Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses
Name
Regev_Integrative annotation.pdf
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1.45 MB
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Author(s)
Cabili, Moran N.
Trapnell, Cole
Goff, Loyal
Koziol, Magdalena J.
Tazon-Vega, Barbara
Regev, Aviv
Rinn, John L.
Date Issued
September 2011
Journal
Genes & Development
Publisher
Cold Spring Harbor Laboratory Press
Citation
Cabili, M. N., C. Trapnell, L. Goff, M. Koziol, B. Tazon-Vega, A. Regev, and J. L. Rinn. “Integrative annotation of human large intergenic noncoding RNAs reveals global properties and specific subclasses.” Genes & Development 25, no. 18 (September 21, 2011): 1915-1927.
Version
Final published version
Abstract
Large intergenic noncoding RNAs (lincRNAs) are emerging as key regulators of diverse cellular processes. Determining the function of individual lincRNAs remains a challenge. Recent advances in RNA sequencing (RNA-seq) and computational methods allow for an unprecedented analysis of such transcripts. Here, we present an integrative approach to define a reference catalog of >8000 human lincRNAs. Our catalog unifies previously existing annotation sources with transcripts we assembled from RNA-seq data collected from ∼4 billion RNA-seq reads across 24 tissues and cell types. We characterize each lincRNA by a panorama of >30 properties, including sequence, structural, transcriptional, and orthology features. We found that lincRNA expression is strikingly tissue-specific compared with coding genes, and that lincRNAs are typically coexpressed with their neighboring genes, albeit to an extent similar to that of pairs of neighboring protein-coding genes. We distinguish an additional subset of transcripts that have high evolutionary conservation but may include short ORFs and may serve as either lincRNAs or small peptides. Our integrated, comprehensive, yet conservative reference catalog of human lincRNAs reveals the global properties of lincRNAs and will facilitate experimental studies and further functional classification of these genes.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Biology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1101/gad.17446611
