Global signatures of protein binding on structured RNAs in Saccharomyces cerevisiae

Protein binding is essential to the transport,decay and regulation of almost all RNA molecules.However,the structural preference of protein binding on RNAs and their cellular functions and dynamics upon changing environmental conditions are poorly understood.Here,we integrated various high-throughput data and introduced a computational framework to describe the global interactions between RNA binding proteins(RBPs)and structured RNAs in yeast at single-nucleotide resolution.We found that on average,in terms of percent total lengths,~15%of mRNA untranslated regions(UTRs),~37%of canonical non-coding RNAs(ncRNAs)and~11%of long ncRNAs(lncRNAs)are bound by proteins.The RBP binding sites,in general,tend to occur at single-stranded loops,with evolutionarily conserved signatures,and often facilitate a specific RNA structure conformation in vivo.We found that four nucleotide modifications of tRNA are significantly associated with RBP binding.We also identified various structural motifs bound by RBPs in the UTRs of mRNAs,associated with localization,degradation and stress responses.Moreover,we identified>200 novel lncRNAs bound by RBPs,and about half of them contain conserved secondary structures.We present the first ensemble pattern of RBP binding sites in the structured non-coding regions of a eukaryotic genome,emphasizing their structural context and cellular functions.     

Nucleotide sequence determination and secondary structure of Xenopus U3 snRNA

Using a combination of RNA sequencing and construction of cDNA clones followed by DNA sequencing, we have determined the primary nucleotide sequence of U3 snRNA in Xenopus laevis and Xenopus borealis. This molecule has a length of 219 nucleotides. Alignment of the Xenopus sequences with U3 snRNA sequences from other organisms reveals three evolutionarily conserved blocks. We have probed the secondary structure of U3 snRNA in intact Xenopus laevis nuclei using single-strand specific chemical reagents; primer extension was used to map the positions of chemical modification. The three blocks of conserved sequences fall within single-stranded regions, and are therefore accessible for interaction with other molecules. Models of U3 snRNA function are discussed in light of these data.     

地中海富盐菌中非编码RNA的鉴定与分析

【目的】通过转录组高通量测序技术(即RNA-seq),结合生物信息学分析和分子生物学方法,在组学水平鉴定极端嗜盐菌中可能的非编码RNA(nc RNA)。【方法】将培养至对数中期的地中海富盐菌在不同盐浓度下处理30分钟,提取RNA,进行链特异的转录组测序和5′端区分的转录组测序,通过生物信息学分析在全基因组范围内鉴定nc RNA,预测其转录边界;然后通过Northern blot和环化RNA反转录聚合酶链式反应(CR-RT-PCR)对部分预测的nc RNA进行实验验证。【结果】比较两种RNA-seq技术在不同培养条件下的RNA测序结果和对转录单元的精细分析,共鉴定到105个高可信度的nc RNA,并发现4个在不同盐度下表达差异较大的nc RNA,通过Northern blot和CR-RT-PCR验证了inc RNA1436和inc RNA1903的表达情况、转录本、转录起始位点及终止位点等。【结论】首次在组学水平鉴定了地中海富盐菌中的nc RNA,不同盐浓度刺激下部分nc RNA的转录差异暗示其有可能参与地中海富盐菌对盐胁迫的适应,高可信度nc RNA的组学发现为今后全面开展嗜盐古菌nc RNA的功能机制研究提供了基础数据及重要的切入点。     

Improved prediction of RNA secondary structure by integrating the free energy model with restraints derived from experimental probing data

