POSTAR: a platform for exploring post-transcriptional regulation coordinated by RNA-binding proteins.

We present POSTAR (http://POSTAR.ncrnalab.org), a resource of POST-trAnscriptional Regulation coordinated by RNA-binding proteins (RBPs). Precise characterization of post-transcriptional regulatory maps has accelerated dramatically in the past few years. Based on new studies and resources, POSTAR supplies the largest collection of experimentally probed (∼23 million) and computationally predicted (approximately 117 million) RBP binding sites in the human and mouse transcriptomes. POSTAR annotates every transcript and its RBP binding sites using extensive information regarding various molecular regulatory events (e.g., splicing, editing, and modification), RNA secondary structures, disease-associated variants, and gene expression and function. Moreover, POSTAR provides a friendly, multi-mode, integrated search interface, which helps users to connect multiple RBP binding sites with post-transcriptional regulatory events, phenotypes, and diseases. Based on our platform, we were able to obtain novel insights into post-transcriptional regulation, such as the putative association between CPSF6 binding, RNA structural domains, and Li-Fraumeni syndrome SNPs. In summary, POSTAR represents an early effort to systematically annotate post-transcriptional regulatory maps and explore the putative roles of RBPs in human diseases.

Large-scale mapping of mammalian transcriptomes identifies conserved genes associated with different cell states.

Distinguishing cell states based only on gene expression data remains a challenging task. This is true even for analyses within a species. In cross-species comparisons, the results obtained by different groups have varied widely. Here, we integrate RNA-seq data from more than 40 cell and tissue types of four mammalian species to identify sets of associated genes as indicators for specific cell states in each species. We employ a statistical method, TROM, to identify both protein-coding and non-coding indicators. Next, we map the cell states within each species and also between species using these indicator genes. We recapitulate known phenotypic similarity between related cell and tissue types and reveal molecular basis for their similarity. We also report novel associations between several tissues and cell types with functional support. Moreover, our identified conserved associated genes are found to be a good resource for studying cell differentiation and reprogramming. Lastly, long non-coding RNAs can serve well as associated genes to indicate cell states. We further infer the biological functions of those non-coding associated genes based on their co-expressed protein-coding genes. This study demonstrates that combining statistical modeling with public RNA-seq data can be powerful for improving our understanding of cell identity control.

A common set of distinct features that characterize noncoding RNAs across multiple species.

To find signature features shared by various ncRNA sub-types and characterize novel ncRNAs, we have developed a method, RNAfeature, to investigate >600 sets of genomic and epigenomic data with various evolutionary and biophysical scores. RNAfeature utilizes a fine-tuned intra-species wrapper algorithm that is followed by a novel feature selection strategy across species. It considers long distance effect of certain features (e.g. histone modification at the promoter region). We finally narrow down on 10 informative features (including sequences, structures, expression profiles and epigenetic signals). These features are complementary to each other and as a whole can accurately distinguish canonical ncRNAs from CDSs and UTRs (accuracies: >92% in human, mouse, worm and fly). Moreover, the feature pattern is conserved across multiple species. For instance, the supervised 10-feature model derived from animal species can predict ncRNAs in Arabidopsis (accuracy: 82%). Subsequently, we integrate the 10 features to define a set of noncoding potential scores, which can identify, evaluate and characterize novel noncoding RNAs. The score covers all transcribed regions (including unconserved ncRNAs), without requiring assembly of the full-length transcripts. Importantly, the noncoding potential allows us to identify and characterize potential functional domains with feature patterns similar to canonical ncRNAs (e.g. tRNA, snRNA, miRNA, etc) on ∼70% of human long ncRNAs (lncRNAs).

CLIPdb: a CLIP-seq database for protein-RNA interactions.

BACKGROUND: RNA-binding proteins (RBPs) play essential roles in gene expression regulation through their interactions with RNA transcripts, including coding, canonical non-coding and long non-coding RNAs. Large amounts of crosslinking immunoprecipitation (CLIP)-seq data (including HITS-CLIP, PAR-CLIP, and iCLIP) have been recently produced to reveal transcriptome-wide binding sites of RBPs at the single-nucleotide level. DESCRIPTION: Here, we constructed a database, CLIPdb, to describe RBP-RNA interactions based on 395 publicly available CLIP-seq data sets for 111 RBPs from four organisms: human, mouse, worm and yeast. We consistently annotated the CLIP-seq data sets and RBPs, and developed a user-friendly interface for rapid navigation of the CLIP-seq data. We applied a unified computational method to identify transcriptome-wide binding sites, making the binding sites directly comparable and the data available for integration across different CLIP-seq studies. The high-resolution binding sites of the RBPs can be visualized on the whole-genome scale using a browser. In addition, users can browse and download the identified binding sites of all profiled RBPs by querying genes of interest, including both protein coding genes and non-coding RNAs. CONCLUSION: Manually curated metadata and uniformly identified binding sites of publicly available CLIP-seq data sets will be a foundation for further integrative and comparative analyses. With maintained up-to-date data sets and improved functionality, CLIPdb ( http://clipdb.ncrnalab.org ) will be a valuable resource for improving the understanding of post-transcriptional regulatory networks.

