Oligonucleotides are designed to target RNA using base pairing rules, however, they are hampered by poor cellular delivery and non-specific stimulation of the immune system. Small molecules are preferred as lead drugs or probes, but cannot be designed from sequence. Herein, we describe an approach termed Inforna that designs lead small molecules for RNA from solely sequence. Inforna was applied to all human microRNA precursors and identified bioactive small molecules that inhibit biogenesis by binding to nuclease processing sites (41% hit rate). Amongst 29 lead interactions, the most avid interaction is between a benzimidazole (1) and precursor microRNA-96. Compound 1 selectively inhibits biogenesis of microRNA-96, upregulating a protein target (FOXO1) and inducing apoptosis in cancer cells. Apoptosis is ablated when FOXO1 mRNA expression is knocked down by an siRNA, validating compound selectivity. Importantly, microRNA profiling shows that 1 only significantly effects microRNA-96 biogenesis and is more selective than an oligonucleotide.

The REDfly database of Drosophila transcriptional cis-regulatory elements provides the broadest and most comprehensive available resource for experimentally validated cis-regulatory modules and transcription factor binding sites among the metazoa. The third major release of the database extends the utility of REDfly as a powerful tool for both computational and experimental studies of transcription regulation. REDfly v3.0 includes the introduction of new data classes to expand the types of regulatory elements annotated in the database along with a roughly 40% increase in the number of records. A completely redesigned interface improves access for casual and power users alike; among other features it now automatically provides graphical views of the genome, displays images of reporter gene expression and implements improved capabilities for database searching and results filtering. REDfly is freely accessible at http://redfly.ccr.buffalo.edu.

Shake‐and‐bake is a direct‐methods phasing algorithm for structure determination based on the minimal principle. SnB is a program based on shake‐and‐bake that has been used successfully to solve more than a dozen structures in a variety of space groups. The focus of this paper is on the details of this program, including its structure, system requirements, running times and the rationale for coding in a combination of C and Fortran. A summary of successful SnB applications is also provided. These include solving two previously unknown 100‐atom structures and re‐solving crambin (a structure containing the equivalent of approximately 400 fully occupied atomic positions) for the first time with a direct‐methods technique.

ORegAnno is an open-source, open-access database and literature curation system for community-based annotation of experimentally identified DNA regulatory regions, transcription factor binding sites and regulatory variants. The current release comprises 30 145 records curated from 922 publications and describing regulatory sequences for over 3853 genes and 465 transcription factors from 19 species. A new feature called the ‘publication queue’ allows users to input relevant papers from scientific literature as targets for annotation. The queue contains 4438 gene regulation papers entered by experts and another 54 351 identified by text-mining methods. Users can enter or ‘check out’ papers from the queue for manual curation using a series of user-friendly annotation pages. A typical record entry consists of species, sequence type, sequence, target gene, binding factor, experimental outcome and one or more lines of experimental evidence. An evidence ontology was developed to describe and categorize these experiments. Records are cross-referenced to Ensembl or Entrez gene identifiers, PubMed and dbSNP and can be visualized in the Ensembl or UCSC genome browsers. All data are freely available through search pages, XML data dumps or web services at: http://www.oreganno.org.

BackgroundTissue gene expression is generally regulated by multiple transcription factors (TFs). A major first step toward understanding how tissues achieve their specificity is to identify, at the genome scale, interacting TFs regulating gene expression in different tissues. Despite previous discoveries, the mechanisms that control tissue gene expression are not fully understood.ResultsWe have integrated a function conservation approach, which is based on evolutionary conservation of biological function, and genes with highest expression level in human tissues to predict TF pairs controlling tissue gene expression. To this end, we have identified 2549 TF pairs associated with a certain tissue. To find interacting TFs controlling tissue gene expression in a broad spatial and temporal manner, we looked for TF pairs common to the same type of tissues and identified 379 such TF pairs, based on which TF-TF interaction networks were further built. We also found that tissue-specific TFs may play an important role in recruiting non-tissue-specific TFs to the TF-TF interaction network, offering the potential for coordinating and controlling tissue gene expression across a variety of conditions.ConclusionThe findings from this study indicate that tissue gene expression is regulated by large sets of interacting TFs either on the same promoter of a gene or through TF-TF interaction networks.

The identification and study of the cis-regulatory elements that control gene expression are important areas of biological research, but few resources exist to facilitate large-scale bioinformatics studies of cis-regulation in metazoan species. Drosophila melanogaster, with its well-annotated genome, exceptional resources for comparative genomics and long history of experimental studies of transcriptional regulation, represents the ideal system for regulatory bioinformatics. We have merged two existing Drosophila resources, the REDfly database of cis-regulatory modules and the FlyReg database of transcription factor binding sites (TFBSs), into a single integrated database containing extensive annotation of empirically validated cis-regulatory modules and their constituent binding sites. With the enhanced functionality made possible through this integration of TFBS data into REDfly, together with additional improvements to the REDfly infrastructure, we have constructed a one-stop portal for Drosophila cis-regulatory data that will serve as a powerful resource for both computational and experimental studies of transcriptional regulation. REDfly is freely accessible at http://redfly.ccr.buffalo.edu.

Purpose Significant cancer-related distress affects 30-60% of women diagnosed with breast cancer. Fewer than 30% of distressed patients receive psychosocial care. Unaddressed distress is associated with poor treatment adherence, reduced quality of life, and increased healthcare costs. This study aimed to evaluate the preliminary efficacy of a new web-based, psychoeducational distress self-management program, CaringGuidance™ After Breast Cancer Diagnosis, on newly diagnosed women's reported distress.Methods One-hundred women, in five states, diagnosed with breast cancer within the prior 3 months, were randomized to 12 weeks of independent use of CaringGuidance™ plus usual care or usual care alone. The primary multidimensional outcome, distress, was measured with the Distress Thermometer (DT), the Center for Epidemiologic Studies Depression Scale (CES-D), and the Impact of Events Scale (IES) at baseline and months 1, 2, and 3. Intervention usage was continually monitored by the data analytic system imbedded within CaringGuidance™. Results Although multilevel models showed no significant overall effects, post hoc analysis showed significant group differences in slopes occurring between study months 2 and 3 on distress (F(1,70) = 4.91, p = .03, η 2 = .065) measured by the DT, and depressive symptoms (F(1, 76) = 4.25, p = .043, η 2 = .053) favoring the intervention. Conclusions Results provide preliminary support for the potential efficacy of CaringGuidance™ plus usual care over usual care alone on distress in women newly diagnosed with breast cancer. This analysis supports and informs future study of this selfmanagement program aimed at filling gaps in clinical distress management.

Bioinformatics studies of transcriptional regulation in the metazoa are significantly hindered by the absence of readily available data on large numbers of transcriptional cis-regulatory modules (CRMs). Even the richly annotated Drosophila melanogaster genome lacks extensive CRM information. We therefore present here a database of Drosophila CRMs curated from the literature complete with both DNA sequence and a searchable description of the gene expression pattern regulated by each CRM. This resource should greatly facilitate the development of computational approaches to CRM discovery as well as bioinformatics analyses of regulatory sequence properties and evolution.