Pipelines#

class pyhmmer.plan7.Pipeline#

An HMMER3 accelerated sequence/profile comparison pipeline.

The Plan7 pipeline handles the platform-accelerated comparison of sequences to profile HMMs. It performs either a search (comparing a single query profile to a target sequence database) or a scan (comparing a single query sequence to a target profile database). The two methods are yielding equivalent results: if you have a collection of \(M\) sequences and \(N\) HMMs to compare, doing a search or a scan should give the same raw scores. The E-values will however be different if Z and domZ where not set manually: \(Z\) will be set to \(M\) for a search, and to \(N\) for a scan.

The main reason for which you should choose search or scan is the relative size of the sequences and HMMs databases. In the original HMMER3 code, the memory was managed in a way that you never had to load the entirety of the target sequences in memory. In PyHMMER, the methods accept both reading the target database from a file, or loading it entirely into memory. A scan is always slower than a search because of the overhead introduced when reconfiguring a profile for a new sequence.

alphabet#

The alphabet for which the pipeline is configured.

Type:: Alphabet

background#

The null background model to use to compute scores.

Type:: Background

randomness#

The random number generator being used by the pipeline.

Type:: Randomness

Changed in version 0.4.2: Added the randomness attribute.

__init__(alphabet, background=None, *, bias_filter=True, null2=True, seed=42, Z=None, domZ=None, F1=0.02, F2=0.001, F3=1e-05, E=10.0, T=None, domE=10.0, domT=None, incE=0.01, incT=None, incdomE=0.01, incdomT=None, bit_cutoffs=None)#

Instantiate and configure a new accelerated comparison pipeline.

Parameters:

alphabet (Alphabet) – The biological alphabet the of the HMMs and sequences that are going to be compared. Used to build the background model.
background (Background, optional) – The background model to use with the pipeline, or None to create and use a default one. The pipeline needs ownership of the background model, so any background model passed there will be copied.

Keyword Arguments:

bias_filter (bool) – Whether or not to enable composition bias filter. Defaults to True.
null2 (bool) – Whether or not to compute biased composition score corrections. Defaults to True.
seed (int, optional) – The seed to use with the random number generator. Pass 0 to use a one-time arbitrary seed, or None to keep the default seed from HMMER.
Z (int, optional) – The effective number of comparisons done, for E-value calculation. Leave as None to auto-detect by counting the number of sequences queried.
domZ (int, optional) – The number of significant sequences found, for domain E-value calculation. Leave as None to auto-detect by counting the number of sequences reported.
F1 (float) – The MSV filter threshold.
F2 (float) – The Viterbi filter threshold.
F3 (float) – The uncorrected Forward filter threshold.
E (float) – The per-target E-value threshold for reporting a hit.
T (float, optional) – The per-target bit score threshold for reporting a hit. If given, takes precedence over E.
domE (float) – The per-domain E-value threshold for reporting a domain hit.
domT (float, optional) – The per-domain bit score threshold for reporting a domain hit. If given, takes precedence over domE.
incE (float) – The per-target E-value threshold for including a hit in the resulting TopHits.
incT (float, optional) – The per-target bit score threshold for including a hit in the resulting TopHits. If given, takes precedence over incE.
incdomE (float) – The per-domain E-value threshold for including a domain in the resulting TopHits.
incdomT (float, optional) – The per-domain bit score thresholds for including a domain in the resulting TopHits. If given, takes precedence over incdomE.
bit_cutoffs (str, optional) – The model-specific thresholding option to use for reporting hits. With None (the default), use global pipeline options; otherwise pass one of "noise", "gathering" or "trusted" to use the appropriate cutoffs.

Hint

In order to run the pipeline in slow/max mode, disable the bias filter, and set the three filtering parameters to \(1.0\):

>>> dna = easel.Alphabet.dna()
>>> max_pli = Pipeline(dna, bias_filter=False, F1=1.0, F2=1.0, F3=1.0)

Changed in version 0.4.6: Added keywords arguments to set the E-value thresholds.

arguments()#

Generate an argv-like list with pipeline options as CLI flags.

