Plan7¶
High-level interface to the Plan7 data model.
Plan7 is the model architecture used by HMMER since HMMER2.
See also
Details about the Plan 7 architecture in the HMMER documentation.
Profile¶
Profile¶
-
class
pyhmmer.plan7.
Profile
¶ A Plan7 search profile.
-
__init__
(M, alphabet)¶ Create a new profile for the given
alphabet
.
-
clear
()¶ Clear internal buffers to reuse the profile without reallocation.
-
configure
(hmm, background, L, multihit=True, local=True)¶ Configure a search profile using the given models.
- Parameters
hmm (
pyhmmer.plan7.HMM
) – The model HMM with core probabilities.bg (
pyhmmer.plan7.Background
) – The null background model.L (
int
) – The expected target sequence length.multihit (
bool
) – Whether or not to use multihit modes.local (
bool
) – Whether or not to use non-local modes.
-
copy
()¶ Return a copy of the profile with the exact same configuration.
-
is_local
()¶ Return whether or not the profile is in a local alignment mode.
-
is_multihit
()¶ Returns whether or not the profile is in a multihit alignment mode.
-
optimized
()¶ Convert the profile to a platform-specific optimized profile.
- Returns
OptimizedProfile
– The platform-specific optimized profile built using the configuration of this profile.
-
consensus
¶ The consensus residue line of the profile, if set.
New in version 0.4.1.
-
consensus_structure
¶ The consensus structure of the profile, if any.
New in version 0.4.1.
-
OptimizedProfile¶
-
class
pyhmmer.plan7.
OptimizedProfile
¶ An optimized profile that uses platform-specific instructions.
-
__init__
(M, alphabet)¶ Create a new optimized profile from scratch.
Optimized profiles use platform-specific code to accelerate the various algorithms. Although you can allocate an optimized profile yourself, the only way to obtain a fully configured profile is to create it with the
Profile.optimized
method, after having configured the profile for a given HMM withProfile.configure
.
-
copy
()¶ Create an exact copy of the optimized profile.
-
is_local
()¶ Return whether or not the profile is in a local alignment mode.
-
ssv_filter
()¶ ssv_filter(self, seq)n–
Compute the SSV filter score for the given sequence.
- Parameters
seq (
DigitalSequence
) –- Returns
Note
- Raises
AlphabetMismatch – when the alphabet of the sequence does not correspond to the profile alphabet.
Caution
This method is not available on the PowerPC platform (calling it will raise a
NotImplementedError
).New in version v0.4.0.
-
write
(fh_filter, fh_profile)¶ Write an optimized profile to two separate files.
HMMER implements an acceleration pipeline using several scoring algorithms. Parameters for MSV (the Multi ungapped Segment Viterbi) are saved independently to the
fh_filter
handle, while the rest of the profile is saved tofh_profile
.
-
Background¶
-
class
pyhmmer.plan7.
Background
¶ The null background model of HMMER.
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 (
pyhmmer.easel.Alphabet
) – The alphabet to create the background model with.uniform (
bool
) – Whether or not to create the null model with uniform frequencies. Defaults toFalse
.
-
copy
()¶ Create a copy of the null model with the same parameters.
-
Pipelines¶
Pipeline¶
-
class
pyhmmer.plan7.
Pipeline
¶ An HMMER3 accelerated sequence/profile comparison pipeline.
-
__init__
(alphabet, background=None, *, bias_filter=True, null2=True, seed=42, Z=None, domZ=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, orNone
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 toTrue
.null2 (
bool
) – Whether or not to compute biased composition score corrections. Defaults toTrue
.seed (
int
, optional) – The seed to use with the random number generator. Pass 0 to use a one-time arbitrary seed, orNone
to keep the default seed from HMMER.Z (
int
, optional) – The effective number of comparisons done, for E-value calculation. Leave asNone
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 asNone
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.
-
clear
()¶ Reset the pipeline configuration to its default state.
-
scan_seq
(query, hmms)¶ Run the pipeline using a query sequence against a profile database.