Identification of high-confidence RNA regulatory elements by combinatorial classification of RNA-protein binding sites.

Crosslinking immunoprecipitation sequencing (CLIP-seq) technologies have enabled researchers to characterize transcriptome-wide binding sites of RNA-binding protein (RBP) with high resolution. We apply a soft-clustering method, RBPgroup, to various CLIP-seq datasets to group together RBPs that specifically bind the same RNA sites. Such combinatorial clustering of RBPs helps interpret CLIP-seq data and suggests functional RNA regulatory elements. Furthermore, we validate two RBP-RBP interactions in cell lines. Our approach links proteins and RNA motifs known to possess similar biochemical and cellular properties and can, when used in conjunction with additional experimental data, identify high-confidence RBP groups and their associated RNA regulatory elements.

Comprehensive analysis of long non-coding RNAs highlights their spatio-temporal expression patterns and evolutional conservation in Sus scrofa.

Despite modest sequence conservation and rapid evolution, long non-coding RNAs (lncRNAs) appear to be conserved in expression pattern and function. However, analysis of lncRNAs across tissues and developmental stages remains largely uncharacterized in mammals. Here, we systematically investigated the lncRNAs of the Guizhou miniature pig (Sus scrofa), which was widely used as biomedical model. We performed RNA sequencing across 9 organs and 3 developmental skeletal muscle, and developed a filtering pipeline to identify 10,813 lncRNAs (9,075 novel). Conservation patterns analysis revealed that 57% of pig lncRNAs showed homology to humans and mice based on genome alignment. 5,455 lncRNAs exhibited typical hallmarks of regulatory molecules, such as high spatio-temporal specificity. Notably, conserved lncRNAs exhibited higher tissue specificity than pig-specific lncRNAs and were significantly enriched in testis and ovary. Weighted co-expression network analysis revealed a set of conserved lncRNAs that are likely involved in postnatal muscle development. Based on the high degree of similarity in the structure, organization, and dynamic expression of pig lncRNAs compared with human and mouse lncRNAs, we propose that these lncRNAs play an important role in organ physiology and development in mammals. Our results provide a resource for studying animal evolution, morphological complexity, breeding, and biomedical research.

Recurrently deregulated lncRNAs in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) cells often invade the portal venous system and subsequently develop into portal vein tumour thrombosis (PVTT). Long noncoding RNAs (lncRNAs) have been associated with HCC, but a comprehensive analysis of their specific association with HCC metastasis has not been conducted. Here, by analysing 60 clinical samples' RNA-seq data from 20 HCC patients, we have identified and characterized 8,603 candidate lncRNAs. The expression patterns of 917 recurrently deregulated lncRNAs are correlated with clinical data in a TCGA cohort and published liver cancer data. Matched array data from the 60 samples show that copy number variations (CNVs) and alterations in DNA methylation contribute to the observed recurrent deregulation of 235 lncRNAs. Many recurrently deregulated lncRNAs are enriched in co-expressed clusters of genes related to cell adhesion, immune response and metabolic processes. Candidate lncRNAs related to metastasis, such as HAND2-AS1, were further validated using RNAi-based loss-of-function assays. Thus, we provide a valuable resource of functional lncRNAs and biomarkers associated with HCC tumorigenesis and metastasis.

CCG: an integrative resource of cancer protein-coding genes and long noncoding RNAs.

The identification of cancer genes remains a main aim of cancer research. With the advances of high-throughput sequencing technologies, thousands of novel cancer genes were identified through recurrent mutation analyses and differential expression analyses between normal tissues and tumors in large populations. Many databases were developed to document the cancer genes. However, no public database providing both cancer protein-coding genes and cancer lncRNAs is available presently. Here, we present the Catalogue of Cancer Genes (CCG) database (http://ccg.xingene.net), a catalogue of cancer genes. It includes both well-supported and candidate cancer protein-coding genes and cancer lncRNAs collected from literature search and public databases. In addition, uniform genomic aberration information (such as somatic mutation and copy number variation) and drug-gene interactions were assigned to cancer genes in the database. CCG represents an effort on integrative assembly of well-supported and candidate cancer protein-coding and long noncoding RNA genes and takes advantages of high-throughput sequencing results on large populations. With the help of CCG, users can easily access a comprehensive list of cancer genes as well as genomic aberration related with these genes. The availability of integrative information will facilitate the understanding of cancer mechanisms. In addition, drug-gene information in CCG provides a useful guide to the development of new anti-cancer drugs and selection of rational combination therapies.