Co-expression network-based approaches have become popular in analyzing microarray data, such as for detecting functional gene modules. However, co-expression networks are often constructed by ad hoc methods, and network-based analyses have not been shown to outperform the conventional cluster analyses, partially due to the lack of an unbiased evaluation metric.

MicroRNAs are short, noncoding RNAs that play important roles in post-transcriptional gene regulation. Although many functions of microRNAs in plants and animals have been revealed in recent years, the transcriptional mechanism of microRNA genes is not well-understood. To elucidate the transcriptional regulation of microRNA genes, we study and characterize, in a genome scale, the promoters of intergenic microRNA genes in Caenorhabditis elegans, Homo sapiens, Arabidopsis thaliana, and Oryza sativa. We show that most known microRNA genes in these four species have the same type of promoters as protein-coding genes have. To further characterize the promoters of microRNA genes, we developed a novel promoter prediction method, called common query voting (CoVote), which is more effective than available promoter prediction methods. Using this new method, we identify putative core promoters of most known microRNA genes in the four model species. Moreover, we characterize the promoters of microRNA genes in these four species. We discover many significant, characteristic sequence motifs in these core promoters, several of which match or resemble the known cis-acting elements for transcription initiation. Among these motifs, some are conserved across different species while some are specific to microRNA genes of individual species. MicroRNAs are a class of short RNA sequences that have many regulatory functions in complex organisms such as plants and animals. However, our knowledge of the transcriptional mechanisms of microRNA genes is limited. Here, we analyze the upstream sequences of known microRNA genes in four model species, i.e., C. elegans, H. sapiens, A. thaliana, and O. sativa, and compare them with the promoter sequences of protein-coding genes and other classes of RNA genes. This analysis provides genome-wide evidence that microRNA genes have the same type of promoter sequences as protein-coding genes, and therefore are likely transcribed by RNA polymerase II (pol II). Second, we present a novel computational method for promoter prediction, which is then applied to locate the core promoters of known microRNA genes in the four model species. Furthermore, we present an analysis of short DNA motifs that appear frequently in the predicted promoters of microRNA genes, and report several interesting motifs that may have some functional meanings. These results are important for understanding the initiation and regulation of microRNA gene transcription.

Motivation: Recent advances in technology have dramatically increased the availability of protein–protein interaction (PPI) data and stimulated the development of many methods for improving the systems level understanding the cell. However, those efforts have been significantly hindered by the high level of noise, sparseness and highly skewed degree distribution of PPI networks. Here, we present a novel algorithm to reduce the noise present in PPI networks. The key idea of our algorithm is that two proteins sharing some higher-order topological similarities, measured by a novel random walk-based procedure, are likely interacting with each other and may belong to the same protein complex. Results: Applying our algorithm to a yeast PPI network, we found that the edges in the reconstructed network have higher biological relevance than in the original network, assessed by multiple types of information, including gene ontology, gene expression, essentiality, conservation between species and known protein complexes. Comparison with existing methods shows that the network reconstructed by our method has the highest quality. Using two independent graph clustering algorithms, we found that the reconstructed network has resulted in significantly improved prediction accuracy of protein complexes. Furthermore, our method is applicable to PPI networks obtained with different experimental systems, such as affinity purification, yeast two-hybrid (Y2H) and protein-fragment complementation assay (PCA), and evidence shows that the predicted edges are likely bona fide physical interactions. Finally, an application to a human PPI network increased the coverage of the network by at least 100%. Availability:www.cs.utsa.edu/∼jruan/RWS/. Contact: Jianhua.Ruan@utsa.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Pseudoknots have generally been excluded from the prediction of RNA secondary structures due to its difficulty in modeling. Although, several dynamic programming algorithms exist for the prediction of pseudoknots using thermodynamic approaches, they are neither reliable nor efficient. On the other hand, comparative methods are more reliable, but are often done in an ad hoc manner and require expert intervention. Maximum weighted matching, an algorithm for pseudoknot prediction with comparative analysis, suffers from low-prediction accuracy in many cases. Results: Here we present an algorithm, iterated loop matching, for reliably and efficiently predicting RNA secondary structures including pseudoknots. The method can utilize either thermodynamic or comparative information or both, thus is able to predict pseudoknots for both aligned and individual sequences. We have tested the algorithm on a number of RNA families. Using 8–12 homologous sequences, the algorithm correctly identifies more than 90% of base-pairs for short sequences and 80% overall. It correctly predicts nearly all pseudoknots and produces very few spurious base-pairs for sequences without pseudoknots. Comparisons show that our algorithm is both more sensitive and more specific than the maximum weighted matching method. In addition, our algorithm has high-prediction accuracy on individual sequences, comparable with the PKNOTS algorithm, while using much less computational resources. Availability: The program has been implemented in ANSI C and is freely available for academic use at http://www.cse.wustl.edu/~zhang/projects/rna/ilm/ Supplementary information:http://www.cse.wustl.edu/~zhang/projects/rna/ilm/

Community structure is an important property of complex networks. The automatic discovery of such structure is a fundamental task in many disciplines, including sociology, biology, engineering, and computer science. Recently, several community discovery algorithms have been proposed based on the optimization of a modularity function $\left(Q\right)$. However, the problem of modularity optimization is NP-hard and the existing approaches often suffer from a prohibitively long running time or poor quality. Furthermore, it has been recently pointed out that algorithms based on optimizing $Q$ will have a resolution limit; i.e., communities below a certain scale may not be detected. In this research, we first propose an efficient heuristic algorithm QCUT, which combines spectral graph partitioning and local search to optimize $Q$. Using both synthetic and real networks, we show that QCUT can find higher modularities and is more scalable than the existing algorithms. Furthermore, using QCUT as an essential component, we propose a recursive algorithm HQCUT to solve the resolution limit problem. We show that HQCUT can successfully detect communities at a much finer scale or with a higher accuracy than the existing algorithms. We also discuss two possible reasons that can cause the resolution limit problem and provide a method to distinguish them. Finally, we apply QCUT and HQCUT to study a protein-protein interaction network and show that the combination of the two algorithms can reveal interesting biological results that may be otherwise undetected.

Motivation: A fundamental problem of computational genomics is identifying the genes that respond to certain endogenous cues and environmental stimuli. This problem can be referred to as targeted gene finding. Since gene regulation is mainly determined by the binding of transcription factors and cis-regulatory DNA sequences, most existing gene annotation methods, which exploit the conservation of open reading frames, are not effective in finding targetgenes. Results: A viable approach to targeted gene finding is to exploit the cis-regulatory elements that are known to be responsible for the transcription of target genes. Given such cis-elements, putative target genes whose promoters contain the elements can be identified. As a case study, we apply the above approach to predict the genes in model plant Arabidopsis thaliana which are inducible by a phytohormone, abscisic acid (ABA), and abiotic stress, such as drought, cold and salinity. We first construct and analyze two ABA specific cis-elements, ABA-responsive element (ABRE) and its coupling element (CE), in A.thaliana, based on their conservation in rice and other cereal plants. We then use the ABRE–CE module to identify putative ABA-responsive genes in A.thaliana. Based on RT–PCR verification and the results from literature, this method has an accuracy rate of 67.5% for the top 40 predictions. The cis-element based targeted gene finding approach is expected to be widely applicable since a large number of cis-elements in many species are available. Contact:zhang@cse.wustl.edu Supplementary information: Supplementary data for this paper are available at Bioinformatics online.