Source code for evcouplings.align.protocol

Protein sequence alignment creation protocols/workflows.

  Thomas A. Hopf
  Anna G. Green - complex protocol, hmm_build_and_search
  Chan Kang - hmm_build_and_search


from collections import OrderedDict, Iterable
import re
from shutil import copy
import os

import numpy as np
import pandas as pd

from evcouplings.align import tools as at
from evcouplings.align.alignment import (
    detect_format, parse_header, read_fasta,
    write_fasta, Alignment

from evcouplings.couplings.mapping import Segment

from evcouplings.utils.config import (
    check_required, InvalidParameterError, MissingParameterError,
    read_config_file, write_config_file

from evcouplings.utils.system import (
    create_prefix_folders, get, valid_file,
    verify_resources, ResourceError

from evcouplings.align.ena import (

def _verify_sequence_id(sequence_id):
    Verify if a target sequence identifier is in proper
    format for the pipeline to run without errors
    (not none, and contains no whitespace)
    id : str
        Target sequence identifier to verify

        If sequence identifier is not valid
    if sequence_id is None:
        raise InvalidParameterError(
            "Target sequence identifier (sequence_id) must be defined and "
            "cannot be None/null."

        if len(sequence_id.split()) != 1 or len(sequence_id) != len(sequence_id.strip()):
            raise InvalidParameterError(
                "Target sequence identifier (sequence_id) may not contain any "
                "whitespace (spaces, tabs, ...)"
    except AttributeError:
        raise InvalidParameterError(
            "Target sequence identifier (sequence_id) must be a string"

def _make_hmmsearch_raw_fasta(alignment_result, prefix):
    HMMsearch results do not contain the query sequence
    so we must construct a raw_fasta file with the query 
    sequence as the first hit, to ensure proper numbering. 
    The search result is filtered to only contain the columns with
    match states to the HMM, which has a one to one mapping to the
    query sequence.

    alignment_result : dict
        Alignment result dictionary, output by run_hmmsearch
    prefix : str
        Prefix for file creation

        path to raw focus alignment file

    def _add_gaps_to_query(query_sequence_ali, ali):

         # get the index of columns that do not contain match states (indicated by an x)
        gap_index = [
            i for i, x in enumerate(ali.annotation["GC"]["RF"]) if x != "x"
        # get the index of columns that contain match states (indicated by an x)
        match_index = [
            i for i, x in enumerate(ali.annotation["GC"]["RF"]) if x == "x"

        # ensure that the length of the match states 
        # match the length of the sequence
        if len(match_index) != query_sequence_ali.L:
            raise ValueError(
                "HMMsearch result {} does not have a one-to-one"
                " mapping to the query sequence columns".format(

        gapped_query_sequence = ""
        seq = list(query_sequence_ali.matrix[0, :])

        # loop through every position in the HMMsearch hits
        for i in range(len(ali.annotation["GC"]["RF"])):
            # if that position should be a gap, add a gap
            if i in gap_index:
                gapped_query_sequence += "-"
            # if that position should be a letter, pop the next
            # letter in the query sequence
                gapped_query_sequence += seq.pop(0)

        new_sequence_ali = Alignment.from_dict({
            query_sequence_ali.ids[0]: gapped_query_sequence
        return new_sequence_ali

    # open the sequence file
    with open(alignment_result["target_sequence_file"]) as a:
        query_sequence_ali = Alignment.from_file(a, format="fasta")

    # if the provided alignment is empty, just return the target sequence 
    raw_focus_alignment_file = prefix + "_raw.fasta"
    if not valid_file(alignment_result["raw_alignment_file"]):
        # write the query sequence to a fasta file
        with open(raw_focus_alignment_file, "w") as of:

        # return as an alignment object
        return raw_focus_alignment_file

    # else, open the HMM search result
    with open(alignment_result["raw_alignment_file"]) as a:
        ali = Alignment.from_file(a, format="stockholm")

    # make sure that the stockholm alignment contains the match annotation
    if not ("GC" in ali.annotation and "RF" in ali.annotation["GC"]):
        raise ValueError(
            "Stockholm alignment {} missing RF"
            " annotation of match states".format(alignment_result["raw_alignment_file"])
    # add insertions to the query sequence in order to preserve correct
    # numbering of match sequences
    gapped_sequence_ali = _add_gaps_to_query(query_sequence_ali, ali)

