A model for the study of ligand binding to the ribosomal RNA helix h44.

Oligonucleotide models of ribosomal RNA domains are powerful tools to study the binding and molecular recognition of antibiotics that interfere with bacterial translation. Techniques such as selective chemical modification, fluorescence labeling and mutations are cumbersome for the whole ribosome but readily applicable to model RNAs, which are readily crystallized and often give rise to higher resolution crystal structures suitable for detailed analysis of ligand-RNA interactions. Here, we have investigated the HX RNA construct which contains two adjacent ligand binding regions of helix h44 in 16S ribosomal RNA. High-resolution crystal structure analysis confirmed that the HX RNA is a faithful structural model of the ribosomal target. Solution studies showed that HX RNA carrying a fluorescent 2-aminopurine modification provides a model system that can be used to monitor ligand binding to both the ribosomal decoding site and, through an indirect effect, the hygromycin B interaction region

Using mixtures of biological samples as process controls for RNA-sequencing experiments

Bland-Altman log-ratio(M) - log average (A) plots comparing gene expression in BLM-1 to BLM-2, which were mixed with a designed ratio of 1:1 brain RNA, 2:1 muscle RNA and 1:2 liver RNA. Points representing gene expression values for genes expressed at 5-fold greater levels in a specific tissue are colored based on the tissue in which they are selectively expressed. Non-tissue selective RNA are omitted for clarity. Library size normalization scales all libraries to a common total number of counts, while upper quartile normalization scales to the 75th percentile of the counts for each library. None of these normalizations accurately reflects the designed ratio of transcripts between samples. (PNG 473 kb

Summarizing performance for genome scale measurement of miRNA: reference samples and metrics

Background: The potential utility of microRNA as biomarkers for early detection of cancer and other diseases is being investigated with genome-scale profiling of differentially expressed microRNA. Processes for measurement assurance are critical components of genome-scale measurements. Here, we evaluated the utility of a set of total RNA samples, designed with between-sample differences in the relative abundance of miRNAs, as process controls. Results: Three pure total human RNA samples (brain, liver, and placenta) and two different mixtures of these components were evaluated as measurement assurance control samples on multiple measurement systems at multiple sites and over multiple rounds. In silico modeling of mixtures provided benchmark values for comparison with physical mixtures. Biomarker development laboratories using next-generation sequencing (NGS) or genome-scale hybridization assays participated in the study and returned data from the samples using their routine workflows. Multiplexed and single assay reverse-transcription PCR (RT-PCR) was used to confirm in silico predicted sample differences. Data visualizations and summary metrics for genome-scale miRNA profiling assessment were developed using this dataset, and a range of performance was observed. These metrics have been incorporated into an online data analysis pipeline and provide a convenient dashboard view of results from experiments following the described design. The website also serves as a repository for the accumulation of performance values providing new participants in the project an opportunity to learn what may be achievable with similar measurement processes. Conclusions: The set of reference samples used in this study provides benchmark values suitable for assessing genome-scale miRNA profiling processes. Incorporation of these metrics into an online resource allows laboratories to periodically evaluate their performance and assess any changes introduced into their measurement process

Summarizing performance for genome scale measurement of miRNA: reference samples and metrics

Background: The potential utility of microRNA as biomarkers for early detection of cancer and other diseases is being investigated with genome-scale profiling of differentially expressed microRNA. Processes for measurement assurance are critical components of genome-scale measurements. Here, we evaluated the utility of a set of total RNA samples, designed with between-sample differences in the relative abundance of miRNAs, as process controls. Results: Three pure total human RNA samples (brain, liver, and placenta) and two different mixtures of these components were evaluated as measurement assurance control samples on multiple measurement systems at multiple sites and over multiple rounds. In silico modeling of mixtures provided benchmark values for comparison with physical mixtures. Biomarker development laboratories using next-generation sequencing (NGS) or genome-scale hybridization assays participated in the study and returned data from the samples using their routine workflows. Multiplexed and single assay reverse-transcription PCR (RT-PCR) was used to confirm in silico predicted sample differences. Data visualizations and summary metrics for genome-scale miRNA profiling assessment were developed using this dataset, and a range of performance was observed. These metrics have been incorporated into an online data analysis pipeline and provide a convenient dashboard view of results from experiments following the described design. The website also serves as a repository for the accumulation of performance values providing new participants in the project an opportunity to learn what may be achievable with similar measurement processes. Conclusions: The set of reference samples used in this study provides benchmark values suitable for assessing genome-scale miRNA profiling processes. Incorporation of these metrics into an online resource allows laboratories to periodically evaluate their performance and assess any changes introduced into their measurement process

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Throwing a wrench in the translational machinery: Discovery of RNA ligands by fluorescence techniques

Ribonucleic acids (RNAs) are underexplored as targets for therapeutics. To date, drug research has focused primarily on protein targets. Inhibition of functional RNA is an alternative approach which can avoid some of the problems of resistance. A few RNAs of interest are the bacterial ribosome, HIV RRE and Tar, human thymidylate synthase mRNA, and the Hepatitis C Virus Internal Ribosome Entry Site. Fluorescently-modified oligoribonucleotides are a relatively cheap method of quickly determining changes in the structure of the RNA. During chemical synthesis, fluorescent modifications can be placed in virtually any position and used to report on local structural changes. This technique was applied to the ribosomal A-site, and expanded to encompass alternative fluorophores and RNA sites. To identify compounds bound to HCV IRES domain IIa, fluorescent and FRET-based constructs were designed. After screening a small compound library in these systems, several were identified as binding. A structure activity relationship based on one class of these binding compounds was determined for the IRES IIa. In order to study the chemical components of RNA binding affinity, fluorescent methods for identifying RNA ligands to two RNA targets were devised. Extension of an oligonucleotide construct based on the ribosomal A site allows monitoring of an additional ligand binding site. FRET-based distance measurements in a viral RNA allow discrimination between functional and nonfunctional conformations, and therefore can be used to identify compounds that stabilize a nonfunctional conformation