Example

>>> alphabet = easel.Alphabet.amino()
>>> plan7.Pipeline(alphabet).arguments()
[]
>>> plan7.Pipeline(alphabet, F1=0.01).arguments()
['--F1', '0.01']

Added in version 0.6.0.

clear()#: Reset the pipeline configuration to its default state.

iterate_hmm(query, sequences, builder=None, select_hits=None)#

Run the pipeline to find homologous sequences to a query HMM.

Parameters:

query (HMM) – The sequence object to use to query the sequence database.
sequences (DigitalSequenceBlock) – The target sequences to query with the HMM.
builder (Builder, optional) – A HMM builder to use to convert the query and subsequent alignments to a HMM. If None is given, this method will create one with the default parameters.
select_hits (callable, optional) – A function or callable object for manually selecting hits during each iteration. It should take a single TopHits argument and change the inclusion of every individual hits by setting the included and dropped flags of each Hit manually.

Returns:

IterativeSearch – An iterator object yielding the hits, sequence alignment, and HMM for each iteration.

Raises:

AlphabetMismatch – When the alphabet of the current pipeline does not match the alphabet of the given query or database sequences.

Hint

This method corresponds to running jackhmmer with the query sequence against the sequences database.

Caution

Default values used for jackhmmer do not correspond to the default parameters used for creating a pipeline in the other cases. To have truly identical results to the jackhmmer results in default mode, create the Pipeline object with incE=0.001 and incdomE=0.001.

See also

The iterate_seq, which does the same operation with a query sequence instead of a query HMM, and contains more details and examples.

Changed in version 0.7.0: Targets must now be inside a DigitalSequenceBlock.

iterate_seq(query, sequences, builder=None, select_hits=None)#

Run the pipeline to find homologous sequences to a query sequence.

This method implements an iterative search over a database to find all sequences homologous to a query sequence. It is very sensitive to the pipeline inclusion thresholds (incE and incdomE).

Since this method returns an iterator, the local results of each iteration will be available for inspection before starting the next one. The select_hits callback in particular can be used for manually including/excluding hits in each iteration, which is not supported in the original jackhmmer, but available on the HMMER web client.

Parameters:

query (DigitalSequence) – The sequence object to use to query the sequence database.
sequences (DigitalSequenceBlock) – The target sequences to query with the query sequence.
builder (Builder, optional) – A HMM builder to use to convert the query and subsequent alignments to a HMM. If None is given, this method will create one with the default parameters.
select_hits (callable, optional) – A function or callable object for manually selecting hits during each iteration. It should take a single TopHits argument and change the inclusion of individual hits with the Hit.include and Hit.drop methods.

Returns:

IterativeSearch – An iterator object yielding the hits, sequence alignment, and HMM for each iteration.

Raises:

AlphabetMismatch – When the alphabet of the current pipeline does not match the alphabet of the given query or database sequences.

Hint

This method corresponds to running jackhmmer with the query sequence against the sequences database.

Caution

Default values used for jackhmmer do not correspond to the default parameters used for creating a pipeline in the other cases. To have truly identical results to the jackhmmer results in default mode, create the Pipeline object with incE=0.001 and incdomE=0.001.

Example

Starting from a pipeline and a query sequence, let’s first obtain the iterator over the successive results:

>>> abc = easel.Alphabet.amino()
>>> pli = plan7.Pipeline(abc, incE=1e-3, incdomE=1e-3)
>>> iterator = pli.iterate_seq(reductase, proteins)

Once this is ready, we can keep iterating until we converge:

>>> converged = False
>>> while not converged:
...     _, hits, _, converged, _ = next(iterator)
...     print(f"Hits: {len(hits)}  Converged: {converged}")
Hits: 1  Converged: False
Hits: 2  Converged: False
Hits: 2  Converged: True

To prevent diverging searches from running infinitely, you could wrap the search in a for loop instead, using a number of maximum iterations as the upper boundary:

>>> iterator = pli.iterate_seq(reductase, proteins)
>>> max_iterations = 10
>>> for n in range(max_iterations):
...     iteration = next(iterator)
...     if iteration.converged:
...         break

Added in version 0.6.0.

