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RNA secondary structure prediction by Statistical Sampling and Applications

Abstract Number:

2242 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Ye Ding (1)

Institutions:

(1) Wadsworth Center, New York State Department of Health, N/A

First Author:

Ye Ding  
Wadsworth Center, New York State Department of Health

Presenting Author:

Ye Ding  
Wadsworth Center, New York State Department of Health

Abstract Text:

RNAs are versatile regulators of gene expression. RNA secondary structures are known to be important for regulatory functions by various types of RNAs. We developed a statistical algorithm to sample rigorously and exactly from the Boltzmann ensemble of secondary structures. The algorithm is the basis for our Sfold RNA folding software (http://sfold.wadsworth.org).

MicroRNAs are small non-coding RNAs that repress protein synthesis by binding to target mRNAs in multicellular eukaryotes. N6-methyladenosine (m6A) is the most prevalent modification in eukaryotic messenger RNAs. Through statistical analyses of high throughput data, we found that the level of miRNA-mediated target suppression is significantly enhanced when m6A is present on target mRNAs, suggesting functional significance of m6A modification in posttranscriptional gene regulation by microRNAs. We also found that methylated targets have more stable structure than non-methylated targets. We propose a model in which m6A alters local target secondary structure to increase accessibility for efficient binding by Argonaute proteins, leading to enhanced miRNA-mediated regulation.

Keywords:

RNA|Secondary structure prediction|algorithm| | |

Sponsors:

Section on Statistics in Genomics and Genetics

Tracks:

Miscellaneous

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