Prediction and characterization of non-coding RNAs in C. elegans by integrating conservation, secondary structure and high throughput sequencing and array data
- Zhi John Lu1,
- Kevin Y. Yip1,
- Guilin Wang1,
- Chong Shou1,
- LaDeana W. Hillier2,
- Ekta Khurana1,
- Ashish Agarwal1,
- Raymond Auerbach1,
- Joel Rozowsky1,
- Chao Cheng1,
- Masaomi Kato1,
- David M. Miller3,
- Frank Slack1,
- Michael Snyder4,
- Robert H. Waterson2,
- Valerie Reinke1 and
- Mark Gerstein1,5
- * Corresponding author; email: zlmg2{at}gersteinlab.org
Abstract
We present an integrative machine learning method, incRNA, for whole-genome identification of non-coding 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 data from the modENCODE consortium, we are able to separate known C. elegans ncRNAs from coding sequences and other genomic elements with a high level of accuracy (97% AUC on an independent validation set), and find >7,000 novel ncRNA candidates, among which >1,000 are located in the intergenic regions of C. elegans genome. Based on the validation set, we estimate that 91% of the ~7000 novel ncRNA candidates are true positives. We then analyze fifteen novel ncRNA candidates by RT-PCR, detecting the expression for fourteen. In addition, we characterize the properties of all the novel ncRNA candidates and find that they have distinct expression patterns across developmental stages and tend to use novel RNA structural families. We also find that they are often targeted by specific transcription factors (~59% of intergenic novel ncRNA candidates). Overall, our study identifies many new potential ncRNAs in C. elegans and provides a method that can be adapted to other organisms.
- Received May 7, 2010.
- Accepted December 3, 2010.
- Copyright © 2010, Cold Spring Harbor Laboratory Press
This manuscript is Open Access.