- Parameters
query (
DigitalSequence
) – The sequence object to use to query the profile database.hmms (iterable of
DigitalSequence
) – The HMM profiles to query. Pass aHMMFile
instance to read from disk iteratively.
- 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 profile.
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.
New in version v0.4.0.
-
search_hmm
(query, sequences)¶ Run the pipeline using a query HMM against a sequence database.
- Parameters
query (
HMM
) – The HMM object to use to query the sequence database.sequences (iterable of
DigitalSequence
) – The sequences to query with the HMM. For instance, pass aSequenceFile
in digital mode to read from disk iteratively.
- 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 HMM.
New in version 0.2.0.
-
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 (iterable of
DigitalSequence
) – The sequences to query. Pass aSequencesFile
instance in digital mode to read from disk iteratively.builder (
Builder
, optional) – A HMM builder to use to convert the query to aHMM
. IfNone
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.
New in version 0.3.0.
-
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 (iterable of
DigitalSequence
) – The sequences to query. Pass aSequenceFile
instance in digital mode to read from disk iteratively.builder (
Builder
, optional) – A HMM builder to use to convert the query to aHMM
. IfNone
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.
New in version 0.2.0.
-
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.
-
Builder¶
-
class
pyhmmer.plan7.
Builder
¶ A factory for constructing new HMMs from raw sequences.
New in version 0.2.0.
-
__init__
(alphabet, *, architecture='fast', weighting='pb', effective_number='entropy', prior_scheme='alpha', symfrac=0.5, fragthresh=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)¶ Create a new sequence builder with the given configuration.
- Parameters
alphabet (
Alphabet
) – The alphabet the builder expects the sequences to be in.- Keyword Arguments
-
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 (
pyhmmer.plan7.background
) – The background model to use to create the HMM.
- Raises
AlphabetMismatch – When either
sequence
orbackground
have the wrong alphabet for this builder.
-
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 (
pyhmmer.plan7.background
) – The background model to use to create the HMM.
- Raises
AlphabetMismatch – When either
msa
orbackground
have the wrong alphabet for this builder.
New in version 0.3.0.
-
Results¶
TopHits¶
-
class
pyhmmer.plan7.
TopHits
¶ A ranked list of top-scoring hits.
TopHits
are thresholded using the parameters from the pipeline, and are sorted by key when you obtain them from aPipeline
instance:>>> abc = thioesterase.alphabet >>> hits = Pipeline(abc).search_hmm(thioesterase, proteins) >>> hits.is_sorted() True
Use
len
to query the number of top hits, and the usual indexing notation to extract a particularHit
:>>> len(hits) 1 >>> hits[0].name b'938293.PRJEB85.HG003687_113'
-
is_sorted
(by='key')¶ Check whether or not the hits are sorted with the given method.
See
sort
for a list of allowed values for theby
argument.
-
sort
(by='key')¶ Sort hits in the current instance using the given method.
- Parameters
by (
str
) – The comparison method to use to compare hits. Allowed values are:key
(the default) to sort by key, orseqidx
to sort by sequence index and alignment position.
-
threshold
(pipeline)¶ Apply score and e-value thresholds using pipeline parameters.
This function is automatically called in
Pipeline.search_hmm
orPipeline.search_seq
, so it should have limited usefulness at the Python level.
-
to_msa
(alphabet, trim=False, digitize=False, all_consensus_cols=False)¶ Create multiple alignment of all included domains.
- Parameters
alphabet (
Alphabet
) – The alphabet of the HMM thisTopHits
was obtained from. It is required to convert back hits to single sequences.trim (
bool
) – Trim off any residues that get assigned to flanking \(N\) and \(C\) states (in profile traces) or \(I_0\) and \(I_m\) (in core traces).digitize (
bool
) – If set toTrue
, returns aDigitalMSA
instead of aTextMSA
.all_consensus_cols (
bool
) – Force a column to be created for every consensus column in the model, even if it means having all gap character in a column.
- Returns
MSA
– A multiple sequence alignment containing the reported hits, either aTextMSA
or aDigitalMSA
depending on the value of thedigitize
argument.
New in version 0.3.0.
-