BACKGROUND: Combined whole-genome sequencing (WGS) and RNA sequencing of cancers offer the opportunity to identify genes with altered expression due to genomic rearrangements. Somatic structural variants (SVs), as identified by WGS, can involve altered gene cis-regulation, gene fusions, copy number alterations, or gene disruption. The absence of computational tools to streamline integrative analysis steps may represent a barrier in identifying genes recurrently altered by genomic rearrangement. RESULTS: Here, we introduce SVExpress, a set of tools for carrying out integrative analysis of SV and gene expression data. SVExpress enables systematic cataloging of genes that consistently show increased or decreased expression in conjunction with the presence of nearby SV breakpoints. SVExpress can evaluate breakpoints in proximity to genes for potential enhancer translocation events or disruption of topologically associated domains, two mechanisms by which SVs may deregulate genes. The output from any commonly used SV calling algorithm may be easily adapted for use with SVExpress. SVExpress can readily analyze genomic datasets involving hundreds of cancer sample profiles. Here, we used SVExpress to analyze SV and expression data across 327 cancer cell lines with combined SV and expression data in the Cancer Cell Line Encyclopedia (CCLE). In the CCLE dataset, hundreds of genes showed altered gene expression in relation to nearby SV breakpoints. Altered genes involved TAD disruption, enhancer hijacking, and gene fusions. When comparing the top set of SV-altered genes from cancer cell lines with the top SV-altered genes previously reported for human tumors from The Cancer Genome Atlas and the Pan-Cancer Analysis of Whole Genomes datasets, a significant number of genes overlapped in the same direction for both cell lines and tumors, while some genes were significant for cell lines but not for human tumors and vice versa. CONCLUSION: Our SVExpress tools allow computational biologists with a working knowledge of R to integrate gene expression with SV breakpoint data to identify recurrently altered genes. SVExpress is freely available for academic or commercial use at https://github.com/chadcreighton/SVExpress . SVExpress is implemented as a set of Excel macros and R code. All source code (R and Visual Basic for Applications) is available.

MicroRNAs are short non-coding RNAs that regulate the stability and translation of mRNAs. Profiling experiments, using microarray or deep sequencing technology, have identified microRNAs that are preferentially expressed in certain tissues, specific stages of development, or disease states such as cancer. Deep sequencing utilizes massively parallel sequencing, generating millions of small RNA sequence reads from a given sample. Profiling of microRNAs by deep sequencing measures absolute abundance and allows for the discovery of novel microRNAs that have eluded previous cloning and standard sequencing efforts. Public databases provide in silico predictions of microRNA gene targets by various algorithms. To better determine which of these predictions represent true positives, microRNA expression data can be integrated with gene expression data to identify putative microRNA:mRNA functional pairs. Here we discuss tools and methodologies for the analysis of microRNA expression data from deep sequencing.

MOTIVATION: Global gene expression profiling, both at the transcript level and at the protein level, can be a valuable tool in the understanding of genes, biological networks, and cellular states. As larger and larger gene expression data sets become available, data mining techniques can be applied to identify patterns of interest in the data. Association rules, used widely in the area of market basket analysis, can be applied to the analysis of expression data as well. Association rules can reveal biologically relevant associations between different genes or between environmental effects and gene expression. An association rule has the form LHS --> RHS, where LHS and RHS are disjoint sets of items, the RHS set being likely to occur whenever the LHS set occurs. Items in gene expression data can include genes that are highly expressed or repressed, as well as relevant facts describing the cellular environment of the genes (e.g. the diagnosis of a tumor sample from which a profile was obtained). RESULTS: We demonstrate an algorithm for efficiently mining association rules from gene expression data, using the data set from Hughes et al. (2000, Cell, 102, 109-126) of 300 expression profiles for yeast. Using the algorithm, we find numerous rules in the data. A cursory analysis of some of these rules reveals numerous associations between certain genes, many of which make sense biologically, others suggesting new hypotheses that may warrant further investigation. In a data set derived from the yeast data set, but with the expression values for each transcript randomly shifted with respect to the experiments, no rules were found, indicating that most all of the rules mined from the actual data set are not likely to have occurred by chance. AVAILABILITY: An implementation of the algorithm using Microsoft SQL Server with Access 2000 is available at http://dot.ped.med.umich.edu:2000/pub/assoc_rules/assoc_rules.zip. Our results from mining the yeast data set are available at http://dot.ped.med.umich.edu:2000/pub/assoc_rules/yeast_results.zip.