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 [email protected]

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 ([email protected])

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.

  [email protected]                  - General discussion
  http://bioperl.org/wiki/Mailing_lists  - About the mailing lists

Support

Please direct usage questions or support issues to the mailing list:

[email protected]

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 <[email protected]>

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    :

_parse_summary_format

 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    :

_parse_short_format

 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    :