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seq.alg – Algorithms for Sequences

Algorithms for Alignments

MergePairwiseAlignments(pairwise_alns, ref_seq)
Parameters:
  • pairwise_alns (AlignmentList) – A list of pairwise alignments
  • ref_seq (SequenceHandle) – The reference sequence
Returns:

The merged alignment

Return type:

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:

    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
    
ValidateSEQRESAlignment(aln, chain=None)

Checks if sequence in alignment has same connectivity as residues in chain. This looks for connected stretches in both the sequence and the chain and returns False if they don’t match. This uses the connectivity of the protein backbone.

Parameters:
  • aln (AlignmentHandle) – Alignment of two sequences with second one expected to map to residues in chain.
  • chain (ChainHandle) – Source of the sequence.
Returns:

True if all residues (beside gapped ones) are connected, False otherwise.

AlignToSEQRES(chain, seqres, try_resnum_first=False, validate=True)

Aligns the residues of chain to the SEQRES sequence, inserting gaps where needed. The function uses the connectivity of the protein backbone to find consecutive peptide fragments. These fragments are then aligned to the SEQRES sequence.

All the non-ligand, peptide-linking residues of the chain must be listed in SEQRES. If there are any additional residues in the chain, the function raises a ValueError.

Parameters:
  • chain (ChainHandle) – Source of the sequence
  • seqres (str) – SEQRES sequence
  • try_resnum_first (bool) – If set to True, this first builds an alignment using residue numbers and checks if the one-letter-codes match. If they all match, this alignment is used (and possibly validated). Otherwise, it displays a warning and falls back to the connectivity-based alignment.
  • validate (bool) – If set to True, the alignment is additionally checked by ValidateSEQRESAlignment() and raises a ValueError if the validation failed.
Returns:

The alignment of the residues in the chain and the SEQRES entries.

Return type:

AlignmentHandle

AlignmentFromChainView(chain, handle_seq_name='handle', view_seq_name='view')

Creates and returns the sequence alignment of the given chain view to the chain handle. The alignment contains two sequences, the first containing all non-ligand peptide-linking residues, the second containing all non-ligand peptide-linking residues that are part of the view.

Parameters:
  • chain (ChainView) – A valid chain
  • handle_seq_name – Name of the handle sequence in the output alignment
  • view_seq_name – Name of the view sequence in the output alignment
Returns:

The alignment

Return type:

AlignmentHandle

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.

Parameters:
  • aln (AlignmentHandle) – An alignment handle
  • 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.
  • prop_name – The property name for assigning the conservation to attached residues. Defaults to ‘cons’.
  • 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).
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:

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
Parameters:
  • seq1 (ConstSequenceHandle) – A valid sequence
  • seq2 (ConstSequenceHandle) – A valid sequence
  • subst_weigth – The substitution weights matrix
  • gap_open – The gap opening penalty. Must be a negative number
  • 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.

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:

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
Parameters:
  • seq1 (ConstSequenceHandle) – A valid sequence
  • seq2 (ConstSequenceHandle) – A valid sequence
  • subst_weigth – The substitution weights matrix
  • gap_open – The gap opening penalty. Must be a negative number
  • gap_ext – The gap extension penalty. Must be a negative number
Returns:

Best-scoring alignment of seq1 and seq2.

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.

Parameters:
  • aln (AlignmentHandle) – Multiple sequence alignment
  • ignore_gaps (bool) – Whether to ignore gaps in the column.
Returns:

List of column entropies

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:

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-----
Parameters:
  • seq1 (ConstSequenceHandle) – A valid sequence
  • seq2 (ConstSequenceHandle) – A valid sequence
  • subst_weigth – The substitution weights matrix
  • gap_open – The gap opening penalty. Must be a negative number
  • gap_ext – The gap extension penalty. Must be a negative number
Returns:

best-scoring alignment of seq1 and seq2.

Renumber(seq_handle, sequence_number_with_attached_view=1, old_number_label=None)

Function to renumber an entity according to an alignment between the model sequence and the full-length target sequence. The aligned model sequence or the alignment itself with an attached view needs to be provided. Upon succcess, the renumbered entity is returned. If an alignment is given, the first sequence of the alignment is considered the full-length sequence and it must match the model sequence wherever it is aligned (i.e. excluding gaps).

from ost.seq.alg import renumber
from ost.bindings.clustalw import *
ent = io.LoadPDB("path_to_model")
s = io.LoadSequence("path_to_full_length_fasta_seqeunce")
pdb_seq = seq.SequenceFromChain("model", ent.chains[0])
aln = ClustalW(s, pdb_seq)
aln.AttachView(1, ent.chains[0].Select(""))
e = Renumber(aln.sequences[1])
io.SavePDB(e, "renum.pdb")
Parameters:
  • seq_handle (SequenceHandle / AlignmentHandle) – Sequence or alignment handle with attached view.
  • sequence_number_with_attached_view (int) – Sequence number for the aln. handle (not used if seq. handle given)
Raises:

RuntimeError if unknown type of seq_handle or if attached view is missing or if the given alignment sequence is inconsistent.