Changed in version 0.7.0: Targets must now be inside a DigitalSequenceBlock.

search_msa(query, sequences, builder=None)#

Run the pipeline using a query alignment against a sequence database.

Parameters:

query (DigitalMSA) – The multiple sequence alignment to use to query the sequence database.
sequences (DigitalSequenceBlock or SequenceFile) – The target sequences to query with the alignment, either pre-loaded in memory inside a pyhmmer.easel.DigitalSequenceBlock, or to be read iteratively from a SequenceFile opened in digital mode.
builder (Builder, optional) – A HMM builder to use to convert the query to a HMM. If None is given, it will use a default one.

Returns:

TopHits – the hits found in the sequence database.

Raises:

AlphabetMismatch – When the alphabet of the current pipeline does not match the alphabet of the given query.
ValueError – When the Builder fails to create an HMM from the given DigitalMSA query.

Hint

This method corresponds to running phmmer with the query multiple sequence alignment against the sequences database.

Caution

Internally, this method will create a new HMM from the query MSA using the Builder.build_msa method. HMMER requires that every HMM has a name, so the Builder will attempt to use the name of the query MSA to name the HMM. Passing an MSA without a name will result in an error.

Added in version 0.3.0.

Changed in version 0.7.0: Targets can be inside a DigitalSequenceBlock or a SequenceFile.

search_seq(query, sequences, builder=None)#

Run the pipeline using a query sequence against a sequence database.

Parameters:

query (DigitalSequence) – The sequence object to use to query the sequence database.
sequences (DigitalSequenceBlock or SequenceFile) – The target sequences to query with the query sequence, either pre-loaded in memory inside a pyhmmer.easel.DigitalSequenceBlock, or to be read iteratively from a SequenceFile opened in digital mode.
builder (Builder, optional) – A HMM builder to use to convert the query to a HMM. If None is given, it will use a default one.

Returns:

TopHits – the hits found in the sequence database.

Raises:

AlphabetMismatch – When the alphabet of the current pipeline does not match the alphabet of the given query.
ValueError – When the Builder fails to create an HMM from the given DigitalSequence query.

Hint

This method corresponds to running phmmer with the query sequence against the sequences database.

Added in version 0.2.0.

Changed in version 0.7.0: Targets can be inside a DigitalSequenceBlock or a SequenceFile.

E#

The per-target E-value threshold for reporting a hit.

Added in version 0.4.6.

Type:: float

F1#

The MSV filter threshold.

Added in version 0.4.1.

Type:: float

F2#

The Viterbi filter threshold.

Added in version 0.4.1.

Type:: float

F3#

The uncorrected Forward filter threshold.

Added in version 0.4.1.

Type:: float

T#

The per-target score threshold for reporting a hit.

If set to a non-None value, this threshold takes precedence over the per-target E-value threshold (Pipeline.E).

Added in version 0.4.8.

Type:: float or None

Z#

The number of effective targets searched.

It is used to compute the independent e-value for each domain, and for an entire hit. If None, the parameter number will be set automatically after all the comparisons have been done. Otherwise, it can be set to an arbitrary number.

Type:: float or None

bias_filter#

Whether or not to enable the biased comp HMM filter.

Added in version 0.4.1.

Type:: bool

bit_cutoffs#

The model-specific thresholding option, if any.

Added in version 0.4.6.

Type:: str or None

domE#

The per-domain E-value threshold for reporting a hit.

Added in version 0.4.6.

Type:: float

domT#

The per-domain score threshold for reporting a hit.

If set to a non-None value, this threshold takes precedence over the per-domain E-value threshold (Pipeline.domE).

Added in version 0.4.8.