Recently, several experimental techniques have emerged for probing RNA structures based on high-throughput sequencing. However, most secondary structure prediction tools that incorporate probing data are designed and optimized for particular types of experiments. For example, RNAstructure-Fold is optimized for SHAPE data, while SeqFold is optimized for PARS data. Here, we report a new RNA secondary structure prediction method, restrained MaxExpect (RME), which can incorporate multiple types of experimental probing data and is based on a free energy model and an MEA (maximizing expected accuracy) algorithm. We first demonstrated that RME substantially improved secondary structure prediction with perfect restraints (base pair information of known structures). Next, we collected structure-probing data from diverse experiments (e.g. SHAPE, PARS and DMS-seq) and transformed them into a unified set of pairing probabilities with a posterior probabilistic model. By using the probability scores as restraints in RME, we compared its secondary structure prediction performance with two other well-known tools, RNAstructure-Fold (based on a free energy minimization algorithm) and SeqFold (based on a sampling algorithm). For SHAPE data, RME and RNAstructure-Fold performed better than SeqFold, because they markedly altered the energy model with the experimental restraints. For high-throughput data (e.g. PARS and DMS-seq) with lower probing efficiency, the secondary structure prediction performances of the tested tools were comparable, with performance improvements for only a portion of the tested RNAs. However, when the effects of tertiary structure and protein interactions were removed, RME showed the highest prediction accuracy in the DMS-accessible regions by incorporating in vivo DMS-seq data.     

Epitranscriptomic technologies and analyses

RNA can interact with RNA-binding proteins(RBPs), mRNA, or other non-coding RNAs(ncRNAs) to form complex regulatory networks. High-throughput CLIP-seq, degradome-seq, and RNA-RNA interactome sequencing methods represent powerful approaches to identify biologically relevant ncRNA-target and protein-ncRNA interactions. However, assigning ncRNAs to their regulatory target genes or interacting RNA-binding proteins(RBPs) remains technically challenging. Chemical modifications to mRNA also play important roles in regulating gene expression. Investigation of the functional roles of these modifications relies highly on the detection methods used. RNA structure is also critical at nearly every step of the RNA life cycle. In this review, we summarize recent advances and limitations in CLIP technologies and discuss the computational challenges of and bioinformatics tools used for decoding the functions and regulatory networks of ncRNAs. We also summarize methods used to detect RNA modifications and to probe RNA structure.     

Sequence and expression analysis of gaps in human chromosome 20

The finished human genome-assemblies comprise several hundred un-sequenced euchromatic gaps, which may be rich in long polypurine/polypyrimidine stretches. Human chromosome 20 (chr 20) currently has three unfinished gaps remaining on its q-arm. All three gaps are within gene-dense regions and/or overlap disease-associated loci, including the DLGAP4 locus. In this study, we sequenced ～ 99% of all three unfinished gaps on human chr 20, determined their complete genomic sizes and assessed epigenetic profiles using a combination of Sanger sequencing, mate pair paired-end high-throughput sequencing and chromatin, methylation and expression analyses. We found histone 3 trimethylated at Lysine 27 to be distributed across all three gaps in immortalized B-lymphocytes. In one gap, five novel CpG islands were predominantly hypermethylated in genomic DNA from peripheral blood lymphocytes and human cerebellum. One of these CpG islands was differentially methylated and paternally hypermethylated. We found all chr 20 gaps to comprise structured non-coding RNAs (ncRNAs) and to be conserved in primates. We verified expression for 13 candidate ncRNAs, some of which showed tissue specificity. Four ncRNAs expressed within the gap at DLGAP4 show elevated expression in the human brain. Our data suggest that unfinished human genome gaps are likely to comprise numerous functional elements.     

RNase J1 endonuclease activity as a probe of RNA secondary structure

Reliable determination of RNA secondary structure depends on both computer algorithms and experimental probing of nucleotides in single- or double-stranded conformation. Here we describe the exploitation of the endonucleolytic activity of the Bacillus subtilis enzyme RNase J1 as a probe of RNA structure. RNase J1 cleaves in single-stranded regions and, in vitro at least, the enzyme has relatively relaxed nucleotide specificity. We confirmed the feasibility of the approach on an RNA of known structure, B. subtilis tRNA^Thr. We then used RNase J1 to solve the secondary structure of the 5′ end of the hbs mRNA. Finally, we showed that RNase J1 can also be used in footprinting experiments by probing the interaction between the 30S ribosomal subunit and the Shine–Dalgarno element of the hbs mRNA.     

