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A sequence-structure hidden Markov model for the analysis of RNA-binding protein data.

Project Description

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.

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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

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