RMBase v2.0: deciphering the map of RNA modifications from epitranscriptome sequencing data.

More than 100 distinct chemical modifications to RNA have been characterized so far. However, the prevalence, mechanisms and functions of various RNA modifications remain largely unknown. To provide transcriptome-wide landscapes of RNA modifications, we developed the RMBase v2.0 (http://rna.sysu.edu.cn/rmbase/), which is a comprehensive database that integrates epitranscriptome sequencing data for the exploration of post-transcriptional modifications of RNAs and their relationships with miRNA binding events, disease-related single-nucleotide polymorphisms (SNPs) and RNA-binding proteins (RBPs). RMBase v2.0 was expanded with ∼600 datasets and ∼1 397 000 modification sites from 47 studies among 13 species, which represents an approximately 10-fold expansion when compared with the previous release. It contains ∼1 373 000 N6-methyladenosines (m6A), ∼5400 N1-methyladenosines (m1A), ∼9600 pseudouridine (Ψ) modifications, ∼1000 5-methylcytosine (m5C) modifications, ∼5100 2'-O-methylations (2'-O-Me), and ∼2800 modifications of other modification types. Moreover, we built a new module called 'Motif' that provides the visualized logos and position weight matrices (PWMs) of the modification motifs. We also constructed a novel module termed 'modRBP' to study the relationships between RNA modifications and RBPs. Additionally, we developed a novel web-based tool named 'modMetagene' to plot the metagenes of RNA modification along a transcript model. This database will help researchers investigate the potential functions and mechanisms of RNA modifications.

ChIPBase v2.0: decoding transcriptional regulatory networks of non-coding RNAs and protein-coding genes from ChIP-seq data.

The abnormal transcriptional regulation of non-coding RNAs (ncRNAs) and protein-coding genes (PCGs) is contributed to various biological processes and linked with human diseases, but the underlying mechanisms remain elusive. In this study, we developed ChIPBase v2.0 (http://rna.sysu.edu.cn/chipbase/) to explore the transcriptional regulatory networks of ncRNAs and PCGs. ChIPBase v2.0 has been expanded with ∼10 200 curated ChIP-seq datasets, which represent about 20 times expansion when comparing to the previous released version. We identified thousands of binding motif matrices and their binding sites from ChIP-seq data of DNA-binding proteins and predicted millions of transcriptional regulatory relationships between transcription factors (TFs) and genes. We constructed 'Regulator' module to predict hundreds of TFs and histone modifications that were involved in or affected transcription of ncRNAs and PCGs. Moreover, we built a web-based tool, Co-Expression, to explore the co-expression patterns between DNA-binding proteins and various types of genes by integrating the gene expression profiles of ∼10 000 tumor samples and ∼9100 normal tissues and cell lines. ChIPBase also provides a ChIP-Function tool and a genome browser to predict functions of diverse genes and visualize various ChIP-seq data. This study will greatly expand our understanding of the transcriptional regulations of ncRNAs and PCGs.

tRF2Cancer: A web server to detect tRNA-derived small RNA fragments (tRFs) and their expression in multiple cancers.

tRNA-derived small RNA fragments (tRFs) are one class of small non-coding RNAs derived from transfer RNAs (tRNAs). tRFs play important roles in cellular processes and are involved in multiple cancers. High-throughput small RNA (sRNA) sequencing experiments can detect all the cellular expressed sRNAs, including tRFs. However, distinguishing genuine tRFs from RNA fragments generated by random degradation remains a major challenge. In this study, we developed an integrated web-based computing system, tRF2Cancer, to accurately identify tRFs from sRNA deep-sequencing data and evaluate their expression in multiple cancers. The binomial test was introduced to evaluate whether reads from a small RNA-seq data set represent tRFs or degraded fragments. A classification method was then used to annotate the types of tRFs based on their sites of origin in pre-tRNA or mature tRNA. We applied the pipeline to analyze 10 991 data sets from 32 types of cancers and identified thousands of expressed tRFs. A tool called 'tRFinCancer' was developed to facilitate the users to inspect the expression of tRFs across different types of cancers. Another tool called 'tRFBrowser' shows both the sites of origin and the distribution of chemical modification sites in tRFs on their source tRNA. The tRF2Cancer web server is available at http://rna.sysu.edu.cn/tRFfinder/.

deepBase v2.0: identification, expression, evolution and function of small RNAs, LncRNAs and circular RNAs from deep-sequencing data.

Small non-coding RNAs (e.g. miRNAs) and long non-coding RNAs (e.g. lincRNAs and circRNAs) are emerging as key regulators of various cellular processes. However, only a very small fraction of these enigmatic RNAs have been well functionally characterized. In this study, we describe deepBase v2.0 (http://biocenter.sysu.edu.cn/deepBase/), an updated platform, to decode evolution, expression patterns and functions of diverse ncRNAs across 19 species. deepBase v2.0 has been updated to provide the most comprehensive collection of ncRNA-derived small RNAs generated from 588 sRNA-Seq datasets. Moreover, we developed a pipeline named lncSeeker to identify 176 680 high-confidence lncRNAs from 14 species. Temporal and spatial expression patterns of various ncRNAs were profiled. We identified approximately 24 280 primate-specific, 5193 rodent-specific lncRNAs, and 55 highly conserved lncRNA orthologs between human and zebrafish. We annotated 14 867 human circRNAs, 1260 of which are orthologous to mouse circRNAs. By combining expression profiles and functional genomic annotations, we developed lncFunction web-server to predict the function of lncRNAs based on protein-lncRNA co-expression networks. This study is expected to provide considerable resources to facilitate future experimental studies and to uncover ncRNA functions.

