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.
Changed in version 0.4.6: Added the
evalue_parameters
andcutoffs
attributes.-
__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.
-
cutoffs
¶ The bitscore cutoffs for this profile, if any.
- Type
Cutoffs
-
evalue_parameters
¶ The e-value parameters for this profile.
- Type
EvalueParameters
-
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
.
-
convert
(profile)¶ Store the given profile into
self
as a platform-specific profile.Use this method to obtained an optimized profile from a
Profile
while recycling the internal vector buffers.See also
The
Profile.optimized
method, which allows getting anOptimizedProfile
directly from a profile without having to allocate first.
-
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
(seq)¶ 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
.
-
consensus
¶ The consensus residue line of the profile, if any.
New in version 0.4.11.
-
consensus_structure
¶ The consensus structure of the profile, if any.
New in version 0.4.11.
-
cutoffs
¶ The bitscore cutoffs for this profile, if any.
- Type
Cutoffs
-
evalue_parameters
¶ The e-value parameters for this profile.
- Type
EvalueParameters
-
offsets
¶ The disk offsets for this optimized profile.
- Type
Offsets
-
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.
-
background
¶ The null background model to use to compute scores.
- Type
-
randomness
¶ The random number generator being used by the pipeline.
- Type
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, 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.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 overE
.domE (
float
) – The per-domain E-value threshold for reporting a domain hit.domT (
float
, optional) – The per-domain bit score threshold for reporint a domain hit. If given, takes precedence overdomE
.incE (
float
) – The per-target E-value threshold for including a hit in the resultingTopHits
.incT (
float
, optional) – The per-target bit score threshold for including a hit in the resultingTopHits
. If given, takes precedence overincE
.incdomE (
float
) – The per-domain E-value threshold for including a domain in the resultingTopHits
.incdomT (
float
, optional) – The per-domain bit score thresholds for including a domain in the resultingTopHits
. If given, takes precedence overincdomE
.bit_cutoffs (
str
, optional) – The model-specific thresholding option to use for reporting hits. WithNone
(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.
-
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
,Profile
orOptimizedProfile
) – The object to use to query the sequence database.sequences (collection of
DigitalSequence
) – The 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.
New in version 0.2.0.
Changed in version 0.4.9: Query can now be a
Profile
or anOptimizedProfile
.
-
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 (collection of
DigitalSequence
) – The sequences to query.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 (collection of
DigitalSequence
) – The sequences to query.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.
-
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
).New in version 0.4.8.
-
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.
-
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
).New in version 0.4.8.
-
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.
-
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
).New in version 0.4.8.
-
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
).New in version 0.4.8.
-
LongTargetsPipeline¶
-
class
pyhmmer.plan7.
LongTargetsPipeline
(Pipeline)¶ An HMMER3 pipeline tuned for long targets.
New in version 0.4.9.
-
__init__
(alphabet, background=None, *, F1=0.02, F2=0.003, F3=3e-05, strand=None, B1=100, B2=240, B3=1000, block_length=262144, **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, 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
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 asNone
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 when reading blocks from target sequences.**kwargs – Any additional parameter will be passed to the
Pipeline
constructor.
-
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.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
()¶ Run the pipeline using a query HMM against a sequence database.
- Parameters
query (
HMM
,Profile
orOptimizedProfile
) – The object to use to query the sequence database.sequences (collection of
DigitalSequence
) – The 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.
New in version 0.2.0.
Changed in version 0.4.9: Query can now be a
Profile
or anOptimizedProfile
.
-
search_msa
()¶ 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 (collection of
DigitalSequence
) – The sequences to query.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
()¶ 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 (collection of
DigitalSequence
) – The sequences to query.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.
-
Builder¶
-
class
pyhmmer.plan7.
Builder
¶ A factory for constructing new HMMs from raw sequences.
-
randomness
¶ The random number generator being used by the builder.
- Type
New 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='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, 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
, orfloat
) – 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. If0
is given, an arbitrary seed will be chosen based on the current time.ere (
double
, optional) – The relative entropy target for effective number weighting, orNone
.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 overwindow_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 (
pyhmmer.plan7.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 aPipeline
.- Raises
AlphabetMismatch – When either
sequence
orbackground
have the wrong alphabet for this builder.
