o Rŀg~@sjdZddlZddlZddlZddlmZddlmZddlm Z GdddZ Gdd d Z d d d Z dS) zExtract 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. N)Counter)BiopythonDeprecationWarning)Seqc@seZdZdZddZdddZdd d Zdd d ZddZddZ dddZ   d ddZ  d!ddZ ddZ ddZd S)" SummaryInfozCalculate 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. cCs||_g|_dS)zInitialize with the alignment to calculate information on. ic_vector attribute. A list of ic content for each column number. N) alignment ic_vector)selfrr G/var/www/html/myenv/lib/python3.10/site-packages/Bio/Align/AlignInfo.py__init__s zSummaryInfo.__init__ffffff?XFc Cstdtd}|j}t|D]u}t}d}|jD]&} z| |} Wn ty,Yqw| dkrA| dkrA|| d7<|d7}qg} d} |D]} || | krX| g} || } qH|| | krc| | qH|ro|dkro||7}qt | dkr| ||kr|| d7}q||7}qt |S)a&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). a;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: >>> from Bio.Seq import Seq >>> from Bio.SeqRecord import SeqRecord >>> from Bio.Align import MultipleSeqAlignment >>> from Bio.Align.AlignInfo import SummaryInfo >>> msa = MultipleSeqAlignment([SeqRecord(Seq('ACGT')), ... SeqRecord(Seq('ATGT')), ... SeqRecord(Seq('ATGT'))]) >>> summary = SummaryInfo(msa) >>> dumb_consensus = summary.dumb_consensus(ambiguous='N') >>> print(dumb_consensus) ANGT >>> alignment = msa.alignment >>> from Bio.motifs import Motif >>> motif = Motif('ACGT', alignment) >>> print(motif.consensus) ATGT >>> print(motif.degenerate_consensus) AYGT >>> counts = motif.counts >>> consensus = counts.calculate_consensus(identity=0.7) >>> print(consensus) ANGT 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.r-. warningswarnrrget_alignment_lengthranger IndexErrorappendlenrr threshold ambiguousrequire_multiple consensuscon_lenn atom_dict num_atomsrecordc max_atomsmax_sizeatomr r r dumb_consensus'sD'+            zSummaryInfo.dumb_consensusc Cstdtd}|j}t|D]m}t}d}|jD]} z| |} Wn ty,Yqw|| d7<|d7}qg} d} |D]} || | krP| g} || } q@|| | kr[| | q@|rg|dkrg||7}qt | dkrz| ||krz|| d7}q||7}qt |S)ahOutput 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. aThe `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: >>> from Bio.Seq import Seq >>> from Bio.SeqRecord import SeqRecord >>> from Bio.Align import MultipleSeqAlignment >>> from Bio.Align.AlignInfo import SummaryInfo >>> msa = MultipleSeqAlignment([SeqRecord(Seq('ACGT')), ... SeqRecord(Seq('AT-T')), ... SeqRecord(Seq('CT-T')), ... SeqRecord(Seq('GT-T'))]) >>> summary = SummaryInfo(msa) >>> gap_consensus = summary.gap_consensus(ambiguous='N') >>> print(gap_consensus) NT-T >>> alignment = msa.alignment >>> from Bio.motifs import Motif >>> motif = Motif('ACGT-', alignment) # include '-' in alphabet >>> print(motif.consensus) AT-T >>> print(motif.degenerate_consensus) VT-T >>> counts = motif.counts >>> consensus = counts.calculate_consensus(identity=0.7) >>> print(consensus) NT-T 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.rrrrrr r r gap_consensuss@ (+             zSummaryInfo.gap_consensusNc stdt|durtdfddD}tt|jD]1}t|dt|jD]$}||j|j|j|j|j|j dd|j|j dd|q*q|S) aGenerate 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. aThe `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: >>> alignment = msa.alignment >>> dictionary = alignment.substitutions 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.Nztargument skip_chars has been deprecated; instead, please use 'letters' to specify the characters you want to includecs i|] }D]}||fdqqS)rr ).0letter1letter2lettersr r ,s z6SummaryInfo.replacement_dictionary..rweight?) rrr ValueErrorrrr_pair_replacementseq annotationsget)r skip_charsr.rep_dictrec_num1rec_num2r r-r replacement_dictionarys*"   z"SummaryInfo.replacement_dictionaryc Cs@t||D]\}}||vr||vr|||f||7<qdS)aCompare 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. N)zip) rseq1seq2weight1weight2 dictionaryr.residue1residue2r r r r3@s  zSummaryInfo._pair_replacementcCs4t}|jD]}||jqt|}d|}|S)zKReturn a string containing the expected letters in the alignment (PRIVATE).r)setrupdater4sortedjoin)r set_lettersr# list_letters all_lettersr r r _get_all_lettersPs   zSummaryInfo._get_all_lettersc CsHtdt|}|std|durg}t|tstdd}|||D]}| |d}q(|rC|}t ||j krBtdn| }g}tt |D]P}t|d} |j D];} z| j|} Wn tynd} Ynw| r| |vr| jd d } z | | | 7<WqZtytd | dwqZ|||| fqOt|S) aCreate 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. aThe `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: >>> alignment = msa.alignment >>> from Bio.motifs import Motif >>> motif = Motif('ACGT', alignment) >>> counts = motif.counts The `counts` object contains the same information as the PSSM returned by `pos_specific_score_matrix`, but note that the indices are reversed: >>> counts[letter][i] == pssm[index][letter] True 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.z&_get_all_letters returned empty stringNz!chars_to_ignore should be a list.rrzi} t ||D]} | | |j|||||} | | |||} | | | <qNt | }g|_t| D]\}}|j| ||qv|S)aCalculate 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. aThe `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: >>> alignment = msa.alignment >>> from Bio.motifs import Motif >>> motif = Motif('ACGT', alignment) >>> information_content = motif.information_content 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. 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.Nrz5Start (%s) and end (%s) are not in the range %s to %sr)rrrrrr4r2rKrOr_get_letter_freqs_get_column_info_contentsumvaluesr enumerater)rstartend e_freq_tablelog_baserU pseudo_countrandom_expectedrJrW info_contentrZ freq_dict column_score total_infoikr r r information_contentsL"   zSummaryInfo.information_contentc CsLt|d}d} d} |dkrtd||D]4} z| j||vr6| jdd} || j|| 7<| | 7} WqtyJtd| j||fdw|r`|D]} | | kr_| |vr_td| qO| dkru|D] }||dkrrtd qf|S|D],}|r|s|r|r||}n|}||||}| |}||||<qw||| ||<qw|S) a^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 rrz5Positive value required for pseudo_count, %s providedr0r1z"Residue %s not found in letters %sNz(%s not found in expected frequency tablezfreq_info[letter] is not 0)rPrQr2r4r5r6rR)rrZ all_recordsr. to_ignorerhrfri freq_info total_countrVr#r0keyletter ajust_freqajusted_letter_count ajusted_totalr r r r_sX       zSummaryInfo._get_letter_freqsc Csd}|r|D]}||kr||vrtd|qd}|D]/}d} ||kr5|r/||||} n|||} | dkrK||t| t|} || 7}q|S)aCalculate 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. rz:Frequency table provided does not contain observed letter gr)r2mathlog) robs_freqrfrgrirVrurmrv inner_log letter_infor r r r`ts*  z$SummaryInfo._get_column_info_contentcCs|jdd|fS)zReturn column of alignment.N)r)rcolr r r get_columnszSummaryInfo.get_column)r r F)NN)rNNr^Nr)rNN)__name__ __module__ __qualname____doc__r r(r)r;r3rKr]rpr_r`rr r r r rs,  l `M a l V ,rc@s0eZdZdZddZddZddZdd Zd S) rSaRepresent 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'] cCstdt||_dS)aWInitialize 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. aThe `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: >>> alignment = msa.alignment >>> from Bio.motifs import Motif >>> motif = Motif('ACGT', alignment) >>> counts = motif.counts The `counts` object contains the same information as the PSSM returned by `pos_specific_score_matrix`, but note that the indices are reversed: >>> counts[letter][i] == pssm[index][letter] True 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.N)rrrpssm)rrr r r r s   z PSSM.__init__cC|j|dS)Nrrrposr r r __getitem__szPSSM.__getitem__cCszd}t|jdd}|D]}|d|7}q |d7}|jD]}|d|d7}|D] }|d|d|7}q)|d7}q|S)N rrz %s z%s z %.1f)rFr)rout all_residuesresitemr r r __str__s  z PSSM.__str__cCr)z4Return the residue letter at the specified position.rrrr r r get_residueszPSSM.get_residueN)rrrrr rrrr r r r rSs % rScCsdtdt|p tj}|js||j|}tt ||jD]\}\}}| d|||fqdS)zJ3 column output: position, aa in representative sequence, ic_vector value.zeThe `print_info_content` function is deprecated and will be removed in a future release of Biopython.z %d %s %.3f N) rrrsysstdoutrrprrcr<write) summary_infofout rep_record rep_sequenceraaicr r r print_info_contents  r)Nr) rrzrr collectionsrBiorBio.SeqrrrSrr r r r s   ^