| Chapter title |
Structural and Functional Annotation of Long Noncoding RNAs.
|
|---|---|
| Chapter number | 4 |
| Book title |
Bioinformatics
|
| Published in |
Methods in molecular biology, January 2017
|
| DOI | 10.1007/978-1-4939-6613-4_4 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6611-0, 978-1-4939-6613-4
|
| Authors |
Martin A. Smith, John S. Mattick, Smith, Martin A., Mattick, John S. |
| Editors |
Jonathan M. Keith |
| Abstract |
Protein-coding RNAs represent only a small fraction of the transcriptional output in higher eukaryotes. The remaining RNA species encompass a broad range of molecular functions and regulatory roles, a consequence of the structural polyvalence of RNA polymers. Albeit several classes of small noncoding RNAs are relatively well characterized, the accessibility of affordable high-throughput sequencing is generating a wealth of novel, unannotated transcripts, especially long noncoding RNAs (lncRNAs) that are derived from genomic regions that are antisense, intronic, intergenic, and overlapping protein-coding loci. Parsing and characterizing the functions of noncoding RNAs-lncRNAs in particular-is one of the great challenges of modern genome biology. Here we discuss concepts and computational methods for the identification of structural domains in lncRNAs from genomic and transcriptomic data. In the first part, we briefly review how to identify RNA structural motifs in individual lncRNAs. In the second part, we describe how to leverage the evolutionary dynamics of structured RNAs in a computationally efficient screen to detect putative functional lncRNA motifs using comparative genomics. |
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|---|---|---|
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| Unknown | 40 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 13 | 32% |
| Researcher | 7 | 17% |
| Student > Bachelor | 6 | 15% |
| Professor > Associate Professor | 3 | 7% |
| Professor | 2 | 5% |
| Other | 2 | 5% |
| Unknown | 8 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
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| Biochemistry, Genetics and Molecular Biology | 10 | 24% |
| Neuroscience | 3 | 7% |
| Computer Science | 2 | 5% |
| Psychology | 1 | 2% |
| Other | 3 | 7% |
| Unknown | 8 | 20% |