Charles E. Grant

MEME Suite: tools for motif discovery and searching

The MEME Suite web server provides a unified portal for online discovery and analysis of sequence motifs representing features such as DNA binding sites and protein interaction domains. The popular MEME motif discovery algorithm is now complemented by the GLAM2 algorithm which allows discovery of motifs containing gaps. Three sequence scanning algorithms—MAST, FIMO and GLAM2SCAN—allow scanning numerous DNA and protein sequence databases for motifs discovered by MEME and GLAM2. Transcription factor motifs (including those discovered using MEME) can be compared with motifs in many popular motif databases using the motif database scanning algorithm Tomtom. Transcription factor motifs can be further analyzed for putative function by association with Gene Ontology (GO) terms using the motif-GO term association tool GOMO. MEME output now contains sequence LOGOS for each discovered motif, as well as buttons to allow motifs to be conveniently submitted to the sequence and motif database scanning algorithms (MAST, FIMO and Tomtom), or to GOMO, for further analysis. GLAM2 output similarly contains buttons for further analysis using GLAM2SCAN and for rerunning GLAM2 with different parameters. All of the motif-based tools are now implemented as web services via Opal. Source code, binaries and a web server are freely available for noncommercial use at http://meme.nbcr.net.

FIMO: scanning for occurrences of a given motif

Summary: A motif is a short DNA or protein sequence that contributes to the biological function of the sequence in which it resides. Over the past several decades, many computational methods have been described for identifying, characterizing and searching with sequence motifs. Critical to nearly any motif-based sequence analysis pipeline is the ability to scan a sequence database for occurrences of a given motif described by a position-specific frequency matrix. Results: We describe Find Individual Motif Occurrences (FIMO), a software tool for scanning DNA or protein sequences with motifs described as position-specific scoring matrices. The program computes a log-likelihood ratio score for each position in a given sequence database, uses established dynamic programming methods to convert this score to a P-value and then applies false discovery rate analysis to estimate a q-value for each position in the given sequence. FIMO provides output in a variety of formats, including HTML, XML and several Santa Cruz Genome Browser formats. The program is efficient, allowing for the scanning of DNA sequences at a rate of 3.5 Mb/s on a single CPU. Availability and Implementation: FIMO is part of the MEME Suite software toolkit. A web server and source code are available at http://meme.sdsc.edu. Contact: t.bailey@imb.uq.edu.au; t.bailey@imb.uq.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.

The MEME Suite

The MEME Suite is a powerful, integrated set of web-based tools for studying sequence motifs in proteins, DNA and RNA. Such motifs encode many biological functions, and their detection and characterization is important in the study of molecular interactions in the cell, including the regulation of gene expression. Since the previous description of the MEME Suite in the 2009 Nucleic Acids Research Web Server Issue, we have added six new tools. Here we describe the capabilities of all the tools within the suite, give advice on their best use and provide several case studies to illustrate how to combine the results of various MEME Suite tools for successful motif-based analyses. The MEME Suite is freely available for academic use at http://meme-suite.org, and source code is also available for download and local installation.

A critical assessment of Mus musculus gene function prediction using integrated genomic evidence

Background:Several years after sequencing the human genome and the mouse genome, much remains to be discovered about the functions of most human and mouse genes. Computational prediction of gene function promises to help focus limited experimental resources on the most likely hypotheses. Several algorithms using diverse genomic data have been applied to this task in model organisms; however, the performance of such approaches in mammals has not yet been evaluated.Results:In this study, a standardized collection of mouse functional genomic data was assembled; nine bioinformatics teams used this data set to independently train classifiers and generate predictions of function, as defined by Gene Ontology (GO) terms, for 21,603 mouse genes; and the best performing submissions were combined in a single set of predictions. We identified strengths and weaknesses of current functional genomic data sets and compared the performance of function prediction algorithms. This analysis inferred functions for 76% of mouse genes, including 5,000 currently uncharacterized genes. At a recall rate of 20%, a unified set of predictions averaged 41% precision, with 26% of GO terms achieving a precision better than 90%.Conclusion:We performed a systematic evaluation of diverse, independently developed computational approaches for predicting gene function from heterogeneous data sources in mammals. The results show that currently available data for mammals allows predictions with both breadth and accuracy. Importantly, many highly novel predictions emerge for the 38% of mouse genes that remain uncharacterized.

Widely distributed noncoding purifying selection in the human genome

It is widely assumed that human noncoding sequences comprise a substantial reservoir for functional variants impacting gene regulation and other chromosomal processes. Evolutionarily conserved noncoding sequences (CNSs) in the human genome have attracted considerable attention for their potential to simplify the search for functional elements and phenotypically important human alleles. A major outstanding question is whether functionally significant human noncoding variation is concentrated in CNSs or distributed more broadly across the genome. Here, we combine wholegenome sequence data from four nonhuman species (chimp, dog, mouse, and rat) with recently available comprehensive human polymorphism data to analyze selection at single-nucleotide resolution. We show that a substantial fraction of active purifying selection in human noncoding sequences occurs outside of CNSs and is diffusely distributed across the genome. This finding suggests the existence of a large complement of human noncoding variants that may impact gene expression and phenotypic traits, the majority of which will escape detection with current approaches to genome analysis.

Improved similarity scores for comparing motifs

MOTIVATION A question that often comes up after applying a motif finder to a set of co-regulated DNA sequences is whether the reported putative motif is similar to any known motif. While several tools have been designed for this task, Habib et al. pointed out that the scores that are commonly used for measuring similarity between motifs do not distinguish between a good alignment of two informative columns (say, all-A) and one of two uninformative columns. This observation explains why tools such as Tomtom occasionally return an alignment of uninformative columns which is clearly spurious. To address this problem, Habib et al. suggested a new score [Bayesian Likelihood 2-Component (BLiC)] which uses a Bayesian information criterion to penalize matches that are also similar to the background distribution. RESULTS We show that the BLiC score exhibits other, highly undesirable properties, and we offer instead a general approach to adjust any motif similarity score so as to reduce the number of reported spurious alignments of uninformative columns. We implement our method in Tomtom and show that, without significantly compromising Tomtoms retrieval accuracy or its runtime, we can drastically reduce the number of uninformative alignments. AVAILABILITY AND IMPLEMENTATION The modified Tomtom is available as part of the MEME Suite at http://meme.nbcr.net.

Crux: Rapid Open Source Protein Tandem Mass Spectrometry Analysis

Efficiently and accurately analyzing big protein tandem mass spectrometry data sets requires robust software that incorporates state-of-the-art computational, machine learning, and statistical methods. The Crux mass spectrometry analysis software toolkit (http://cruxtoolkit.sourceforge.net) is an open source project that aims to provide users with a cross-platform suite of analysis tools for interpreting protein mass spectrometry data.

Assessing phylogenetic motif models for predicting transcription factor binding sites

Motivation: A variety of algorithms have been developed to predict transcription factor binding sites (TFBSs) within the genome by exploiting the evolutionary information implicit in multiple alignments of the genomes of related species. One such approach uses an extension of the standard position-specific motif model that incorporates phylogenetic information via a phylogenetic tree and a model of evolution. However, these phylogenetic motif models (PMMs) have never been rigorously benchmarked in order to determine whether they lead to better prediction of TFBSs than obtained using simple position weight matrix scanning. Results: We evaluate three PMM-based prediction algorithms, each of which uses a different treatment of gapped alignments, and we compare their prediction accuracy with that of a non-phylogenetic motif scanning approach. Surprisingly, all of these algorithms appear to be inferior to simple motif scanning, when accuracy is measured using a gold standard of validated yeast TFBSs. However, the PMM scanners perform much better than simple motif scanning when we abandon the gold standard and consider the number of statistically significant sites predicted, using column-shuffled ‘random’ motifs to measure significance. These results suggest that the common practice of measuring the accuracy of binding site predictors using collections of known sites may be dangerously misleading since such collections may be missing ‘weak’ sites, which are exactly the type of sites needed to discriminate among predictors. We then extend our previous theoretical model of the statistical power of PMM-based prediction algorithms to allow for loss of binding sites during evolution, and show that it gives a more accurate upper bound on scanner accuracy. Finally, utilizing our theoretical model, we introduce a new method for predicting the number of real binding sites in a genome. The results suggest that the number of true sites for a yeast TF is in general several times greater than the number of known sites listed in the Saccharomyces cerevisiae Database (SCPD). Among the three scanning algorithms that we test, the MONKEY algorithm has the highest accuracy for predicting yeast TFBSs. Contact: j.hawkins@imb.uq.edu.au

MCAST: scanning for cis-regulatory motif clusters

UNLABELLED Precise regulatory control of genes, particularly in eukaryotes, frequently requires the joint action of multiple sequence-specific transcription factors. A cis-regulatory module (CRM) is a genomic locus that is responsible for gene regulation and that contains multiple transcription factor binding sites in close proximity. Given a collection of known transcription factor binding motifs, many bioinformatics methods have been proposed over the past 15 years for identifying within a genomic sequence candidate CRMs consisting of clusters of those motifs. RESULTS The MCAST algorithm uses a hidden Markov model with a P-value-based scoring scheme to identify candidate CRMs. Here, we introduce a new version of MCAST that offers improved graphical output, a dynamic background model, statistical confidence estimates based on false discovery rate estimation and, most significantly, the ability to predict CRMs while taking into account epigenomic data such as DNase I sensitivity or histone modification data. We demonstrate the validity of MCASTs statistical confidence estimates and the utility of epigenomic priors in identifying CRMs. AVAILABILITY AND IMPLEMENTATION MCAST is part of the MEME Suite software toolkit. A web server and source code are available at http://meme-suite.org and http://alternate.meme-suite.org CONTACT t.bailey@imb.uq.edu.au or william-noble@uw.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

MoMo: Discovery of statistically significant post-translational modification motifs

Motivation Post-translational modifications (PTMs) of proteins are associated with many significant biological functions and can be identified in high throughput using tandem mass spectrometry. Many PTMs are associated with short sequence patterns called “motifs” that help localize the modifying enzyme. Accordingly, many algorithms have been designed to identify these motifs from mass spectrometry data. Accurate statistical confidence estimates for discovered motifs are critically important for proper interpretation and in the design of downstream experimental validation. Results We describe a method for assigning statistical confidence estimates to PTM motifs, and we demonstrate that this method provides accurate p-values on both simulated and real data. Our methods are implemented in MoMo, a software tool for discovering motifs among sets of PTMs that we make available as a web server and as downloadable source code. MoMo reimplements the two most widely used PTM motif discovery algorithms—motif-x and MoDL—while offering many enhancements. Relative to motif-x, MoMo offers improved statistical confidence estimates and more accurate calculation of motif scores. The MoMo web server offers more proteome databases, more input formats, larger inputs and longer running times than the motif-x web server. Finally, our study demonstrates that the confidence estimates produced by motif-x are inaccurate. This inaccuracy stems in part from the common practice of drawing “background” peptides from an unshuffled proteome database. Our results thus suggest that many of the hundreds of papers that use motif-x to find motifs may be reporting results that lack statistical support. Availability http://meme-suite.org Contact timothybailey@unr.edu