Type:: float or None

domZ#

The number of significant targets.

It is used to compute the conditional e-value for each domain. If None, the parameter number will be set automatically after all the comparisons have been done, and all hits have been thresholded. Otherwise, it can be set to an arbitrary number.

Type:: float or None

incE#

The per-target E-value threshold for including a hit.

Added in version 0.4.6.

Type:: float

incT#

The per-target score threshold for including a hit.

If set to a non-None value, this threshold takes precedence over the per-target E-value inclusion threshold (Pipeline.incE).

Added in version 0.4.8.

Type:: float or None

incdomE#

The per-domain E-value threshold for including a hit.

Added in version 0.4.6.

Type:: float

incdomT#

The per-domain score threshold for including a hit.

If set to a non-None value, this threshold takes precedence over the per-domain E-value inclusion threshold (Pipeline.incdomE).

Added in version 0.4.8.

Type:: float or None

null2#

Whether or not to enable the null2 score correction.

Added in version 0.4.1.

Type:: bool

scan_seq#

Run the pipeline using a query sequence against a profile database.

Parameters:

query (DigitalSequence) – The sequence object to use to query the profile database.
targets (OptimizedProfileBlock or HMMPressedFile) – The optimized profiles to query, either pre-loaded in memory as an OptimizedProfileBlock, or to be read iteratively from a pressed HMM file.

Returns:

TopHits – the hits found in the profile database.

Raises:

AlphabetMismatch – When the alphabet of the current pipeline does not match the alphabet of the given query or profiles.

Caution

In the current version, this method is not optimized to use the pressed database, even if it exists. This will cause the MSV and SSV filters to be rebuilt at each iteration, which could be slow. Consider at least pre-fetching the HMM database if calling this method several times in a row.

Hint

This method corresponds to running hmmscan with the query sequence against the hmms database.

Added in version 0.4.0.

Changed in version 0.7.0: Require optimized profiles to be inside an OptimizedProfileBlock.

search_hmm#

Run the pipeline using a query HMM against a sequence database.

Parameters:

query (HMM, Profile or OptimizedProfile) – The object to use to query the sequence database.
sequences (DigitalSequenceBlock or SequenceFile) – The target sequences to query with the HMM, either pre-loaded in memory inside a DigitalSequenceBlock, or to be read iteratively from a SequenceFile opened in digital mode.

Returns:

TopHits – the hits found in the sequence database.

Raises:

ValueError – When the pipeline is configured to use model-specific reporting thresholds but the HMM query doesn’t have the right cutoffs available.
AlphabetMismatch – When the alphabet of the current pipeline does not match the alphabet of the given HMM.

Hint

This method corresponds to running hmmsearch with the query HMM against the sequences database.

Added in version 0.2.0.

Changed in version 0.4.9: Query can now be a Profile or an OptimizedProfile.

Changed in version 0.7.0: Targets can be inside a DigitalSequenceBlock or a SequenceFile.

seed#

The seed given at pipeline initialization.

Setting this attribute to a different value will cause the random number generator to be reseeded immediately.

Added in version 0.2.0.

Changed in version 0.4.2: Avoid shadowing initial null seeds given on pipeline initialization.

Type:: int

class pyhmmer.plan7.LongTargetsPipeline(Pipeline)#

An HMMER3 pipeline tuned for long targets.

The default HMMER3 pipeline is configured not to accept target sequences longer than 100,000 residues. Although there is no strong limitation for this threshold, comparing a sequence of \(L\) residues to a profile with \(M\) nodes requires the allocation of a \(L imes M\) dynamic programming matrix.

For sequences too long, it’s actually more efficient memory-wise to use a sliding window to match the profile to the sequence. The usual comparison pipeline is then used to perform the comparison on each window, and results are merged once the entire sequence is done being processed. The context size \(C\) is large enough to accommodate for the entire profile, so that there is no risk of missing a hit in the overlaps between windows. The window size \(W\) can be changed with the block_length argument when instantiating a new LongTargetsPipeline object.