RMBase: a resource for decoding the landscape of RNA modifications from high-throughput sequencing data.

Although more than 100 different types of RNA modifications have been characterized across all living organisms, surprisingly little is known about the modified positions and their functions. Recently, various high-throughput modification sequencing methods have been developed to identify diverse post-transcriptional modifications of RNA molecules. In this study, we developed a novel resource, RMBase (RNA Modification Base, http://mirlab.sysu.edu.cn/rmbase/), to decode the genome-wide landscape of RNA modifications identified from high-throughput modification data generated by 18 independent studies. The current release of RMBase includes ∼ 9500 pseudouridine (Ψ) modifications generated from Pseudo-seq and CeU-seq sequencing data, ∼ 1000 5-methylcytosines (m(5)C) predicted from Aza-IP data, ∼ 124 200 N6-Methyladenosine (m(6)A) modifications discovered from m(6)A-seq and ∼ 1210 2'-O-methylations (2'-O-Me) identified from RiboMeth-seq data and public resources. Moreover, RMBase provides a comprehensive listing of other experimentally supported types of RNA modifications by integrating various resources. It provides web interfaces to show thousands of relationships between RNA modification sites and microRNA target sites. It can also be used to illustrate the disease-related SNPs residing in the modification sites/regions. RMBase provides a genome browser and a web-based modTool to query, annotate and visualize various RNA modifications. This database will help expand our understanding of potential functions of RNA modifications.

Serum total bilirubin and long-term outcome in patients undergoing percutaneous coronary intervention.

PURPOSE: The purpose of this study was to investigate the associated between serum total bilirubin (STB) levels and long-term outcomes in patients with acute coronary syndrome (ACS) after percutaneous coronary intervention (PCI). METHODS: A total of 1,273 consecutive patients were enrolled. Patients were grouped according to their baseline STB levels: Group 1 (STB < 3.4 µmol/L), Group 2 (3.4 µmol/L ≤ STB ≤ 10.3 µmol/L), Group 3 (10.3 µmol/L < STB ≤ 17.1 µmol/L), and Group 4 (STB < 17.1 µmol/L) and the rate of major adverse cardiovascular events (MACE) was determined RESULTS: A total of 1,152 patients were successfully followed up (90.5%) for a mean period of 30 ± 5 months, including 187 patients experiencing a major adverse cardiovascular event (MACE: death from any cause, myocardial infarction, repeat revascularization or readmission). The MACE rate in Groups 3 and 4 was lower than in Groups 1 and 2 (P < 0.01). After adjusted the confounding factors with Cox regression analysis, the MACE rates in Groups 2-4 were still lower than in Group 1 (Group 2, RR=0.293, 95% CI 0.167-0.517, P < 0.01; Group 3, RR=0.142, 95% CI 0.065-0.312, P < 0.01; Group 4, RR=0.134, 95% CI 0.071-0.252, P < 0.01). The cumulative survival rates of Groups 3 and 4 were higher than that of Groups 1and 2 (P < 0.01). CONCLUSIONS: High STB concentration is associated with lower MACE in patients with ACS after PCI.

Discovery of Protein-lncRNA Interactions by Integrating Large-Scale CLIP-Seq and RNA-Seq Datasets.

Long non-coding RNAs (lncRNAs) are emerging as important regulatory molecules in developmental, physiological, and pathological processes. However, the precise mechanism and functions of most of lncRNAs remain largely unknown. Recent advances in high-throughput sequencing of immunoprecipitated RNAs after cross-linking (CLIP-Seq) provide powerful ways to identify biologically relevant protein-lncRNA interactions. In this study, by analyzing millions of RNA-binding protein (RBP) binding sites from 117 CLIP-Seq datasets generated by 50 independent studies, we identified 22,735 RBP-lncRNA regulatory relationships. We found that one single lncRNA will generally be bound and regulated by one or multiple RBPs, the combination of which may coordinately regulate gene expression. We also revealed the expression correlation of these interaction networks by mining expression profiles of over 6000 normal and tumor samples from 14 cancer types. Our combined analysis of CLIP-Seq data and genome-wide association studies data discovered hundreds of disease-related single nucleotide polymorphisms resided in the RBP binding sites of lncRNAs. Finally, we developed interactive web implementations to provide visualization, analysis, and downloading of the aforementioned large-scale datasets. Our study represented an important step in identification and analysis of RBP-lncRNA interactions and showed that these interactions may play crucial roles in cancer and genetic diseases.