Large-scale mapping of mammalian transcriptomes identifies conserved genes associated with different cell states

Yang, Yang; Yang, Yucheng; Yuan, Jiapei; Lu, Zhi John; Li, Jingyi Jessica

doi:10.1093/nar/gkw1256

Cited by 11 publications

(14 citation statements)

References 94 publications

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“…We adapted a published statistical method, Transcriptome Overlap Measure (TROM), to integrate a number of public RNA‐seq data sets from various samples to find closely related samples (Yang et al ., ). This method identified associated genes or lncRNAs with the stress conditions, as these coding genes or lncRNAs have a relatively high abundance in the sample under stress, but relatively low abundance in other samples.…”

Section: Methodsmentioning

confidence: 97%

Stress‐responsive regulation of long non‐coding RNA polyadenylation in Oryza sativa

Yuan

Yang

et al. 2018

The Plant Journal

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Recently, long non-coding RNAs (lncRNAs) have been demonstrated to be involved in many biological processes of plants; however, a systematic study on transcriptional and, in particular, post-transcriptional regulation of stress-responsive lncRNAs in Oryza sativa (rice) is lacking. We sequenced three types of RNA libraries (poly(A)+, poly(A)- and nuclear RNAs) under four abiotic stresses (cold, heat, drought and salt). Based on an integrative bioinformatics approach and ~200 high-throughput data sets, ~170 of which have been published, we revealed over 7000 lncRNAs, nearly half of which were identified for the first time. Notably, we found that the majority of the ~500 poly(A) lncRNAs that were differentially expressed under stress were significantly downregulated, but approximately 25% were found to have upregulated non-poly(A) forms. Moreover, hundreds of lncRNAs with downregulated polyadenylation (DPA) tend to be highly conserved, show significant nuclear retention and are co-expressed with protein-coding genes that function under stress. Remarkably, these DPA lncRNAs are significantly enriched in quantitative trait loci (QTLs) for stress tolerance or development, suggesting their potential important roles in rice growth under various stresses. In particular, we observed substantially accumulated DPA lncRNAs in plants exposed to drought and salt, which is consistent with the severe reduction of RNA 3'-end processing factors under these conditions. Taken together, the results of this study reveal that polyadenylation and subcellular localization of many rice lncRNAs are likely to be regulated at the post-transcriptional level. Our findings strongly suggest that many upregulated/downregulated lncRNAs previously identified by traditional RNA-seq analyses need to be carefully reviewed to assess the influence of post-transcriptional modification.

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Section: Methodsmentioning

confidence: 97%

Stress‐responsive regulation of long non‐coding RNA polyadenylation in Oryza sativa

Yuan

Yang

et al. 2018

The Plant Journal

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“…To facilitate in-depth investigations of RNA functions and regulations by RISE users, we annotated the genes and interacting regions involved in the RRIs with an array of molecular details. We retrieved RBP binding sites from CLIPdb ( 30 ), RNA editing sites from RADAR ( 31 ) and DARNED ( 32 ), RNA modification sites from RMBase ( 33 ), single nucleotide polymorphisms (SNPs) from dbSNP version 142 ( 34 ), pan-cancer somatic mutations ( 35 ) and gene expression levels in various cell and tissue types from recent publications ( 36 ). Finally, the integrative visualization of the RRIs was implemented using a Circos plot ( 37 ) and a set of table views.…”

Section: Data Collection and Analysismentioning

confidence: 99%

RISE: a database of RNA interactome from sequencing experiments

Gong

Шао

et al. 2017

Nucleic Acids Research

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We present RISE (http://rise.zhanglab.net), a database of RNA Interactome from Sequencing Experiments. RNA-RNA interactions (RRIs) are essential for RNA regulation and function. RISE provides a comprehensive collection of RRIs that mainly come from recent transcriptome-wide sequencing-based experiments like PARIS, SPLASH, LIGR-seq, and MARIO, as well as targeted studies like RIA-seq, RAP-RNA and CLASH. It also includes interactions aggregated from other primary databases and publications. The RISE database currently contains 328,811 RNA-RNA interactions mainly in human, mouse and yeast. While most existing RNA databases mainly contain interactions of miRNA targeting, notably, more than half of the RRIs in RISE are among mRNA and long non-coding RNAs. We compared different RRI datasets in RISE and found limited overlaps in interactions resolved by different techniques and in different cell lines. It may suggest technology preference and also dynamic natures of RRIs. We also analyzed the basic features of the human and mouse RRI networks and found that they tend to be scale-free, small-world, hierarchical and modular. The analysis may nominate important RNAs or RRIs for further investigation. Finally, RISE provides a Circos plot and several table views for integrative visualization, with extensive molecular and functional annotations to facilitate exploration of biological functions for any RRI of interest.

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“… 93 Another approach extended the discovery of cell type-regulatory modules by looking at gene expression in other mammalian species, and discovered many primate-specific long non-coding RNAs (lncRNAs) with putative cell type-specific functions. 94 Indeed, lncRNAs are also expressed in a cell type-specific pattern, 26 , 95 and are good candidates for cell type-specific control. 96 , 97 However, a comprehensive explanation of how TFs, lincRNAs, and other non-coding RNAs can control cell type, and why transdifferentiation is rare in the adult organism remains unclear.…”

Section: Exogenous Expression Of Transcription Factors Can Drive Convmentioning

confidence: 99%

Genomic and molecular control of cell type and cell type conversions

et al. 2017

Cell Regeneration

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Organisms are made of a limited number of cell types that combine to form higher order tissues and organs. Cell types have traditionally been defined by their morphologies or biological activity, yet the underlying molecular controls of cell type remain unclear. The onset of single cell technologies, and more recently genomics (particularly single cell genomics), has substantially increased the understanding of the concept of cell type, but has also increased the complexity of this understanding. These new technologies have added a new genome wide molecular dimension to the description of cell type, with genome-wide expression and epigenetic data acting as a cell type ‘fingerprint’ to describe the cell state. Using these genomic fingerprints cell types are being increasingly defined based on specific genomic and molecular criteria, without necessarily a distinct biological function. In this review, we will discuss the molecular definitions of cell types and cell type control, and particularly how endogenous and exogenous transcription factors can control cell types and cell type conversions.

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Large-scale mapping of mammalian transcriptomes identifies conserved genes associated with different cell states

Cited by 11 publications

References 94 publications

Stress‐responsive regulation of long non‐coding RNA polyadenylation in Oryza sativa

Stress‐responsive regulation of long non‐coding RNA polyadenylation in Oryza sativa

RISE: a database of RNA interactome from sequencing experiments

Genomic and molecular control of cell type and cell type conversions

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