Measuring motif similarity is essential for identifying functionally related transcription factors (TFs) and RNA-binding proteins, and for annotating de novo motifs. Here, we describe Motif Similarity Based on Affinity of Targets (MoSBAT), an approach for measuring the similarity of motifs by computing their affinity profiles across a large number of random sequences. We show that MoSBAT successfully associates de novo ChIP-seq motifs with their respective TFs, accurately identifies motifs that are obtained from the same TF in different in vitro assays, and quantitatively reflects the similarity of in vitro binding preferences for pairs of TFs. AVAILABILITY AND IMPLEMENTATION: MoSBAT is available as a webserver at mosbat.ccbr.utoronto.ca, and for download at github.com/csglab/MoSBAT. CONTACT: [email protected] or [email protected] information: Supplementary data are available at Bioinformatics online.

UNLABELLED: Current methods for motif discovery from chromatin immunoprecipitation followed by sequencing (ChIP-seq) data often identify non-targeted transcription factor (TF) motifs, and are even further limited when peak sequences are similar due to common ancestry rather than common binding factors. The latter aspect particularly affects a large number of proteins from the Cys2His2 zinc finger (C2H2-ZF) class of TFs, as their binding sites are often dominated by endogenous retroelements that have highly similar sequences. Here, we present recognition code-assisted discovery of regulatory elements (RCADE) for motif discovery from C2H2-ZF ChIP-seq data. RCADE combines predictions from a DNA recognition code of C2H2-ZFs with ChIP-seq data to identify models that represent the genuine DNA binding preferences of C2H2-ZF proteins. We show that RCADE is able to identify generalizable binding models even from peaks that are exclusively located within the repeat regions of the genome, where state-of-the-art motif finding approaches largely fail. AVAILABILITY AND IMPLEMENTATION: RCADE is available as a webserver and also for download at http://rcade.ccbr.utoronto.ca/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: [email protected].

SUMMARY: Computational gene function prediction can serve to focus experimental resources on high-priority experimental tasks. FuncBase is a web resource for viewing quantitative machine learning-based gene function annotations. Quantitative annotations of genes, including fungal and mammalian genes, with Gene Ontology terms are accompanied by a community feedback system. Evidence underlying function annotations is shown. For example, a custom Cytoscape viewer shows functional linkage graphs relevant to the gene or function of interest. FuncBase provides links to external resources, and may be accessed directly or via links from species-specific databases. AVAILABILITY: FuncBase as well as all underlying data and annotations are freely available via http://func.med.harvard.edu/

Metazoan genomes encode hundreds of RNA-binding proteins (RBPs). These proteins regulate post-transcriptional gene expression and have critical roles in numerous cellular processes including mRNA splicing, export, stability and translation. Despite their ubiquity and importance, the binding preferences for most RBPs are not well characterized. In vitro and in vivo studies, using affinity selection-based approaches, have successfully identified RNA sequence associated with specific RBPs; however, it is difficult to infer RBP sequence and structural preferences without specifically designed motif finding methods. In this study, we introduce a new motif-finding method, RNAcontext, designed to elucidate RBP-specific sequence and structural preferences with greater accuracy than existing approaches. We evaluated RNAcontext on recently published in vitro and in vivo RNA affinity selected data and demonstrate that RNAcontext identifies known binding preferences for several control proteins including HuR, PTB, and Vts1p and predicts new RNA structure preferences for SF2/ASF, RBM4, FUSIP1 and SLM2. The predicted preferences for SF2/ASF are consistent with its recently reported in vivo binding sites. RNAcontext is an accurate and efficient motif finding method ideally suited for using large-scale RNA-binding affinity datasets to determine the relative binding preferences of RBPs for a wide range of RNA sequences and structures.

MOTIVATION: Recognition of specific DNA sequences is a central mechanism by which transcription factors (TFs) control gene expression. Many TF-binding preferences, however, are unknown or poorly characterized, in part due to the difficulty associated with determining their specificity experimentally, and an incomplete understanding of the mechanisms governing sequence specificity. New techniques that estimate the affinity of TFs to all possible k-mers provide a new opportunity to study DNA-protein interaction mechanisms, and may facilitate inference of binding preferences for members of a given TF family when such information is available for other family members. RESULTS: We employed a new dataset consisting of the relative preferences of mouse homeodomains for all eight-base DNA sequences in order to ask how well we can predict the binding profiles of homeodomains when only their protein sequences are given. We evaluated a panel of standard statistical inference techniques, as well as variations of the protein features considered. Nearest neighbour among functionally important residues emerged among the most effective methods. Our results underscore the complexity of TF-DNA recognition, and suggest a rational approach for future analyses of TF families.

BACKGROUND: The relative preference of nucleosomes to form on individual DNA sequences plays a major role in genome packaging. A wide variety of DNA sequence features are believed to influence nucleosome formation, including periodic dinucleotide signals, poly-A stretches and other short motifs, and sequence properties that influence DNA structure, including base content. It was recently shown by Kaplan et al. that a probabilistic model using composition of all 5-mers within a nucleosome-sized tiling window accurately predicts intrinsic nucleosome occupancy across an entire genome in vitro. However, the model is complicated, and it is not clear which specific DNA sequence properties are most important for intrinsic nucleosome-forming preferences. RESULTS: We find that a simple linear combination of only 14 simple DNA sequence attributes (G+C content, two transformations of dinucleotide composition, and the frequency of eleven 4-bp sequences) explains nucleosome occupancy in vitro and in vivo in a manner comparable to the Kaplan model. G+C content and frequency of AAAA are the most important features. G+C content is dominant, alone explaining approximately 50% of the variation in nucleosome occupancy in vitro. CONCLUSIONS: Our findings provide a dramatically simplified means to predict and understand intrinsic nucleosome occupancy. G+C content may dominate because it both reduces frequency of poly-A-like stretches and correlates with many other DNA structural characteristics. Since G+C content is enriched or depleted at many types of features in diverse eukaryotic genomes, our results suggest that variation in nucleotide composition may have a widespread and direct influence on chromatin structure.

We address the challenge of assessing conservation of gene expression in complex, non-homogeneous datasets. Recent studies have demonstrated the success of probabilistic models in studying the evolution of gene expression in simple eukaryotic organisms such as yeast, for which measurements are typically scalar and independent. Models capable of studying expression evolution in much more complex organisms such as vertebrates are particularly important given the medical and scientific interest in species such as human and mouse. We present a statistical model that makes a number of significant extensions to previous models to enable characterization of changes in expression among highly complex organisms. We demonstrate the efficacy of our method on a microarray dataset containing diverse tissues from multiple vertebrate species. We anticipate that the model will be invaluable in the study of gene expression patterns in other diverse organisms as well, such as worms and insects.

BACKGROUND: Accurate identification of novel, functional noncoding (nc) RNA features in genome sequence has proven more difficult than for exons. Current algorithms identify and score potential RNA secondary structures on the basis of thermodynamic stability, conservation, and/or covariance in sequence alignments. Neither the algorithms nor the information gained from the individual inputs have been independently assessed. Furthermore, due to issues in modelling background signal, it has been difficult to gauge the precision of these algorithms on a genomic scale, in which even a seemingly small false-positive rate can result in a vast excess of false discoveries. RESULTS: We developed a shuffling algorithm, shuffle-pair.pl, that simultaneously preserves dinucleotide frequency, gaps, and local conservation in pairwise sequence alignments. We used shuffle-pair.pl to assess precision and recall of six ncRNA search tools (MSARI, QRNA, ddbRNA, RNAz, Evofold, and several variants of simple thermodynamic stability on a test set of 3046 alignments of known ncRNAs. Relative to mononucleotide shuffling, preservation of dinucleotide content in shuffling the alignments resulted in a drastic increase in estimated false-positive detection rates for ncRNA elements, precluding evaluation of higher order alignments, which cannot not be adequately shuffled maintaining both dinucleotides and alignment structure. On pairwise alignments, none of the covariance-based tools performed markedly better than thermodynamic scoring alone. Although the high false-positive rates call into question the veracity of any individual predicted secondary structural element in our analysis, we nevertheless identified intriguing global trends in human genome alignments. The distribution of ncRNA prediction scores in 75-base windows overlapping UTRs, introns, and intergenic regions analyzed using both thermodynamic stability and EvoFold (which has no thermodynamic component) was significantly higher for real than shuffled sequence, while the distribution for coding sequences was lower than that of corresponding shuffles. CONCLUSION: Accurate prediction of novel RNA structural elements in genome sequence remains a difficult problem, and development of an appropriate negative-control strategy for multiple alignments is an important practical challenge. Nonetheless, the general trends we observed for the distributions of predicted ncRNAs across genomic features are biologically meaningful, supporting the presence of secondary structural elements in many 3' UTRs, and providing evidence for evolutionary selection against secondary structures in coding regions.

BACKGROUND: For effective exposition of biological information, especially with regard to analysis of large-scale data types, researchers need immediate access to multiple categorical knowledge bases and need summary information presented to them on collections of genes, as opposed to the typical one gene at a time. RESULTS: We present here a web-based tool (FunSpec) for statistical evaluation of groups of genes and proteins (e.g. co-regulated genes, protein complexes, genetic interactors) with respect to existing annotations (e.g. functional roles, biochemical properties, localization). FunSpec is available online at http://funspec.med.utoronto.ca CONCLUSION: FunSpec is helpful for interpretation of any data type that generates groups of related genes and proteins, such as gene expression clustering and protein complexes, and is useful for predictive methods employing "guilt-by-association."