Added in version 0.4.9.

Added in version 0.10.8: The window_length and window_beta keyword arguments.

__init__(alphabet, background=None, *, F1=0.02, F2=0.003, F3=3e-05, strand=None, B1=100, B2=240, B3=1000, block_length=262144, window_length=None, window_beta=None, **kwargs)#

Instantiate and configure a new long targets pipeline.

Parameters:

alphabet (Alphabet) – The biological alphabet the of the HMMs and sequences that are going to be compared. Used to build the background model. A nucleotide alphabet is expected.
background (Background, optional) – The background model to use with the pipeline, or None to create and use a default one. The pipeline needs ownership of the background model, so any background model passed there will be copied.

Keyword Arguments:

strand (str, optional) – The strand to use when processing nucleotide sequences. Use "watson" to use only the coding strand, "crick" to use only the reverse strand, or leave as None to process both strands.
B1 (int) – The window length to use for the biased-composition modifier of the MSV filter.
B2 (int) – The window length to use for the biased-composition modifier of the Viterbi filter.
B3 (int) – The window length to use for the biased-composition modifier of the Forward filter.
block_length (int) – The number of residues to use as the window size \(W\) when reading blocks from the long target sequences.
window_length (int) – The window length to use to compute E-values.
window_beta (float) – The tail mass at which window length is determined.
**kwargs – Any additional parameter will be passed to the Pipeline constructor.

arguments()#

Generate an argv-like list with pipeline options as CLI flags.

Example

>>> alphabet = easel.Alphabet.dna()
>>> plan7.LongTargetsPipeline(alphabet).arguments()
[]
>>> plan7.LongTargetsPipeline(alphabet, B1=200).arguments()
['--B1', '200']

Added in version 0.6.0.

search_msa(query, sequences, builder=None)#

Run the pipeline using a query alignment against a sequence database.

Parameters:

query (DigitalMSA) – The multiple sequence alignment to use to query the sequence database.
sequences (DigitalSequenceBlock) – The target sequences to query with the query alignment.
builder (Builder, optional) – A HMM builder to use to convert the query to a HMM. If None is given, it will use a default one.

Returns:

TopHits – the hits found in the sequence database.

Raises:

AlphabetMismatch – When the alphabet of the current pipeline does not match the alphabet of the given query.

Hint

This method corresponds to running nhmmer with the query multiple sequence alignment against the sequences database.

Changed in version 0.7.0: Targets must now be inside a DigitalSequenceBlock.

search_seq(query, sequences, builder=None)#

Run the pipeline using a query sequence against a sequence database.

Parameters:

query (DigitalSequence) – The sequence object to use to query the sequence database.
sequences (DigitalSequenceBlock) – The target sequences to query with the query sequence.
builder (Builder, optional) – A HMM builder to use to convert the query to a HMM. If None is given, it will use a default one.

Returns:

TopHits – the hits found in the sequence database.

Raises:

AlphabetMismatch – When the alphabet of the current pipeline does not match the alphabet of the given query.

Hint

This method corresponds to running nhmmer with the query sequence against the sequences database.

Changed in version 0.7.0: Targets must now be inside a DigitalSequenceBlock.

B1#

The window length for biased-composition modifier of the MSV.

Type:: int

B2#

The window length for biased-composition modifier of the Viterbi.

Type:: int

B3#

The window length for biased-composition modifier of the Forward.

Type:: int

scan_seq#

Run the pipeline using a query sequence against a profile database.

This is currently unsupported for LongTargetsPipeline.

search_hmm#

Run the pipeline using a query HMM against a sequence database.

Parameters:

query (HMM, Profile or OptimizedProfile) – The object to use to query the sequence database.
sequences (DigitalSequenceBlock) – The target sequences to query with the HMM.

Returns:

TopHits – the hits found in the sequence database.

Raises:

ValueError – When the pipeline is configured to use model-specific reporting thresholds but the HMM query doesn’t have the right cutoffs available.
AlphabetMismatch – When the alphabet of the current pipeline does not match the alphabet of the given HMM.

