Prediction and characterization of noncoding RNAs in C. elegans by integrating conservation, secondary structure, and high-throughput sequencing and array data

We present an integrative machine learning method, incRNA, for whole-genome identification of noncoding RNAs (ncRNAs). It combines a large amount of expression data, RNA secondary-structure stability, and evolutionary conservation at the protein and nucleic-acid level. Using the incRNA model and dat...

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Detalhes bibliográficos
Main Authors: Lu, Zhi John, Yip, Kevin Y., Wang, Guilin, Shou, Chong, Hillier, LaDeana W., Khurana, Ekta, Agarwal, Ashish, Auerbach, Raymond, Rozowsky, Joel, Cheng, Chao, Kato, Masaomi, Miller, David M., Slack, Frank, Snyder, Michael, Waterston, Robert H., Reinke, Valerie, Gerstein, Mark B.
Formato: Artigo
Idioma:Inglês
Publicado em: Cold Spring Harbor Laboratory Press 2011
Assuntos:
Method
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC3032931/
https://ncbi.nlm.nih.gov/pubmed/21177971
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1101/gr.110189.110
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