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What it does

This calls the SignalP v3.0 tool for prediction of signal peptides, which uses both a Neural Network (NN) and Hidden Markov Model (HMM) to produce two sets of scores.

The input is a FASTA file of protein sequences, and the output is tabular with twenty columns (one row per protein):

Column	Description
1	Sequence identifier
2-14	Neural Network (NN) predictions (13 columns)
15-20	Hidden Markov Model (HMM) predictions (6 columns)

Internally the input FASTA file is divided into parts (to allow multiple processors to be used), and the proteins truncated as specified (see below). The raw output from SignalP is then reformatted into a tabular layout suitable for Galaxy (see below).

Neural Network Scores

For each organism class (Eukaryote, Gram-negative and Gram-positive), two different neural networks are used, one for predicting the actual signal peptide and one for predicting the position of the signal peptidase I (SPase I) cleavage site.

The NN output comprises three different scores (C-max, S-max and Y-max) and two scores derived from them (S-mean and D-score).

Column	Name	Description
2-4	C-score	The C-score is the 'cleavage site' score. For each position in the submitted sequence, a C-score is reported, which should only be significantly high at the cleavage site. Confusion is often seen with the position numbering of the cleavage site. When a cleavage site position is referred to by a single number, the number indicates the first residue in the mature protein, meaning, that a predicted cleavage site between amino acid 26-27 is reported as 27, corresponding to the mature protein starting at (and including) position 27.
5-7	S-score	The S-score for the signal peptide prediction is calculated for every single amino acid position in the submitted sequence (not shown in the output via Galaxy), with high scores indicating that the corresponding amino acid is part of a signal peptide, and low scores indicating that the amino acid is part of a mature protein.
8-10	Y-max	Y-max is a derivative of the C-score combined with the S-score resulting in a better cleavage site prediction than the raw C-score alone. This is due to the fact that multiple high-peaking C-scores can be found in one sequence, where only one is the true cleavage site. The cleavage site is assigned from the Y-score where the slope of the S-score is steep and a significant C-score is found.
11-12	S-mean	The S-mean is the average of the S-score, ranging from the N-terminal amino acid to the amino acid assigned with the highest Y-max score, thus the S-mean score is calculated for the length of the predicted signal peptide. The S-mean score was in SignalP version 2.0 used as the criteria for discrimination of secretory and non-secretory proteins.
13-14	D-score	The D-score was introduced in SignalP version 3.0 and is a simple average of the S-mean and Y-max score. The score shows superior discrimination performance of secretory and non-secretory proteins to that of the S-mean score which was used in SignalP version 1 and 2.

For non-secretory proteins all the scores represented in the SignalP3-NN output should ideally be very low.

Hidden Markov Model Scores

The hidden Markov model calculates the probability of whether the submitted sequence contains a signal peptide or not. The eukaryotic HMM model also reports the probability of a signal anchor, previously named uncleaved signal peptides. Furthermore, the cleavage site is assigned by a probability score together with scores for the n-region, h-region, and c-region of the signal peptide, if such one is found.

The 'type' column uses 'S' for a signal peptide (i.e. secretory protein) and 'Q' for non-secretory protein.

Notes

The raw output 'short' output from TMHMM v2.0 looks something like this (21 columns space separated - shown here formatted nicely). Notice that the identifiers are given twice, the first time truncated (as part of the NN predictions) and the second time in full (in the HMM predictions).

# SignalP-NN euk predictions														# SignalP-HMM euk predictions
# name	Cmax	pos	?	Ymax	pos	?	Smax	pos	?	Smean	?	D	?	# name	!	Cmax	pos	?	Sprob	?
gi\|2781234\|pdb\|1JLY\|	0.061	17	N	0.043	17	N	0.199	1	N	0.067	N	0.055	N	gi\|2781234\|pdb\|1JLY\|B	Q	0.000	17	N	0.000	N
gi\|4959044\|gb\|AAD342	0.099	191	N	0.012	38	N	0.023	12	N	0.014	N	0.013	N	gi\|4959044\|gb\|AAD34209.1\|AF069992_1	Q	0.000	0	N	0.000	N
gi\|671626\|emb\|CAA856	0.139	381	N	0.020	8	N	0.121	4	N	0.067	N	0.044	N	gi\|671626\|emb\|CAA85685.1\|	Q	0.000	0	N	0.000	N
gi\|3298468\|dbj\|BAA31	0.208	24	N	0.184	38	N	0.980	32	Y	0.613	Y	0.398	N	gi\|3298468\|dbj\|BAA31520.1\|	Q	0.066	24	N	0.139	N

In order to make this easier to use in Galaxy, the wrapper script simplifies this to remove the redundant column and use tabs for separation. It also includes a header line with unique column names.

#ID	NN_Cmax_score	NN_Cmax_pos	NN_Cmax_pred	NN_Ymax_score	NN_Ymax_pos	NN_Ymax_pred	NN_Smax_score	NN_Smax_pos	NN_Smax_pred	NN_Smean_score	NN_Smean_pred	NN_D_score	NN_D_pred	HMM_type	HMM_Cmax_score	HMM_Cmax_pos	HMM_Cmax_pred	HMM_Sprob_score	HMM_Sprob_pred
gi\|2781234\|pdb\|1JLY\|B	0.061	17	N	0.043	17	N	0.199	1	N	0.067	N	0.055	N	Q	0.000	17	N	0.000	N
gi\|4959044\|gb\|AAD34209.1\|AF069992_1	0.099	191	N	0.012	38	N	0.023	12	N	0.014	N	0.013	N	Q	0.000	0	N	0.000	N
gi\|671626\|emb\|CAA85685.1\|	0.139	381	N	0.020	8	N	0.121	4	N	0.067	N	0.044	N	Q	0.000	0	N	0.000	N
gi\|3298468\|dbj\|BAA31520.1\|	0.208	24	N	0.184	38	N	0.980	32	Y	0.613	Y	0.398	N	Q	0.066	24	N	0.139	N

Truncation

Signal peptides are found at the start of a protein, so there is limited value in providing the full length sequence, and providing the full sequence slows down the analysis. Furthermore, SignalP has an upper bound on the sequence length it will accept (6000bp). Thus for practical reasons it is useful to truncate the proteins before passing them to SignalP. However, the precise point they are truncated does have a small influence on some score values, and thus to the results.

References

If you use this Galaxy tool in work leading to a scientific publication please cite the following papers:

Peter J.A. Cock, Björn A. Grüning, Konrad Paszkiewicz and Leighton Pritchard (2013). Galaxy tools and workflows for sequence analysis with applications in molecular plant pathology. PeerJ 1:e167 https://doi.org/10.7717/peerj.167

Bendtsen, Nielsen, von Heijne, and Brunak (2004). Improved prediction of signal peptides: SignalP 3.0. J. Mol. Biol., 340:783-795. https://doi.org/10.1016/j.jmb.2004.05.028

Nielsen, Engelbrecht, Brunak and von Heijne (1997). Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein Engineering, 10:1-6. https://doi.org/10.1093/protein/10.1.1

Nielsen and Krogh (1998). Prediction of signal peptides and signal anchors by a hidden Markov model. Proceedings of the Sixth International Conference on Intelligent Systems for Molecular Biology (ISMB 6), AAAI Press, Menlo Park, California, pp. 122-130. http://www.ncbi.nlm.nih.gov/pubmed/9783217

This wrapper is available to install into other Galaxy Instances via the Galaxy Tool Shed at http://toolshed.g2.bx.psu.edu/view/peterjc/tmhmm_and_signalp