:mod:`seq.alg ` -- Algorithms for Sequences ================================================================================ .. module:: ost.seq.alg :synopsis: Algorithms for sequences Algorithms for Alignments -------------------------------------------------------------------------------- .. function:: MergePairwiseAlignments(pairwise_alns, ref_seq) :param pairwise_alns: A list of pairwise alignments :type pairwise_alns: :class:`~ost.seq.AlignmentList` :param ref_seq: The reference sequence :type ref_seq: :class:`~ost.seq.SequenceHandle` :returns: The merged alignment :rtype: :class:`~ost.seq.AlignmentHandle` Merge a list of pairwise alignments into a multiple sequence alignments. This function uses the reference sequence as the anchor and inserts gaps where needed. This is also known as the *star method*. The resulting multiple sequence alignment provides a simple way to map between residues of pairwise alignments, e.g. to compare distances in two structural templates. There are a few things to keep in mind when using this function: - The reference sequence mustn't contain any gaps - The first sequence of each pairwise alignments corresponds to the reference sequence. Apart from the presence of gaps, these two sequences must be completely identical. - If the reference sequence has an offset, the first sequence of each pairwise alignment must have the same offset. This offset is inherited by the first sequence of the final output alignment. - The resulting multiple sequence alignment is by no means optimal. For better results, consider using a multiple-sequence alignment program such as MUSCLE or ClustalW. - Residues in columns where the reference sequence has gaps should not be considered as aligned. There is no information in the pairwise alignment to guide the merging, the result is undefined. **Example:** .. code-block:: python ref_seq = ost.seq.CreateSequence('ref', 'acdefghiklmn') seq_a1 = seq.CreateSequence('A1', 'acdefghikl-mn') seq_a2 = seq.CreateSequence('A2', 'atd-fghikllmn') seq_b1 = seq.CreateSequence('B1', 'acdefg-hiklmn') seq_b2 = seq.CreateSequence('B2', 'acd---qhirlmn') aln_a = seq.CreateAlignment() aln_a.AddSequence(seq_a1) aln_a.AddSequence(seq_a2) print(aln_a) # >>> A1 acdefghikl-mn # >>> A2 atd-fghikllmn aln_b = seq.CreateAlignment() aln_b.AddSequence(seq_b1) aln_b.AddSequence(seq_b2) print(aln_b) # >>> B1 acdefg-hiklmn # >>> B2 acd---qhirlmn aln_list = ost.seq.AlignmentList() aln_list.append(aln_a) aln_list.append(aln_b) merged_aln = ost.seq.alg.MergePairwiseAlignments(aln_list, ref_seq) print(merged_aln) # >>> ref acdefg-hikl-mn # >>> A2 atd-fg-hikllmn # >>> B2 acd---qhirl-mn .. autofunction:: ValidateSEQRESAlignment .. autofunction:: AlignToSEQRES .. autofunction:: AlignmentFromChainView .. function:: Conservation(aln, assign=true, prop_name="cons", ignore_gap=false) Calculates conservation scores for each column in the alignment, according to the ConSurf method (Armon et al., J. Mol. Biol. (2001) 307, 447-463). The conservation score is a value between 0 and 1. The bigger the number the more conserved the aligned residues are. :param aln: An alignment handle :type aln: :class:`~ost.seq.AlignmentHandle` :param assign: If true, the conservation scores are assigned to attached residues. The name of the property can be changed with the *prop_name* parameter. Useful when coloring entities based on sequence conservation. :param prop_name: The property name for assigning the conservation to attached residues. Defaults to 'cons'. :param ignore_gap: If true, the dissimilarity between two gaps is increased to 6.0 instead of 0.5 as defined in the original version. Without this, a stretch where in the alignment there is only one sequence which is aligned to only gaps, is considered highly conserved (depending on the number of gap sequences). .. function:: LocalAlign(seq1, seq2, subst_weight, gap_open=-5, gap_ext=-2) Performs a Smith/Waterman local alignment of *seq1* and *seq2* and returns the best-scoring alignments as a list of pairwise alignments. **Example:** .. code-block:: python seq_a = seq.CreateSequence('A', 'acdefghiklmn') seq_b = seq.CreateSequence('B', 'acdhiklmn') alns = seq.alg.LocalAlign(seq_a, seq_b, seq.alg.BLOSUM62) print(alns[0].ToString(80)) # >>> A acdefghiklmn # >>> B acd---hiklmn :param seq1: A valid sequence :type seq1: :class:`~ost.seq.ConstSequenceHandle` :param seq2: A valid sequence :type seq2: :class:`~ost.seq.ConstSequenceHandle` :param