Dataset Information

Prediction of novel long non-coding RNAs based on RNA-Seq data of mouse Klf1 knockout study.

ABSTRACT: BACKGROUND: Study on long non-coding RNAs (lncRNAs) has been promoted by high-throughput RNA sequencing (RNA-Seq). However, it is still not trivial to identify lncRNAs from the RNA-Seq data and it remains a challenge to uncover their functions. RESULTS: We present a computational pipeline for detecting novel lncRNAs from the RNA-Seq data. First, the genome-guided transcriptome reconstruction is used to generate initially assembled transcripts. The possible partial transcripts and artefacts are filtered according to the quantified expression level. After that, novel lncRNAs are detected by further filtering known transcripts and those with high protein coding potential, using a newly developed program called lncRScan. We applied our pipeline to a mouse Klf1 knockout dataset, and discussed the plausible functions of the novel lncRNAs we detected by differential expression analysis. We identified 308 novel lncRNA candidates, which have shorter transcript length, fewer exons, shorter putative open reading frame, compared with known protein-coding transcripts. Of the lncRNAs, 52 large intergenic ncRNAs (lincRNAs) show lower expression level than the protein-coding ones and 13 lncRNAs represent significant differential expression between the wild-type and Klf1 knockout conditions. CONCLUSIONS: Our method can predict a set of novel lncRNAs from the RNA-Seq data. Some of the lncRNAs are showed differentially expressed between the wild-type and Klf1 knockout strains, suggested that those novel lncRNAs can be given high priority in further functional studies.

SUBMITTER: Sun L

PROVIDER: S-EPMC3577497 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Prediction of novel long non-coding RNAs based on RNA-Seq data of mouse Klf1 knockout study.

Sun Lei L Zhang Zhihua Z Bailey Timothy L TL Perkins Andrew C AC Tallack Michael R MR Xu Zhao Z Liu Hui H

BMC bioinformatics 20121213

<h4>Background</h4>Study on long non-coding RNAs (lncRNAs) has been promoted by high-throughput RNA sequencing (RNA-Seq). However, it is still not trivial to identify lncRNAs from the RNA-Seq data and it remains a challenge to uncover their functions.<h4>Results</h4>We present a computational pipeline for detecting novel lncRNAs from the RNA-Seq data. First, the genome-guided transcriptome reconstruction is used to generate initially assembled transcripts. The possible partial transcripts and ar ...[more]

PMID: 23237380

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Project description:Background:Nrf2 (nuclear factor, erythroid 2 like 2) is believed to play a major role in neurodegenerative diseases. The present study attempts to investigate the hippocampal circRNA and lncRNA expression profiles associated with Nrf2-mediated neuroprotection. Methods:The hippocampal mRNA, circRNA and lncRNA expression profiles of Nrf2 (-/-) mice were determined by a microarray analysis. Bioinformatics analyses, including identification of differentially expressed mRNAs (DEmRNAs), circRNAs (DEcircRNAs) and lncRNAs (DElncRNAs), DEcircRNA-miRNA-DEmRNA interaction network construction, DElncRNA-DEmRNA co-expression network construction, and biological function annotation, were conducted. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to validate the dysregulated expression of circRNAs and lncRNAs derived from the microarray data of the hippocampus of Nrf2 (-/-) mice. Results:Compared to wild-type Nrf2 (+/+) mice, 412 DEmRNAs (109 up- and 303 down-regulated mRNAs), 1279 DEcircRNAs (632 up- and 647 down-regulated circRNAs), and 303 DElncRNAs (50 up- and 253 down-regulated lncRNAs) were identified in the hippocampus of Nrf2 (-/-) mice. Additionally, in the qRT-PCR validation results, the expression patterns of selected DEcircRNAs and DElncRNAs were generally consistent with results in the microarray data. The DEcircRNA-miRNA-DEmRNA interaction networks revealed that mmu_circRNA_44531, mmu_circRNA_34132, mmu_circRNA_000903, mmu_circRNA_018676, mmu_circRNA_45901, mmu_circRNA_33836, mmu_circRNA_ 34137, mmu_circRNA_34106, mmu_circRNA_008691, and mmu_circRNA_003237 were predicted to compete with 47, 54, 45, 57, 63, 81, 121, 85, 181, and 43 DEmRNAs, respectively. ENSMUST00000125413, NR_028123, uc008nfy.1, AK076764, AK142725, AK080547, and AK035903 were co-expressed with 178, 89, 149, 179, 142, 55, and 112 DEmRNAs in the Nrf2 (-/-) hippocampus, respectively. Conclusion:Our study might contribute to exploring the key circRNAs and lncRNAs associated with Nrf2-mediated neuroprotection.

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Prediction of novel long non-coding RNAs based on RNA-Seq data of mouse Klf1 knockout study.

Publications

Prediction of novel long non-coding RNAs based on RNA-Seq data of mouse Klf1 knockout study.

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