UNLABELLED: miR2GO is a web-based platform for comparative analyses of human miRNA functions. It includes two programs: miRmut2GO and miRpair2GO. miRmut2GO implements a knowledge-based method to assess the functional effects of genetic and somatic mutations in microRNA seed regions. The functional effects of a mutation are analysed by semantic comparison of enriched gene ontology (GO) annotations of the target gene sets for the wild-type and mutated alleles. miRpair2GO compares the functions of two different miRNAs based on the enriched functional annotations of their target gene sets. AVAILABILITY AND IMPLEMENTATION: The miR2GO web server is available at http://compbio.uthsc.edu/miR2GO.

SUMMARY: The Bayesian Network Webserver (BNW) is a platform for comprehensive network modeling of systems genetics and other biological datasets. It allows users to quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data and use the model to understand relationships between network variables. Many datasets, including those used to create genetic network models, contain both discrete (e.g. genotype) and continuous (e.g. gene expression traits) variables, and BNW allows for modeling hybrid datasets. Users of BNW can incorporate prior knowledge during structure learning through an easy-to-use structural constraint interface. After structure learning, users are immediately presented with an interactive network model, which can be used to make testable hypotheses about network relationships. AVAILABILITY AND IMPLEMENTATION: BNW, including a downloadable structure learning package, is available at http://compbio.uthsc.edu/BNW. (The BNW interface for adding structural constraints uses HTML5 features that are not supported by current version of Internet Explorer. We suggest using other browsers (e.g. Google Chrome or Mozilla Firefox) when accessing BNW). CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Cluster analysis of gene expression data is a useful tool for identifying biologically relevant groups of genes that show similar expression patterns under multiple experimental conditions. Performance of clustering algorithms is largely dependent on selected similarity measure. Efficiency in handling outliers is a major contributor to the success of a similarity measure. In gene expression data, there may be pairs of genes that have completely different expression values over a few samples under certain experimental condition(s), although they exhibit similar behavior over the other samples. Depending on the algorithms, these outliers are either placed in single element clusters (hierarchical clustering), are allowed to be in a cluster that is more similar compared to others (partitioning clustering) or they may be completely discarded from grouping (density-based, grid-based and graph-based clustering). In all these cases outliers affect the outcome of a clustering result. Measurement errors or conditional changes during microarray experiments may cause a single sample, if not more, differing in expression level to a great extent compared to the other samples. Expression value of the single or a very few outlier samples may cause a gene to be an outlier. We formulate a new weighted function based method to reduce the effect of outliers on similarity measures. The better the similarity measure is in measuring similarity between genes in the presence of outliers, the better the performance of the clustering algorithm will be in forming biologically relevant groups of genes. The effectiveness of the weighted function based method has been demonstrated with the clustering algorithms, viz. , K-means [ 1 ], Minimization of Disagreement (MIND) [ 2 ], Divisive Correlation Clustering Algorithm (DCCA) [ 3 ], Average Correlation Clustering Algorithm (ACCA) [ 4 ] and Bi-Correlation Clustering Algorithm (BCCA) [ 5 ] on a yeast gene expression dataset (Yeast Cheng and Church dataset from Yeast Functional Genomics Database [ http://yfgdb.princeton.edu/ ]). Assessment of the results has been done by using P-values on functional annotations. P-values less than 5.0 × 10 are reported as enriched functional categories. Figure 1 shows the number of functionally enriched attributes in the most enriched clusters obtained by each of the clustering and biclustering algorithms on the yeast gene expression dataset. The results suggest that the new weighted function based method significantly improves performance of all the cases, in terms of finding biologically relevant groups of genes. Number of functionally enriched attributes in the most enriched clusters obtained by different clustering and biclustering algorithms.

MOTIVATION: Biclustering has been emerged as a powerful tool for identification of a group of co-expressed genes under a subset of experimental conditions (measurements) present in a gene expression dataset. Several biclustering algorithms have been proposed till date. In this article, we address some of the important shortcomings of these existing biclustering algorithms and propose a new correlation-based biclustering algorithm called bi-correlation clustering algorithm (BCCA). RESULTS: BCCA has been able to produce a diverse set of biclusters of co-regulated genes over a subset of samples where all the genes in a bicluster have a similar change of expression pattern over the subset of samples. Moreover, the genes in a bicluster have common transcription factor binding sites in the corresponding promoter sequences. The presence of common transcription factors binding sites, in the corresponding promoter sequences, is an evidence that a group of genes in a bicluster are co-regulated. Biclusters determined by BCCA also show highly enriched functional categories. Using different gene expression datasets, we demonstrate strength and superiority of BCCA over some existing biclustering algorithms. AVAILABILITY: The software for BCCA has been developed using C and Visual Basic languages, and can be executed on the Microsoft Windows platforms. The software may be downloaded as a zip file from http://www.isical.ac.in/ approximately rajat. Then it needs to be installed. Two word files (included in the zip file) need to be consulted before installation and execution of the software. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Cluster analysis (of gene-expression data) is a useful tool for identifying biologically relevant groups of genes that show similar expression patterns under multiple experimental conditions. Various methods have been proposed for clustering gene-expression data. However most of these algorithms have several shortcomings for gene-expression data clustering. In the present article, we focus on several shortcomings of conventional clustering algorithms and propose a new one that is able to produce better clustering solution than that produced by some others. RESULTS: We present the Divisive Correlation Clustering Algorithm (DCCA) that is suitable for finding a group of genes having similar pattern of variation in their expression values. To detect clusters with high correlation and biological significance, we use the correlation clustering concept introduced by Bansal et al. Our proposed algorithm DCCA produces a clustering solution without taking number of clusters to be created as an input. DCCA uses the correlation matrix in such a way that all genes in a cluster have highest average correlation with genes in that cluster. To test the performance of the DCCA, we have applied DCCA and some well-known conventional methods to an artificial dataset, and nine gene-expression datasets, and compared the performance of the algorithms. The clustering results of the DCCA are found to be more significantly relevant to the biological annotations than those of the other methods. All these facts show the superiority of the DCCA over some others for the clustering of gene-expression data. AVAILABILITY: The software has been developed using C and Visual Basic languages, and can be executed on the Microsoft Windows platforms. The software may be downloaded as a zip file from http://www.isical.ac.in/~rajat. Then it needs to be installed. Two word files (included in the zip file) need to be consulted before installation and execution of the software.

We introduce a new class of pulse-mode circuit, called forgetful logic. Forgetful logic circuits can be used to implement more complex waveform signaling in pulse-mode artificial neural network circuits. The basic operation of forgetful logic is first explained. Its application is then illustrated by numerous examples.