    # write a new alignment file with the query sequence as 
    # the first entry
    with open(raw_focus_alignment_file, "w") as of:

    return raw_focus_alignment_file

[docs]def fetch_sequence(sequence_id, sequence_file, sequence_download_url, out_file): """ Fetch sequence either from database based on identifier, or from input sequence file. Parameters ---------- sequence_id : str Identifier of sequence that should be retrieved sequence_file : str File containing sequence. If None, sqeuence will be downloaded from sequence_download_url sequence_download_url : str URL from which to download missing sequence. Must contain "{}" at the position where sequence ID will be inserted into download URL (using str.format). out_file : str Output file in which sequence will be stored, if sequence_file is not existing. Returns ------- str Path of file with stored sequence (can be sequence_file or out_file) tuple (str, str) Identifier of sequence as stored in file, and sequence """ if sequence_file is None: get( sequence_download_url.format(sequence_id), out_file, allow_redirects=True ) else: # if we have sequence file, try to copy it try: copy(sequence_file, out_file) except FileNotFoundError: raise ResourceError( "sequence_file does not exist: {}".format( sequence_file ) ) # also make sure input file has something in it verify_resources( "Input sequence missing", out_file ) with open(out_file) as f: seq = next(read_fasta(f)) return out_file, seq
[docs]def cut_sequence(sequence, sequence_id, region=None, first_index=None, out_file=None): """ Cut a given sequence to sub-range and save it in a file Parameters ---------- sequence : str Full sequence that will be cut sequence_id : str Identifier of sequence, used to construct header in output file region : tuple(int, int), optional (default: None) Region that will be cut out of full sequence. If None, full sequence will be returned. first_index : int, optional (default: None) Define index of first position in sequence. Will be set to 1 if None. out_file : str, optional (default: None) Save sequence in a FASTA file (header: >sequence_id/start_region-end_region) Returns ------ str Subsequence contained in region tuple(int, int) Region. If no input region is given, this will be (1, len(sequence)); otherwise, the input region is returned. Raises ------ InvalidParameterError Upon invalid region specification (violating boundaries of sequence) """ cut_seq = None # (not using 1 as default value to allow parameter # to be unspecified in config file) if first_index is None: first_index = 1 # last index is *inclusive*! if region is None: region = (first_index, first_index + len(sequence) - 1) cut_seq = sequence else: start, end = region str_start = start - first_index str_end = end - first_index + 1 cut_seq = sequence[str_start:str_end] # make sure bounds are valid given the sequence that we have if str_start < 0 or str_end > len(sequence): raise InvalidParameterError( "Invalid sequence range: " "region={} first_index={} len(sequence)={}".format( region, first_index, len(sequence) ) ) # save sequence to file if out_file is not None: with open(out_file, "w") as f: header = "{}/{}-{}".format(sequence_id, *region) write_fasta([(header, cut_seq)], f) return region, cut_seq
[docs]def search_thresholds(use_bitscores, seq_threshold, domain_threshold, seq_len): """ Set homology search inclusion parameters. HMMER hits get included in the HMM according to a two-step rule 1. sequence passes sequence-level treshold 2. domain passes domain-level threshold Therefore, search thresholds are set based on the following logic: 1. If only sequence threshold is given, a MissingParameterException is raised 2. If only bitscore threshold is given, sequence threshold is set to the same 3. If both thresholds are given, they are according to defined values Valid inputs for bitscore thresholds: 1. int or str: taken as absolute score threshold 2. float: taken as relative threshold (absolute threshold derived by multiplication with domain length) Valid inputs for integer thresholds: 1. int: Used as negative exponent, threshold will be set to 1E-<exponent> 2. float or str: Interpreted literally Parameters ---------- use_bitscores : bool Use bitscore threshold instead of E-value threshold domain_threshold : str or int or float Domain-level threshold. See rules above. seq_threshold : str or int or float Sequence-level threshold. See rules above. seq_len : int Length of sequence. Used to calculate absolute bitscore threshold for relative bitscore thresholds. Returns ------- tuple (str, str) Sequence- and domain-level thresholds ready to be fed into HMMER """ def transform_bitscore(x): if isinstance(x, float): # float: interpret as relative fraction of length return "{:.1f}".format(x * seq_len) else: # otherwise interpret as absolute score return str(x) def transform_evalue(x): if isinstance(x, int): # if integer, interpret as negative exponent return "1E{}".format(-x) else: # otherwise interpret literally # (mantissa-exponent string or float) return str(x).upper() if domain_threshold is None: raise MissingParameterError( "domain_threshold must be explicitly defined " "and may not be None/empty" ) if use_bitscores: transform = transform_bitscore else: transform = transform_evalue if seq_threshold is not None: seq_threshold = transform(seq_threshold) if domain_threshold is not None: domain_threshold = transform(domain_threshold) # set "outer" sequence threshold so that it matches domain threshold if domain_threshold is not None and seq_threshold is None: seq_threshold = domain_threshold return seq_threshold, domain_threshold
[docs]def extract_header_annotation(alignment, from_annotation=True): """ Extract Uniprot/Uniref sequence annotation from Stockholm file (as output by jackhmmer). This function may not work for other formats. Parameters ---------- alignment : Alignment Multiple sequence alignment object from_annotation : bool, optional (default: True) Use annotation line (in Stockholm file) rather than sequence ID line (e.g. in FASTA file) Returns ------- pandas.DataFrame Table containing all annotation (one row per sequence in alignment, in order of occurrence) """ columns = [ ("GN", "gene"), ("OS", "organism"), ("PE", "existence_evidence"), ("SV", "sequence_version"), ("n", "num_cluster_members"), ("Tax", "taxon"), ("RepID", "representative_member") ] col_to_descr = OrderedDict(columns) regex = re.compile("\s({})=".format( "|".join(col_to_descr.keys())) ) # collect rows for dataframe in here res = [] for i, id_ in enumerate(alignment.ids): # annotation line for current sequence seq_id = None anno = None # look for annotation either in separate # annotation line or in full sequence ID line if from_annotation: seq_id = id_ # query level by level to avoid creating new keys # in DefaultOrderedDict if ("GS" in alignment.annotation and id_ in alignment.annotation["GS"] and "DE" in alignment.annotation["GS"][id_]): anno = alignment.annotation["GS"][id_]["DE"] else: split = id_.split(maxsplit=1) if len(split) == 2: seq_id, anno = split else: seq_id = id_ anno = None # extract info from line if we got one if anno is not None: # do split on known field names o keep things # simpler than a gigantic full regex to match # (some fields are allowed to be missing) pairs = re.split(regex, anno) pairs = ["id", seq_id, "name"] + pairs # create feature-value map feat_map = dict(zip(pairs[::2], pairs[1::2])) res.append(feat_map) else: res.append({"id": seq_id}) df = pd.DataFrame(res) return df.loc[:, ["id", "name"] + list(col_to_descr.keys())]
[docs]def describe_seq_identities(alignment, target_seq_index=0): """ Calculate sequence identities of any sequence to target sequence and create result dataframe. Parameters ---------- alignment : Alignment Alignment for which description statistics will be calculated Returns ------- pandas.DataFrame Table giving the identity to target sequence for each sequence in alignment (in order of occurrence) """ id_to_query = alignment.identities_to( alignment[target_seq_index] ) return pd.DataFrame( {"id": alignment.ids, "identity_to_query": id_to_query} )
[docs]def describe_frequencies(alignment, first_index, target_seq_index=None): """ Get parameters of alignment such as gaps, coverage, conservation and summarize. Parameters ---------- alignment : Alignment Alignment for which description statistics will be calculated first_index : int Sequence index of first residue in target sequence target_seq_index : int, optional (default: None) If given, will add the symbol in the target sequence into a separate column of the output table Returns ------- pandas.DataFrame Table detailing conservation and symbol frequencies for all positions in the alignment """ fi = alignment.frequencies conservation = alignment.conservation() fi_cols = {c: fi[:, i] for c, i in alignment.alphabet_map.items()} if target_seq_index is not None: target_seq = alignment[target_seq_index] else: target_seq = np.full((alignment.L), np.nan) info = pd.DataFrame( { "i": range(first_index, first_index + alignment.L), "A_i": target_seq, "conservation": conservation, **fi_cols } ) # reorder columns info = info.loc[:, ["i", "A_i", "conservation"] + list(alignment.alphabet)] return info
[docs]def describe_coverage(alignment, prefix, first_index, minimum_column_coverage): """ Produce "classical" buildali coverage statistics, i.e. number of sequences, how many residues have too many gaps, etc. Only to be applied to alignments focused around the target sequence. Parameters ---------- alignment : Alignment Alignment for which coverage statistics will be calculated prefix : str Prefix of alignment file that will be stored as identifier in table first_index : int Sequence index of first position of target sequence minimum_column_coverage : Iterable(float) or float Minimum column coverage threshold(s) that will be tested (creating one row for each threshold in output table). .. note:: ``int`` values given to this function instead of a float will be divided by 100 to create the corresponding floating point representation. This parameter is 1.0 - maximum fraction of gaps per column. Returns ------- pd.DataFrame Table with coverage statistics for different gap thresholds """ res = [] NO_MEFF = np.nan if not isinstance(minimum_column_coverage, Iterable): minimum_column_coverage = [minimum_column_coverage] pos = np.arange(first_index, first_index + alignment.L) f_gap = alignment.frequencies[:, alignment.alphabet_map[alignment._match_gap]] for threshold in minimum_column_coverage: if isinstance(threshold, int): threshold /= 100 # all positions that have enough sequence information (i.e. little gaps), # and their indeces uppercase = f_gap <= 1 - threshold uppercase_idx = np.nonzero(uppercase)[0] # where does coverage of sequence by good alignment start and end? cov_first_idx, cov_last_idx = uppercase_idx[0], uppercase_idx[-1] # calculate indeces in sequence numbering space first, last = pos[cov_first_idx], pos[cov_last_idx] # how many lowercase positions in covered region? num_lc_cov = np.sum(~uppercase[cov_first_idx:cov_last_idx + 1]) # total number of upper- and lowercase positions, # and relative percentage num_cov = uppercase.sum() num_lc = (~uppercase).sum() perc_cov = num_cov / len(uppercase) res.append( (prefix, threshold, alignment.N, alignment.L, num_cov, num_lc, perc_cov, first, last, last - first + 1, num_lc_cov, NO_MEFF) ) df = pd.DataFrame( res, columns=[ "prefix", "minimum_column_coverage", "num_seqs", "seqlen", "num_cov", "num_lc", "perc_cov", "1st_uc", "last_uc", "len_cov", "num_lc_cov", "N_eff", ] ) return df
[docs]def existing(**kwargs): """ Protocol: Use external sequence alignment and extract all relevant information from there (e.g. sequence, region, etc.), then apply gap & fragment filtering as usual Parameters ---------- Mandatory kwargs arguments: See list below in code where calling check_required Returns ------- outcfg : dict Output configuration of the pipeline, including the following fields: * sequence_id (passed through from input) * alignment_file * raw_focus_alignment_file * statistics_file * sequence_file * first_index * target_sequence_file * annotation_file (None) * frequencies_file * identities_file * focus_mode * focus_sequence * segments """ check_required( kwargs, [ "prefix", "input_alignment", "sequence_id", "first_index", "extract_annotation" ] ) prefix = kwargs["prefix"] # make sure output directory exists create_prefix_folders(prefix) # this file is starting point of pipeline; # check if input alignment actually exists input_alignment = kwargs["input_alignment"] verify_resources( "Input alignment does not exist", input_alignment ) # first try to autodetect format of alignment with open(input_alignment) as f: format = detect_format(f) if format is None: raise InvalidParameterError( "Format of input alignment {} could not be " "automatically detected.".format( input_alignment ) ) with open(input_alignment) as f: ali_raw = Alignment.from_file(f, format) # save annotation in sequence headers (species etc.) annotation_file = None if kwargs["extract_annotation"]: annotation_file = prefix + "_annotation.csv" from_anno_line = (format == "stockholm") annotation = extract_header_annotation( ali_raw, from_annotation=from_anno_line ) annotation.to_csv(annotation_file, index=False) # Target sequence of alignment sequence_id = kwargs["sequence_id"] # check if sequence identifier is valid _verify_sequence_id(sequence_id) # First, find focus sequence in alignment focus_index = None for i, id_ in enumerate(ali_raw.ids): if id_.startswith(sequence_id): focus_index = i break # if we didn't find it, cannot continue if focus_index is None: raise InvalidParameterError( "Target sequence {} could not be found in alignment" .format(sequence_id) ) # identify what columns (non-gap) to keep for focus focus_seq = ali_raw[focus_index] focus_cols = np.array( [c not in [ali_raw._match_gap, ali_raw._insert_gap] for c in focus_seq] ) # extract focus alignment focus_ali = focus_seq_nogap = "".join(focus_ali[focus_index]) # determine region of sequence. If first_index is given, # use that in any case, otherwise try to autodetect full_focus_header = ali_raw.ids[focus_index] focus_id = full_focus_header.split()[0] # try to extract region from sequence header id_, region_start, region_end = parse_header(focus_id) # override with first_index if given if kwargs["first_index"] is not None: region_start = kwargs["first_index"] region_end = region_start + len(focus_seq_nogap) - 1 if region_start is None or region_end is None: raise InvalidParameterError( "Could not extract region information " + "from sequence header {} ".format(full_focus_header) + "and first_index parameter is not given." ) # resubstitute full sequence ID from identifier # and region information header = "{}/{}-{}".format( id_, region_start, region_end ) focus_ali.ids[focus_index] = header # write target sequence to file target_sequence_file = prefix + ".fa" with open(target_sequence_file, "w") as f: write_fasta( [(header, focus_seq_nogap)], f ) # apply sequence identity and fragment filters, # and gap threshold mod_outcfg, ali = modify_alignment( focus_ali, focus_index, id_, region_start, **kwargs ) # generate output configuration of protocol outcfg = { **mod_outcfg, "sequence_id": sequence_id, "sequence_file": target_sequence_file, "first_index": region_start, "target_sequence_file": target_sequence_file, "focus_sequence": header, "focus_mode": True, } if annotation_file is not None: outcfg["annotation_file"] = annotation_file # dump config to YAML file for debugging/logging write_config_file(prefix + ".align_existing.outcfg", outcfg) # return results of protocol return outcfg
[docs]def modify_alignment(focus_ali, target_seq_index, target_seq_id, region_start, **kwargs): """ Apply pairwise identity filtering, fragment filtering, and exclusion of columns with too many gaps to a sequence alignment. Also generates files describing properties of the alignment such as frequency distributions, conservation, and "old-style" alignment statistics files. .. note:: assumes focus alignment (otherwise unprocessed) as input. .. todo:: come up with something more clever to filter fragments than fixed width (e.g. use 95% quantile of length distribution as reference point) Parameters ---------- focus_ali : Alignment Focus-mode input alignment target_seq_index : int Index of target sequence in alignment target_seq_id : str Identifier of target sequence (without range) region_start : int Index of first sequence position in target sequence kwargs : See required arguments in source code Returns ------- outcfg : Dict File products generated by the function: * alignment_file * statistics_file * frequencies_file * identities_file * raw_focus_alignment_file ali : Alignment Final processed alignment """ check_required( kwargs, [ "prefix", "seqid_filter", "hhfilter", "minimum_sequence_coverage", "minimum_column_coverage", "compute_num_effective_seqs", "theta", ] ) prefix = kwargs["prefix"] create_prefix_folders(prefix) focus_fasta_file = prefix + "_raw_focus.fasta" outcfg = { "alignment_file": prefix + ".a2m", "statistics_file": prefix + "_alignment_statistics.csv", "frequencies_file": prefix + "_frequencies.csv", "identities_file": prefix + "_identities.csv", "raw_focus_alignment_file": focus_fasta_file, } # swap target sequence to first position if it is not # the first sequence in alignment; # this is particularly important for hhfilter run # because target sequence might otherwise be filtered out if target_seq_index != 0: indices = np.arange(0, len(focus_ali)) indices[0] = target_seq_index indices[target_seq_index] = 0 target_seq_index = 0 focus_ali = with open(focus_fasta_file, "w") as f: focus_ali.write(f, "fasta") # apply pairwise identity filter (using hhfilter) if kwargs["seqid_filter"] is not None: filtered_file = prefix + "_filtered.a3m" at.run_hhfilter( focus_fasta_file, filtered_file, threshold=kwargs["seqid_filter"], columns="first", binary=kwargs["hhfilter"] ) with open(filtered_file) as f: focus_ali = Alignment.from_file(f, "a3m") # final FASTA alignment before applying A2M format modifications filtered_fasta_file = prefix + "_raw_focus_filtered.fasta" with open(filtered_fasta_file, "w") as f: focus_ali.write(f, "fasta") ali = focus_ali # filter fragments # come up with something more clever here than fixed width # (e.g. use 95% quantile of length distribution as reference point) min_cov = kwargs["minimum_sequence_coverage"] if min_cov is not None: if isinstance(min_cov, int): min_cov /= 100 keep_seqs = (1 - ali.count("-", axis="seq")) >= min_cov ali = # Calculate frequencies, conservation and identity to query # on final alignment (except for lowercase modification) # Note: running hhfilter might cause a loss of the target seque # if it is not the first sequence in the file! To be sure that # nothing goes wrong, target_seq_index should always be 0. describe_seq_identities( ali, target_seq_index=target_seq_index ).to_csv( outcfg["identities_file"], float_format="%.3f", index=False ) describe_frequencies( ali, region_start, target_seq_index=target_seq_index ).to_csv( outcfg["frequencies_file"], float_format="%.3f", index=False ) coverage_stats = describe_coverage( ali, prefix, region_start, kwargs["minimum_column_coverage"] ) # keep list of uppercase sequence positions in alignment pos_list = np.arange(region_start, region_start + ali.L, dtype="int32") # Make columns with too many gaps lowercase min_col_cov = kwargs["minimum_column_coverage"] if min_col_cov is not None: if isinstance(min_col_cov, int): min_col_cov /= 100 lc_cols = ali.count(ali._match_gap, axis="pos") > 1 - min_col_cov ali = ali.lowercase_columns(lc_cols) # if we remove columns, we have to update list of positions pos_list = pos_list[~lc_cols] else: lc_cols = None # compute effective number of sequences # (this is intended for cases where coupling stage is # not run, but this number is wanted nonetheless) if kwargs["compute_num_effective_seqs"]: # make sure we only compute N_eff on the columns # that would be used for model inference, dispose # the rest if lc_cols is None: cut_ali = ali else: cut_ali = # compute sequence weights cut_ali.set_weights(kwargs["theta"]) # N_eff := sum of all sequence weights n_eff = float(cut_ali.weights.sum()) # patch into coverage statistics (N_eff column) coverage_stats.loc[:, "N_eff"] = n_eff else: n_eff = None # save coverage statistics to file coverage_stats.to_csv( outcfg["statistics_file"], float_format="%.3f", index=False ) # store description of final sequence alignment in outcfg # (note these parameters will be updated by couplings protocol) outcfg.update( { "num_sites": len(pos_list), "num_sequences": len(ali), "effective_sequences": n_eff, "region_start": region_start, } ) # create segment in outcfg outcfg["segments"] = [ Segment( "aa", target_seq_id, region_start, region_start + ali.L - 1, pos_list ).to_list() ] with open(outcfg["alignment_file"], "w") as f: ali.write(f, "fasta") return outcfg, ali
[docs]def standard(**kwargs): """ Protocol: Standard buildali4 workflow (run iterative jackhmmer search against sequence database, than determine which sequences and columns to include in the calculation based on coverage and maximum gap thresholds). Parameters ---------- Mandatory kwargs arguments: See list below in code where calling check_required Returns ------- outcfg : dict Output configuration of the pipeline, including the following fields: * sequence_id (passed through from input) * first_index (passed through from input) * alignment_file * raw_alignment_file * raw_focus_alignment_file * statistics_file * target_sequence_file * sequence_file * annotation_file * frequencies_file * identities_file * hittable_file * focus_mode * focus_sequence * segments ali : Alignment Final sequence alignment """ check_required( kwargs, [ "prefix", "extract_annotation", ] ) prefix = kwargs["prefix"] # make sure output directory exists create_prefix_folders(prefix) # first step of protocol is to get alignment using # jackhmmer; initialize output configuration with # results of this search jackhmmer_outcfg = jackhmmer_search(**kwargs) stockholm_file = jackhmmer_outcfg["raw_alignment_file"] segment = Segment.from_list(jackhmmer_outcfg["segments"][0]) target_seq_id = segment.sequence_id region_start = segment.region_start region_end = segment.region_end # read in stockholm format (with full annotation) with open(stockholm_file) as a: ali_raw = Alignment.from_file(a, "stockholm") # and store as FASTA file first (disabled for now # since equivalent information easily be obtained # from Stockholm file """ ali_raw_fasta_file = prefix + "_raw.fasta" with open(ali_raw_fasta_file, "w") as