NAME

genome_imim_es.pm - Library for genome_imim_es services.

SYNOPSIS

Usage:

 my $obj = Object->new ('src_human','Uniprot');
 print $obj->toString;
 my $aaSeq = inb_bsc_es::getAminoAcidSequence(
        'input' => $obj
        ) -> {'sequence'};
 print $aaSeq->SequenceString;

refreshWSDL parameter:

All methods have an additional parameter 'refreshWSDL' used to refresh the service WSDL. Default value is "false".

 my $aaSeq = inb_bsc_es::getAminoAcidSequence(
        'input' => $obj,
	'refreshWSDL' => "true"
        ) -> {'sequence'};

See also runService.

DESCRIPTION

Library to run available services from genome_imim_es.

METHODS

fromFASTAToAminoAcidSequence

Converts aminoacid FASTA sequence into an aminoacid sequence

 (sequence => AminoAcidSequence) = fromFASTAToAminoAcidSequence 
		(
			sequence => FASTA_AA
		)

fromFASTAToAminoAcidSequenceCollection

Converts aminoacid FASTA sequences into a collection of aminoacid sequences

 (sequences => [AminoAcidSequence]) = fromFASTAToAminoAcidSequenceCollection 
		(
			sequences => FASTA_AA_multi
		)

fromFASTAToDNASequence

Converts a DNA FASTA sequence into a DNA sequence

 (sequence => DNASequence) = fromFASTAToDNASequence 
		(
			sequence => FASTA_NA
		)

fromFASTAToDNASequenceCollection

Converts DNA FASTA sequences into a collection of DNA sequences

 (sequences => [DNASequence]) = fromFASTAToDNASequenceCollection 
		(
			sequences => FASTA_NA_multi
		)

fromGenericSequenceCollectionToFASTA

Converts a collection of generic sequences into FASTA sequences

 (sequences => FASTA) = fromGenericSequenceCollectionToFASTA 
		(
			sequences => [GenericSequence]
		)

fromGenericSequenceToFASTA

Converts a generic sequence into a FASTA sequence

 (sequence => FASTA) = fromGenericSequenceToFASTA 
		(
			sequence => GenericSequence
		)

fromMetaAlignmentsToScoreMatrix

Parses a collection of meta-alignment outputs to produce a sequence similarity score matrix

 (matrix => Distance_Matrix) = fromMetaAlignmentsToScoreMatrix 
		(
			similarity_results => [Meta_Alignment_Text]
		)

fromMetaAlignmentsToTextScoreMatrix

Parses a collection of meta-alignment outputs to produce a text-formatted sequence similarity score matrix

 (microarraydata => MicroArrayData_Text) = fromMetaAlignmentsToTextScoreMatrix 
		(
			similarity_results => [Meta_Alignment_Text]
		)

parseMotifMatricesFromMEME

Parses the score or probability motif matrices from MEME output

 (motif_weight_matrices => [Matrix]) = parseMotifMatricesFromMEME 
		(
			meme_predictions => MEME_Text,
			matrixmode => String
		)

runGeneIDGFF

Ab initio gene prediction tool that returns the gene predictions in GFF format.

 (geneid_predictions => GFF) = runGeneIDGFF 
		(
			sequence => DNASequence,
			engine => String,
			strand => String,
			exons => String,
			signals => String,
			profile => String
		)

runMatScanGFF

Analyzes a DNA sequence for putative transcription factor binding sites from Transfac or Jaspar and reports them in GFF format

 (matscan_predictions => GFF) = runMatScanGFF 
		(
			sequence => DNASequence,
			matrixmode => String,
			motifdatabase => String,
			strand => String,
			threshold => Float
		)

runMatScanGFFCollection

Analyzes a collection of DNA sequences for putative transcription factor binding sites from Transfac or Jaspar and reports them in GFF format

 (matscan_predictions => [GFF]) = runMatScanGFFCollection 
		(
			sequences => [DNASequence],
			motifdatabase => String,
			strand => String,
			threshold => Float,
			matrixmode => String
		)

runMatScanGFFCollectionVsInputMatrices

Analyzes a collection of DNA sequences for putative motifs (transcription or splicing factor binding sites) and reports them in GFF format. The collection of motifs is given by the user as a set of Position Weight Matrices (PWMs)