Hint

This method corresponds to running nhmmer with the query HMM against the sequences database.

Changed in version 0.7.0: Targets must now be inside a DigitalSequenceBlock.

strand#

The strand to process, or None for both.

Type:: str or None

window_beta#

The tail mass at which window length is determined.

Type:: float

window_length#

The window length for nucleotide sequences.

Type:: int or None

class pyhmmer.plan7.Builder#

A factory for constructing new HMMs from raw sequences.

alphabet#

The alphabet the builder is configured to use to convert sequences to HMMs.

Type:: Alphabet

randomness#

The random number generator being used by the builder.

Type:: Randomness

score_matrix#

The name of the substitution matrix used to build HMMs for single sequences.

Type:: str

popen#

The gap open probability to use when building HMMs from single sequences.

Type:: float

pextend#

The gap extend probability to use when building HMMs from single sequences.

Type:: float

Added in version 0.2.0.

Changed in version 0.4.2: Added the randomness attribute.

__init__(alphabet, *, architecture='fast', weighting='pb', effective_number='entropy', prior_scheme='alphabet', symfrac=0.5, fragthres=0.5, wid=0.62, esigma=45.0, eid=0.62, EmL=200, EmN=200, EvL=200, EvN=200, EfL=100, EfN=200, Eft=0.04, seed=42, ere=None, popen=None, pextend=None, score_matrix=None, window_length=None, window_beta=None)#

Create a new sequence builder with the given configuration.

Parameters:

alphabet (Alphabet) – The alphabet the builder expects the sequences to be in.

Keyword Arguments:

architecture (str) – The algorithm to use to determine the model architecture, either "fast" (the default), or "hand".
weighting (str) – The algorithm to use for relative sequence weighting, either "pb" (the default), "gsc", "blosum", "none", or "given".
effective_number (str, int, or float) – The algorithm to use to determine the effective sequence number, either "entropy" (the default), "exp", "clust", "none". A number can also be given directly to set the effective sequence number manually.
prior_scheme (str) – The choice of mixture Dirichlet prior when parameterizing from counts, either "laplace" or "alphabet" (the default).
symfrac (float) – The residue occurrence threshold for fast architecture determination.
fragthresh (float) – A threshold such that a sequence is called a fragment when \(L \le fragthresh imes alen\).
wid (double) – The percent identity threshold for BLOSUM relative weighting.
esigma (float) – The minimum total relative entropy parameter for effective number entropy weights.
eid (float) – The percent identity threshold for effective number clustering.
EmL (int) – The length of sequences generated for MSV fitting.
EmN (int) – The number of sequences generated for MSV fitting.
EvL (int) – The lenght of sequences generated for Viterbi fitting.
EvN (int) – The number of sequences generated for Viterbi fitting.
EfL (int) – The lenght of sequences generated for Forward fitting.
EfN (int) – The number of sequences generated for Forward fitting.
Eft (float) – The tail mass used for Forward fitting.
seed (int) – The seed to use to initialize the internal random number generator. If 0 is given, an arbitrary seed will be chosen based on the current time.
ere (double, optional) – The relative entropy target for effective number weighting, or None.
popen (float, optional) – The gap open probability to use when building HMMs from single sequences. The default value depends on the alphabet: 0.02 for proteins, 0.03125 for nucleotides.
pextend (float, optional) – The gap extend probability to use when building HMMs from single sequences. Default depends on the alphabet: 0.4 for proteins, 0.75 for nucleotides.
score_matrix (str, optional) – The name of the score matrix to use when building HMMs from single sequences. The only allowed value for nucleotide alphabets is DNA1. For proteins, PAM30, PAM70, PAM120, PAM240, BLOSUM45, BLOSUM50, BLOSUM62 (the default), BLOSUM80 or BLOSUM90 can be used.
window_length (float, optional) – The window length for nucleotide sequences, essentially the max expected hit length. If given, takes precedence over window_beta.
window_beta (float, optional) – The tail mass at which window length is determined for nucleotide sequences.

build(sequence, background)#

Build a new HMM from sequence using the builder configuration.