subst_weigth: The substitution weights matrix :type subst_weight: :class:`SubstWeightMatrix` :param gap_open: The gap opening penalty. Must be a negative number :param gap_ext: The gap extension penalty. Must be a negative number :returns: A list of best-scoring, non-overlapping alignments of *seq1* and *seq2*. Since alignments always start with a replacement, the start is stored in the sequence offset of the two sequences. .. function:: GlobalAlign(seq1, seq2, subst_weight, gap_open=-5, gap_ext=-2) Performs a Needleman/Wunsch global alignment of *seq1* and *seq2* and returns the best-scoring alignment. **Example:** .. code-block:: python seq_a = seq.CreateSequence('A', 'acdefghiklmn') seq_b = seq.CreateSequence('B', 'acdhiklmn') alns = seq.alg.GlobalAlign(seq_a, seq_b, seq.alg.BLOSUM62) print(alns[0].ToString(80)) # >>> A acdefghiklmn # >>> B acd---hiklmn :param seq1: A valid sequence :type seq1: :class:`~ost.seq.ConstSequenceHandle` :param seq2: A valid sequence :type seq2: :class:`~ost.seq.ConstSequenceHandle` :param subst_weigth: The substitution weights matrix :type subst_weight: :class:`SubstWeightMatrix` :param gap_open: The gap opening penalty. Must be a negative number :param gap_ext: The gap extension penalty. Must be a negative number :returns: Best-scoring alignment of *seq1* and *seq2*. .. function:: ShannonEntropy(aln, ignore_gaps=True) Returns the per-column Shannon entropies of the alignment. The entropy describes how conserved a certain column in the alignment is. The higher the entropy is, the less conserved the column. For a column with no amino aids, the entropy value is set to NAN. :param aln: Multiple sequence alignment :type aln: :class:`~ost.seq.AlignmentHandle` :param ignore_gaps: Whether to ignore gaps in the column. :type ignore_gaps: bool :returns: List of column entropies .. function:: SemiGlobalAlign(seq1, seq2, subst_weight, gap_open=-5, gap_ext=-2) Performs a semi-global alignment of *seq1* and *seq2* and returns the best- scoring alignment. The algorithm is Needleman/Wunsch same as GlobalAlign, but without any gap penalty for starting or ending gaps. This is prefereble whenever one of the sequences is significantly shorted than the other. This make it also suitable for fragment assembly. **Example:** .. code-block:: python seq_a = seq.CreateSequence('A', 'abcdefghijklmnok') seq_b = seq.CreateSequence('B', 'cdehijk') alns = seq.alg.GlobalAlign(seq_a, seq_b, seq.alg.BLOSUM62) print(alns[0].ToString(80)) # >>> A abcdefghijklmnok # >>> B --cde--hi-----jk alns = seq.alg.SemiGlobalAlign(seq_a, seq_b, seq.alg.BLOSUM62) print(alns[0].ToString(80)) # >>> A abcdefghijklmnok # >>> B --cde--hijk----- :param seq1: A valid sequence :type seq1: :class:`~ost.seq.ConstSequenceHandle` :param seq2: A valid sequence :type seq2: :class:`~ost.seq.ConstSequenceHandle` :param subst_weigth: The substitution weights matrix :type subst_weight: :class:`SubstWeightMatrix` :param gap_open: The gap opening penalty. Must be a negative number :param gap_ext: The gap extension penalty. Must be a negative number :returns: best-scoring alignment of *seq1* and *seq2*. .. autofunction:: ost.seq.alg.renumber.Renumber .. function:: SequenceIdentity(aln, ref_mode=seq.alg.RefMode.ALIGNMENT, seq_a=0, seq_b=1) Calculates the sequence identity between two sequences at index seq_a and seq_b in a multiple sequence alignment. :param aln: multiple sequence alignment :type aln: :class:`~ost.seq.AlignmentHandle` :param ref_mode: influences the way the sequence identity is calculated. When set to `seq.alg.RefMode.LONGER_SEQUENCE`, the sequence identity is calculated as the number of matches divided by the length of the longer sequence. If set to `seq.alg.RefMode.ALIGNMENT` (the default), the sequence identity is calculated as the number of matches divided by the number of aligned residues. :type ref_mode: int :param seq_a: the index of the first sequence :type seq_a: int :param seq_b: the index of the second sequence :type seq_b: int :returns: sequence identity in the range 0 to 100. :rtype: float .. function:: SequenceSimilarity(aln, subst_weight, normalize=false, seq_a=0, seq_b=1) Calculates the sequence similarity between two sequences at index seq_a and seq_b in a multiple sequence alignment. :param aln: Multiple sequence alignment :type aln: :class:`~ost.seq.AlignmentHandle` :param subst_weight: the substitution weight matrix (see the :ref:`BLOSUM Matrix` section below) :type subst_weight: :class:`~SubstWeightMatrix` :param normalize: if set to True, normalize to the range of the substitution weight matrix :type normalize: bool :param seq_a: the index of the first sequence :type seq_a: int :param seq_b: the index of the second sequence :type seq_b: int :returns: sequence similarity :rtype: float .. _substitution-weight-matrices: Substitution Weight Matrices and BLOSUM Matrices -------------------------------------------------------------------------------- .. autoclass:: SubstWeightMatrix :members: .. _blosum: Four preset BLOSUM (BLOcks SUbstitution Matrix) matrices are available at different levels of sequence identity: - BLOSUM45 - BLOSUM62 - BLOSUM80 - BLOSUM100 .. _contact-prediction: Contact Prediction -------------------------------------------------------------------------------- This is a set of functions for predicting pairwise contacts from a multiple sequence alignment (MSA). The core method here is mutual information which uses coevolution to predict contacts. Mutual information is complemented by two other methods which score pairs of columns of a MSA from the likelyhood of certain amino acid pairs to form contacts (statistical potential) and the likelyhood of finding certain substitutions of aminio-acid pairs in columns of the MSA corresponding to interacting residues. .. class:: ContactPredictionScoreResult Object containing the results form a contact prediction. .. attribute:: matrix An *NxN* :class:`~ost.FloatMatrix` where *N* is the length of the alignment. The element *i,j* corresponds to the score of the corresponding columns of the MSA. High scores correspond to high likelyhood of a contact. .. attribute:: sorted_indices List of all indices pairs *i,j*, containing (N*N-1)/2 elements, as the **matrix** is symmetrical and elements in the diagonal are ignored. The indices are sorted from the pair most likely to form a contact to the least likely one. .. method:: GetScore(i,j) returns **matrix(i,j)** :param i: First index :param j: Second index :type i: :class:`int` :type j: :class:`int` .. method:: SetScore(i,j,score) Sets **matrix(i,j)** to **score** :param i: First index :param j: Second index :param score: The score :type i: :class:`int` :type j: :class:`int` :type score: :class:`float` .. autofunction:: PredictContacts .. function:: CalculateMutualInformation(aln, \ weights=LoadConstantContactWeightMatrix(), \ apc_correction=true, zpx_transformation=true, \ small_number_correction=0.05) Calculates the mutual information (MI) from a multiple sequence alignemnt. Contributions of each pair of amino-acids are weighted using the matrix **weights** (weighted mutual information). The average product correction (**apc_correction**) correction and transformation into Z-scores (**zpx_transofrmation**) increase prediciton accuracy by reducing the effect of phylogeny and other noise sources. The small number correction reduces noise for alignments with small number of sequences of low diversity. :param aln: The multiple sequences alignment :type aln: :class:`~ost.seq.AlignmentHandle` :param weights: The weight matrix :type weights: :class`ContactWeightMatrix` :param apc_correction: Whether to use the APC correction :type apc_correction: :class:`bool` :param zpx_transformation: Whether to transform the scores into Z-scores :type zpx_transformation: :class:`bool` :param small_number_correction: initial values for the probabilities of having a given pair of amino acids *p(a,b)*. :type small_number_correction: :class:`float` .. autofunction:: CalculateContactProbability .. function:: CalculateContactScore(aln, \ weights=LoadDefaultContactWeightMatrix()) Calculates the Contact Score (*CoSc*) from a multiple sequence alignment. For each pair of residues *(i,j)* (pair of columns in the MSA), *CoSc(i,j)* is the average over the values of the **weights** corresponding to the amino acid pairs in the columns. :param aln: The multiple sequences alignment :type aln: :class:`~ost.seq.AlignmentHandle` :param weights: The contact weight matrix :type weights: :class`ContactWeightMatrix` .. function:: CalculateContactSubstitutionScore(aln, ref_seq_index=0, \ weights=LoadDefaultPairSubstWeightMatrix()) Calculates the Contact Substitution Score (*CoEvoSc*) from a multiple sequence alignment. For each pair of residues *(i,j)* (pair of columns in the MSA), *CoEvoSc(i,j)* is the average over the values of the **weights** corresponding to substituting the amino acid pair