 (matscan_predictions => [GFF]) = runMatScanGFFCollectionVsInputMatrices 
		(
			sequences => [DNASequence],
			motif_weight_matrices => [Matrix],
			strand => String,
			threshold => Float,
			matrixmode => String
		)

runMemeHTML

Analyzes a set of protein or DNA sequences for similarities among them and produces a description (motif) for each pattern it discovers. The results are returned in HTML format

 (meme_predictions => text_html) = runMemeHTML 
		(
			sequences => [GenericSequence],
			minimumsitesforeachmotif => Integer,
			maximumnumberofmotifs => Integer,
			motifdistribution => String,
			backgroundmarkovmodeltrainingvalueisthemodelorder => String,
			motifE__valuecutoff => String,
			maximumsitesforeachmotif => Integer,
			maximumoptimumwidth => Integer,
			minimumoptimumwidth => Integer
		)

runMemeText

Analyzes a set of protein or DNA sequences for similarities among them and produces a description (motif) for each pattern it discovers. The results are returned in MEME text format

 (meme_predictions => MEME_Text) = runMemeText 
		(
			sequences => [GenericSequence],
			minimumsitesforeachmotif => Integer,
			maximumnumberofmotifs => Integer,
			motifdistribution => String,
			backgroundmarkovmodeltrainingvalueisthemodelorder => String,
			motifE__valuecutoff => String,
			maximumsitesforeachmotif => Integer,
			maximumoptimumwidth => Integer,
			minimumoptimumwidth => Integer
		)

runMetaAlignment

Produces alignments of sequences of TF binding sites and returns the predictions in 'Meta-alignment' format. You can use runMatScanGFF to produce the input GFF files, specifying the 'log-likelihood' matrix mode.

 (meta_predictions => Meta_Alignment_Text) = runMetaAlignment 
		(
			map2 => GFF,
			map1 => GFF,
			lambapenalty => Float,
			mupenalty => Float,
			alphapenalty => Float
		)

runMetaAlignmentGFF

Produces alignments of sequences of TF binding sites and returns the predictions in GFF format. You can use runMatScanGFF to produce the input GFF files, specifying the 'log-likelihood' matrix mode

 (meta_predictions => GFF) = runMetaAlignmentGFF 
		(
			map2 => GFF,
			map1 => GFF,
			lambapenalty => Float,
			mupenalty => Float,
			alphapenalty => Float
		)

runMultiPairwiseMetaAlignment

Runs Meta-alignment software on a multiple running mode, receiving a collection of maps, making pairs of them and, foreach pair, producing, in 'Meta-alignment' format, alignments of sequences of TF binding sites

 (meta_predictions => [Meta_Alignment_Text]) = runMultiPairwiseMetaAlignment 
		(
			maps => [GFF],
			lambapenalty => Float,
			alphapenalty => Float,
			mupenalty => Float
		)

runMultiPairwiseMetaAlignmentGFF

Runs Meta-alignment software on a multiple running mode, receiving a collection of maps, making pairs of them and, foreach pair, producing, in GFF format, alignments of sequences of TF binding sites

 (meta_predictions => [GFF]) = runMultiPairwiseMetaAlignmentGFF 
		(
			maps => [GFF],
			lambapenalty => Float,
			mupenalty => Float,
			alphapenalty => Float
		)

runSGP2GFF

Ab initio gene prediction service that runs geneid with synteny evidences and returns the output predictions in GFF format. To generate the synteny evidences, use a tblastx service

 (geneid_predictions => GFF) = runSGP2GFF 
		(
			sequence => DNASequence,
			tblastx_report => BLAST__Text,
			profile => String
		)

translateGeneIDGFFPredictions

Translates the GeneID gene predictions, given in GFF format, into a set of aminoacid sequences

 (peptides => [AminoAcidSequence]) = translateGeneIDGFFPredictions 
		(
			geneid_predictions => GFF,
			sequence => DNASequence,
			translationtable => String
		)