Parameters:

sequence (DigitalSequence) – A single biological sequence in digital mode to build a HMM with.
background (Background) – The background model to use to create the HMM.

Returns:

(HMM, Profile, OptimizedProfile) – A tuple containing the new HMM as well as profiles to be used directly in a Pipeline.

Raises:

AlphabetMismatch – When either sequence or background have the wrong alphabet for this builder.
ValueError – When the HMM cannot be created successfully from the input; the error message contains more details.

Hint

The score matrix and the gap probabilities used here can be set when initializing the builder, or changed by setting a new value to the right attribute:

>>> alphabet = easel.Alphabet.amino()
>>> background = plan7.Background(alphabet)
>>> builder = plan7.Builder(alphabet, popen=0.05)
>>> builder.score_matrix = "BLOSUM90"
>>> hmm, _, _ = builder.build(proteins[0], background)

Changed in version 0.4.6: Sets the HMM.creation_time attribute with the current time.

Changed in version 0.4.10: The score system is now cached between calls to Builder.build.

build_msa(msa, background)#

Build a new HMM from msa using the builder configuration.

Parameters:

msa (DigitalMSA) – A multiple sequence alignment in digital mode to build a HMM with.
background (Background) – The background model to use to create the HMM.

Returns:

(HMM, Profile, OptimizedProfile) – A tuple containing the new HMM as well as profiles to be used directly in a Pipeline.

Raises:

AlphabetMismatch – When either msa or background have the wrong alphabet for this builder.
ValueError – When the HMM cannot be created successfully from the input; the error message contains more details.

Caution

HMMER requires that every HMM has a name, so the Builder will attempt to use the name of the sequence to name the HMM. Passing an MSA without a name will result in an error:

>>> alphabet = easel.Alphabet.amino()
>>> msa = easel.TextMSA(sequences=[
...   easel.TextSequence(name=b"ustiA", sequence="YAIG"),
...   easel.TextSequence(name=b"ustiB", sequence="YVIG")
... ])
>>> builder = plan7.Builder(alphabet)
>>> background = plan7.Background(alphabet)
>>> builder.build_msa(msa.digitize(alphabet), background)
Traceback (most recent call last):
  ...
ValueError: Could not build HMM: Unable to name the HMM.

Added in version 0.3.0.

Changed in version 0.4.6: Sets the HMM.creation_time attribute with the current time.

copy()#: Create a duplicate Builder instance with the same arguments.

seed#

The seed given at builder initialization.

Setting this attribute to a different value will cause the internal random number generator to be reseeded immediately.

Changed in version 0.4.2: Avoid shadowing initial null seeds given on builder initialization.

Type:: int

window_beta#

The tail mass at which window length is determined.

Type:: float

window_length#

The window length for nucleotide sequences.

Type:: int or None

class pyhmmer.plan7.Background#

The null background model of HMMER.

alphabet#

The alphabet of the background model.

Type:: Alphabet

uniform#

Whether or not the null model has been created with uniform frequencies.

Type:: bool

residue_frequencies#

The null1 background residue frequencies.

Type:: VectorF

Changed in version 0.4.0: Added the residue_frequencies attribute.

__init__(alphabet, uniform=False)#

Create a new background model for the given alphabet.

Parameters:

alphabet (Alphabet) – The alphabet to create the background model with.
uniform (bool) – Whether or not to create the null model with uniform frequencies. Defaults to False.

copy()#: Create a copy of the null model with the same parameters.

L#

The mean of the null model length distribution, in residues.

Type:: int

omega#

The prior on null2/null3.

Added in version 0.4.0.

Type:: float

transition_probability#

The null1 transition probability (\(\frac{L}{L+1}\)).

Added in version 0.4.0.

Type:: float

Pipelines#

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