Dataset Information

Computational identification of putative lincRNAs in mouse embryonic stem cell.

ABSTRACT: As the regulatory factors, lncRNAs play critical roles in embryonic stem cells. And lincRNAs are most widely studied lncRNAs, however, there might still might exist a large member of uncovered lncRNAs. In this study, we constructed the de novo assembly of transcriptome to detect 6,701 putative long intergenic non-coding transcripts (lincRNAs) expressed in mouse embryonic stem cells (ESCs), which might be incomplete with the lack coverage of 5' ends assessed by CAGE peaks. Comparing the TSS proximal regions between the known lincRNAs and their closet protein coding transcripts, our results revealed that the lincRNA TSS proximal regions are associated with the characteristic genomic and epigenetic features. Subsequently, 1,293 lincRNAs were corrected at their 5' ends using the putative lincRNA TSS regions predicted by the TSS proximal region prediction model based on genomic and epigenetic features. Finally, 43 putative lincRNAs were annotated by Gene Ontology terms. In conclusion, this work provides a novel catalog of mouse ESCs-expressed lincRNAs with the relatively complete transcript length, which might be useful for the investigation of transcriptional and post-transcriptional regulation of lincRNA in mouse ESCs and even mammalian development.

SUBMITTER: Liu H

PROVIDER: S-EPMC5054606 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Computational identification of putative lincRNAs in mouse embryonic stem cell.

Liu Hui H Lyu Jie J Liu Hongbo H Gao Yang Y Guo Jing J He Hongjuan H Han Zhengbin Z Zhang Yan Y Wu Qiong Q

Scientific reports 20161007

As the regulatory factors, lncRNAs play critical roles in embryonic stem cells. And lincRNAs are most widely studied lncRNAs, however, there might still might exist a large member of uncovered lncRNAs. In this study, we constructed the de novo assembly of transcriptome to detect 6,701 putative long intergenic non-coding transcripts (lincRNAs) expressed in mouse embryonic stem cells (ESCs), which might be incomplete with the lack coverage of 5' ends assessed by CAGE peaks. Comparing the TSS proxi ...[more]

PMID: 27713513

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Project description:BackgroundLittle is known about the genes that drive embryonic stem cell differentiation. However, such knowledge is necessary if we are to exploit the therapeutic potential of stem cells. To uncover the genetic determinants of mouse embryonic stem cell (mESC) differentiation, we have generated and analyzed 11-point time-series of DNA microarray data for three biologically equivalent but genetically distinct mESC lines (R1, J1, and V6.5) undergoing undirected differentiation into embryoid bodies (EBs) over a period of two weeks.ResultsWe identified the initial 12 hour period as reflecting the early stages of mESC differentiation and studied probe sets showing consistent changes of gene expression in that period. Gene function analysis indicated significant up-regulation of genes related to regulation of transcription and mRNA splicing, and down-regulation of genes related to intracellular signaling. Phylogenetic analysis indicated that the genes showing the largest expression changes were more likely to have originated in metazoans. The probe sets with the most consistent gene changes in the three cell lines represented 24 down-regulated and 12 up-regulated genes, all with closely related human homologues. Whereas some of these genes are known to be involved in embryonic developmental processes (e.g. Klf4, Otx2, Smn1, Socs3, Tagln, Tdgf1), our analysis points to others (such as transcription factor Phf21a, extracellular matrix related Lama1 and Cyr61, or endoplasmic reticulum related Sc4mol and Scd2) that have not been previously related to mESC function. The majority of identified functions were related to transcriptional regulation, intracellular signaling, and cytoskeleton. Genes involved in other cellular functions important in ESC differentiation such as chromatin remodeling and transmembrane receptors were not observed in this set.ConclusionOur analysis profiles for the first time gene expression at a very early stage of mESC differentiation, and identifies a functional and phylogenetic signature for the genes involved. The data generated constitute a valuable resource for further studies. All DNA microarray data used in this study are available in the StemBase database of stem cell gene expression data 1 and in the NCBI's GEO database.

Dataset Information

Computational identification of putative lincRNAs in mouse embryonic stem cell.

Publications

Computational identification of putative lincRNAs in mouse embryonic stem cell.

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