in the reference sequence (given by **ref_seq_index**) with all other pairs in columns *(i,j)* of the **aln**. :param aln: The multiple sequences alignment :type aln: :class:`~ost.seq.AlignmentHandle` :param weights: The pair substitution weight matrix :type weights: :class`ContactWeightMatrix` .. function:: LoadDefaultContactWeightMatrix() :returns: *CPE*, a :class:`ContactWeightMatrix` that was calculated from a large (>15000) set of high quality crystal structures as *CPE=log(CF(a,b)/NCF(a,b))* and then normalised so that all its elements are comprised between 0 and 1. *CF(a,b)* is the frequency of amino acids *a* and *b* for pairs of contacting residues and *NCF(a,b)* is the frequency of amino acids *a* and *b* for pairs of non-contacting residues. Apart from weights for the standard amino acids, this matrix gives a weight of 0 to all pairs for which at least one amino-acid is a gap. .. function:: LoadConstantContactWeightMatrix() :returns: A :class:`ContactWeightMatrix`. This matrix gives a weight of one to all pairs of standard amino-acids and a weight of 0 to pairs for which at least one amino-acid is a gap. .. function:: LoadDefaultPairSubstWeightMatrix() :returns: *CRPE*, a :class:`PairSubstWeightMatrix` that was calculated from a large (>15000) set of high quality crystal structures as *CRPE=log(CRF(ab->cd)/NCRF(ab->cd))* and then normalised so that all its elements are comprised between 0 and 1. *CRF(ab->cd)* is the frequency of replacement of a pair of amino acids *a* and *b* by a pair *c* and *d* in columns of the MSA corresponding to contacting residues and *NCRF(ab->cd)* is the frequency of replacement of a pair of amino acids *a* and *b* by a pair *c* and *d* in columns of the MSA corresponding to non-contacting residues. Apart from weights for the standard amino acids, this matrix gives a weight of 0 to all pair substitutions for which at least one amino-acid is a gap. .. class:: PairSubstWeightMatrix(weights, aa_list) This class is used to associate a weight to any substitution from one amino-acid pair *(a,b)* to any other pair *(c,d)*. .. attribute:: weights A :class:`~ost.FloatMatrix4` of size *NxNxNxN*, where *N=len(aa_list)* .. attribute:: aa_list A :class:`CharList` of one letter codes of the amino acids for which weights are found in the **weights** matrix. .. class:: ContactWeightMatrix(weights, aa_list) This class is used to associate a weight to any pair of amino-acids. .. attribute:: weights A :class:`~ost.FloatMatrix` of size *NxN*, where *N=len(aa_list)* .. attribute:: aa_list A :class:`CharList` of one letter codes of the amino acids for which weights are found in the **weights** matrix. Get and analyze distance matrices from alignments -------------------------------------------------------------------------------- Given a multiple sequence alignment between a reference sequence (first sequence in alignment) and a list of structures (remaining sequences in alignment with an attached view to the structure), this set of functions can be used to analyze differences between the structures. **Example:** .. code-block:: python # SETUP: aln is multiple sequence alignment, where first sequence is the # reference sequence and all others have a structure attached # clip alignment to only have parts with at least 3 sequences (incl. ref.) # -> aln will be cut and clip_start is 1st column of aln that was kept clip_start = seq.alg.ClipAlignment(aln, 3) # get variance measure and distance to mean for each residue pair d_map = seq.alg.CreateDistanceMap(aln) var_map = seq.alg.CreateVarianceMap(d_map) dist_to_mean = seq.alg.CreateDist2Mean(d_map) # report min. and max. variances print("MIN-MAX:", var_map.Min(), "-", var_map.Max()) # get data and json-strings for further processing var_map_data = var_map.GetData() var_map_json = var_map.GetJsonString() dist_to_mean_data = dist_to_mean.GetData() dist_to_mean_json = dist_to_mean.GetJsonString() .. function:: ClipAlignment(aln, n_seq_thresh=2, set_offset=true, \ remove_empty=true) Clips alignment so that first and last column have at least the desired number of structures. :param aln: Multiple sequence alignment. Will be cut! :type aln: :class:`~ost.seq.AlignmentHandle` :param n_seq_thresh: Minimal number of sequences desired. :type n_seq_thresh: :class:`int` :param set_offset: Shall we update offsets for attached views? :type set_offset: :class:`bool` :param remove_empty: Shall we remove sequences with only gaps