Long noncoding RNAs in lung cancer – what we know in 2015

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Integrating Large-Scale RNA-Seq and CLIP-Seq Datasets Enables Study of lncRNA

Long non-coding RNAs (lncRNAs) are emerging as important regulatory molecules in developmental, physiological, and pathological processes. However, the precise mechanism More »

Scientists discover long-sought genetic mechanism for cancer progression

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MEG3 long noncoding RNA regulates the TGF-β pathway genes through formation of RNA-DNA triplex structures

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LncRNA Regulator Of Brown Fat Identified

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CANTATAdb – a Collection of Plant Long Non-coding RNAs

Long non-coding RNAs (lncRNAs) represent a class of potent regulators of gene expression that are found in a wide array of eukaryotes, however, our knowledge about these molecules in plants is still very limited. In particular, a number of model plant species still lack comprehensive datasets of lncRNAs and their annotations and very little is known about their biological roles. To meet these short-comings, researchers at the Adam Mickiewicz University created an online database of lncRNAs in ten model plant species. The lncRNAs were identified computationally using dozens of publicly available RNA-Seq libraries. Expression values, coding potential, sequence alignments as well as other types of data provide annotation for the identified lncRNAs. In order to better characterize them, the researchers investigated their potential roles in splicing modulation and deregulation of miRNA functions. The data are freely available for searching, browsing and downloading from an online database called CANTATAdb.

lncRNAA view of the search page, with search options, search summary and search results components marked.

Availability – CANTATAdb is located at: http://cantata.amu.edu.pl, http://yeti.amu.edu.pl/CANTATA/

  • Szcześniak MW, Rosikiewicz W, Makałowska I. (2015) CANTATAdb: a Collection of Plant Long Non-coding RNAs. Plant Cell Physiol [Epub ahead of print]. [article]

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, 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.

lncRNAA system-level overview of the deepBase v2.0 core framework. A total of 558 small RNA datasets and 478 RNA-seq datasets were retrieved from NCBI GEO or SRA database. All the small and large noncoding RNAs were identified. The expression, evolution and functions of these ncRNAs were further analyzed. All the results generated by deepBase v2.0 were deposited in MySQL relational databases and displayed in the visual browser and web pages.

Availability – deepBase v2.0 is available at: http://biocenter.sysu.edu.cn/deepBase/

  • Zheng LL, Li JH, Wu J, Sun WJ, Liu S, Wang ZL, Zhou H, Yang JH, Qu LH. (2015) deepBase v2.0: identification, expression, evolution and function of small RNAs, LncRNAs and circular RNAs from deep-sequencing data. Nucleic Acids Res [Epub ahead of print]. [article]

Integrating Large-Scale RNA-Seq and CLIP-Seq Datasets Enables Study of lncRNA


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, researchers at Sun Yat-sen University analyzed millions of RNA-binding protein (RBP) binding sites from 117 CLIP-Seq datasets generated by 50 independent studies and identified 22,735 RBP-lncRNA regulatory relationships.

The researchers 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. They 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, the researchers developed interactive web implementations to provide visualization, analysis, and downloading of the aforementioned large-scale datasets.

Availability – StarBase V2.0 is available at: http://starbase.sysu.edu.cn/rbpLncRNA.php

  • Li JH, Liu S, Zheng LL, Wu J, Sun WJ, Wang ZL, Zhou H, Qu LH, Yang JH. (2015) Discovery of Protein-lncRNA Interactions by Integrating Large-Scale CLIP-Seq and RNA-Seq Datasets. Front Bioeng Biotechnol 2:88. [article]

C-It-Loci – a knowledge database for tissue-enriched loci

Increasing evidences suggest that most of the genome is transcribed into RNAs, but many of them are not translated into proteins. All those RNAs that do not become proteins are called ‘non-coding RNAs (ncRNAs)’, which outnumbers protein-coding genes. Interestingly, these ncRNAs are shown to be more tissue specifically expressed than protein-coding genes. Given that tissue-specific expressions of transcripts suggest their importance in the expressed tissue, researchers are conducting biological experiments to elucidate the function of such ncRNAs. Owing greatly to the advancement of next-generation techniques, especially RNA-seq, the amount of high-throughput data are increasing rapidly. However, due to the complexity of the data as well as its high volume, it is not easy to re-analyze such data to extract tissue-specific expressions of ncRNAs from published datasets.

Here, researchers from Goethe University Frankfurt introduce a new knowledge database called ‘C-It-Loci’, which allows a user to screen for tissue-specific transcripts across three organisms: human, mouse and zebrafish. C-It-Loci is intuitive and easy to use to identify not only protein-coding genes but also ncRNAs from various tissues. C-It-Loci defines homology through sequence and positional conservation to allow for the extraction of species-conserved loci. C-It-Loci can be used as a starting point for further biological experiments.


Scheme of C-It-Loci. (a) Flowchart of building of C-It-Loci. All the analyzed results were imported as MySQL data tables into C-It-Loci. (b) Definition of CGP. The genomic coordinates from one protein-coding gene (‘Gene A’) to the immediately downstream protein-coding gene (‘Gene B’) are defined as one locus unit. When homologous protein-coding genes are found in another species for both protein-coding genes in the locus, this locus is defined as ‘conserved locus’, which we called ‘C-It-Loci Genomic Positions (CGP)’

Availability – C-It-Loci is freely available online without registration at http://c-it-loci.uni-frankfurt.de


  • Weirick T, John D, Dimmeler S, Uchida S. (2015) C-It-Loci: a knowledge database for tissue-enriched loci. Bioinformatics [Epub ahead of print]. [abstract]

Co-LncRNA – investigating the lncRNA combinatorial effects in GO annotations and KEGG pathways based on human RNA-Seq data

Long non-coding RNAs (lncRNAs) are emerging as key regulators of diverse biological processes and diseases. However, the combinatorial effects of these molecules in a specific biological function are poorly understood. Identifying co-expressed protein-coding genes of lncRNAs would provide ample insight into lncRNA functions.

To facilitate such an effort, researchers at Harbin Medical University, China have developed Co-LncRNA, which is a web-based computational tool that allows users to identify GO annotations and KEGG pathways that may be affected by co-expressed protein-coding genes of a single or multiple lncRNAs. LncRNA co-expressed protein-coding genes were first identified in publicly available human RNA-Seq datasets, including 241 datasets across 6560 total individuals representing 28 tissue types/cell lines. Then, the lncRNA combinatorial effects in a given GO annotations or KEGG pathways are taken into account by the simultaneous analysis of multiple lncRNAs in user-selected individual or multiple datasets, which is realized by enrichment analysis.

In addition, this software provides a graphical overview of pathways that are modulated by lncRNAs, as well as a specific tool to display the relevant networks between lncRNAs and their co-expressed protein-coding genes. Co-LncRNA also supports users in uploading their own lncRNA and protein-coding gene expression profiles to investigate the lncRNA combinatorial effects. It will be continuously updated with more human RNA-Seq datasets on an annual basis. Taken together, Co-LncRNA provides a web-based application for investigating lncRNA combinatorial effects, which could shed light on their biological roles and could be a valuable resource for this community.


Flowchart used in Co-LncRNA for investigating the combinatorial effects of lncRNAs in GO annotations and KEGG pathways.


  • Zhao Z, Bai J, Wu A, Wang Y, Zhang J, Wang Z, Li Y, Xu J, Li X. (2015) Co-LncRNA: investigating the lncRNA combinatorial effects in GO annotations and KEGG pathways based on human RNA-Seq data. Database (Oxford). 2015 Sep 10. [article]

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