ChemMine Tools is an online service for small molecule data analysis. It provides a web interface to a set of cheminformatics and data mining tools that are useful for various analysis routines performed in chemical genomics and drug discovery. The service also offers programmable access options via the R library ChemmineR. The primary functionalities of ChemMine Tools fall into five major application areas: data visualization, structure comparisons, similarity searching, compound clustering and prediction of chemical properties. First, users can upload compound data sets to the online Compound Workbench. Numerous utilities are provided for compound viewing, structure drawing and format interconversion. Second, pairwise structural similarities among compounds can be quantified. Third, interfaces to ultra-fast structure similarity search algorithms are available to efficiently mine the chemical space in the public domain. These include fingerprint and embedding/indexing algorithms. Fourth, the service includes a Clustering Toolbox that integrates cheminformatic algorithms with data mining utilities to enable systematic structure and activity based analyses of custom compound sets. Fifth, physicochemical property descriptors of custom compound sets can be calculated. These descriptors are important for assessing the bioactivity profile of compounds in silico and quantitative structure—activity relationship (QSAR) analyses. ChemMine Tools is available at: http://chemmine.ucr.edu .

Background: Next-generation sequencing (NGS) has revolutionized how research is carried out in many areas of biology and medicine. However, the analysis of NGS data remains a major obstacle to the efficient utilization of the technology, as it requires complex multi-step processing of big data demanding considerable computational expertise from users. While substantial effort has been invested on the development of software dedicated to the individual analysis steps of NGS experiments, insufficient resources are currently available for integrating the individual software components within the widely used R/Bioconductor environment into automated workflows capable of running the analysis of most types of NGS applications from start-to-finish in a time-efficient and reproducible manner. Results: To address this need, we have developed the R/Bioconductor package systemPipeR. It is an extensible environment for both building and running end-to-end analysis workflows with automated report generation for a wide range of NGS applications. Its unique features include a uniform workflow interface across different NGS applications, automated report generation, and support for running both R and command-line software on local computers and computer clusters. A flexible sample annotation infrastructure efficiently handles complex sample sets and experimental designs. To simplify the analysis of widely used NGS applications, the package provides pre-configured workflows and reporting templates for RNA-Seq, ChIP-Seq, VAR-Seq and Ribo-Seq. Additional workflow templates will be provided in the future. Conclusions: systemPipeR accelerates the extraction of reproducible analysis results from NGS experiments. By combining the capabilities of many R/Bioconductor and command-line tools, it makes efficient use of existing software resources without limiting the user to a set of predefined methods or environments. systemPipeR is freely available for all common operating systems from Bioconductor (http://bioconductor.org/packages/devel/systemPipeR).

Significance: Mosquitoes transmit some of the most dangerous human diseases. Microarray analysis of developmental gene dynamics in the femaleAedes aegyptifat body has demonstrated that 6,146 genes are differentially expressed during juvenile hormone-dependent posteclosion development. These genes show striking temporal and functional separation. A RNAi microarray screen revealed a differential action of Met in the down- and up-regulation of genes expressed during early and late PE, respectively. Thus, this study provides substantial evidence of a central role for insect-specific JH and its receptor Met in the regulation of female mosquito reproductive biology.

The advent of high-throughput sequencing (HTS) methods has enabled direct approaches to quantitatively profile small RNA populations. However, these methods have been limited by several factors, including representational artifacts and lack of established statistical methods of analysis. Furthermore, massive HTS data sets present new problems related to data processing and mapping to a reference genome. Here, we show that cluster-based sequencing-by-synthesis technology is highly reproducible as a quantitative profiling tool for several classes of small RNA from Arabidopsis thaliana. We introduce the use of synthetic RNA oligoribonucleotide standards to facilitate objective normalization between HTS data sets, and adapt microarray-type methods for statistical analysis of multiple samples. These methods were tested successfully using mutants with small RNA biogenesis (miRNA-defective dcl1 mutant and siRNA-defective dcl2 dcl3 dcl4 triple mutant) or effector protein (ago1 mutant) deficiencies. Computational methods were also developed to rapidly and accurately parse, quantify, and map small RNA data.

Motivation: The ability to accurately measure structural similarities among small molecules is important for many analysis routines in drug discovery and chemical genomics. Algorithms used for this purpose include fragment-based fingerprint and graph-based maximum common substructure (MCS) methods. MCS approaches provide one of the most accurate similarity measures. However, their rigid matching policies limit them to the identification of perfect MCSs. To eliminate this restriction, we introduce a new mismatch tolerant search method for identifying flexible MCSs (FMCSs) containing a user-definable number of atom and/or bond mismatches. Results: The fmcsR package provides an R interface, with the time-consuming steps of the FMCS algorithm implemented in C++. It includes utilities for pairwise compound comparisons, structure similarity searching, clustering and visualization of MCSs. In comparison with an existing MCS tool, fmcsR shows better time performance over a wide range of compound sizes. When mismatching of atoms or bonds is turned on, the compute times increase as expected, and the resulting FMCSs are often substantially larger than their strict MCS counterparts. Based on extensive virtual screening (VS) tests, the flexible matching feature enhances the enrichment of active structures at the top of MCS-based similarity search results. With respect to overall and early enrichment performance, FMCS outperforms most of the seven other VS methods considered in these tests. Availability: fmcsR is freely available for all common operating systems from the Bioconductor site (http://www.bioconductor.org/packages/devel/bioc/html/fmcsR.html). Contact: thomas.girke@ucr.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Development of the Arabidopsis Small RNA Project (ASRP) Database, which provides information and tools for the analysis of microRNA, endogenous siRNA and other small RNA-related features, has been driven by the introduction of high-throughput sequencing technology. To accommodate the demands of increased data, numerous improvements and updates have been made to ASRP, including new ways to access data, more efficient algorithms for handling data, and increased integration with community-wide resources. New search and visualization tools have also been developed to improve access to small RNA classes and their targets. ASRP is publicly available through a web interface at http://asrp.cgrb.oregonstate.edu/db/