in cut aln? :type remove_empty: :class:`bool` :returns: Starting column (0-indexed), where cut region starts (w.r.t. original aln). -1, if there is no region in the alignment with at least the desired number of structures. :rtype: :class:`int` .. function:: CreateDistanceMap(aln) Create distance map from a multiple sequence alignment. The algorithm requires that the sequence alignment consists of at least two sequences. The sequence at index 0 serves as a frame of reference. All the other sequences must have an attached view and a properly set sequence offset (see :meth:`~ost.seq.AlignmentHandle.SetSequenceOffset`). For each of the attached views, the C-alpha distance pairs are extracted and mapped onto the corresponding C-alpha distances in the reference sequence. :param aln: Multiple sequence alignment. :type aln: :class:`~ost.seq.AlignmentHandle` :returns: Distance map. :rtype: :class:`DistanceMap` :raises: Exception if *aln* has less than 2 sequences or any sequence (apart from index 0) is lacking an attached view. .. function:: CreateVarianceMap(d_map, sigma=25) :returns: Variance measure for each entry in *d_map*. :rtype: :class:`VarianceMap` :param d_map: Distance map as created with :func:`CreateDistanceMap`. :type d_map: :class:`DistanceMap` :param sigma: Used for weighting of variance measure (see :meth:`Distances.GetWeightedStdDev`) :type sigma: :class:`float` :raises: Exception if *d_map* has no entries. .. function:: CreateDist2Mean(d_map) :returns: Distances to mean for each structure in *d_map*. Structures are in the same order as passed when creating *d_map*. :rtype: :class:`Dist2Mean` :param d_map: Distance map as created with :func:`CreateDistanceMap`. :type d_map: :class:`DistanceMap` :raises: Exception if *d_map* has no entries. .. function:: CreateMeanlDDTHA(d_map) :returns: lDDT calculation based on CA carbons of the structures with lddt distance threshold of 15 Angstrom and distance difference thresholds of [0.5, 1.0, 2.0, 4.0]. The reported values for a certain structure are the mean per-residue lDDT values given all other structures as reference. Structures are in the same order as passed when creating *d_map*. :rtype: :class:`MeanlDDT` :param d_map: Distance map as created with :func:`CreateDistanceMap`. :type d_map: :class:`DistanceMap` :raises: Exception if *d_map* has no entries. .. class:: Distances Container used by :class:`DistanceMap` to store a pair wise distance for each structure. Each structure is identified by its index in the originally used alignment (see :func:`CreateDistanceMap`). .. method:: GetDataSize() :returns: Number of pairwise distances. :rtype: :class:`int` .. method:: GetAverage() :returns: Average of all distances. :rtype: :class:`float` :raises: Exception if there are no distances. .. method:: GetMin() GetMax() :returns: Minimal/maximal distance. :rtype: :class:`tuple` (distance (:class:`float`), index (:class:`int`)) :raises: Exception if there are no distances. .. method:: GetDataElement(index) :returns: Element at given *index*. :rtype: :class:`tuple` (distance (:class:`float`), index (:class:`int`)) :param index: Index within list of distances (must be < :meth:`GetDataSize`). :type index: :class:`int` :raises: Exception if there are no distances or *index* out of bounds. .. method:: GetStdDev() :returns: Standard deviation of all distances. :rtype: :class:`float` :raises: Exception if there are no distances. .. method:: GetWeightedStdDev(sigma) :returns: Standard deviation of all distances multiplied by exp( :meth:`GetAverage` / (-2*sigma) ). :rtype: :class:`float` :param sigma: Defines weight. :type sigma: :class:`float` :raises: Exception if there are no distances. .. method:: GetNormStdDev() :returns: Standard deviation of all distances divided by :meth:`GetAverage`. :rtype: :class:`float` :raises: Exception if there are no distances. .. class:: DistanceMap Container returned by :func:`CreateDistanceMap`. Essentially a symmetric :meth:`GetSize` x :meth:`GetSize` matrix containing up to :meth:`GetNumStructures` distances (list stored as :class:`Distances`). Indexing of residues starts at 0 and corresponds to the positions in the originally used alignment (see :func:`CreateDistanceMap`). .. method:: GetDistances(i_res1, i_res2) :returns: List of distances for given pair of residue indices. :rtype: :class:`Distances` :param i_res1: Index of residue. :type i_res1: :class:`int` :param i_res2: Index of residue. :type i_res2: :class:`int` .. method:: GetSize() :returns: Number of residues in map. :rtype: :class:`int` .. method:: GetNumStructures() :returns: Number of structures originally used when creating the map (see :func:`CreateDistanceMap`). :rtype: :class:`int` .. class:: VarianceMap Container returned by :func:`CreateVarianceMap`. Like :class:`DistanceMap`, it is a symmetric :meth:`GetSize` x :meth:`GetSize` matrix containing variance measures. Indexing of residues is as in :class:`DistanceMap`. .. method:: Get(i_res1, i_res2) :returns: Variance measure for given pair of residue indices. :rtype: :class:`float` :param i_res1: Index of residue. :type i_res1: :class:`int` :param i_res2: Index of residue. :type i_res2: :class:`int` .. method:: GetSize() :returns: Number of residues in map. :rtype: :class:`int` .. method:: Min() Max() :returns: Minimal/maximal variance in the map. :rtype: :class:`float` .. method:: ExportDat(file_name) ExportCsv(file_name) ExportJson(file_name) Write all variance measures into a file. The possible formats are: - "dat" file: a list of "*i_res1+1* *i_res2+1* variance" lines - "csv" file: a list of ";" separated variances (one line for each *i_res1*) - "json" file: a JSON formatted file (see :meth:`GetJsonString`) :param file_name: Path to file to be created. :type file_name: :class:`str` :raises: Exception if the file cannot be opened for writing. .. method:: GetJsonString() :returns: A JSON formatted list of :meth:`GetSize` lists with :meth:`GetSize` variances :rtype: :class:`str` .. method:: GetData() Gets all the data in this map at once. Note that this is much faster (10x speedup observed) than parsing :meth:`GetJsonString` or using :meth:`Get` on each element. :returns: A list of :meth:`GetSize` lists with :meth:`GetSize` variances. :rtype: :class:`list` of :class:`list` of :class:`float` .. method:: GetSubData(num_res_to_avg) Gets subset of data in this map by averaging neighboring values for *num_res_to_avg* residues. :returns: A list of ceil(:meth:`GetSize`/*num_res_to_avg*) lists with ceil(:meth:`GetSize`/*num_res_to_avg*) variances. :rtype: :class:`list` of :class:`list` of :class:`float` .. class:: Dist2Mean Container returned by :func:`CreateDist2Mean`. Stores distances to mean for :meth:`GetNumResidues` residues of :meth:`GetNumStructures` structures. Indexing of residues is as in :class:`DistanceMap`. Indexing of structures goes from 0 to :meth:`GetNumStructures` - 1 and is in the same order as the structures in the originally used alignment. .. method:: Get(i_res, i_str) :returns: Distance to mean for given residue and structure indices. :rtype: :class:`float` :param i_res: Index of residue. :type i_res: :class:`int` :param i_str: Index of structure. :type i_str: :class:`int` .. method:: GetNumResidues() :returns: Number of residues. :rtype: :class:`int` .. method:: GetNumStructures() :returns: Number of structures. :rtype: :class:`int` .. method:: ExportDat(file_name) ExportCsv(file_name) ExportJson(file_name) Write all distance measures into a file. The possible formats are: - "dat" file: a list of "*i_res+1* distances" lines (distances are space separated) - "csv" file: a list of ";" separated distances (one line for each *i_res*) - "json" file: a JSON formatted file (see :meth:`GetJsonString`) :param file_name: Path to file to be created. :type file_name: :class:`str` :raises: Exception if the file cannot be opened for writing. .. method:: GetJsonString() :returns: A JSON formatted list of :meth:`GetNumResidues` lists with :meth:`GetNumStructures` distances. :rtype: :class:`str` .. method:: GetData() Gets all the data in this map at once. Note that this is much faster (10x speedup observed) than parsing :meth:`GetJsonString` or using :meth:`Get` on each element. :returns: A list of :meth:`GetNumResidues` lists with :meth:`GetNumStructures` distances. :rtype: :class:`list` of :class:`list` of :class:`float` .. method:: GetSubData(num_res_to_avg) Gets subset of data in this map by averaging neighboring values for *num_res_to_avg* residues. :returns: A list of ceil(:meth:`GetNumResidues`/*num_res_to_avg*) lists with :meth:`GetNumStructures` distances. :rtype: :class:`list` of :class:`list` of :class:`float` .. class:: MeanlDDT Container returned by :func:`CreateMeanlDDTHA`. Stores mean lDDT values for :meth:`GetNumResidues` residues of :meth:`GetNumStructures` structures. Has the exact same functionality and behaviour as :class:`Dist2Mean` HMM