NAME¶
Bio::Tools::Signalp::ExtendedSignalp - enhanced parser for Signalp output
SYNOPSIS¶
use Bio::Tools::Signalp::ExtendedSignalp;
my $params = [qw(maxC maxY maxS meanS D)];
my $parser = new Bio::Tools::Signalp::ExtendedSignalp(
-fh => $filehandle
-factors => $params
);
$parser->factors($params);
while( my $sp_feat = $parser->next_feature ) {
#do something
#eg
push @sp_feat, $sp_feat;
}
DESCRIPTION¶
# Please direct questions and support issues to
bioperl-l@bioperl.org
Parser module for Signalp.
Based on the EnsEMBL module Bio::EnsEMBL::Pipeline::Runnable::Protein::Signalp
originally written by Marc Sohrmann (ms2 a sanger.ac.uk) Written in BioPipe by
Balamurugan Kumarasamy (savikalpa a fugu-sg.org) Cared for by the Fugu
Informatics team (fuguteam@fugu-sg.org)
You may distribute this module under the same terms as perl itself
Compared to the original SignalP, this method allow the user to filter results
out based on maxC maxY maxS meanS and D factor cutoff for the Neural Network
(NN) method only. The HMM method does not give any filters with 'YES' or 'NO'
as result.
The user must be aware that the filters can only by applied on NN method. Also,
to ensure the compatibility with original Signalp parsing module, the user
must know that by default, if filters are empty, max Y and mean S filters are
automatically used to filter results.
If the used gives a list, then the parser will only report protein having 'YES'
for each factor.
This module supports parsing for full, summary and short output form signalp.
Actually, full and summary are equivalent in terms of filtering results.
FEEDBACK¶
Mailing Lists¶
User feedback is an integral part of the evolution of this and other Bioperl
modules. Send your comments and suggestions preferably to the Bioperl mailing
list. Your participation is much appreciated.
bioperl-l@bioperl.org - General discussion
http://bioperl.org/wiki/Mailing_lists - About the mailing lists
Support¶
Please direct usage questions or support issues to the mailing list:
bioperl-l@bioperl.org
rather than to the module maintainer directly. Many experienced and reponsive
experts will be able look at the problem and quickly address it. Please
include a thorough description of the problem with code and data examples if
at all possible.
Reporting Bugs¶
Report bugs to the Bioperl bug tracking system to help us keep track of the bugs
and their resolution. Bug reports can be submitted via the web:
https://github.com/bioperl/bioperl-live/issues
AUTHOR¶
Based on the Bio::Tools::Signalp module
Emmanuel Quevillon <emmanuel.quevillon@versailles.inra.fr>
APPENDIX¶
The rest of the documentation details each of the object methods.
Internal methods are usually preceded with a _
new¶
Title : new
Usage : my $obj = new Bio::Tools::Signalp::ExtendedSignalp();
Function: Builds a new Bio::Tools::Signalp::ExtendedSignalp object
Returns : Bio::Tools::Signalp::ExtendedSignalp
Args : -fh/-file => $val, # for initing input, see Bio::Root::IO
next_feature¶
Title : next_feature
Usage : my $feat = $signalp->next_feature
Function: Get the next result feature from parser data
Returns : Bio::SeqFeature::Generic
Args : none
_filterok¶
Title : _filterok
Usage : my $feat = $signalp->_filterok
Function: Check if the factors required by the user are all ok.
Returns : 1/0
Args : hash reference
factors¶
Title : factors
Usage : my $feat = $signalp->factors
Function: Get/Set the filters required from the user
Returns : hash
Args : array reference
_parsed¶
Title : _parsed
Usage : obj->_parsed()
Function: Get/Set if the result is parsed or not
Returns : 1/0 scalar
Args : On set 1
_parse¶
Title : _parse
Usage : obj->_parse
Function: Parse the SignalP result
Returns :
Args :
Title : _parse_summary_format
Usage : $self->_parse_summary_format
Function: Method to parse summary/full format from signalp output
It automatically fills filtered features.
Returns :
Args :
_parse_nn_result¶
Title : _parse_nn_result
Usage : obj->_parse_nn_result
Function: Parses the Neuronal Network (NN) part of the result
Returns : Hash reference
Args :
_parse_hmm_result¶
Title : _parse_hmm_result
Usage : obj->_parse_hmm_result
Function: Parses the Hiden Markov Model (HMM) part of the result
Returns : Hash reference
Args :
Title : _parse_short_format
Usage : $self->_parse_short_format
Function: Method to parse short format from signalp output
It automatically fills filtered features.
Returns :
Args :
create_feature¶
Title : create_feature
Usage : obj->create_feature(\%feature)
Function: Internal(not to be used directly)
Returns :
Args :
seqname¶
Title : seqname
Usage : obj->seqname($name)
Function: Internal(not to be used directly)
Returns :
Args :