Dataset Information

LncFunNet: an integrated computational framework for identification of functional long noncoding RNAs in mouse skeletal muscle cells.

ABSTRACT: Long noncoding RNAs (lncRNAs) are key regulators of diverse cellular processes. Recent advances in high-throughput sequencing have allowed for an unprecedented discovery of novel lncRNAs. To identify functional lncRNAs from thousands of candidates for further functional validation is still a challenging task. Here, we present a novel computational framework, lncFunNet (lncRNA Functional inference through integrated Network) that integrates ChIP-seq, CLIP-seq and RNA-seq data to predict, prioritize and annotate lncRNA functions. In mouse embryonic stem cells (mESCs), using lncFunNet we not only recovered most of the functional lncRNAs known to maintain mESC pluripotency but also predicted a plethora of novel functional lncRNAs. Similarly, in mouse myoblast C2C12 cells, applying lncFunNet led to prediction of reservoirs of functional lncRNAs in both proliferating myoblasts (MBs) and differentiating myotubes (MTs). Further analyses demonstrated that these lncRNAs are frequently bound by key transcription factors, interact with miRNAs and constitute key nodes in biological network motifs. Further experimentations validated their dynamic expression profiles and functionality during myoblast differentiation. Collectively, our studies demonstrate the use of lncFunNet to annotate and identify functional lncRNAs in a given biological system.

SUBMITTER: Zhou J

PROVIDER: S-EPMC5499579 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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LncFunNet: an integrated computational framework for identification of functional long noncoding RNAs in mouse skeletal muscle cells.

Zhou Jiajian J Zhang Suyang S Wang Huating H Sun Hao H

Nucleic acids research 20170701 12

Long noncoding RNAs (lncRNAs) are key regulators of diverse cellular processes. Recent advances in high-throughput sequencing have allowed for an unprecedented discovery of novel lncRNAs. To identify functional lncRNAs from thousands of candidates for further functional validation is still a challenging task. Here, we present a novel computational framework, lncFunNet (lncRNA Functional inference through integrated Network) that integrates ChIP-seq, CLIP-seq and RNA-seq data to predict, prioriti ...[more]

PMID: 28379566

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Project description:Gene transcripts or mRNAs and long noncoding RNAs (lncRNAs) are differentially expressed during porcine skeletal muscle development. However, only a few studies have been conducted on skeletal muscle transcriptome in pigs based on timepoints according to the growth curve for porcine. Here, we investigated gene expression in Qingyu pigs at three different growth stages: the inflection point with the maximum growth rate (MGI), the inflection point of the gradually increasing stage to the rapidly increasing stage (GRI), and the inflection point of the rapidly increasing stage to the slowly increasing stage (RSI). Subsequently, we explored gene expression profiles during muscle development at the MGI, GRI and RSI stages by Ribo-Zero RNA sequencing. Qingyu pigs reached the MGI, GRI and RSI stages at 156.40, 23.82 and 288.97 days of age with 51.73, 3.14 and 107.03 kg body weight, respectively. A total of 14,530 mRNAs and 11,970 lncRNAs were identified at the three stages, and 645, 323 differentially expressed genes (DEGs) and 696, 760 differentially expressed lncRNAs (DELs) were identified in the GRI vs. MGI, and RSI vs. MGI, comparisons. Functional enrichment analysis revealed that genes involved in immune system development and energy metabolism (mainly relate to amino acid, carbohydrate and lipid) were enriched at the GRI and MGI stages, respectively, whereas genes involved in lipid metabolism were enriched at the RSI stage. We further characterized G1430, an abundant lncRNA. The full-length sequence (316 nt) of lncRNA G1430 was determined by rapid amplification of cDNA ends (RACE). Subcellular distribution analysis by quantitative real-time PCR (qRT-PCR) revealed that G1430 is a cytoplasmic lncRNA. Binding site prediction and dual luciferase assay showed that lncRNA G1430 directly binds to microRNA 133a (miR-133a). Our findings provide the basis for further investigation of the regulatory mechanisms and molecular genetics of muscle development in pigs.

Project description:Background: Adrenocortical adenocarcinoma (ACC) is known to be a relatively uncommon malignant tumor of the adrenal gland with patients having a poor prognosis. At present, few reports are available concerning the m6A modifications of lncRNAs as well as their clinical and immunological significance in the occurrence and progression of ACC. Materials and Methods: In the present research, 21 m6A-related genes were analyzed. Both multivariate and univariate Cox regression analyses were conducted to examine the prognostic m6A-related lncRNAs. A sum of 165 m6A-related lncRNAs was obtained from The Cancer Genome Atlas (TCGA) dataset. Based on the expressions of m6A-related lncRNAs, all ACC patients were classified into distinct subgroups using the consistent clustering method. Finally, m6A-related lncRNAs that were shown to have prognostic value were utilized to develop an m6A-related lncRNA risk model, which may be employed in the prediction of prognosis and survival. Results: Using TCGA data set, 26 m6A-associated lncRNAs having putative prognostic values were identified according to their expression levels, TCGA-AAC patients were classified into two clusters with the aid of consistent clustering analysis. The correlation between the two clusters was low, in which cluster1 consisted of 42% of all ACC patients. The survival analysis showed that cluster1 was associated with an unfavorable prognosis relative to cluster2. A risk model was constructed incorporating 26 m6A-associated lncRNAs that were correlated with patient prognosis. The model was subsequently validated by univariate and multivariate Cox, receiver operating characteristic (ROC) curve, and survival analyses. We also observed that the m6A-related risk model performed well in anticipating prognoses and survival status of patients with AAC. The overall survival (OS) of the high-risk cohort, as predicted by the model, was lower as opposed to that of the low-risk cohort. Conclusion: In the present research, we developed a risk model consisting of 4 m6A-related long-noncoding RNAs (lncRNAs), which can exert independent predictive values in patients with ACC. Our findings demonstrated that these 4 m6A-related lncRNAs perform integral functions in the tumor immune microenvironment, and also revealed the possibility of using these lncRNAs to guide the development of prognostic classifications and therapy approaches for ACC patients.

Dataset Information

LncFunNet: an integrated computational framework for identification of functional long noncoding RNAs in mouse skeletal muscle cells.

Publications

LncFunNet: an integrated computational framework for identification of functional long noncoding RNAs in mouse skeletal muscle cells.

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