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 (
pyhmmer.plan7.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 aPipeline
.- Raises
AlphabetMismatch – When either
msa
orbackground
have the wrong alphabet for this builder.
New in version 0.3.0.
Changed in version 0.4.6: Sets the
HMM.creation_time
attribute with the current time.
-
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
-
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.
-
Hit¶
-
class
pyhmmer.plan7.
Hit
¶ A high-scoring database hit found by the comparison pipeline.
-
is_dropped
()¶ Check if the hit was dropped.
-
is_duplicate
()¶ Check if the hit is a duplicate.
-
is_included
()¶ Check if the hit should be included with respect to the thresholds.
-
is_new
()¶ Check if the hit is a new hit.
-
is_reported
()¶ Check if the hit should be reported with respect to the thresholds.
-
Domains¶
Traces¶
TraceAligner¶
-
class
pyhmmer.plan7.
TraceAligner
¶ A factory for aligning several sequences to a reference model.
Example
>>> aligner = TraceAligner() >>> traces = aligner.compute_traces(thioesterase, proteins[:100]) >>> msa = aligner.align_traces(thioesterase, proteins[:100], traces)
New in version 0.4.7.
-
align_traces
(hmm, sequences, traces, trim=False, digitize=False, all_consensus_cols=False)¶ Compute traces for a collection of sequences relative to an HMM.
- Parameters
hmm (
HMM
) – The reference HMM to use for the alignment.sequences (collection of
DigitalSequence
) – The sequences to align to the HMM.traces (
Traces
) – The traces corresponding to the alignment ofsequences
tohmm
, obtained by a previous call tocompute_traces
.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. Note that this is enabled by default forhmmalign
since HMMER v3.1, but disabled here.
- Returns
MSA
– A multiple sequence alignment containing the aligned sequences, either aTextMSA
or aDigitalMSA
depending on the value of thedigitize
argument.- Raises
AlphabetMismatch – when the alphabet of any of the sequences does not correspond to the HMM alphabet.
-
compute_traces
(hmm, sequences)¶ Compute traces for a collection of sequences relative to an HMM.
- Parameters
hmm (
HMM
) – The reference HMM to use for the alignment.sequences (collection of
DigitalSequence
) – The sequences to align to the HMM.
- Returns
Traces
– The traces for each sequence.- Raises
AlphabetMismatch – when the alphabet of any of the sequences does not correspond to the HMM alphabet.
-
Traces¶
-
class
pyhmmer.plan7.
Traces
¶ A list of tracebacks obtained by aligning several sequences to a model.
New in version 0.4.7.
Miscellaneous¶
Cutoffs¶
-
class
pyhmmer.plan7.
Cutoffs
¶ A mutable view over the score cutoffs of a
HMM
or aProfile
.New in version 0.4.6.
-
as_vector
()¶ Return a view over the score cutoffs as a
VectorF
.
-
gathering_available
()¶ Check whether the gathering thresholds are available.
-
noise_available
()¶ Check whether the noise cutoffs are available.
-
trusted_available
()¶ Check whether the trusted cutoffs are available.
-
gathering
¶ The gathering thresholds, if any.
Example
This property can be used to set the gathering cutoffs by passing it an iterable of two
float
:>>> thioesterase.cutoffs.gathering = (180.0, 120.0) >>> thioesterase.cutoffs.gathering_available() True >>> thioesterase.cutoffs.gathering (180.0, 120.0)
Set the attribute to
None
or delete it withdel
to clear the gathering thresholds:>>> thioesterase.cutoffs.gathering = None >>> thioesterase.cutoffs.gathering_available() False
New in version 0.4.8.
-
EvalueParameters¶
-
class
pyhmmer.plan7.
EvalueParameters
¶ A mutable view over the e-value parameters of a
HMM
or aProfile
.The e-value for each filter is estimated based off a maximum likelihood distribution fitted for each profile HMM, either a Gumbel distribution for the MSV and Viterbi filters, or an exponential distribution for the Forward filter.
New in version 0.4.6.
-
as_vector
()¶ Return a view over the e-value parameters as a
VectorF
.
-