High-throughput single-nucleotide structural mapping by capillary automated footprinting analysis

The use of capillary electrophoresis with fluorescently labeled nucleic acids revolutionized DNA sequencing, effectively fueling the genomic revolution. We present an application of this technology for the high-throughput structural analysis of nucleic acids by chemical and enzymatic mapping ('footprinting'). We achieve the throughput and data quality necessary for genomic-scale structural analysis by combining fluorophore labeling of nucleic acids with novel quantitation algorithms. We implemented these algorithms in the CAFA (capillary automated footprinting analysis) open-source software that is downloadable gratis from https://simtk.org/home/cafa. The accuracy, throughput and reproducibility of CAFA analysis are demonstrated using hydroxyl radical footprinting of RNA. The versatility of CAFA is illustrated by dimethyl sulfate mapping of RNA secondary structure and DNase I mapping of a protein binding to a specific sequence of DNA. Our experimental and computational approach facilitates the acquisition of high-throughput chemical probing data for solution structural analysis of nucleic acids.     

Integration of expressed sequence tag data flanking predicted RNA secondary structures facilitates novel non-coding RNA discovery

Many computational methods have been used to predict novel non-coding RNAs (ncRNAs), but none, to our knowledge, have explicitly investigated the impact of integrating existing cDNA-based Expressed Sequence Tag (EST) data that flank structural RNA predictions. To determine whether flanking EST data can assist in microRNA (miRNA) prediction, we identified genomic sites encoding putative miRNAs by combining functional RNA predictions with flanking ESTs data in a model consistent with miRNAs undergoing cleavage during maturation. In both human and mouse genomes, we observed that the inclusion of flanking ESTs adjacent to and not overlapping predicted miRNAs significantly improved the performance of various methods of miRNA prediction, including direct high-throughput sequencing of small RNA libraries. We analyzed the expression of hundreds of miRNAs predicted to be expressed during myogenic differentiation using a customized microarray and identified several known and predicted myogenic miRNA hairpins. Our results indicate that integrating ESTs flanking structural RNA predictions improves the quality of cleaved miRNA predictions and suggest that this strategy can be used to predict other non-coding RNAs undergoing cleavage during maturation.     

CoRAL: predicting non-coding RNAs from small RNA-sequencing data

The surprising observation that virtually the entire human genome is transcribed means we know little about the function of many emerging classes of RNAs, except their astounding diversities. Traditional RNA function prediction methods rely on sequence or alignment information, which are limited in their abilities to classify the various collections of non-coding RNAs (ncRNAs). To address this, we developed Classification of RNAs by Analysis of Length (CoRAL), a machine learning-based approach for classification of RNA molecules. CoRAL uses biologically interpretable features including fragment length and cleavage specificity to distinguish between different ncRNA populations. We evaluated CoRAL using genome-wide small RNA sequencing data sets from four human tissue types and were able to classify six different types of RNAs with ∼80% cross-validation accuracy. Analysis by CoRAL revealed that microRNAs, small nucleolar and transposon-derived RNAs are highly discernible and consistent across all human tissue types assessed, whereas long intergenic ncRNAs, small cytoplasmic RNAs and small nuclear RNAs show less consistent patterns. The ability to reliably annotate loci across tissue types demonstrates the potential of CoRAL to characterize ncRNAs using small RNA sequencing data in less well-characterized organisms.     

Statistical prediction of single-stranded regions in RNA secondary structure and application to predicting effective antisense target sites and beyond

Single-stranded regions in RNA secondary structure are important for RNA–RNA and RNA–protein interactions. We present a probability profile approach for the prediction of these regions based on a statistical algorithm for sampling RNA secondary structures. For the prediction of phylogenetically-determined single-stranded regions in secondary structures of representative RNA sequences, the probability profile offers substantial improvement over the minimum free energy structure. In designing antisense oligonucleotides, a practical problem is how to select a secondary structure for the target mRNA from the optimal structure(s) and many suboptimal structures with similar free energies. By summarizing the information from a statistical sample of probable secondary structures in a single plot, the probability profile not only presents a solution to this dilemma, but also reveals ‘well-determined’ single-stranded regions through the assignment of probabilities as measures of confidence in predictions. In antisense application to the rabbit β-globin mRNA, a significant correlation between hybridization potential predicted by the probability profile and the degree of inhibition of in vitro translation suggests that the probability profile approach is valuable for the identification of effective antisense target sites. Coupling computational design with DNA–RNA array technique provides a rational, efficient framework for antisense oligonucleotide screening. This framework has the potential for high-throughput applications to functional genomics and drug target validation.