Recently, more than 1000 large intergenic noncoding RNAs (lincRNAs) have been reported. These RNAs are evolutionarily conserved in mammalian genomes and thus presumably function in diverse biological processes. Here, we report the identification of lincRNAs that are regulated by p53. One of these lincRNAs (lincRNA-p21) serves as a repressor in p53-dependent transcriptional responses. Inhibition of lincRNA-p21 affects the expression of hundreds of gene targets enriched for genes normally repressed by p53. The observed transcriptional repression by lincRNA-p21 is mediated through the physical association with hnRNP-K. This interaction is required for proper genomic localization of hnRNP-K at repressed genes and regulation of p53 mediates apoptosis. We propose a model whereby transcription factors activate lincRNAs that serve as key repressors by physically associating with repressive complexes and modulate their localization to sets of previously active genes.

A Large Intergenic Noncoding RNA Induced by p53 Mediates Global Gene Repression in the p53 Response

As high-throughput transcriptome sequencing provides evidence for novel transcripts in many species, there is a renewed need for accurate methods to classify small genomic regions as protein-coding or non-coding. We present PhyloCSF, a novel comparative genomics method that analyzes a multi-species nucleotide sequence alignment to determine whether it is likely to represent a conserved protein-coding region, based on a formal statistical comparison of phylogenetic codon models. We show that PhyloCSF's classification performance in 12-species Drosophila genome alignments exceeds all other methods we compared in a previous study, and we provide a software implementation for use by the community. We anticipate that this method will be widely applicable as the transcriptomes of many additional species, tissues, and subcellular compartments are sequenced, particularly in the context of ENCODE and modENCODE.

PhyloCSF: a comparative genomics method to distinguish protein-coding and non-coding regions

:Sites of transcription of polyadenylated and nonpolyadenylated RNAs for 10 human chromosomes were mapped at 5–base pair resolution in eight cell lines. Unannotated, nonpolyadenylated transcripts comprise the major proportion of the transcriptional output of the human genome. Of all transcribed sequences, 19.4, 43.7, and 36.9% were observed to be polyadenylated, nonpolyadenylated, and bimorphic, respectively. Half of all transcribed sequences are found only in the nucleus and for the most part are unannotated. Overall, the transcribed portions of the human genome are predominantly composed of interlaced networks of both poly Aþ and poly A– annotated transcripts and unannotated transcripts of unknown function. This organization has important implications for interpreting genotype-phenotype associations, regulation of gene expression, and the definition of a gene. The current classification of protein-coding and noncoding genomic regions is based on intron-exon structures of well-characterized protein-coding genes. Noncoding genomic regions, which account for 98% to 99% of the human genome, consist of introns found within protein-coding transcripts and the intergenic re

Transcriptional Maps of 10 Human Chromosomes at

Many non-coding RNA genes and cis-acting regulatory elements of mRNAs contain RNA secondary structures that are critical for their function. Such functional RNAs can be predicted on the basis of thermodynamic stability and evolutionary conservation. We present a web server that uses the RNAz algorithm to detect functional RNA structures in multiple alignments of nucleotide sequences. The server provides access to a complete and fully automatic analysis pipeline that allows not only to analyze single alignments in a variety of formats, but also to conduct complex screens of large genomic regions. Results are presented on a website that is illustrated by various structure representations and can be downloaded for local view. The web server is available at: rna.tbi.univie.ac.at/RNAz.

The RNAz web server: prediction of thermodynamically stable and evolutionarily conserved RNA structures

We identified lncRNA candidates in four economically important cereals (Poaceae): 7,196 in Zea mays, 1,974 in Sorghum bicolor, 4,236 in Setaria italica and 2,542 in Oryza sativa, using computational methods; we then compared these RNAs across the species. Our approach involved screening a reference-guided transcriptome assembly of RNA-Seq data for RNAs that were at least 200 bases in length with at most 70 amino acids in open reading frames and with a lack of homology in the Uniprot database. A sequence composition analysis of the lncRNA candidates, in comparison to protein-coding transcripts, highlighted distinctive features, including a low GC content, a paucity of introns and a hexamer usage bias, consistent with what has been found for mammalian lncRNAs. RepeatMasker identified from 1% (rice) to 19% (maize) of the candidate lncRNAs as being transcribed from transposable elements, based on a dataset with 3,853 transposable elements. We compared the candidate lncRNAs with 25,141 miRNAs from miRBase, and found that less than 1% of them could be potential miRNA precursors. The cross-species comparisons, which included a sequence- and structure-based lncRNA homology search, synteny analysis, and lncRNA secondary structure prediction, uncovered some limited sequence similarity. In sub-regions, we predicted conserved secondary structures using covariation analysis. We used the comparative sequence and synteny analyses to predict the existence of lncRNAs in S. italica; experimental tests confirmed the presence of these RNAs. Our results are consistent with a model of very rapid evolution of lncRNAs.

Genome-wide identification and evolutionary analysis of long non-coding RNAs in cereals

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Ying Sun

Author Tools

Chat about Author

Papers

Genome-wide identification and evolutionary analysis of long non-coding RNAs in cereals