Finding RNA structure in the unstructured RBPome

BACKGROUND: RNA-binding proteins (RBPs) play vital roles in many processes in the cell. Different RBPs bind RNA with different sequence and structure specificities. While sequence specificities for a large set of 205 RBPs have been reported through the RNAcompete compendium, structure specificities are known for only a small fraction. The main limitation lies in the design of the RNAcompete technology, which tests RBP binding against unstructured RNA probes, making it difficult to infer structural preferences from these data. We recently developed RCK, an algorithm to infer sequence and structural binding models from RNAcompete data. The set of binding models enables, for the first time, a large-scale assessment of RNA structure in the RBPome. RESULTS: We re-validate and uncover the role of RNA structure in the RPBome through novel analysis of the largest-scale dataset to date. First, we show that RNA structure exists in presumably unstructured RNA probes and that its variability is correlated with RNA-binding. Second, we examine the structural binding preferences of RBPs and discover an overall preference to bind RNA loops. Third, we significantly improve protein-binding prediction using RNA structure, both in vitro and in vivo. Lastly, we demonstrate that RNA structural binding preferences can be inferred for new proteins from solely their amino acid content. CONCLUSIONS: By counter-intuitively demonstrating through our analysis that we can predict both the RNA structure of and RBP binding to these putatively unstructured RNAs, we transform a compendium of RNA-binding proteins into a valuable resource for structure-based binding models. We uncover the important role RNA structure plays in protein-RNA interaction for hundreds of RNA-binding proteins

Flexible microwave system to measure the electron number density and quantify the communications impact of electric thruster plasma plumes

An advanced microwave interferometric system operating in the Ku (12–18 GHz) band has been implemented for use in very large vacuum chambers to determine the effects of electromagnetic wave propagation through a plasma plume created by a space electric propulsion thruster. This diagnostic tool is used to nonintrusively obtain the local electron number density as well as provide information necessary for understanding impact to communications and other spacecraft electromagnetic systems. The use of a nonintrusive electromagnetic measurement provides highly accurate line integrated density and avoids problems caused by intrusive measurement techniques. If the plasma is symmetrical, local plasma density can also be determined accurately using well known inversion techniques. A network analyzer acts as a transmitter and receiver while a two axis positioning system maps the amplitude and phase variation of a transmitted signal over one plane of the plasma plume. The utilization of a 6 m×9 m vacuum chamber effectively minimizes plasma boundary effects, but the longer cable path lengths have required a frequency conversion circuit to reduce power loss and phase uncertainty at high frequencies. Two studies are presented: the first is a measurement of the local electron density in the plume of a 1 kW arcjet and the second is a measurement of attenuation in the plume of a stationary plasma thruster. Both the arcjet and SPT emit a steady state conical unmagnetized plasma that is radially symmetric. The arcjet peak density is 1015–1016 m−3 along centerline and the SPT peak density is 1016–1017 m−3 along centerline. © 1997 American Institute of Physics.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/71094/2/RSINAK-68-2-1189-1.pd

Plume characterization of the SPT-100

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76626/1/AIAA-1996-3298-655.pd

RF signal impact study of an SPT

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76213/1/AIAA-1996-2706-928.pd

RCAS: an RNA centric annotation system for transcriptome-wide regions of interest

In the field of RNA, the technologies for studying the transcriptome have created a tremendous potential for deciphering the puzzles of the RNA biology. Along with the excitement, the unprecedented volume of RNA related omics data is creating great challenges in bioinformatics analyses. Here, we present the RNA Centric Annotation System (RCAS), an R package, which is designed to ease the process of creating gene-centric annotations and analysis for the genomic regions of interest obtained from various RNA-based omics technologies. The design of RCAS is modular, which enables flexible usage and convenient integration with other bioinformatics workflows. RCAS is an R/Bioconductor package but we also created graphical user interfaces including a Galaxy wrapper and a stand-alone web service. The application of RCAS on published datasets shows that RCAS is not only able to reproduce published findings but also helps generate novel knowledge and hypotheses. The meta-gene profiles, gene-centric annotation, motif analysis and gene-set analysis provided by RCAS provide contextual knowledge which is necessary for understanding the functional aspects of different biological events that involve RNAs. In addition, the array of different interfaces and deployment options adds the convenience of use for different levels of users. RCAS is available at http://bioconductor.org/packages/release/bioc/html/RCAS.html and http://rcas.mdc-berlin.de

Chloroplast cold-resistance is mediated by the acidic domain of the RNA binding protein CP31A