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.

matrix

An NxN 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.

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.

GetScore(i, j)

returns matrix(i,j)

Parameters:
  • i (int) – First index
  • j (int) – Second index
SetScore(i, j, score)

Sets matrix(i,j) to score

Parameters:
  • i (int) – First index
  • j (int) – Second index
  • score (float) – The score
PredictContacts(ali)

Predicts contacts from a multiple sequence alignment using a combination of Mutual Information (MI) and the Contact Substitution Score (CoEvoSc). MI is calculated with the APC and small number corrections as well as with a transformation into Z-scores. The CoEvoSc is calculated using the default PairSubstWeightMatrix (see seq.alg.LoadDefaultPairSubstWeightMatrix). The final score for a pair of columns (i,j) of ali is obtained from:

Sc(i,j)=MI(i,j)exp(CoEvoSc(i,j)) if (i,j) >=0

Sc(i,j)=MI(i,j)exp(1-CoEvoSc(i,j)) if (i,j) <0

Parameters:ali (AlignmentHandle) – The multiple sequence alignment
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.

Parameters:
  • aln (AlignmentHandle) – The multiple sequences alignment
  • weights (:class`ContactWeightMatrix`) – The weight matrix
  • apc_correction (bool) – Whether to use the APC correction
  • zpx_transformation (bool) – Whether to transform the scores into Z-scores
  • small_number_correction (float) – initial values for the probabilities of having a given pair of amino acids p(a,b).
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.

Parameters:
  • aln (AlignmentHandle) – The multiple sequences alignment
  • weights (:class`ContactWeightMatrix`) – The contact weight matrix
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.

Parameters:
  • aln (AlignmentHandle) – The multiple sequences alignment
  • weights (:class`ContactWeightMatrix`) – The pair substitution weight matrix
LoadDefaultContactWeightMatrix()
Returns:CPE, a 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.
LoadConstantContactWeightMatrix()
Returns:A 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.
LoadDefaultPairSubstWeightMatrix()
Returns:CRPE, a 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).

weights

A FloatMatrix4 of size NxNxNxN, where N=len(aa_list)

aa_list

A 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.

weights

A FloatMatrix of size NxN, where N=len(aa_list)

aa_list

A 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:

# 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()
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.

Parameters:
  • aln (AlignmentHandle) – Multiple sequence alignment. Will be cut!
  • n_seq_thresh (int) – Minimal number of sequences desired.
  • set_offset (bool) – Shall we update offsets for attached views?
  • remove_empty (bool) – Shall we remove sequences with only gaps in cut aln?
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.

Return type:

int

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 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.

Parameters:aln (AlignmentHandle) – Multiple sequence alignment.
Returns:Distance map.
Return type:DistanceMap
Raises:Exception if aln has less than 2 sequences or any sequence (apart from index 0) is lacking an attached view.
CreateVarianceMap(d_map, sigma=25)
Returns:

Variance measure for each entry in d_map.

Return type:

VarianceMap

Parameters:
Raises:

Exception if d_map has no entries.

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.
Return type:Dist2Mean
Parameters:d_map (DistanceMap) – Distance map as created with CreateDistanceMap().
Raises:Exception if d_map has no entries.
class Distances

Container used by DistanceMap to store a pair wise distance for each structure. Each structure is identified by its index in the originally used alignment (see CreateDistanceMap()).

GetDataSize()
Returns:Number of pairwise distances.
Return type:int
GetAverage()
Returns:Average of all distances.
Return type:float
Raises:Exception if there are no distances.
GetMin()
GetMax()
Returns:Minimal/maximal distance.
Return type:tuple (distance (float), index (int))
Raises:Exception if there are no distances.
GetDataElement(index)
Returns:Element at given index.
Return type:tuple (distance (float), index (int))
Parameters:index (int) – Index within list of distances (must be < GetDataSize()).
Raises:Exception if there are no distances or index out of bounds.
GetStdDev()
Returns:Standard deviation of all distances.
Return type:float
Raises:Exception if there are no distances.
GetWeightedStdDev(sigma)
Returns:Standard deviation of all distances multiplied by exp( GetAverage() / (-2*sigma) ).
Return type:float
Parameters:sigma (float) – Defines weight.
Raises:Exception if there are no distances.
GetNormStdDev()
Returns:Standard deviation of all distances divided by GetAverage().
Return type:float
Raises:Exception if there are no distances.
class DistanceMap

Container returned by CreateDistanceMap(). Essentially a symmetric GetSize() x GetSize() matrix containing up to GetNumStructures() distances (list stored as Distances). Indexing of residues starts at 0 and corresponds to the positions in the originally used alignment (see CreateDistanceMap()).

GetDistances(i_res1, i_res2)
Returns:

List of distances for given pair of residue indices.

Return type:

Distances

Parameters:
  • i_res1 (int) – Index of residue.
  • i_res2 (int) – Index of residue.
GetSize()
Returns:Number of residues in map.
Return type:int
GetNumStructures()
Returns:Number of structures originally used when creating the map (see CreateDistanceMap()).
Return type:int
class VarianceMap

Container returned by CreateVarianceMap(). Like DistanceMap, it is a symmetric GetSize() x GetSize() matrix containing variance measures. Indexing of residues is as in DistanceMap.

Get(i_res1, i_res2)
Returns:

Variance measure for given pair of residue indices.

Return type:

float

Parameters:
  • i_res1 (int) – Index of residue.
  • i_res2 (int) – Index of residue.
GetSize()
Returns:Number of residues in map.
Return type:int
Min()
Max()
Returns:Minimal/maximal variance in the map.
Return type:float
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 GetJsonString())
Parameters:file_name (str) – Path to file to be created.
Raises:Exception if the file cannot be opened for writing.
GetJsonString()
Returns:A JSON formatted list of GetSize() lists with GetSize() variances
Return type:str
GetData()

Gets all the data in this map at once. Note that this is much faster (10x speedup observed) than parsing GetJsonString() or using Get() on each element.

Returns:A list of GetSize() lists with GetSize() variances.
Return type:list of list of float
class Dist2Mean

Container returned by CreateDist2Mean(). Stores distances to mean for GetNumResidues() residues of GetNumStructures() structures. Indexing of residues is as in DistanceMap. Indexing of structures goes from 0 to GetNumStructures() - 1 and is in the same order as the structures in the originally used alignment.

Get(i_res, i_str)
Returns:

Distance to mean for given residue and structure indices.

Return type:

float

Parameters:
  • i_res (int) – Index of residue.
  • i_str (int) – Index of structure.
GetNumResidues()
Returns:Number of residues.
Return type:int
GetNumStructures()
Returns:Number of structures.
Return type:int
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 GetJsonString())
Parameters:file_name (str) – Path to file to be created.
Raises:Exception if the file cannot be opened for writing.
GetJsonString()
Returns:A JSON formatted list of GetNumResidues() lists with GetNumStructures() distances.
Return type:str
GetData()

Gets all the data in this map at once. Note that this is much faster (10x speedup observed) than parsing GetJsonString() or using Get() on each element.

Returns:A list of GetNumResidues() lists with GetNumStructures() distances.
Return type:list of list of float

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 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.

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.

Parameters:
  • profile_0 (ost.seq.ProfileHandle) – First profile to be scored
  • profile_1 (ost.seq.ProfileHandle) – Second profile to be scored
  • aln (ost.seq.AlignmentHandle) – Alignment connecting the two profiles
  • s_0_idx (int) – Idx of sequence in aln that describes profile_0
  • s_1_idx (int) – Idx of sequence in aln that describes profile_1
  • match_score_offset (float) – Offset which is applied to each column alignment score
  • correl_score_weight (float) – Prefactor to control contribution of correlation score to total score
  • del_start_penalty_factor (float) – Factor which is applied for each transition score starting a deletion
  • del_extend_penalty_factor (float) – Factor which is applied for each transition score extending a deletion
  • ins_start_penalty_factor (float) – Factor which is applied for each transition score starting an insertion
  • ins_extend_penalty_factor (float) – Factor which is applied for each transition score extending an insertion
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:

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))
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.

Parameters:profile (ost.seq.ProfileHandle) – Profile to add pseudo counts
Raises:Exception if profile doesn’t have HMM information assigned
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.

Parameters:profile (ost.seq.ProfileHandle) – Profile to add pseudo counts
Raises:Exception if profile doesn’t have HMM information assigned
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 .

Parameters:profile (ost.seq.ProfileHandle) – Profile to add pseudo counts
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.

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”

Parameters:filename (str) – Filename of CRF file
Save(filename)

Saves database in OST-internal binary format which can be loaded faster than a crf file.

Parameters:filename (str) – Filename to save db
Load(filename)

Static load function that loads database in OST-internal binary format.

Parameters:filename (str) – Filename of db
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 .

Parameters:
Raises:

Exception if profile doesn’t have HMM information assigned

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