A sequence-structure hidden Markov model for the analysis of RNA-binding protein data.
RNA-binding proteins (RBPs) play a vital role in the post-transcriptional control of RNAs. They are known to recognize RNA molecules by their nucleotide sequence as well as their three-dimensional structure. ssHMM is an RNA motif finder that combines a hidden Markov model (HMM) with Gibbs sampling to learn the joint sequence and structure binding preferences of RBPs from high-throughput RNA-binding experiments, such as CLIP-Seq. The model can be visualized as an intuitive graph illustrating the interplay between RNA sequence and structure.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|sshmm-1.0.5-py2.7-linux-x86_64.egg (159.5 kB) Copy SHA256 hash SHA256||Egg||2.7||Nov 22, 2017|
|sshmm-1.0.5.tar.gz (105.2 kB) Copy SHA256 hash SHA256||Source||None||Nov 22, 2017|