# Copyright 2000 Brad Chapman. All rights reserved. # # This file is part of the Biopython distribution and governed by your # choice of the "Biopython License Agreement" or the "BSD 3-Clause License". # Please see the LICENSE file that should have been included as part of this # package. """Extract information from alignment objects. In order to try and avoid huge alignment objects with tons of functions, functions which return summary type information about alignments should be put into classes in this module. """ import math import sys import warnings from collections import Counter from Bio import BiopythonDeprecationWarning from Bio.Seq import Seq class SummaryInfo: """Calculate summary info about the alignment. This class should be used to calculate information summarizing the results of an alignment. This may either be straight consensus info or more complicated things. """ def __init__(self, alignment): """Initialize with the alignment to calculate information on. ic_vector attribute. A list of ic content for each column number. """ self.alignment = alignment self.ic_vector = [] def dumb_consensus(self, threshold=0.7, ambiguous="X", require_multiple=False): """Output a fast consensus sequence of the alignment. This doesn't do anything fancy at all. It will just go through the sequence residue by residue and count up the number of each type of residue (ie. A or G or T or C for DNA) in all sequences in the alignment. If the percentage of the most common residue type is greater then the passed threshold, then we will add that residue type, otherwise an ambiguous character will be added. This could be made a lot fancier (ie. to take a substitution matrix into account), but it just meant for a quick and dirty consensus. Arguments: - threshold - The threshold value that is required to add a particular atom. - ambiguous - The ambiguous character to be added when the threshold is not reached. - require_multiple - If set as True, this will require that more than 1 sequence be part of an alignment to put it in the consensus (ie. not just 1 sequence and gaps). """ warnings.warn( "The `dumb_consensus` method is deprecated and will be removed " "in a future release of Biopython. As an alternative, you can " "convert the multiple sequence alignment object to a new-style " "Alignment object by via its `.alignment` property, and then " "create a Motif object. You can then use the `.consensus` or " "`.degenerate_consensus` property of the Motif object to get a " "consensus sequence. For more control over how the consensus " "sequence is calculated, you can call the `calculate_consensus` " "method on the `.counts` property of the Motif object. This is an " "example for a multiple sequence alignment `msa` of DNA " "nucleotides:" "\n" ">>> from Bio.Seq import Seq\n" ">>> from Bio.SeqRecord import SeqRecord\n" ">>> from Bio.Align import MultipleSeqAlignment\n" ">>> from Bio.Align.AlignInfo import SummaryInfo\n" ">>> msa = MultipleSeqAlignment([SeqRecord(Seq('ACGT')),\n" "... SeqRecord(Seq('ATGT')),\n" "... SeqRecord(Seq('ATGT'))])\n" ">>> summary = SummaryInfo(msa)\n" ">>> dumb_consensus = summary.dumb_consensus(ambiguous='N')\n" ">>> print(dumb_consensus)\n" "ANGT\n" ">>> alignment = msa.alignment\n" ">>> from Bio.motifs import Motif\n" ">>> motif = Motif('ACGT', alignment)\n" ">>> print(motif.consensus)\n" "ATGT\n" ">>> print(motif.degenerate_consensus)\n" "AYGT\n" ">>> counts = motif.counts\n" ">>> consensus = counts.calculate_consensus(identity=0.7)\n" ">>> print(consensus)\n" "ANGT\n" "\n" "If your multiple sequence alignment object was obtained using " "Bio.AlignIO, then you can obtain a new-style Alignment object " "directly by using Bio.Align.read instead of Bio.AlignIO.read, " "or Bio.Align.parse instead of Bio.AlignIO.parse.", BiopythonDeprecationWarning, ) # Iddo Friedberg, 1-JUL-2004: changed ambiguous default to "X" consensus = "" # find the length of the consensus we are creating con_len = self.alignment.get_alignment_length() # go through each seq item for n in range(con_len): # keep track of the counts of the different atoms we get atom_dict = Counter() num_atoms = 0 for record in self.alignment: # make sure we haven't run past the end of any sequences # if they are of different lengths try: c = record[n] except IndexError: continue if c != "-" and c != ".": atom_dict[c] += 1 num_atoms += 1 max_atoms = [] max_size = 0 for atom in atom_dict: if atom_dict[atom] > max_size: max_atoms = [atom] max_size = atom_dict[atom] elif atom_dict[atom] == max_size: max_atoms.append(atom) if require_multiple and num_atoms == 1: consensus += ambiguous elif len(max_atoms) == 1 and max_size / num_atoms >= threshold: consensus += max_atoms[0] else: consensus += ambiguous return Seq(consensus) def gap_consensus(self, threshold=0.7, ambiguous="X", require_multiple=False): """Output a fast consensus sequence of the alignment, allowing gaps. Same as dumb_consensus(), but allows gap on the output. Things to do: - Let the user define that with only one gap, the result character in consensus is gap. - Let the user select gap character, now it takes the same as input. """ warnings.warn( "The `gap_consensus` method is deprecated and will be removed " "in a future release of Biopython. As an alternative, you can " "convert the multiple sequence alignment object to a new-style " "Alignment object by via its `.alignment` property, and then " "create a Motif object. You can then use the `.consensus` or " "`.degenerate_consensus` property of the Motif object to get a " "consensus sequence. For more control over how the consensus " "sequence is calculated, you can call the `calculate_consensus` " "method on the `.counts` property of the Motif object. This is an " "example for a multiple sequence alignment `msa` of DNA " "nucleotides:" "\n" ">>> from Bio.Seq import Seq\n" ">>> from Bio.SeqRecord import SeqRecord\n" ">>> from Bio.Align import MultipleSeqAlignment\n" ">>> from Bio.Align.AlignInfo import SummaryInfo\n" ">>> msa = MultipleSeqAlignment([SeqRecord(Seq('ACGT')),\n" "... SeqRecord(Seq('AT-T')),\n" "... SeqRecord(Seq('CT-T')),\n" "... SeqRecord(Seq('GT-T'))])\n" ">>> summary = SummaryInfo(msa)\n" ">>> gap_consensus = summary.gap_consensus(ambiguous='N')\n" ">>> print(gap_consensus)\n" "NT-T\n" ">>> alignment = msa.alignment\n" ">>> from Bio.motifs import Motif\n" ">>> motif = Motif('ACGT-', alignment) # include '-' in alphabet\n" ">>> print(motif.consensus)\n" "AT-T\n" ">>> print(motif.degenerate_consensus)\n" "VT-T\n" ">>> counts = motif.counts\n" ">>> consensus = counts.calculate_consensus(identity=0.7)\n" ">>> print(consensus)\n" "NT-T\n" "\n" "If your multiple sequence alignment object was obtained using " "Bio.AlignIO, then you can obtain a new-style Alignment object " "directly by using Bio.Align.read instead of Bio.AlignIO.read, " "or Bio.Align.parse instead of Bio.AlignIO.parse.", BiopythonDeprecationWarning, ) consensus = "" # find the length of the consensus we are creating con_len = self.alignment.get_alignment_length() # go through each seq item for n in range(con_len): # keep track of the counts of the different atoms we get atom_dict = Counter() num_atoms = 0 for record in self.alignment: # make sure we haven't run past the end of any sequences # if they are of different lengths try: c = record[n] except IndexError: continue atom_dict[c] += 1 num_atoms += 1 max_atoms = [] max_size = 0 for atom in atom_dict: if atom_dict[atom] > max_size: max_atoms = [atom] max_size = atom_dict[atom] elif atom_dict[atom] == max_size: max_atoms.append(atom) if require_multiple and num_atoms == 1: consensus += ambiguous elif len(max_atoms) == 1 and max_size / num_atoms >= threshold: consensus += max_atoms[0] else: consensus += ambiguous return Seq(consensus) def replacement_dictionary(self, skip_chars=None, letters=None): """Generate a replacement dictionary to plug into a substitution matrix. This should look at an alignment, and be able to generate the number of substitutions of different residues for each other in the aligned object. Will then return a dictionary with this information:: {('A', 'C') : 10, ('C', 'A') : 12, ('G', 'C') : 15 ....} This also treats weighted sequences. The following example shows how we calculate the replacement dictionary. Given the following multiple sequence alignment:: GTATC 0.5 AT--C 0.8 CTGTC 1.0 For the first column we have:: ('A', 'G') : 0.5 * 0.8 = 0.4 ('C', 'G') : 0.5 * 1.0 = 0.5 ('A', 'C') : 0.8 * 1.0 = 0.8 We then continue this for all of the columns in the alignment, summing the information for each substitution in each column, until we end up with the replacement dictionary. Arguments: - skip_chars - Not used; setting it to anything other than None will raise a ValueError - letters - An iterable (e.g. a string or list of characters to include. """ warnings.warn( "The `replacement_dictionary` method is deprecated and will be " "removed in a future release of Biopython. As an alternative, you " "can convert the multiple sequence alignment object to a new-style " "Alignment object by via its `.alignment` property, and then " "use the `.substitutions` property of the `Alignment` object. " "For example, for a multiple sequence alignment `msa` of DNA " "nucleotides, you would do: " "\n" ">>> alignment = msa.alignment\n" ">>> dictionary = alignment.substitutions\n" "\n" "If your multiple sequence alignment object was obtained using " "Bio.AlignIO, then you can obtain a new-style Alignment object " "directly by using Bio.Align.read instead of Bio.AlignIO.read, " "or Bio.Align.parse instead of Bio.AlignIO.parse.", BiopythonDeprecationWarning, ) if skip_chars is not None: raise ValueError( "argument skip_chars has been deprecated; instead, please use 'letters' to specify the characters you want to include" ) rep_dict = {(letter1, letter2): 0 for letter1 in letters for letter2 in letters} # iterate through each record for rec_num1 in range(len(self.alignment)): # iterate through each record from one beyond the current record # to the end of the list of records for rec_num2 in range(rec_num1 + 1, len(self.alignment)): # for each pair of records, compare the sequences and add # the pertinent info to the dictionary self._pair_replacement( self.alignment[rec_num1].seq, self.alignment[rec_num2].seq, self.alignment[rec_num1].annotations.get("weight", 1.0), self.alignment[rec_num2].annotations.get("weight", 1.0), rep_dict, letters, ) return rep_dict def _pair_replacement(self, seq1, seq2, weight1, weight2, dictionary, letters): """Compare two sequences and generate info on the replacements seen (PRIVATE). Arguments: - seq1, seq2 - The two sequences to compare. - weight1, weight2 - The relative weights of seq1 and seq2. - dictionary - The dictionary containing the starting replacement info that we will modify. - letters - A list of characters to include when calculating replacements. """ # loop through each residue in the sequences for residue1, residue2 in zip(seq1, seq2): if residue1 in letters and residue2 in letters: dictionary[(residue1, residue2)] += weight1 * weight2 def _get_all_letters(self): """Return a string containing the expected letters in the alignment (PRIVATE).""" set_letters = set() for record in self.alignment: set_letters.update(record.seq) list_letters = sorted(set_letters) all_letters = "".join(list_letters) return all_letters def pos_specific_score_matrix(self, axis_seq=None, chars_to_ignore=None): """Create a position specific score matrix object for the alignment. This creates a position specific score matrix (pssm) which is an alternative method to look at a consensus sequence. Arguments: - chars_to_ignore - A list of all characters not to include in the pssm. - axis_seq - An optional argument specifying the sequence to put on the axis of the PSSM. This should be a Seq object. If nothing is specified, the consensus sequence, calculated with default parameters, will be used. Returns: - A PSSM (position specific score matrix) object. """ # determine all of the letters we have to deal with warnings.warn( "The `pos_specific_score_matrix` method is deprecated and will be " "removed in a future release of Biopython. As an alternative, you " "can convert the multiple sequence alignment object to a new-style " "Alignment object by via its `.alignment` property, and then " "create a Motif object. For example, for a multiple sequence " "alignment `msa` of DNA nucleotides, you would do: " "\n" ">>> alignment = msa.alignment\n" ">>> from Bio.motifs import Motif\n" ">>> motif = Motif('ACGT', alignment)\n" ">>> counts = motif.counts\n" "\n" "The `counts` object contains the same information as the PSSM " "returned by `pos_specific_score_matrix`, but note that the " "indices are reversed:\n" "\n" ">>> counts[letter][i] == pssm[index][letter]\n" "True\n" "\n" "If your multiple sequence alignment object was obtained using " "Bio.AlignIO, then you can obtain a new-style Alignment object " "directly by using Bio.Align.read instead of Bio.AlignIO.read, " "or Bio.Align.parse instead of Bio.AlignIO.parse.", BiopythonDeprecationWarning, ) all_letters = self._get_all_letters() if not all_letters: raise ValueError("_get_all_letters returned empty string") if chars_to_ignore is None: chars_to_ignore = [] if not isinstance(chars_to_ignore, list): raise TypeError("chars_to_ignore should be a list.") gap_char = "-" chars_to_ignore.append(gap_char) for char in chars_to_ignore: all_letters = all_letters.replace(char, "") if axis_seq: left_seq = axis_seq if len(axis_seq) != self.alignment.get_alignment_length(): raise ValueError( "Axis sequence length does not equal the get_alignment_length" ) else: left_seq = self.dumb_consensus() pssm_info = [] # now start looping through all of the sequences and getting info for residue_num in range(len(left_seq)): score_dict = dict.fromkeys(all_letters, 0) for record in self.alignment: try: this_residue = record.seq[residue_num] # if we hit an index error we've run out of sequence and # should not add new residues except IndexError: this_residue = None if this_residue and this_residue not in chars_to_ignore: weight = record.annotations.get("weight", 1.0) try: score_dict[this_residue] += weight except KeyError: raise ValueError( "Residue %s not found" % this_residue ) from None pssm_info.append((left_seq[residue_num], score_dict)) return PSSM(pssm_info) def information_content( self, start=0, end=None, e_freq_table=None, log_base=2, chars_to_ignore=None, pseudo_count=0, ): """Calculate the information content for each residue along an alignment. Arguments: - start, end - The starting an ending points to calculate the information content. These points should be relative to the first sequence in the alignment, starting at zero (ie. even if the 'real' first position in the seq is 203 in the initial sequence, for the info content, we need to use zero). This defaults to the entire length of the first sequence. - e_freq_table - A dictionary specifying the expected frequencies for each letter (e.g. {'G' : 0.4, 'C' : 0.4, 'T' : 0.1, 'A' : 0.1}). Gap characters should not be included, since these should not have expected frequencies. - log_base - The base of the logarithm to use in calculating the information content. This defaults to 2 so the info is in bits. - chars_to_ignore - A listing of characters which should be ignored in calculating the info content. Defaults to none. Returns: - A number representing the info content for the specified region. Please see the Biopython manual for more information on how information content is calculated. """ warnings.warn( "The `information_content` method and `ic_vector` attribute of the " "`SummaryInfo` class are deprecated and will be removed in a " "future release of Biopython. As an alternative, you can convert " "the multiple sequence alignment object to a new-style Alignment " "object by via its `.alignment` property, and use the " "`information_content` attribute of the Alignment obecjt. " "For example, for a multiple sequence alignment `msa` of " "DNA nucleotides, you would do: " "\n" ">>> alignment = msa.alignment\n" ">>> from Bio.motifs import Motif\n" ">>> motif = Motif('ACGT', alignment)\n" ">>> information_content = motif.information_content\n" "\n" "The `information_content` object contains the same values as the " "`ic_vector` attribute of the `SummaryInfo` object. Its sum is " "equal to the value return by the `information_content` method. " "\n" "If your multiple sequence alignment object was obtained using " "Bio.AlignIO, then you can obtain a new-style Alignment object " "directly by using Bio.Align.read instead of Bio.AlignIO.read, " "or Bio.Align.parse instead of Bio.AlignIO.parse.", BiopythonDeprecationWarning, ) # if no end was specified, then we default to the end of the sequence if end is None: end = len(self.alignment[0].seq) if chars_to_ignore is None: chars_to_ignore = [] if start < 0 or end > len(self.alignment[0].seq): raise ValueError( "Start (%s) and end (%s) are not in the range %s to %s" % (start, end, 0, len(self.alignment[0].seq)) ) # determine random expected frequencies, if necessary random_expected = None # determine all of the letters we have to deal with all_letters = self._get_all_letters() for char in chars_to_ignore: all_letters = all_letters.replace(char, "") info_content = {} for residue_num in range(start, end): freq_dict = self._get_letter_freqs( residue_num, self.alignment, all_letters, chars_to_ignore, pseudo_count, e_freq_table, random_expected, ) # print(freq_dict, end="") column_score = self._get_column_info_content( freq_dict, e_freq_table, log_base, random_expected ) info_content[residue_num] = column_score # sum up the score total_info = sum(info_content.values()) # fill in the ic_vector member: holds IC for each column # reset ic_vector to empty list at each call self.ic_vector = [] for i, k in enumerate(info_content): self.ic_vector.append(info_content[i + start]) return total_info def _get_letter_freqs( self, residue_num, all_records, letters, to_ignore, pseudo_count=0, e_freq_table=None, random_expected=None, ): """Determine the frequency of specific letters in the alignment (PRIVATE). Arguments: - residue_num - The number of the column we are getting frequencies from. - all_records - All of the SeqRecords in the alignment. - letters - The letters we are interested in getting the frequency for. - to_ignore - Letters we are specifically supposed to ignore. - pseudo_count - Optional argument specifying the Pseudo count (k) to add in order to prevent a frequency of 0 for a letter. - e_freq_table - An optional argument specifying a dictionary with the expected frequencies for each letter. - random_expected - Optional argument that specify the frequency to use when e_freq_table is not defined. This will calculate the frequencies of each of the specified letters in the alignment at the given frequency, and return this as a dictionary where the keys are the letters and the values are the frequencies. Pseudo count can be added to prevent a null frequency """ freq_info = dict.fromkeys(letters, 0) total_count = 0 gap_char = "-" if pseudo_count < 0: raise ValueError( "Positive value required for pseudo_count, %s provided" % (pseudo_count) ) # collect the count info into the dictionary for all the records for record in all_records: try: if record.seq[residue_num] not in to_ignore: weight = record.annotations.get("weight", 1.0) freq_info[record.seq[residue_num]] += weight total_count += weight except KeyError: raise ValueError( "Residue %s not found in letters %s" % (record.seq[residue_num], letters) ) from None if e_freq_table: # check if all the residus in freq_info are in e_freq_table for key in freq_info: if key != gap_char and key not in e_freq_table: raise ValueError("%s not found in expected frequency table" % key) if total_count == 0: # This column must be entirely ignored characters for letter in freq_info: if freq_info[letter] != 0: raise ValueError("freq_info[letter] is not 0") # TODO - Map this to NA or NaN? else: # now convert the counts into frequencies for letter in freq_info: if pseudo_count and (random_expected or e_freq_table): # use either the expected random freq or the if e_freq_table: ajust_freq = e_freq_table[letter] else: ajust_freq = random_expected ajusted_letter_count = freq_info[letter] + ajust_freq * pseudo_count ajusted_total = total_count + pseudo_count freq_info[letter] = ajusted_letter_count / ajusted_total else: freq_info[letter] = freq_info[letter] / total_count return freq_info def _get_column_info_content( self, obs_freq, e_freq_table, log_base, random_expected ): """Calculate the information content for a column (PRIVATE). Arguments: - obs_freq - The frequencies observed for each letter in the column. - e_freq_table - An optional argument specifying a dictionary