New SLiMSuite, SeqSuite and RJESuite releases are now available

New releases of SLiMSuite, SeqSuite and RJESuite are now available from the Edwards Lab software page.

Please note that the documentation (particularly the manuals) are still lagging a bit behind, so do report anything that does not make sense. The default settings also need to be verified as there is a chance that some of these may have inadvertently changed over the years. (The same core code is now used for the webservers, which often have different defaults.) Checking these along with updating and checking the servers themselves are ongoing priorities.

A full list of updated modules is given below. As well as SLiMMaker now handling end of sequence characters, the biggest changes this release are updates to CompariMotif to (3.7) output unmatched input motifs and (3.8) improve handling of partially overlapping ambiguous positions (e.g. [AGS] and [ST]). The motivation behind both these changes is the ongoing benchmarking (and preparation for publication) of QSLiMFinder and the creation of SLiMBench for benchmarking motif prediction methods. A QSLiMFinder section has been added to the SLiMFinder Manual (section 5.4). SLiMBench is still a work in progress and will be documented in a later release.

Updates since last release:

• comparimotif_V3: Updated from Version 3.6.
→ Version 3.7: Added coreIC and output of unmatched motifs.
→ Version 3.8: Added overlaps=T/F : Whether to include overlapping ambiguities (e.g. [KR] vs [HK]) as match [True]
→ Version 3.8: Changed scoring of overlapping ambiguities - uses IC of all possible ambiguities. Added "Ugly" match type.

• slimbench: Created.
→ Version 0.0: Initial Compilation.
→ Version 0.1: Functional version with benchmarking dataset generation.
→ Version 1.0: Consolidation of "working" version with additional basic benchmarking analysis.
→ Version 1.1: Added simulated dataset construction and benchmarking.
→ Version 1.2: Added MinIC filtering to benchmark assessment. Sorted beginning/end of line for reduced ELMs.
→ Version 1.3: Made SimCount a list rather than Integer. Sorted CompariMotif assessment issue.
→ Version 1.4: Added ICCut and SLiMLenCut as lists and output columns.
→ Version 1.5: Added Summary Results output table. Removed PropRes.

• slimmaker: Updated from Version 1.0.
→ Version 1.1: Modified to work with end of line characters.

• slimsearch: Updated from Version 1.5.
→ Version 1.6: Minor tweaks to Log output. Add option for UPC number in occ output.

• rje: Updated from Version 4.1.
→ Version 4.2: Modified INI reading across the board to look in ../settings/ and look for defaults.ini as well as rje.ini.
→ Version 4.2: Enabled handing on -ini FILE in addition to ini=FILE.
→ Version 4.3: Added ilist and nlist types to cmdRead for objects. (Lists of integers and floats). Add ratio() function.

• rje_blast: Updated from Version 1.13.
→ Version 1.14: Added blast.checkProg(qtype,stype) to check whether blastp setting matches sequence formats.

• rje_db: Created.
→ Version 0.0: Initial Compilation.
→ Version 0.1: Added merge tables option.
→ Version 0.2: Miscellaneous updates to various methods.
→ Version 0.3: Minor doc tweaks and added keepFields().

• rje_seq: Updated from Version 3.12.
→ Version 3.13: Updated sequence type checking for use with GABLAM 2.10.

• rje_seqlist: Created.
→ Version 0.0: Initial Compilation. Based on rje_seq 3.10.
→ Version 0.1: Added basic species filtering and sequence output.
→ Version 0.2: Added upper case filtering.
→ Version 0.3: Added accnum filtering and sequence renaming.
→ Version 0.4: Added sequence redundancy filtering.
→ Version 0.5: Added newgene=X for sequence renaming (newgene_spcode__newaccXXX). NewAcc no longer fixed Upper Case.
→ Version 1.0: Upgraded to "ready" Version 1.0. Added concatenate=T and split=X options for sequence concatenation.
→ Version 1.0: Added reading of sequence type from rje_seq.py and mixed=T/F.
→ Version 1.1: Added shortName() and modified SeqDict.

• rje_sequence: Updated from Version 2.0.
→ Version 2.1: Added re_unirefprot = re.compile('^([A-Za-z0-9\-]+)\s+([A-Za-z0-9]+)_([A-Za-z0-9]+)\s+')

• rje_slim: Updated from Version 1.5.
→ Version 1.6: Fixed splitting bug introduced by lower case motifs.

• rje_slimcore: Updated from Version 1.8.
→ Version 1.9: Minor modifications to Log output. Updated motifSeq() function to output unmasked sequences.

• rje_slimlist: Updated from Version 0.6.
→ Version 1.0: Functional module with lower case motif splitting fixed and ? -> .{0,1} replacement.

• rje_zen: Updated from Version 1.0.
→ Version 1.1: Added a few more words here and there.

SLiMMaker now handles end of sequence characters

SLiMMaker is a simple tool for generating regular expression motifs from aligned peptide sequences. It was originally made for making new SLiM definitions based on a set of aligned occurrences and therefore the ends of these peptides are typically not the actual ends of sequences. Sometimes, however, such as in the KDEL ER retrieval motif, they are at (or near) the end (or beginning) of a protein and you might want that taken into consideration when generating a motif.

The SLiMMaker website will now accept beginning (^) and end ($) of seuqence characters. The peptides still need to be aligned, so if using them you should fill in any non-conforming peptides with an X. If the peptides are of different lengths and the end of lines character ends up appearing within an ambiguous position with regular amino acids (e.g. [$AGS]), SLiMMaker will truncate the regular expression at that point (not including that position) - otherwise it just gets too messy!

This upgrade will appear in the next distributions of SLiMSuite and SeqSuite. As always, if it is not clear then just try it out with some test sequences. (And report any odd behaviour.)

"Iterative" SLiMMaker function added

The SLiMMaker website (and download once the new release is put up) now has an "iterate" function that will produce both a motif and a set of sequences, all of which match that motif. Basically, the input sequences matching the motif produced by SLiMMaker keep getting put back through SLiMMaker using the same settings until the motif produced matches all of the input (or there is no motif produced). Obviously, if the first SLiM produced matches all of the input, this mode will behave just like the original.

At some point, I will add some more documentation, including some examples.

SLiMSuite servers and programs

An emerging field of biology is the role of intrinsically disordered regions in protein function and, specifically, protein-protein interactions (PPI) [1-2]. Of particular interest, Short, Linear Motifs (SLiMs) playing a vital role in disorder-mediated PPI, acting as ligands for molecular signalling, post-translational modifications and subcellular targeting [3]. SLiMs have extremely compact protein interaction interfaces, generally encoded by less than 4 major affinity-/specificity-determining residues within a stretch of 2-10 residues [4]. Their small size enables high functional density and evolutionary plasticity, which is frequently exploited by rapidly evolving pathogens that use them to hijack cellular processes [5]. These same features also make experimental discovery a challenge and considerable attention has therefore been given to computational methods for SLiM prediction and analysis [6].

A number of these tools have been developed by the Edwards and Shields labs [7-11] and made available as part of the SLiMSuite package and online as webservers (http://bioware.ucd.ie) [9-10,12-14], with two new tools, SLiMPrints and QSLiMFinder, currently in preparation for submission, and SLiMMaker to be added soon. The main tools that form the SLiMSuite package/servers are as follows:

• SLiMFinder [8,13]: de novo SLiM prediction based on a statistical model of over-represented motifs in unrelated proteins.
  
• SLiMDisc [7,12]: de novo SLiM prediction based on heuristic ranking of over-represented motifs in unrelated proteins.
  
• SLiMPred [11]: de novo SLiM/MoRF prediction in single proteins based machine learning of motif attributes.
  
• SLiMSearch [10]: biological context (disorder & conservation) for searches of pre-defined motifs with under- and over-representation statistics, correcting for evolutionary relationships.
  
• SLiMSearch 2.0 [14]: biological context (disorder & conservation) and ranking for proteome-wide searches of pre-defined motifs.
  
• SLiMPrints (in prep.): de novo SLiM/MoRF prediction in single proteins from statistical clustering of conserved disordered residues.
  
• QSLiMFinder (server coming soon): Query-based variant of SLiMFinder with increased sensitivity and specificity.
  
• CompariMotif [9]: Motif-motif comparison tool.
  
• SLiMMaker (coming soon): Simple tool for converting aligned peptides or SLiM occurrences into a regular expression motif.
  
• GOPHER [12]: Automated orthologue prediction and alignment algorithm. Used for conservation-based masking (SLiMFinder/SLiMSearch) and prediction (SLiMPrints).
  
• GABLAM [7] (server coming soon): BLAST-based protein similarity scoring and clustering. Used for SLiMFinder and SLiMSearch adjustments for evolutionary relationships.
  

Personnel (and funding applications) permitting, a number of improvements for these resources are planned, including updates to the underlying databases for proteome-wide predictions (SLiMSearch 1.0 & 2.0), conservation analyses (SLiMSearch 1.0 & 2.0, SLiMPrints, GOPHER) and SLiM comparisons (CompariMotif). We also intend to improve the integration of different tools, allowing seamless continuation of analyses. Motif predictions ((Q)SLiMFinder/SLiMPrints/SLiMPred) will be able to be searched directly against known motifs (CompariMotif) or proteomes (SLiMSearch); GOPHER alignments will be accessible for SLiMPrints analyses and even SLiMSearch/(Q)SLiMFinder input; outputs of motif occurrences ((Q)SLiMFinder/SLiMSearch) can be used to redefine motifs using SLiMMaker etc. If you have any other suggestions for improvements, please let us know.


References:

[1] Tompa P (2011) Unstructural biology coming of age. Curr Opin Struct Biol 21: 419; [2] Babu MM et al. (2011) Intrinsically disordered proteins: regulation and disease. Curr Opin Struct Biol 21:432; [3] Diella F et al. (2008) Understanding eukaryotic linear motifs and their role in cell signaling and regulation. Front Biosci 13:6580; [4] Davey NE et al. (2012) Attributes of short linear motifs. Mol Biosyst 8:268; [5] Davey NE, Trave G & Gibson TJ (2011) How viruses hijack cell regulation. Trends Biochem Sci 36:159; [6] Davey NE, Edwards RJ & Shields DC (2010) Computational identification and analysis of protein short linear motifs. Front Biosci 15:801; [7] Davey NE, Shields DC & Edwards RJ (2006): SLiMDisc: short, linear motif discovery, correcting for common evolutionary descent. Nucleic Acids Res. 34:3546; [8] Edwards RJ, Davey NE & Shields DC (2007): SLiMFinder: A probabilistic method for identifying over-represented, convergently evolved, short linear motifs in proteins. PLoS ONE 2:e967; [9] Edwards RJ, Davey NE & Shields DC (2008): CompariMotif: Quick and easy comparisons of sequence motifs. Bioinformatics 24:1307; [10] Davey NE et al. (2010): SLiMSearch: a webserver for finding novel occurrences of short linear motifs in proteins, incorporating sequence context. Lecture Notes in Bioinformatics 6282:50; [11] Mooney C et al. (2012): Prediction of short linear protein binding regions. J Mol Biol 415:193; [12] Davey NE, Edwards RJ & Shields DC (2007): The SLiMDisc server: short, linear motif discovery in proteins. Nuc Acids Res 35:W455; [13] Davey NE et al. (2010): SLiMFinder: a web server to find novel, significantly over-represented, short protein motifs. Nuc Acids Res 38:W534; [14] Davey NE et al. (2011): SLiMSearch 2.0: biological context for short linear motifs in proteins. Nuc Acids Res 39:W56.

SLiMMaker: regular expressions from aligned peptide sequences

SLiMMaker has a fairly simple function of reading in a set of sequences and generating a regular expression motif from them. It is designed with protein sequences in mind but should work for DNA sequences too. Input sequences can be in fasta format or just plain text (with no sequence headers) and should be aligned already. Gapped positions will be ignored (treated as Xs) and variable length wildcards are not returned.

SLiMMaker considers each column of the input in turn and compresses it into a regular expression element according to some simple rules, screening out rare amino acids and converting particularly degenerate positions into wildcards. Each amino acid in the column that occurs at least X times (as defined by minseq=X) is considered for the regular expression definition for that position. The full set of amino acids meeting this criterion is then assessed for whether to keep it as a defined position, or convert into a wildcard.

First, if the number of different amino acids meeting this criterion is zero or above a second threshold (maxaa=X), the position is defined as a wildcard. Second, the proportion of input sequences matching the amino acid set is compared to a minimum frequency criterion (minfreq=X). Failing to meet this minimum frequency will again result in a wildcard. Otherwise, the amino acid set is added to the SLiM definition as either a fixed position (if only one amino acid met the minseq criterion) or as a degenerate position. Finally, leading and trailing wildcards are removed.

By default, each defined position in a motif will contain amino acids that (a) occur in at least three sequences each, (b) have a combined frequency of >=75%, and (c) have 5 or fewer different amino acids (that occur in 3+ sequences).

Note. The final motif only contains defined positions that match a given frequency of the input (75% by default). Because positions are considered independently, however, the final motif might occur in fewer than 75% of the input sequences. Results will indicate the coverage of the input data but SLiMSearch can be used to check the occurrence stats more thoroughly.

Citation: SLiMMaker is part of the ongoing benchmarking of QSLiMFinder, which should be submitted for publication soon. In the meantime, please cite the SLiMMaker URL: http://bioware.soton.ac.uk/slimmaker.html.

Availability: SLiMMaker is available on request and will shortly be part of the SLiMSuite package.