MOTIVATION: Next-generation sequencing (NGS) methods have the potential for changing the landscape of biomedical science, but at the same time pose several problems in analysis and interpretation. Currently, there are many commercial and public software packages that analyze NGS data. However, the limitations of these applications include output which is insufficiently annotated and of difficult functional comprehension to end users. RESULTS: We developed GAMES (Genomic Analysis of Mutations Extracted by Sequencing), a pipeline aiming to serve as an efficient middleman between data deluge and investigators. GAMES attains multiple levels of filtering and annotation, such as aligning the reads to a reference genome, performing quality control and mutational analysis, integrating results with genome annotations and sorting each mismatch/deletion according to a range of parameters. Variations are matched to known polymorphisms. The prediction of functional mutations is achieved by using different approaches. Overall GAMES enables an effective complexity reduction in large-scale DNA-sequencing projects. AVAILABILITY: GAMES is available free of charge to academic users and may be obtained from http://aqua.unife.it/GAMES.

MOTIVATION: Non-coding microRNAs (miRNAs) act as regulators of global protein output. While their major effect is on protein levels of target genes, it has been proven that they also specifically impact on the messenger RNA level of targets. Prominent interest in miRNAs strongly motivates the need for increasing the options available to detect their cellular activity. RESULTS: We used the effect of miRNAs over their targets for the detection of miRNA activity using mRNAs expression profiles. Here we describe the method, called T-REX (from Targets' Reverse EXpression), compare it to other similar applications, show its effectiveness and apply it to build activity maps. We used six different target predictions from each of four algorithms: TargetScan, PicTar, DIANA-microT and DIANA Union. First, we proved the sensitivity and specificity of our technique in miRNA over-expression and knock-out animal models. Then, we used whole transcriptome data from acute myeloid leukemia to show that we could identify critical miRNAs in a real life, complex, clinically relevant dataset. Finally, we studied 66 different cellular conditions to confirm and extend the current knowledge on the role of miRNAs in cellular physiology and in cancer. AVAILABILITY: Software is available at http://aqua.unife.it and is free for all users with no login requirement.

Abstract Summary: The study of complex hereditary diseases is a very challenging area of research. The expanding set of in silico approaches offers a flourishing ground for the acceleration of meaningful findings in this area by exploitation of rich and diverse sources of omic data. These approaches are cheap, flexible, extensible, often complementary and can continuously integrate new information and tests to improve the selection of genes responsible for hereditary diseases. Following this principle, we improved and extended our web-service TOM for the identification of candidate genes in the study of complex hereditary diseases. Availability: Our tool is freely available online at http://www.micrel.deis.unibo.it/~tom/. Contact: [email protected] Supplementary information: Manuals and sample data are available in the Help section of the tool's web page.

MOTIVATION: Microarray and other genome-wide technologies allow a global view of gene expression that can be used in several ways and whose potential has not been yet fully discovered. Functional insight into expression profiles is routinely obtained by using gene ontology terms associated to the cellular genes. In this article, we deal with functional data mining from expression profiles, proposing a novel approach that studies the correlations between genes and their relations to Gene Ontology (GO). We implemented this approach in a public web-based application named Fun&Co. By using Fun&Co, the user dissects in a pair-wise manner gene expression patterns and links correlated pairs to gene ontology terms. The proof of principle for our study was accomplished by dissecting molecular pathways in muscles. In particular, we identified specific cellular pathways by comparing the three different types of muscle in a pairwise fashion. In fact, we were interested in the specific molecular mechanisms regulating the cardiovascular system (cardiomyocytes and smooth muscle cells). RESULTS: We applied here Fun&Co to the molecular study of cardiovascular system and the identification of the specific molecular pathways in heart, skeletal and smooth muscles (using 317 microarrays) and to reveal functional differences between the three different kinds of muscle cells. AVAILABILITY: Application is online at http://tommy.unife.it. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Through the use of DNA microarrays it is now possible to obtain quantitative measurements of the expression of thousands of genes from a biological sample. This technology yields a global view of gene expression that can be used in several ways. Functional insight into expression profiles is routinely obtained by using Gene Ontology terms associated to the cellular genes. In this paper, we deal with functional data mining from expression profiles, proposing a novel approach that studies the correlations between genes and their relations to Gene Ontology (GO). By using this "functional correlations comparison" we explore all possible pairs of genes identifying the affected biological processes by analyzing in a pair-wise manner gene expression patterns and linking correlated pairs with Gene Ontology terms. RESULTS: We apply here this "functional correlations comparison" approach to identify the existing correlations in hepatocarcinoma (161 microarray experiments) and to reveal functional differences between normal liver and cancer tissues. The number of well-correlated pairs in each GO term highlights several differences in genetic interactions between cancer and normal tissues. We performed a bootstrap analysis in order to compute false detection rates (FDR) and confidence limits. CONCLUSION: Experimental results show the main advantage of the applied method: it both picks up general and specific GO terms (in particular it shows a fine resolution in the specific GO terms). The results obtained by this novel method are highly coherent with the ones proposed by other cancer biology studies. But additionally they highlight the most specific and interesting GO terms helping the biologist to focus his/her studies on the most relevant biological processes.

