Welcome to the ssHMM documentation!

ssHMM stands for “Sequence-Structure Hidden Markov Model”. It is an RNA motif finder and recovers sequence-structure motifs from 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 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.


ssHMM was developed for the analysis of data from RNA-binding assays. Its aim is to help biologists to derive a binding motif for one or a number of RNA-binding proteins. ssHMM was written in Python and is a pure command-line tool.