Chloroplast RNA metabolism is characterized by long-lived mRNAs that undergo a multitude of post-transcriptional processing events. Chloroplast RNA accumulation responds to environmental cues, foremost light and temperature. A large number of nuclear-encoded RNA-binding proteins (RBPs) are required for chloroplast RNA metabolism, but we do not yet know how chloroplast RBPs convert abiotic signals into gene expression changes. Previous studies showed that the chloroplast ribonucleoprotein 31A (CP31A) is required for the stabilization of multiple chloroplast mRNAs in the cold, and that the phosphorylation of CP31A at various residues within its N-terminal acidic domain (AD) can alter its affinity for RNA in vitro. Loss of CP31A leads to cold sensitive plants that exhibit bleached tissue at the center of the vegetative rosette. Here, by applying RIP-Seq, we demonstrated that CP31A shows increased affinity for a large number of chloroplast RNAs in vivo in the cold. Among the main targets of CP31A were RNAs encoding subunits of the NDH complex and loss of CP31A lead to reduced accumulation of ndh transcripts. Deletion analyses revealed that cold-dependent RNA binding and cold resistance of chloroplast development both depend on the AD of CP31A. Together, our analysis established the AD of CP31A as a key mediator of cold acclimation of the chloroplast transcriptome

Determinants of promoter and enhancer transcription directionality in metazoans

Divergent transcription from promoters and enhancers is pervasive in many species, but it remains unclear if it is a general feature of all eukaryotic cis regulatory elements. To address this, here we define cis regulatory elements in C. elegans, D. melanogaster and H. sapiens and investigate the determinants of their transcription directionality. In all three species, we find that divergent transcription is initiated from two separate core promoter sequences and promoter regions display competition between histone modifications on the +1 and -1 nucleosomes. In contrast, promoter directionality, sequence composition surrounding promoters, and positional enrichment of chromatin states, are different across species. Integrative models of H3K4me3 levels and core promoter sequence are highly predictive of promoter and enhancer directionality and support two directional classes, skewed and balanced. The relative importance of features to these models are clearly distinct for promoters and enhancers. Differences in regulatory architecture within and between metazoans are therefore abundant, arguing against a unified eukaryotic model

Inferring time series chromatin states for promoter-enhancer pairs based on Hi-C data

BACKGROUND: Co-localized combinations of histone modifications (“chromatin states”) have been shown to correlate with promoter and enhancer activity. Changes in chromatin states over multiple time points ("chromatin state trajectories") have previously been analyzed at promoter and enhancers separately. With the advent of time series Hi-C data it is now possible to connect promoters and enhancers and to analyze chromatin state trajectories at promoter-enhancer pairs. RESULTS: We present TimelessFlex, a framework for investigating chromatin state trajectories at promoters and enhancers and at promoter-enhancer pairs based on Hi-C information. TimelessFlex extends our previous approach Timeless, a Bayesian network for clustering multiple histone modification data sets at promoter and enhancer feature regions. We utilize time series ATAC-seq data measuring open chromatin to define promoters and enhancer candidates. We developed an expectation-maximization algorithm to assign promoters and enhancers to each other based on Hi-C interactions and jointly cluster their feature regions into paired chromatin state trajectories. We find jointly clustered promoter-enhancer pairs showing the same activation patterns on both sides but with a stronger trend at the enhancer side. While the promoter side remains accessible across the time series, the enhancer side becomes dynamically more open towards the gene activation time point. Promoter cluster patterns show strong correlations with gene expression signals, whereas Hi-C signals get only slightly stronger towards activation. The code of the framework is available at https://github.com/henriettemiko/TimelessFlex. CONCLUSIONS: TimelessFlex clusters time series histone modifications at promoter-enhancer pairs based on Hi-C and it can identify distinct chromatin states at promoter and enhancer feature regions and their changes over time

Performance assessment of promoter predictions on ENCODE regions in the EGASP experiment

BACKGROUND: This study analyzes the predictions of a number of promoter predictors on the ENCODE regions of the human genome as part of the ENCODE Genome Annotation Assessment Project (EGASP). The systems analyzed operate on various principles and we assessed the effectiveness of different conceptual strategies used to correlate produced promoter predictions with the manually annotated 5' gene ends. RESULTS: The predictions were assessed relative to the manual HAVANA annotation of the 5' gene ends. These 5' gene ends were used as the estimated reference transcription start sites. With the maximum allowed distance for predictions of 1,000 nucleotides from the reference transcription start sites, the sensitivity of predictors was in the range 32% to 56%, while the positive predictive value was in the range 79% to 93%. The average distance mismatch of predictions from the reference transcription start sites was in the range 259 to 305 nucleotides. At the same time, using transcription start site estimates from DBTSS and H-Invitational databases as promoter predictions, we obtained a sensitivity of 58%, a positive predictive value of 92%, and an average distance from the annotated transcription start sites of 117 nucleotides. In this experiment, the best performing promoter predictors were those that combined promoter prediction with gene prediction. The main reason for this is the reduced promoter search space that resulted in smaller numbers of false positive predictions. CONCLUSION: The main finding, now supported by comprehensive data, is that the accuracy of human promoter predictors for high-throughput annotation purposes can be significantly improved if promoter prediction is combined with gene prediction. Based on the lessons learned in this experiment, we propose a framework for the preparation of the next similar promoter prediction assessment