Algorithms for phylogenetic footprinting.

Phylogenetic footprinting is a technique that identifies regulatory elements by finding unusually well conserved regions in a set of orthologous noncoding DNA sequences from multiple species. We introduce a new motif-finding problem, the Substring Parsimony Problem, which is a formalization of the ideas behind phylogenetic footprinting, and we present an exact dynamic programming algorithm to solve it. We then present a number of algorithmic optimizations that allow our program to run quickly on most biologically interesting datasets. We show how to handle data sets in which only an unknown subset of the sequences contains the regulatory element. Finally, we describe how to empirically assess the statistical significance of the motifs found. Each technique is implemented and successfully identifies a number of known binding sites, as well as several highly conserved but uncharacterized regions. The program is available at http://bio.cs.washington.edu/software.html.     

Overlapping splicing regulatory motifs—combinatorial effects on splicing

Regulation of splicing in eukaryotes occurs through the coordinated action of multiple splicing factors. Exons and introns contain numerous putative binding sites for splicing regulatory proteins. Regulation of splicing is presumably achieved by the combinatorial output of the binding of splicing factors to the corresponding binding sites. Although putative regulatory sites often overlap, no extensive study has examined whether overlapping regulatory sequences provide yet another dimension to splicing regulation. Here we analyzed experimentally-identified splicing regulatory sequences using a computational method based on the natural distribution of nucleotides and splicing regulatory sequences. We uncovered positive and negative interplay between overlapping regulatory sequences. Examination of these overlapping motifs revealed a unique spatial distribution, especially near splice donor sites of exons with weak splice donor sites. The positively selected overlapping splicing regulatory motifs were highly conserved among different species, implying functionality. Overall, these results suggest that overlap of two splicing regulatory binding sites is an evolutionary conserved widespread mechanism of splicing regulation. Finally, over-abundant motif overlaps were experimentally tested in a reporting minigene revealing that overlaps may facilitate a mode of splicing that did not occur in the presence of only one of the two regulatory sequences that comprise it.     

Alignment of U3 region sequences of mammalian type C viruses: identification of highly conserved motifs and implications for enhancer design.

We aligned published sequences for the U3 region of 35 type C mammalian retroviruses. The alignment reveals that certain sequence motifs within the U3 region are strikingly conserved. A number of these motifs correspond to previously identified sites. In particular, we found that the enhancer region of most of the viruses examined contains a binding site for leukemia virus factor b, a viral corelike element, the consensus motif for nuclear factor 1, and the glucocorticoid response element. Most viruses containing more than one copy of enhancer sequences include these binding sites in both copies of the repeat. We consider this set of binding sites to constitute a framework for the enhancers of this set of viruses. Other highly conserved motifs in the U3 region include the retrovirus inverted repeat sequence, a negative regulatory element, and the CCAAT and TATA boxes. In addition, we identified two novel motifs in the promoter region that were exceptionally highly conserved but have not been previously described.     

Separating real motifs from their artifacts

The typical output of many computational methods to identify binding sites is a long list of motifs containing some real motifs (those most likely to correspond to the actual binding sites) along with a large number of random variations of these. We present a statistical method to separate real motifs from their artifacts. This produces a short list of high quality motifs that is sufficient to explain the over-representation of all motifs in the given sequences. Using synthetic data sets, we show that the output of our method is very accurate. On various sets of upstream sequences in S. cerevisiae, our program identifies several known binding sites, as well as a number of significant novel motifs.     

Prediction of consensus structural motifs in a family of coregulated RNA sequences

Given a set of homologous or functionally related RNA sequences, the consensus motifs may represent the binding sites of RNA regulatory proteins. Unlike DNA motifs, RNA motifs are more conserved in structures than in sequences. Knowing the structural motifs can help us gain a deeper insight of the regulation activities. There have been various studies of RNA secondary structure prediction, but most of them are not focused on finding motifs from sets of functionally related sequences. Although recent research shows some new approaches to RNA motif finding, they are limited to finding relatively simple structures, e.g. stem-loops. In this paper, we propose a novel genetic programming approach to RNA secondary structure prediction. It is capable of finding more complex structures than stem-loops. To demonstrate the performance of our new approach as well as to keep the consistency of our comparative study, we first tested it on the same data sets previously used to verify the current prediction systems. To show the flexibility of our new approach, we also tested it on a data set that contains pseudoknot motifs which most current systems cannot identify. A web-based user interface of the prediction system is set up at http://bioinfo. cis.nctu.edu.tw/service/gprm/.     

fdrMotif: identifying cis-elements by an EM algorithm coupled with false discovery rate control

MOTIVATION: Most de novo motif identification methods optimize the motif model first and then separately test the statistical significance of the motif score. In the first stage, a motif abundance parameter needs to be specified or modeled. In the second stage, a Z-score or P-value is used as the test statistic. Error rates under multiple comparisons are not fully considered. Methodology: We propose a simple but novel approach, fdrMotif, that selects as many binding sites as possible while controlling a user-specified false discovery rate (FDR). Unlike existing iterative methods, fdrMotif combines model optimization [e.g. position weight matrix (PWM)] and significance testing at each step. By monitoring the proportion of binding sites selected in many sets of background sequences, fdrMotif controls the FDR in the original data. The model is then updated using an expectation (E)- and maximization (M)-like procedure. We propose a new normalization procedure in the E-step for updating the model. This process is repeated until either the model converges or the number of iterations exceeds a maximum. RESULTS: Simulation studies suggest that our normalization procedure assigns larger weights to the binding sites than do two other commonly used normalization procedures. Furthermore, fdrMotif requires only a user-specified FDR and an initial PWM. When tested on 542 high confidence experimental p53 binding loci, fdrMotif identified 569 p53 binding sites in 505 (93.2%) sequences. In comparison, MEME identified more binding sites but in fewer ChIP sequences than fdrMotif. When tested on 500 sets of simulated 'ChIP' sequences with embedded known p53 binding sites, fdrMotif, compared to MEME, has higher sensitivity with similar positive predictive value. Furthermore, fdrMotif is robust to noise: it selected nearly identical binding sites in data adulterated with 50% added background sequences and the unadulterated data. We suggest that fdrMotif represents an improvement over MEME. AVAILABILITY: C code can be found at: http://www.niehs.nih.gov/research/resources/software/fdrMotif/.     

iGibbs: improving Gibbs motif sampler for proteins by sequence clustering and iterative pattern sampling

The motif prediction problem is to predict short, conserved subsequences that are part of a family of sequences, and it is a very important biological problem. Gibbs is one of the first successful motif algorithms and it runs very fast compared with other algorithms, and its search behavior is based on the well-studied Gibbs random sampling. However, motif prediction is a very difficult problem and Gibbs may not predict true motifs in some cases. Thus, the authors explored a possibility of improving the prediction accuracy of Gibbs while retaining its fast runtime performance. In this paper, the authors considered Gibbs only for proteins, not for DNA binding sites. The authors have developed iGibbs, an integrated motif search framework for proteins that employs two previous techniques of their own: one for guiding motif search by clustering sequences and another by pattern refinement. These two techniques are combined to a new double clustering approach to guiding motif search. The unique feature of their framework is that users do not have to specify the number of motifs to be predicted when motifs occur in different subsets of the input sequences since it automatically clusters input sequences into clusters and predict motifs from the clusters. Tests on the PROSITE database show that their framework improved the prediction accuracy of Gibbs significantly. Compared with more exhaustive search methods like MEME, iGibbs predicted motifs more accurately and runs one order of magnitude faster.     

DNA motif representation with nucleotide dependency

The problem of discovering novel motifs of binding sites is important to the understanding of gene regulatory networks. Motifs are generally represented by matrices (position weight matrix (PWM) or position specific scoring matrix (PSSM) or strings. However, these representations cannot model biological binding sites well because they fail to capture nucleotide interdependence. It has been pointed out by many researchers that the nucleotides of the DNA binding site cannot be treated independently, e.g. the binding sites of zinc finger in proteins. In this paper, a new representation called Scored Position Specific Pattern (SPSP), which is a generalization of the matrix and string representations, is introduced which takes into consideration the dependent occurrences of neighboring nucleotides. Even though the problem of discovering the optimal motif in SPSP representation is proved to be NP-hard, we introduce a heuristic algorithm called SPSP-Finder, which can effectively find optimal motifs in most simulated cases and some real cases for which existing popular motif finding software, such as Weeder, MEME and AlignACE, fail.     

An efficient algorithm for the identification of structured motifs in DNA promoter sequences

We propose a new algorithm for identifying cis-regulatory modules in genomic sequences. The proposed algorithm, named RISO, uses a new data structure, called box-link, to store the information about conserved regions that occur in a well-ordered and regularly spaced manner in the data set sequences. This type of conserved regions, called structured motifs, is extremely relevant in the research of gene regulatory mechanisms since it can effectively represent promoter models. The complexity analysis shows a time and space gain over the best known exact algorithms that is exponential in the spacings between binding sites. A full implementation of the algorithm was developed and made available online. Experimental results show that the algorithm is much faster than existing ones, sometimes by more than four orders of magnitude. The application of the method to biological data sets shows its ability to extract relevant consensi.     

Comparative sequence analysis of imprinted genes between human and mouse to reveal imprinting signatures

We performed a comparative genomic sequence analysis between human and mouse for 24 imprinted genes on human chromosomes 1, 6, 7, 11, 13, 14, 15, 18, 19, and 20. The MEME program was used to search for motifs within conserved sequences among the imprinted genes and we then used the MAST program to analyze for the presence or absence of motifs in the imprinted genes and 128 nonimprinted genes. Our analysis identified 15 motifs that were significantly enriched in the imprinted genes. We generated a logistic regression model by combining multiple motifs as input variables and the 24 imprinted genes and the 128 nonimprinted genes as a training set. The accuracy, sensitivity, and specificity of our model were 98, 92, and 99%, respectively. The model was further validated by an open test on 12 additional imprinted genes. The motifs identified in this study are novel imprinting signatures, which should improve our understanding of genomic imprinting and the role of genomic imprinting in human diseases.     

A graph theoretical approach for predicting common RNA secondary structure motifs including pseudoknots in unaligned sequences

MOTIVATION: RNA structure motifs contained in mRNAs have been found to play important roles in regulating gene expression. However, identification of novel RNA regulatory motifs using computational methods has not been widely explored. Effective tools for predicting novel RNA regulatory motifs based on genomic sequences are needed. RESULTS: We present a new method for predicting common RNA secondary structure motifs in a set of functionally or evolutionarily related RNA sequences. This method is based on comparison of stems (palindromic helices) between sequences and is implemented by applying graph-theoretical approaches. It first finds all possible stable stems in each sequence and compares stems pairwise between sequences by some defined features to find stems conserved across any two sequences. Then by applying a maximum clique finding algorithm, it finds all significant stems conserved across at least k sequences. Finally, it assembles in topological order all possible compatible conserved stems shared by at least k sequences and reports a number of the best assembled stem sets as the best candidate common structure motifs. This method does not require prior structural alignment of the sequences and is able to detect pseudoknot structures. We have tested this approach on some RNA sequences with known secondary structures, in which it is capable of detecting the real structures completely or partially correctly and outperforms other existing programs for similar purposes. AVAILABILITY: The algorithm has been implemented in C++ in a program called comRNA, which is available at http://ural.wustl.edu/softwares.html