f: ali_raw.write(f, "fasta") """ # save annotation in sequence headers (species etc.) if kwargs["extract_annotation"]: annotation_file = prefix + "_annotation.csv" annotation = extract_header_annotation(ali_raw) annotation.to_csv(annotation_file, index=False) # center alignment around focus/search sequence focus_cols = np.array([c != "-" for c in ali_raw[0]]) focus_ali = target_seq_index = 0 mod_outcfg, ali = modify_alignment( focus_ali, target_seq_index, target_seq_id, region_start, **kwargs ) # merge results of jackhmmer_search and modify_alignment stage outcfg = { **jackhmmer_outcfg, **mod_outcfg, "annotation_file": annotation_file } # dump output config to YAML file for debugging/logging write_config_file(prefix + ".align_standard.outcfg", outcfg) # return results of protocol return outcfg
[docs]def complex(**kwargs): """ Protocol: Run monomer alignment protocol and postprocess it for EVcomplex calculations Parameters ---------- Mandatory kwargs arguments: See list below in code where calling check_required Returns ------- outcfg : dict Output configuration of the alignment protocol, and the following additional field: genome_location_file : path to file containing the genomic locations for CDs's corresponding to identifiers in the alignment. """ check_required( kwargs, [ "prefix", "alignment_protocol", "uniprot_to_embl_table", "ena_genome_location_table" ] ) verify_resources( "Uniprot to EMBL mapping table does not exist", kwargs["uniprot_to_embl_table"] ) verify_resources( "ENA genome location table does not exist", kwargs["ena_genome_location_table"] ) prefix = kwargs["prefix"] # make sure output directory exists create_prefix_folders(prefix) # run the regular alignment protocol # (standard, existing, ...) alignment_protocol = kwargs["alignment_protocol"] if alignment_protocol not in PROTOCOLS: raise InvalidParameterError( "Invalid choice for alignment protocol: {}".format( alignment_protocol ) ) outcfg = PROTOCOLS[kwargs["alignment_protocol"]](**kwargs) # if the user selected the existing alignment protocol # they can supply an input annotation file # which overwrites the annotation file generated by the existing protocol if alignment_protocol == "existing": check_required(kwargs, ["override_annotation_file"]) if kwargs["override_annotation_file"] is not None: verify_resources( "Override annotation file does not exist", kwargs["override_annotation_file"] ) outcfg["annotation_file"] = prefix + "_annotation.csv" annotation_data = pd.read_csv(kwargs["override_annotation_file"]) annotation_data.to_csv(outcfg["annotation_file"]) # extract cds identifiers for alignment uniprot IDs cds_ids = extract_cds_ids( outcfg["alignment_file"], kwargs["uniprot_to_embl_table"] ) # extract genome location information from ENA genome_location_filename = prefix + "_genome_location.csv" genome_location_table = extract_embl_annotation( cds_ids, kwargs["ena_genome_location_table"], genome_location_filename ) genome_location_table = add_full_header( genome_location_table, outcfg["alignment_file"] ) genome_location_table.to_csv(genome_location_filename) outcfg["genome_location_file"] = genome_location_filename # dump output config to YAML file for debugging/logging write_config_file(prefix + ".align_complex.outcfg", outcfg) return outcfg
# list of available alignment protocols PROTOCOLS = { # standard buildali protocol (iterative hmmer search) "standard": standard, # build raw multiple sequence alignment using jackmmer "jackhmmer_search": jackhmmer_search, # start from an existing (external) alignment "existing": existing, # run alignment protocol and postprocess output for # complex pipeline "complex": complex, }
[docs]def run(**kwargs): """ Run alignment protocol to generate multiple sequence alignment from input sequence. Parameters ---------- Mandatory kwargs arguments: protocol: Alignment protocol to run prefix: Output prefix for all generated files Optional: Returns ------- Alignment Dictionary with results of stage in following fields (in brackets - not returned by all protocols): * alignment_file * [raw_alignment_file] * statistics_file * target_sequence_file * sequence_file * [annotation_file] * frequencies_file * identities_file * [hittable_file] * focus_mode * focus_sequence * segments """ check_required(kwargs, ["protocol"]) if kwargs["protocol"] not in PROTOCOLS: raise InvalidParameterError( "Invalid protocol selection: " + "{}. Valid protocols are: {}".format( kwargs["protocol"], ", ".join(PROTOCOLS.keys()) ) ) return PROTOCOLS[kwargs["protocol"]](**kwargs)