Algorithms -------------------------------------------------------------------------------- Openstructure implements basic HMM-related functionality that aims at calculating an HMM-HMM alignment score as described in Soding, Bioinformatics (2005) 21(7), 951-60. This is the score which is optimized in the Viterbi algorithm of the hhalign tool. As a prerequisite, OpenStructure also implements adding pseudo counts to :class:`ost.seq.ProfileHandle` in order to avoid zero probabilities for unobserved transitions/emissions. Given these requirements, all functions in this section require HMM related data (transition probabilities, neff values, etc.) to be set, which is the case if you load a file in hhm format. .. method:: HMMScore(profile_0, profile_1, aln, s_0_idx, s_1_idx, \ match_score_offset=-0.03,correl_score_weight=0.1, \ del_start_penalty_factor=0.6, \ del_extend_penalty_factor=0.6, \ ins_start_penalty_factor=0.6, \ ins_extend_penalty_factor=0.6) Scores an HMM-HMM alignment given in *aln* between *profile_0* and *profile_1*. The score is described in Soding, Bioinformatics (2005) 21(7), 951-60 and consists of three components: * sum of column alignment scores of all aligned columns, the *match_score_offset* is applied to each of those scores * sum of transition probability scores, the prefactor of those scores can be controlled with penalty factors (*del_start_penalty_factor* etc.) * correlation score which rewards conserved columns occuring in clusters, *correl_score_weight* controls its contribution to the total score You have to make sure that proper pseudo counts are already assigned before calling this function. You can find a usage example in this documentation. This score is not necessarily consistent with the output generated with hhalign, i.e. you take the hhalign output alignment and directly feed it into this function with the same profiles and expect an equal score. The reason is that by default, hhalign performs a re-alignment step but the output score actually relates to the initial alignment coming from the Viterbi alignment. To get consistent results, run hhalign with the -norealign flag. :param profile_0: First profile to be scored :param profile_1: Second profile to be scored :param aln: Alignment connecting the two profiles :param s_0_idx: Idx of sequence in *aln* that describes *profile_0* :param s_1_idx: Idx of sequence in *aln* that describes *profile_1* :param match_score_offset: Offset which is applied to each column alignment score :param correl_score_weight: Prefactor to control contribution of correlation score to total score :param del_start_penalty_factor: Factor which is applied for each transition score starting a deletion :param del_extend_penalty_factor: Factor which is applied for each transition score extending a deletion :param ins_start_penalty_factor: Factor which is applied for each transition score starting an insertion :param ins_extend_penalty_factor: Factor which is applied for each transition score extending an insertion :type profile_0: :class:`ost.seq.ProfileHandle` :type profile_1: :class:`ost.seq.ProfileHandle` :type aln: :class:`ost.seq.AlignmentHandle` :type s_0_idx: :class:`int` :type s_1_idx: :class:`int` :type match_score_offset: :class:`float` :type correl_score_weight: :class:`float` :type del_start_penalty_factor: :class:`float` :type del_extend_penalty_factor: :class:`float` :type ins_start_penalty_factor: :class:`float` :type ins_extend_penalty_factor: :class:`float` :raises: Exception if profiles don't have HMM information assigned or specified sequences in *aln* don't match with profile SEQRES. Potentially set sequence offsets are taken into account. **Example with pseudo count assignment:** .. code-block:: python from ost import io, seq prof_query = io.LoadSequenceProfile("query.hhm") prof_tpl = io.LoadSequenceProfile("tpl.hhm") aln = io.LoadAlignment("aln.fasta") # assign pseudo counts to transition probabilities seq.alg.AddTransitionPseudoCounts(prof_query) seq.alg.AddTransitionPseudoCounts(prof_tpl) # hhblits/hhalign 3 assign different pseudo counts to # query and template. The reason is computational efficiency. # The more expensive Angermueller et al. pseudo counts # are assigned to the query. path_to_crf = "/path/to/hh-suite/data/context_data.crf" lib = seq.alg.ContextProfileDB.FromCRF(path_to_crf) seq.alg.AddAAPseudoCounts(prof_query, lib) # templates are assigned the computationally cheaper pseudo # counts derived from a Gonnet substitution matrix seq.alg.AddAAPseudoCounts(prof_tpl) # assign null model pseudo counts # this should be done AFTER you assigned pseudo counts to emission # probabilities as this affects the result seq.alg.AddNullPseudoCounts(prof_query) seq.alg.AddNullPseudoCounts(prof_tpl) print("score:", seq.alg.HMMScore(prof_query, prof_tpl, aln, 0, 1)) .. method:: AddNullPseudoCounts(profile) Adds pseudo counts to null model in *profile* as implemented in hhalign. Conceptually we're mixing the original null model with the frequencies observed in the columns of *profile*. The weight of the original null model depends on the neff value of *profile*. This function should be called AFTER you already assigned pseudo counts to the emission probabilities as this affects the result. :param profile: Profile to add pseudo counts :type profile: :class:`ost.seq.ProfileHandle` :raises: Exception if profile doesn't have HMM information assigned .. method:: AddTransitionPseudoCounts(profile, gapb=1.0, gapd=0.15, gape=1.0) Adds pseudo counts to the transition probabilities in *profile* as implemented in hhalign with equivalent parameter naming and default parameterization. The original transition probabilities are mixed with prior probabilities that are controlled by *gapd* and *gape*. Priors: * priorM2I = priorM2D = *gapd* * 0.0286 * priorM2M = 1.0 - priorM2D - priorM2I * priorI2I = priorD2D = 1.0 * *gape* / (gape - 1.0 + 1.0/0.75) * priorI2M = priorD2M = 1.0 - priorI2I Transition probabilities of column i starting from a match state are then estimated with pM2X = (neff[i] - 1) * pM2X + *gape* * priorM2X. Starting from an insertion/deletion state we have pI2X = neff_ins[i] * pI2X + *gape* * priorI2X. In the end, all probabilities are normalized such that (pM2M, pM2I, pM2D) sum up to one, (pI2M, pI2I) sum up to one and (pD2I, pD2D) sum up to one. :param profile: Profile to add pseudo counts :type profile: :class:`ost.seq.ProfileHandle` :raises: Exception if profile doesn't have HMM information assigned .. method:: AddAAPseudoCounts(profile, a=1.0, b=1.5, c=1.0) Adds pseudo counts to the emission probabilities in *profile* by mixing in probabilities from the Gonnet matrix as implemented in hhalign with equivalent parameter naming and default parameterization. We only implement the diversity-dependent mode for the mixing factor tau (default in hhalign), which for column *i* depends on *neff[i]* , *a* , *b* and *c* . :param profile: Profile to add pseudo counts :type profile: :class:`ost.seq.ProfileHandle` :raises: Exception if profile doesn't have HMM information assigned .. class:: ContextProfileDB Database that contains context profiles which will be used to add pseudo counts as described by Angermueller et al., Bioinformatics (2012) 28, 3240-3247. .. method:: FromCRF(filename) Static load function which reads a crf file provided in an hh-suite installation. Default location: "path/to/hhsuite/data/context_data.crf" :param filename: Filename of CRF file :type filename: :class:`str` .. method:: Save(filename) Saves database in OST-internal binary format which can be loaded faster than a crf file. :param filename: Filename to save db :type filename: :class:`str` .. method:: Load(filename) Static load function that loads database in OST-internal binary format. :param filename: Filename of db :type filename: :class:`str` .. method:: AddAAPseudoCounts(profile, db, a=0.9, b=4.0, c=1.0) Adds pseudo counts to the emission probabilities in *profile* by utilizing context profiles as described in Angermueller et al., Bioinformatics (2012) 28, 3240-3247. We only implement the diversity-dependent mode for the mixing factor tau (default in hhalign), which for column *i* depends on *neff[i]* , *a* , *b* and *c* . :param profile: Profile to add pseudo counts :type profile: :class:`ost.seq.ProfileHandle` :param db: Database of context profiles :type db: :class:`ContextProfileDB` :raises: Exception if profile doesn't have HMM information assigned AAIndex annotations ------------------- .. autoclass:: ost.seq.alg.aaindex.AAIndex :members: :special-members: __getitem__ The annotations/scores can either refer to single amino acids or represent pairwise values. The two types are: .. autoclass:: ost.seq.alg.aaindex.AnnoType :members: :undoc-members: The actual data of an entry in the aaindex database is stored in a :class:`aaindex.AAIndexData` object: .. autoclass:: ost.seq.alg.aaindex.AAIndexData :members: