UNLABELLED: MicroRNAs (miRNAs) represent an important class of small non-coding RNAs regulating gene expression in eukaryotes. Present algorithms typically rely on genomic data to identify miRNAs and require extensive installation procedures. Niche model organisms lacking genomic sequences cannot be analyzed by such tools. Here we introduce the MIRPIPE application enabling rapid and simple browser-based miRNA homology detection and quantification. MIRPIPE features automatic trimming of raw RNA-Seq reads originating from various sequencing instruments, processing of isomiRs and quantification of detected miRNAs versus public- or user-uploaded reference databases. AVAILABILITY AND IMPLEMENTATION: The Web service is freely available at http://bioinformatics.mpi-bn.mpg.de. MIRPIPE was implemented in Perl and integrated into Galaxy. An offline version for local execution is also available from our Web site.

BACKGROUND: New experimental methods must be developed to study interaction networks in systems biology. To reduce biological noise, individual subjects, such as single cells, should be analyzed using high throughput approaches. The measurement of several correlative physical properties would further improve data consistency. Accordingly, a considerable quantity of data must be acquired, correlated, catalogued and stored in a database for subsequent analysis. RESULTS: We have developed openBEB (open Biological Experiment Browser), a software framework for data acquisition, coordination, annotation and synchronization with database solutions such as openBIS. OpenBEB consists of two main parts: A core program and a plug-in manager. Whereas the data-type independent core of openBEB maintains a local container of raw-data and metadata and provides annotation and data management tools, all data-specific tasks are performed by plug-ins. The open architecture of openBEB enables the fast integration of plug-ins, e.g., for data acquisition or visualization. A macro-interpreter allows the automation and coordination of the different modules. An update and deployment mechanism keeps the core program, the plug-ins and the metadata definition files in sync with a central repository. CONCLUSIONS: The versatility, the simple deployment and update mechanism, and the scalability in terms of module integration offered by openBEB make this software interesting for a large scientific community. OpenBEB targets three types of researcher, ideally working closely together: (i) Engineers and scientists developing new methods and instruments, e.g., for systems-biology, (ii) scientists performing biological experiments, (iii) theoreticians and mathematicians analyzing data. The design of openBEB enables the rapid development of plug-ins, which will inherently benefit from the "house keeping" abilities of the core program. We report the use of openBEB to combine live cell microscopy, microfluidic control and visual proteomics. In this example, measurements from diverse complementary techniques are combined and correlated.

MOTIVATION: Due to the development of high-throughput technologies such as microarrays, it has become possible to determine genome-wide expression changes in a single experiment. Although much attention has been paid to identify differentially expressed genes, the functions of tens of thousands of genes in different species still remain unknown. RESULTS: C-It is a knowledge database that has its focus on 'uncharacterized genes'. C-It contains expression profiles of various tissues from human, mouse, rat, chicken and zebrafish. By applying our previously introduced algorithm DGSA (Database-Dependent Gene Selection and Analysis), it is possible to screen for uncharacterized, tissue-enriched genes in the species mentioned above. C-It is designed to include further expression studies, which might provide more comprehensive coverage of gene expression patterns and tissue-enriched splicing isoforms. We propose that C-It will be an excellent starting point to study uncharacterized genes. AVAILABILITY: C-It is freely available online without registration at http://C-It.mpi-bn.mpg.dehttp://C-It.mpi-bn.mpg.de.

BACKGROUND: Several tools have been developed to explore and search Gene Ontology (GO) databases allowing efficient GO enrichment analysis and GO tree visualization. Nevertheless, identification of highly specific GO-terms in complex data sets is relatively complicated and the display of GO term assignments and GO enrichment analysis by simple tables or pie charts is not optimal. Valuable information such as the hierarchical position of a single GO term within the GO tree (topological ordering), or enrichment within a complex set of biological experiments is not displayed. Pie charts based on GO tree levels are, themselves, one-dimensional graphs, which cannot properly or efficiently represent the hierarchical specificity for the biological system being studied. RESULTS: Here we present a new method, which we name PCA2GO, capable of GO analysis using complex multidimensional experimental settings. We employed principal component analysis (PCA) and developed a new score, which takes into account the relative frequency of certain GO terms and their specificity (hierarchical position) within the GO graph. We evaluated the correlation between our representation score R and a standard measure of enrichment, namely p-values to convey the versatility of our approach to other methods and point out differences between our method and commonly used enrichment analyses. Although p values and the R score formally measure different quantities they should be correlated, because relative frequencies of GO terms occurrences within a dataset are an indirect measure of protein numbers related to this term. Therefore they are also related to enrichment. We showed that our score enables us to identify more specific GO-terms i.e. those positioned further down the GO-graph than other common tools used for this purpose. PCA2GO allows visualization and detection of multidimensional dependencies both within the acyclic graph (GO tree) and the experimental settings. Our method is intended for the analysis of several experimental sets, not for one set, like standard enrichment tools. To demonstrate the usefulness of our approach we performed a PCA2GO analysis of a fractionated cardiomyocyte protein dataset, which was identified by enhanced liquid chromatography-mass spectrometry (GeLC-MS). The analysis enabled us to detect distinct groups of proteins, which accurately reflect properties of biochemical cell fractions. CONCLUSIONS: We conclude that PCA2GO is an alternative efficient GO analysis tool with unique features for detection and visualization of multidimensional dependencies within the dataset under study. PCA2GO reveals strongly correlated GO terms within the experimental setting (in this case different fractions) by PCA group formation and improves detection of more specific GO terms within experiment dependent GO term groups than standard p value calculations.

SUMMARY: The Exon Array Analyzer (EAA) is a web server, which provides a user-friendly interface to identify alternative splicing events analyzed with Affymetrix Exon Arrays. The EAA implements the Splice Index algorithm to identify differential expressed exons. The use of various filters allows reduction of the number of false positive hits. Results are presented with detailed annotation information and graphics to identify splice events and to facilitate biological validations. To demonstrate the versatility of the EAA, we analyzed exon arrays of 11 different murine tissues using sample data provided by Affymetrix (http://www.affymetrix.com). Data from the heart were compared with other tissues to identify exons that undergo heart-specific alternatively splicing, resulting in the identification of 885 differentially expressed probe sets in 649 genes. AVAILABILITY: The web interface is available at http://EAA.mpi-bn.mpg.de/. Detailed documentation is available on the EAA web site (http://EAA.mpi-bn.mpg.de/supp.php) including screen shots, example analyzes and step by step instructions. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.