MicroRNA.org (http://www.microrna.org) is a comprehensive resource of microRNA target predictions and expression profiles. Target predictions are based on a development of the miRanda algorithm which incorporates current biological knowledge on target rules and on the use of an up-to-date compendium of mammalian microRNAs. MicroRNA expression profiles are derived from a comprehensive sequencing project of a large set of mammalian tissues and cell lines of normal and disease origin. Using an improved graphical interface, a user can explore (i) the set of genes that are potentially regulated by a particular microRNA, (ii) the implied cooperativity of multiple microRNAs on a particular mRNA and (iii) microRNA expression profiles in various tissues. To facilitate future updates and development, the microRNA.org database structure and software architecture is flexibly designed to incorporate new expression and target discoveries. The web resource provides users with functional information about the growing number of microRNAs and their interaction with target genes in many species and facilitates novel discoveries in microRNA gene regulation.

mirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.

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A large number of computational methods have been developed for analyzing differential gene expression in RNA-seq data. We describe a comprehensive evaluation of common methods using the SEQC benchmark dataset and ENCODE data. We consider a number of key features, including normalization, accuracy of differential expression detection and differential expression analysis when one condition has no detectable expression. We find significant differences among the methods, but note that array-based methods adapted to RNA-seq data perform comparably to methods designed for RNA-seq. Our results demonstrate that increasing the number of replicate samples significantly improves detection power over increased sequencing depth.

The Biomolecular Interaction Network Database (BIND; http://binddb. org) is a database designed to store full descriptions of interactions, molecular complexes and pathways. Development of the BIND 2.0 data model has led to the incorporation of virtually all components of molecular mechanisms including interactions between any two molecules composed of proteins, nucleic acids and small molecules. Chemical reactions, photochemical activation and conformational changes can also be described. Everything from small molecule biochemistry to signal transduction is abstracted in such a way that graph theory methods may be applied for data mining. The database can be used to study networks of interactions, to map pathways across taxonomic branches and to generate information for kinetic simulations. BIND anticipates the coming large influx of interaction information from high-throughput proteomics efforts including detailed information about post-translational modifications from mass spectrometry. Version 2.0 of the BIND data model is discussed as well as implementation, content and the open nature of the BIND project. The BIND data specification is available as ASN.1 and XML DTD.

The miR-17~92 cluster is frequently amplified or overexpressed in human cancers and has emerged as the prototypical oncogenic polycistron microRNA (miRNA). miR-17~92 is a direct transcriptional target of c-Myc, and experiments in a mouse model of B-cell lymphomas have shown cooperation between these two oncogenes. However, both the molecular mechanism underlying this cooperation and the individual miRNAs that are responsible for it are unknown. By using a conditional knockout allele of miR-17~92, we show here that sustained expression of endogenous miR-17~92 is required to suppress apoptosis in Myc-driven B-cell lymphomas. Furthermore, we show that among the six miRNAs that are encoded by miR-17~92, miR-19a and miR-19b are absolutely required and largely sufficient to recapitulate the oncogenic properties of the entire cluster. Finally, by combining computational target prediction, gene expression profiling, and an in vitro screening strategy, we identify a subset of miR-19 targets that mediate its prosurvival activity.Supplemental material is available at http://www.genesdev.org. The experiments presented in this study were designed to examine the role of the endogenous miR-17;92 allele in Myc-driven lymphomas, and to determine the relative contribution of each of the six constituent miRNAs to the overall oncogenic potential of the cluster.Our results show that, in the context of Myc-driven B-cell lymphomas, genetic ablation of the endogenous miR-17;92 locus leads to a dramatic reduction of tumor cell growth in vitro and suppresses tumorigenicity in vivo, two effects that are largely the consequence of increased cell death. We also demonstrate that, among the six miRNAs encoded by the miR-17;92 cluster, the members of the miR-19 family (miR-19a and miR-19b) are essential to mediate the oncogenic activity of the entire cluster, and that they do so at least in part by modulating the expression of the tumor suppressor gene Pten (phosphatase and tensin homologous). Results and DiscussionGeneration of miR-17;92 flox/flox ;Em-Myc miceTo investigate the role of miR-17;92 in Myc-induced cancers, we employed the Em-Myc mouse model of B-cell lymphomas (Adams et al. 1985). Em-Myc mice express a c-Myc transgene under the control of the B-cell-specific Em enhancer and develop B-cell lymphomas within 4-6 mo of age (Adams et al. 1985). Em-Myc mice were crossed to mice carrying a conditional miR-17;92 knockout allele (miR-17;92 fl ) ( Fig. 1B; Ventura et al. 2008). To temporally control the deletion of the floxed miR-17;92 allele, these mice were further crossed to mice carrying a 4-hydroxytamoxifen (4-OHT)-inducible Cre-recombinase estrogen receptor-T2 (Cre-ER T2 ) knock-in allele targeted to the ubiquitously expressed ROSA26 locus (R26-Cre-ER T2 mice, hereafter referred to as Cre-ER) (Ventura et al. 2007).As expected, Em-Myc; miR-17;92 fl/fl ; Cre-ER mice developed B-cell lymphomas with similar latency and phenotype as the parental Em-Myc strain (data not shown). From these mice, we derived two independent lymphoma lines (...

