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<tool id="DTmhmm" name="Transmembrane detection" version="1.0.0">
	<description/>
	<requirements>
		<container type="docker">jjkoehorst/sappdocker:TMHMM</container>
	</requirements>
	<command>java -jar /tmhmm/tmhmm-0.0.1-SNAPSHOT-jar-with-dependencies.jar
		-input $input -output $output -format TURTLE
	</command>
	<inputs>
		<param format="ttl" label="genome ttl with orf prediction" name="input" type="data"/>
	</inputs>
	<outputs>
		<data format="ttl" label="TMHMM: ${input.name}" name="output"/>
	</outputs>
	<help>Be aware that this can only be used for academic users; other
		users are
		requested to contact CBS Software Package Manager at
		[e-mail].
		We are investigating alternative prediction
		applications, please contact
		us if you are aware of such method.
	</help>
	<citations>
		<citation type="bibtex">@article{Krogh2001,
			abstract = {We describe and
			validate a new membrane protein topology
			prediction method, TMHMM,
			based on a hidden Markov model. We present
			a detailed analysis of
			TMHMM's performance, and show that it
			correctly predicts 97-98 \% of
			the transmembrane helices.
			Additionally, TMHMM can discriminate
			between soluble and membrane
			proteins with both specificity and
			sensitivity better than 99 \%,
			although the accuracy drops when signal
			peptides are present. This
			high degree of accuracy allowed us to
			predict reliably integral
			membrane proteins in a large collection of
			genomes. Based on these
			predictions, we estimate that 20-30 \% of all
			genes in most genomes
			encode membrane proteins, which is in agreement
			with previous
			estimates. We further discovered that proteins with
			N(in)-C(in)
			topologies are strongly preferred in all examined
			organisms, except
			Caenorhabditis elegans, where the large number of
			7TM receptors
			increases the counts for N(out)-C(in) topologies. We
			discuss the
			possible relevance of this finding for our understanding
			of membrane
			protein assembly mechanisms. A TMHMM prediction service is
			available
			at http://www.cbs.dtu.dk/services/TMHMM/.},
			author = {Krogh,
			A and Larsson, B and von Heijne, G and Sonnhammer, E L},
			doi =
			{10.1006/jmbi.2000.4315},
			issn = {0022-2836},
			journal = {Journal of
			molecular biology},
			keywords = {Animals,Bacterial Proteins,Bacterial
			Proteins:
			chemistry,Computational Biology,Computational Biology:
			methods,Databases as Topic,Fungal Proteins,Fungal Proteins:
			chemistry,Genome,Internet,Markov Chains,Membrane Proteins,Membrane
			Proteins: chemistry,Plant Proteins,Plant Proteins:
			chemistry,Porins,Porins: chemistry,Protein Sorting Signals,Protein
			Structure, Secondary,Reproducibility of Results,Research
			Design,Sensitivity and Specificity,Software,Solubility},
			month = jan,
			number = {3},
			pages = {567--80},
			pmid = {11152613},
			title = {{Predicting
			transmembrane protein topology with a hidden Markov
			model: application
			to complete genomes.}},
			url =
			{http://www.sciencedirect.com/science/article/pii/S0022283600943158},
			volume = {305},
			year = {2001}
			}

		</citation>
	</citations>
</tool>