We have recently reported that the statistical analysis of the frequency distribution of short oligonucleotides within mammalian and viral genomes allows the production of sets of DNA sequences enriched in signals for transcription factors. Such statistical approaches could facilitate the identification of new promoter regions playing a role in the transcriptional regulation of gene expression. In the case of mammalian oligonucleotides, we found that the published set of frequent decamers enriched in transcriptional motifs is not suitable for studies on genes of Homo sapiens and evolutionarily related genomes, because it contains decameric sequences belonging to genomic repeats. We report here that most of the decameric sequences of DNA repeats belong to Alu repeats. Accordingly, we produced a subset of Alu-free frequent decamers. In addition, we eliminated from the subset of Alu-free frequent decamers those that are frequently present within other common human repeats, including (GT)n, (AT)n, (CA)n, (ATT)n, (CAA)n and (GTT)n. The Alu-free (repeats-free) subset of frequent mammalian decamers is enriched in signals for transcription factors and allows the identification of putative signals in genes, such as those coding for plasminogen activator, adenosine deaminase and p53, that contain a large number of Alu-like repeats interspersed within our genomic sequences. The newly generated compilation of frequent decamers described here might be used to locate genomic regions playing functional roles in the expression of genes of Homo sapiens and related primates.

We studied the frequency distribution of 1,048,576 oligonucleotides 10 bp long in a sample of 1.961 Mbase of genes from plants, made of 635 sequences extracted from GenBank 71.0, with the aim of detecting transcription control signals. Among all decamers, 3255, or 0.3%, had a frequency 10 times higher than the mean and were subjected to further statistical analysis. For each of the 3255 decamers (parents), we counted the individual frequencies of the 30 decamers (progeny) differing from the parent by one base mutation, and calculated two variance/mean chi-squares for the progeny, with and without the parent decamer. By studying the distribution of the ratio between the two chi-squares we observed that out of 3255 decamers > 10 times frequent than average, 432 had a chi-square ratio > 1.9. In this residual set, which corresponds to < 0.04 per cent of all possible decamers, only 15 known eukaryotic transcription control elements were found; on the other hand, it included 29 decanucleotides that matched with decanucleotides of a set of Drosophila, 24 with a set from mammals, 13 with a set from yeast and four with a set of viruses--all sets identified with the statistical procedures here described. These decanucloetides are highly repetitive and seem to be present throughout all higher organisms, whereas they are uncommon in mammalian viruses.

We studied the frequency distribution of 1,048,576 oligonucleotides 10 bp long in a sample of 1.072 x 10(6) bases of genes from non-mammalian vertebrates, made of 322 sequences extracted from EMBL(R) 29.0, with the aim of detecting transcription control signals. Among all decamers, 2097 (0.2%) had a frequency 10 times higher than the mean and were subjected to further statistical analysis. For each of the 2097 decamers (parents), we counted the individual frequencies of the 30 decamers differing from the parent by one base mutation (progeny) and we calculated two variance/mean chi squares for the progeny, with and without the parent decamer. By studying the distribution of the ratio between the two chi squares we observed that out of 2097 decamers that occurred > 10 times more frequently than average, 1017 had a chi square ratio of between 1 and 1.5; in this final set, which corresponds to < 0.097% of all possible decamers, 75 decamers were found to contain 100 transcription control elements, like CCAAT and others. The final set contains a high excess of signals when compared to 100 random sets of 1017 decamers. Some of the decamers selected with the procedure are members of consensus sequences rather than unique sequences.

The large body of nucleic acid sequence data now available offers a unique opportunity for the characterization of individual oligonucleotides which may be specific to sequence functional domains. We have prepared algorithms for the study of the frequency distribution of all oligonucleotides of length 2-6 in DNA sequences. We have implemented them in the study of 634 mammalian DNA sequences spanning 1.782 Mb, and have obtained the distribution of the ratio between the observed frequency of oligonucleotides and their expected frequency based on independent nucleotide probabilities. We then studied the distribution of oligonucleotides (or k-tuples) of each length in a subset of 129 complete mammalian genes spanning 0.607 Mb. Eight distinct genomic regions, namely 5'-non-transcribed, first exon, first intron, intermediate exons, intermediate introns, last intron, last exon and 3'-non-transcribed, were considered. We observed that some oligonucleotides show a statistical behaviour and a regional distribution similar to that of known signal sequences. Moreover the frequency distribution of oligonucleotides of length 5 and 6 tends to become bimodal, indicating the existence of a population of very frequent oligonucleotides.