with the expected frequencies for each letter. - log_base - The base of the logarithm to use in calculating the info content. """ gap_char = "-" if e_freq_table: # check the expected freq information to make sure it is good for key in obs_freq: if key != gap_char and key not in e_freq_table: raise ValueError( f"Frequency table provided does not contain observed letter {key}" ) total_info = 0.0 for letter in obs_freq: inner_log = 0.0 # if we have expected frequencies, modify the log value by them # gap characters do not have expected frequencies, so they # should just be the observed frequency. if letter != gap_char: if e_freq_table: inner_log = obs_freq[letter] / e_freq_table[letter] else: inner_log = obs_freq[letter] / random_expected # if the observed frequency is zero, we don't add any info to the # total information content if inner_log > 0: letter_info = ( obs_freq[letter] * math.log(inner_log) / math.log(log_base) ) total_info += letter_info return total_info def get_column(self, col): """Return column of alignment.""" # TODO - Deprecate this and implement slicing? return self.alignment[:, col] class PSSM: """Represent a position specific score matrix. This class is meant to make it easy to access the info within a PSSM and also make it easy to print out the information in a nice table. Let's say you had an alignment like this:: GTATC AT--C CTGTC The position specific score matrix (when printed) looks like:: G A T C G 1 1 0 1 T 0 0 3 0 A 1 1 0 0 T 0 0 2 0 C 0 0 0 3 You can access a single element of the PSSM using the following:: your_pssm[sequence_number][residue_count_name] For instance, to get the 'T' residue for the second element in the above alignment you would need to do: your_pssm[1]['T'] """ def __init__(self, pssm): """Initialize with pssm data to represent. The pssm passed should be a list with the following structure: list[0] - The letter of the residue being represented (for instance, from the example above, the first few list[0]s would be GTAT... list[1] - A dictionary with the letter substitutions and counts. """ warnings.warn( "The `PSSM` class is deprecated and will be removed in a future " "release of Biopython. As an alternative, you can convert the " "multiple sequence alignment object to a new-style Alignment " "object by via its `.alignment` property, and then create a Motif " "object. For example, for a multiple sequence alignment `msa` of " "DNA nucleotides, you would do: " "\n" ">>> alignment = msa.alignment\n" ">>> from Bio.motifs import Motif\n" ">>> motif = Motif('ACGT', alignment)\n" ">>> counts = motif.counts\n" "\n" "The `counts` object contains the same information as the PSSM " "returned by `pos_specific_score_matrix`, but note that the " "indices are reversed:\n" "\n" ">>> counts[letter][i] == pssm[index][letter]\n" "True\n" "\n" "If your multiple sequence alignment object was obtained using " "Bio.AlignIO, then you can obtain a new-style Alignment object " "directly by using Bio.Align.read instead of Bio.AlignIO.read, " "or Bio.Align.parse instead of Bio.AlignIO.parse.", BiopythonDeprecationWarning, ) self.pssm = pssm def __getitem__(self, pos): return self.pssm[pos][1] def __str__(self): out = " " all_residues = sorted(self.pssm[0][1]) # first print out the top header for res in all_residues: out += " %s" % res out += "\n" # for each item, write out the substitutions for item in self.pssm: out += "%s " % item[0] for res in all_residues: out += " %.1f" % item[1][res] out += "\n" return out def get_residue(self, pos): """Return the residue letter at the specified position.""" return self.pssm[pos][0] def print_info_content(summary_info, fout=None, rep_record=0): """3 column output: position, aa in representative sequence, ic_vector value.""" warnings.warn( "The `print_info_content` function is deprecated and will be removed " "in a future release of Biopython.", BiopythonDeprecationWarning, ) fout = fout or sys.stdout if not summary_info.ic_vector: summary_info.information_content() rep_sequence = summary_info.alignment[rep_record] for pos, (aa, ic) in enumerate(zip(rep_sequence, summary_info.ic_vector)): fout.write("%d %s %.3f\n" % (pos, aa, ic))