header = get_new_header(original_header, autocolour_columns)
                line_parts = new_header
            else:
                line_parts = get_data(line, name_subs, original_header, new_header)
            csv_lines.append((','.join(line_parts)))

    print()
    print('Writing Microreact table to: {}'.format(args.csv_out))
    with open(args.csv_out, 'wt') as output_csv:
        for line in csv_lines:
            output_csv.write(line)
            output_csv.write('\n')

    print() 

# 需要導入模塊: from Bio import Phylo [as 別名]
# 或者: from Bio.Phylo import write [as 別名]
def codon_align(self, alignment_tool="mafft", prune=True, discard_premature_stops=False):
        '''
        takes a nucleotide alignment, translates it, aligns the amino acids, pads the gaps
        note that this suppresses any compensated frameshift mutations

        Parameters:
        - alignment_tool: ['mafft', 'muscle'] the commandline tool to use
        '''
        cwd = os.getcwd()
        make_dir(self.run_dir)
        os.chdir(self.run_dir)

        # translate
        aa_seqs = {}
        for seq in self.seqs.values():
            tempseq = seq.seq.translate(table="Bacterial")
            # use only sequences that translate without trouble
            if not discard_premature_stops or '*' not in str(tempseq)[:-1] or prune==False:
                aa_seqs[seq.id]=SeqRecord(tempseq,id=seq.id)
            else:
                print(seq.id,"has premature stops, discarding")

        tmpfname = 'temp_in.fasta'
        SeqIO.write(aa_seqs.values(), tmpfname,'fasta')

        if alignment_tool=='mafft':
            os.system('mafft --reorder --amino temp_in.fasta 1> temp_out.fasta')
            aln_aa = AlignIO.read('temp_out.fasta', "fasta")
        elif alignment_tool=='muscle':
            from Bio.Align.Applications import MuscleCommandline
            cline = MuscleCommandline(input=tmpfname, out=tmpfname[:-5]+'aligned.fasta')
            cline()
            aln_aa = AlignIO.read(tmpfname[:-5]+'aligned.fasta', "fasta")
        else:
            print 'Alignment tool not supported:'+alignment_tool
            #return

        #generate nucleotide alignment
        self.aln = pad_nucleotide_sequences(aln_aa, self.seqs)
        os.chdir(cwd)
        remove_dir(self.run_dir) 

# 需要導入模塊: from Bio import Phylo [as 別名]
# 或者: from Bio.Phylo import write [as 別名]
def align(self):
        '''
        align sequencences in self.seqs using mafft
        '''
        cwd = os.getcwd()
        make_dir(self.run_dir)
        os.chdir(self.run_dir)

        SeqIO.write(self.seqs.values(), "temp_in.fasta", "fasta")
        os.system('mafft --reorder --anysymbol temp_in.fasta 1> temp_out.fasta 2> mafft.log')

        self.aln = AlignIO.read('temp_out.fasta', 'fasta')
        os.chdir(cwd)
        remove_dir(self.run_dir) 

# 需要導入模塊: from Bio import Phylo [as 別名]
# 或者: from Bio.Phylo import write [as 別名]
def cluster_align_makeTree( path, folders_dict, parallel, disable_cluster_postprocessing, simple_tree):
    """
    create gene clusters as nucleotide/ amino_acid fasta files
    and build individual gene trees based on fna files
    """
    proc= multiprocessing.Process(target=create_geneCluster_fa, args=(path, folders_dict))
    proc.start(); proc.join()

    ## align, build_tree, make_geneTree_json
    cluster_seqs_path = path+'geneCluster/'
    if os.path.exists(cluster_seqs_path+'gene_diversity.txt'):
        os.system('rm '+cluster_seqs_path+'gene_diversity.txt')

    if 0:
        with open(cluster_seqs_path+'cluster_correl_stats.txt', 'wb') as cluster_correl_stats_file:
            cluster_correl_stats_file.write('%s\n'%'\t'.join(
                        ['clusterID', 'random_alnID', 'diversity_nuc', \
                        'mean_seqLen', 'std_seqLen', 'bestSplit_paraNodes', 'bestSplit_branchLen'
                        ]))

    fna_file_list=glob.glob(cluster_seqs_path+"*.fna")
    multips(align_and_makeTree, parallel, fna_file_list,
        cluster_seqs_path, simple_tree)

    ## if cluster_postprocessing skipped, rename allclusters.tsv and allclusters.cpk as the final cluster file
    if disable_cluster_postprocessing:
        update_diversity_cpk(path)
        clustering_path= '%s%s'%(path,'protein_faa/diamond_matches/')
        os.system('cp %sallclusters.tsv %sallclusters_final.tsv'%(clustering_path,clustering_path))
        os.system('cp %sallclusters.cpk %sallclusters_postprocessed.cpk'%(clustering_path,clustering_path)) 

# 需要導入模塊: from Bio import Phylo [as 別名]
# 或者: from Bio.Phylo import write [as 別名]
def update_diversity_cpk(path):
    ## write gene_diversity_Dt cpk file
    output_path = path+'geneCluster/'
    with open(output_path+'gene_diversity.txt', 'rb') as infile:
        write_pickle(output_path+'gene_diversity.cpk',{ i.rstrip().split('\t')[0]:i.rstrip().split('\t')[1] for i in infile}) 

# 需要導入模塊: from Bio import Phylo [as 別名]
# 或者: from Bio.Phylo import write [as 別名]
def write_final_cluster(path):
    geneCluster_dt=load_sorted_clusters(path)
    outfileName='allclusters_final.tsv'
    with open(path+'protein_faa/diamond_matches/'+outfileName, 'wb') as outfile:
        for clusterID, cluster_stat in geneCluster_dt:
            outfile.write('\t'.join([gene for gene in cluster_stat[1]]))
            outfile.write('\n')