Data-directed RNA secondary structure prediction using probabilistic modeling.

ABSTRACT: Structure dictates the function of many RNAs, but secondary RNA structure analysis is either labor intensive and costly or relies on computational predictions that are often inaccurate. These limitations are alleviated by integration of structure probing data into prediction algorithms. However, existing algorithms are optimized for a specific type of probing data. Recently, new chemistries combined with advances in sequencing have facilitated structure probing at unprecedented scale and sensitivity. These novel technologies and anticipated wealth of data highlight a need for algorithms that readily accommodate more complex and diverse input sources. We implemented and investigated a recently outlined probabilistic framework for RNA secondary structure prediction and extended it to accommo

SUBMITTER: Deng F 

PROVIDER: S-EPMC4931104 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature