Dataset Information

NONCODE 2016: an informative and valuable data source of long non-coding RNAs.

ABSTRACT: NONCODE (http://www.bioinfo.org/noncode/) is an interactive database that aims to present the most complete collection and annotation of non-coding RNAs, especially long non-coding RNAs (lncRNAs). The recently reduced cost of RNA sequencing has produced an explosion of newly identified data. Revolutionary third-generation sequencing methods have also contributed to more accurate annotations. Accumulative experimental data also provides more comprehensive knowledge of lncRNA functions. In this update, NONCODE has added six new species, bringing the total to 16 species altogether. The lncRNAs in NONCODE have increased from 210 831 to 527,336. For human and mouse, the lncRNA numbers are 167,150 and 130,558, respectively. NONCODE 2016 has also introduced three important new features: (i) conservation annotation; (ii) the relationships between lncRNAs and diseases; and (iii) an interface to choose high-quality datasets through predicted scores, literature support and long-read sequencing method support. NONCODE is also accessible through http://www.noncode.org/.

SUBMITTER: Zhao Y

PROVIDER: S-EPMC4702886 | biostudies-literature | 2016 Jan

REPOSITORIES: biostudies-literature

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NONCODE 2016: an informative and valuable data source of long non-coding RNAs.

Zhao Yi Y Li Hui H Fang Shuangsang S Kang Yue Y Wu Wei W Hao Yajing Y Li Ziyang Z Bu Dechao D Sun Ninghui N Zhang Michael Q MQ Chen Runsheng R

Nucleic acids research 20151119 D1

NONCODE (http://www.bioinfo.org/noncode/) is an interactive database that aims to present the most complete collection and annotation of non-coding RNAs, especially long non-coding RNAs (lncRNAs). The recently reduced cost of RNA sequencing has produced an explosion of newly identified data. Revolutionary third-generation sequencing methods have also contributed to more accurate annotations. Accumulative experimental data also provides more comprehensive knowledge of lncRNA functions. In this up ...[more]

PMID: 26586799

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Project description:BackgroundThe role of long non-coding RNAs (lncRNAs) in controlling gene expression has garnered increased interest in recent years. Sequencing projects, such as Fantom3 for mouse and H-InvDB for human, have generated abundant data on transcribed components of mammalian cells, the majority of which appear not to be protein-coding. However, much of the non-protein-coding transcriptome could merely be a consequence of 'transcription noise'. It is therefore essential to use bioinformatic approaches to identify the likely functional candidates in a high throughput manner.Principal findingsWe derived a scheme for classifying and annotating likely functional lncRNAs in mammals. Using the available experimental full-length cDNA data sets for human and mouse, we identified 78 lncRNAs that are either syntenically conserved between human and mouse, or that originate from the same protein-coding genes. Of these, 11 have significant sequence homology. We found that these lncRNAs exhibit: (i) patterns of codon substitution typical of non-coding transcripts; (ii) preservation of sequences in distant mammals such as dog and cow, (iii) significant sequence conservation relative to their corresponding flanking regions (in 50% cases, flanking regions do not have homology at all; and in the remaining, the degree of conservation is significantly less); (iv) existence mostly as single-exon forms (8/11); and, (v) presence of conserved and stable secondary structure motifs within them. We further identified orthologous protein-coding genes that are contributing to the pool of lncRNAs; of which, genes implicated in carcinogenesis are significantly over-represented.ConclusionOur comparative mammalian genomics approach coupled with evolutionary analysis identified a small population of conserved long non-protein-coding RNAs (lncRNAs) that are potentially functional across Mammalia. Additionally, our analysis indicates that amongst the orthologous protein-coding genes that produce lncRNAs, those implicated in cancer pathogenesis are significantly over-represented, suggesting that these lncRNAs could play an important role in cancer pathomechanisms.

Project description:Acute myeloid leukemia (AML) represents 80% of adult leukemias and 15-20% of childhood leukemias. AML are characterized by the presence of 20% blasts or more in the bone marrow, or defining cytogenetic abnormalities. Laboratory diagnoses of myelodysplastic syndromes (MDS) depend on morphological changes based on dysplasia in peripheral blood and bone marrow, including peripheral blood smears, bone marrow aspirate smears, and bone marrow biopsies. As leukemic cells are not functional, the patient develops anemia, neutropenia, and thrombocytopenia, leading to fatigue, recurrent infections, and hemorrhage. The genetic background and associated mutations in AML blasts determine the clinical course of the disease. Over the last decade, non-coding RNAs transcripts that do not codify for proteins but play a role in regulation of functions have been shown to have multiple applications in the diagnosis, prognosis and therapeutic approach of various types of cancers, including myeloid malignancies. After a comprehensive review of current literature, we found reports of multiple long non-coding RNAs (lncRNAs) that can differentiate between AML types and how their exogenous modulation can dramatically change the behavior of AML cells. These lncRNAs include: H19, LINC00877, RP11-84C10, CRINDE, RP11848P1.3, ZNF667-AS1, AC111000.4-202, SFMBT2, LINC02082-201, MEG3, AC009495.2, PVT1, HOTTIP, SNHG5, and CCAT1. In addition, by performing an analysis on available AML data in The Cancer Genome Atlas (TCGA), we found 10 lncRNAs with significantly differential expression between patients in favorable, intermediate/normal, or poor cytogenetic risk categories. These are: DANCR, PRDM16-DT, SNHG6, OIP5-AS1, SNHG16, JPX, FTX, KCNQ1OT1, TP73-AS1, and GAS5. The identification of a molecular signature based on lncRNAs has the potential for have deep clinical significance, as it could potentially help better define the evolution from low-grade MDS to high-grade MDS to AML, changing the course of therapy. This would allow clinicians to provide a more personalized, patient-tailored therapeutic approach, moving from transfusion-based therapy, as is the case for low-grade MDS, to the introduction of azacytidine-based chemotherapy or allogeneic stem cell transplantation, which is the current treatment for high-grade MDS.

Dataset Information

NONCODE 2016: an informative and valuable data source of long non-coding RNAs.

Publications

NONCODE 2016: an informative and valuable data source of long non-coding RNAs.

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