Bioinformatics Advance Access originally published online on March 28, 2008
Bioinformatics 2008 24(10):1307-1309; doi:10.1093/bioinformatics/btn105
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CompariMotif: quick and easy comparisons of sequence motifs
1UCD Complex and Adaptive Systems Laboratory and UCD Conway Institute of Biomolecular and Biomedical Sciences, University College Dublin, Dublin 4, Ireland and 2School of Biological Sciences, University of Southampton, Boldrewood Campus, Southampton SO167PX, UK
*To whom correspondence should be addressed.
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ABSTRACT |
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 TOP ABSTRACT 1 INTRODUCTION2 METHODS3 RESULTS AND DISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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Summary: CompariMotif is a novel tool for making motif–motif comparisons, identifying and describing similarities between regular expression motifs. CompariMotif can identify a number of different relationships between motifs, including exact matches, variants of degenerate motifs and complex overlapping motifs. Motif relationships are scored using shared information content, allowing the best matches to be easily identified in large comparisons. Many input and search options are available, enabling a list of motifs to be compared to itself (to identify recurring motifs) or to datasets of known motifs.
Availability: CompariMotif can be run online at http://bioware.ucd.ie/ and is freely available for academic use as a set of open source Python modules under a GNU General Public License from http://bioinformatics.ucd.ie/shields/software/comparimotif/
Contact: r.edwards{at}southampton.ac.uk
Supplementary information: Further details are available at http://bioinformatics.ucd.ie/shields/software/comparimotif/
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1 INTRODUCTION |
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TOPABSTRACT1 INTRODUCTION2 METHODS3 RESULTS AND DISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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Short linear motifs (SLiMs) in proteins are functional microdomains of fundamental importance in many biological systems (Neduva and Russell, 2005). SLiMs typically consist of a 3 to 10 amino acid stretch of the primary protein sequence, of which as few as two sites may be important for activity. SLiMs can usually tolerate a number of alternative amino acids at one or more positions, making precise definitions extremely difficult. Because of this, and the way that SLiMs are commonly represented as regular expressions (e.g. R[SFYW].S.P), it can be hard to judge whether a given motif is similar to another. With the emergence of high-throughput SLiM prediction tools (Davey et al., 2006; Edwards et al., 2007; Neduva and Russell, 2006; Neduva et al., 2005), the need to quickly and easily identify recurring and/or previously described motifs is obvious.
CompariMotif is a novel tool for making motif–motif comparisons that identifies and scores similarities between motifs. When a new SLiM has been predicted computationally or discovered by experimental studies, CompariMotif enables similar motifs to be readily identified from published resources, such as the Eukaryotic Linear Motif (ELM) database (Puntervoll et al., 2003), Minimotif Miner (Balla et al., 2006) or PhosphoMotif Finder (Amanchy et al., 2007). Alternatively, comparing a list of motifs with itself might identify recurring motifs of interest. Although designed for protein motifs, which are the focus of this article, CompariMotif also has an option allowing the comparison of nucleotide motifs expressed as regular expressions. Currently, position-specific scoring matrix (PSSM) representations of motifs are not supported.
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2 METHODS |
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TOPABSTRACT1 INTRODUCTION2 METHODS3 RESULTS AND DISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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Motifs are reformatted to standardize the regular expressions used and then the two motif sets are compared in a pairwise fashion, with all ‘query’ motifs compared to all ‘search’ motifs. First, the pair is assessed for a precise match (one motif is either the same as, or an exact substring of, the other). If a pair of motifs has no exact match but contains enough common amino acids (in any position) to have a potential match, then CompariMotif adopts a sliding window comparison in which every possible overlap between the two motifs are compared against each other (Fig. 1, see Manual and website for details). Matches must meet a minimum match requirement in terms of the numbers of positions that match, as determined by the user. Fixed positions in motifs are often more important than ambiguous ones, especially when the motif has been experimentally determined. For this reason, it is also possible to stipulate that all fixed positions in one or other motif (or both) match exactly to fixed positions in the compared motifs. When motifs have flexible wildcard positions, all variants of the motif are compared separately and the best match (if any) is used. Positions representing sequence termini must match other termini. Note, however, that motifs representing post-translational modifications etc. are not given special treatment and the user should pay special attention to whether specific important residues are included in a match.
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For every comparison, each position in each motif is rated according to its relationship with the compared position in the other motif. This determines whether positions are matches, mismatches or some combination of variant/degenerate versions of ambiguous positions. If there are any mismatches—two defined positions that have no common amino acids—then the motif pair comparison is rejected. (This requirement can be relaxed by the user.) Otherwise, each positional comparison is rated for information content:
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The IC for the match, ICm is simply the sum of the component ICi values. Multiple variants and/or sliding windows can produce multiple matches and so the comparison with the best overall ICm is selected as the best match for that motif pair. If two or more comparisons have the same ICm, matches are ranked by the total number of matching positions and then by the number of exactly matching fixed positions. The best match (if any) that meets the minimum criteria set by the user is used to define the relationship between the two motifs, which is translated into a text description (Table 1, Fig. 2). These relationships are asymmetrical and comprised of one of four ‘match type’ keywords plus one of four ‘match length’ keywords, giving 16 categories in total. Because the raw ICm score for a given pairwise comparison is highly dependent on both the length and degeneracy of the matching motifs, an additional normalized IC score is calculated which divided the ICm by the lower IC of the two matching motifs. This reports ICm as a proportion of the maximum possible ICm value for that pair of motifs, given their length and degeneracy. This normalized IC is multiplied by the number of matched positions to give a heuristic CompariMotif Score to aid ranking of large results sets.
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3 RESULTS AND DISCUSSION |
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TOPABSTRACT1 INTRODUCTION2 METHODS3 RESULTS AND DISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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A typical application for CompariMotif is given in the SLiMFinder paper (see Example 1 in Edwards et al., 2007), in which HPRD interaction datasets for 14-3-3 proteins (Mishra et al., 2006) were analysed using SLiMFinder, returning several significant motifs (P < 0.05, see Table 2 in Edwards et al., 2007). These motifs were compared to the ELM database (Puntervoll et al., 2003) using CompariMotif with a ‘normalized IC’ cut-off of 0.4. Results were constrained such that fixed positions in an ELM must match a fixed position in the SLiMFinder motif. In total, eight out of 10 SLiMFinder motifs had matches with 17 ELMs. The eight motifs with matches fell into three main clusters: (1) three motifs matching known 14-3-3 motifs (Fig. 3), (2) three motifs matching SH3 binding motifs and (3) two motifs matching the highly degenerate LIG_PCNA_1 motif. In addition to the 14-3-3 and SH3 ELMs, matches to five phosphorylation ELMs were also identified; phosphorylation of the 14-3-3 motif is important for ligand recognition. A full visualization of these results with Cytoscape (Shannon et al., 2003) can be found at the website and in the manual. These comparisons took < 2 s to run on an Intel(R) Xeon(TM) dual 3.20GHz processor with 3Gb RAM.
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It is beyond the scope of this applications note to discuss these results in detail. They do, however, highlight the ease with which CompariMotif can help to make sense of motif discovery results. As a simple, quick and high-throughput tool, CompariMotif can be an invaluable initial step in making sense of such data. Because of this, CompariMotif is now directly linked to both SLiMDisc and SLiMFinder web implementations (Davey et al., 2007).
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ACKNOWLEDGEMENTS |
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TOPABSTRACT1 INTRODUCTION2 METHODS3 RESULTS AND DISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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This work was funded by Science Foundation Ireland.
Conflict of Interest: none declared.
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FOOTNOTES |
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Associate Editor: Burkhard Rost
Received on February 11, 2008; revised on March 18, 2008; accepted on March 19, 2008
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REFERENCES |
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TOPABSTRACT1 INTRODUCTION2 METHODS3 RESULTS AND DISCUSSIONACKNOWLEDGEMENTSREFERENCES |
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Amanchy R, et al. A curated compendium of phosphorylation motifs. Nat. Biotechnol (2007) 25:285–286.[CrossRef][Web of Science][Medline]
Balla S, et al. Minimotif Miner: a tool for investigating protein function. Nat. Methods (2006) 3:175–177.[CrossRef][Web of Science][Medline]
Davey NE, et al. SLiMDisc: short, linear motif discovery, correcting for common evolutionary descent. Nucleic Acids Res (2006) 34:3546–3554.
Davey NE, et al. The SLiMDisc server: short, linear motif discovery in proteins. Nucleic Acids Res (2007) 35:W455–459.
Edwards RJ, et al. SLiMFinder: a probabilistic method for identifying over-represented, convergently evolved, short linear motifs in proteins. PLoS ONE (2007) 2:e967.[CrossRef]
Mishra GR, et al. Human protein reference database–2006 update. Nucleic Acids Res (2006) 34:D411–414.
Neduva V, Russell RB. Linear motifs: evolutionary interaction switches. FEBS Lett (2005) 579:3342–3345.[CrossRef][Web of Science][Medline]
Neduva V, Russell RB. DILIMOT: discovery of linear motifs in proteins. Nucleic Acids Res (2006) 34:W350–355.
Neduva V, et al. Systematic discovery of new recognition peptides mediating protein interaction networks. PLoS Biol (2005) 3:e405.[CrossRef][Medline]
Puntervoll P, et al. ELM server: a new resource for investigating short functional sites in modular eukaryotic proteins. Nucleic Acids Res (2003) 31:3625–3630.
Shannon CE. The mathematical theory of communication. 1963 MD. Comput (1997) 14:306–317.
Shannon P, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res (2003) 13:2498–2504.
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