Transfection of small RNAs (si/miRNAs) into cells typically lowers expression of many genes. Unexpectedly, increased expression of genes also occurs. We investigated whether this upregulation results from a saturation effect, i.e. competition for intracellular small RNA processing machinery between the transfected si/miRNAs and the endogenous pool of microRNAs (miRNAs). To test this hypothesis, we analyzed genome-wide transcript responses from more than 150 published transfection experiments in 7 different cell types. We show that endogenous miRNA targets have significantly higher expression levels following transfection, consistent with an impaired effectiveness of endogenous miRNA repression. Further confirmation comes from concentration and temporal dependence. Strikingly, the profile of endogenous miRNAs can largely be inferred by correlating miRNA sites with gene expression changes after transfections. The saturation and competition effects present practical implications for miRNA target prediction, the design of si/shRNA genomic screens and siRNA therapeutics.

The Biomolecular Interaction Network Database (BIND) (http://bind.ca) archives biomolecular interaction, reaction, complex and pathway information. Our aim is to curate the details about molecular interactions that arise from published experimental research and to provide this information, as well as tools to enable data analysis, freely to researchers worldwide. BIND data are curated into a comprehensive machinereadable archive of computable information and provides users with methods to discover interactions and molecular mechanisms. BIND has worked to develop new methods for visualization that amplify the underlying annotation of genes and proteins to facilitate the study of molecular interaction networks. BIND has maintained an open database policy since its inception in 1999. Data growth has proceeded at a tremendous rate, approaching over 100 000 records. New services provided include a new BIND Query and Submission interface, a Standard Object Access Protocol service and the Small Molecule Interaction Database (http://smid.blueprint.org) that allows users to determine probable small molecule binding sites of new sequences and examine conserved binding residues. INTRODUCTIONIn light of the vast scientific resources made available through genomics, the science of deciphering molecular mechanisms is expanding rapidly. Scientists who once hunted for disease genes or sought to distinguish key concepts in evolution are now turning their attention to the details of molecular assembly and mechanism to further understand medicine and the key concepts underlying biology. The Biomolecular Interaction Network Database (BIND) was designed to store complete information about molecular assembly through a database structure in order to archive interactions and reactions arising from biopolymers (protein, RNA and DNA), as well as small molecules, lipids and carbohydrates. Detailed information about molecular mechanism, such as the chemical product(s) of an enzymatic reaction, can be encoded in BIND. The underlying ontology of the BIND database is chemistry, and as such, BIND is capable of storing information about molecular interactions to atomic resolution. The taxonomic scope of BIND is