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.
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
sshmm-1.0.5.tar.gz
(105.2 kB
view hashes)
Built Distribution
sshmm-1.0.5-py2.7-linux-x86_64.egg
(159.5 kB
view hashes)
Close
Hashes for sshmm-1.0.5-py2.7-linux-x86_64.egg
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 86d70f06238168852b0342ea733f6893e8baa49be151aab77d6f49cbab6ff227 |
|
| MD5 | 92f2e9691fdd53086c8e5eea47e7d123 |
|
| BLAKE2b-256 | 6d93d1879de96986253da47c110755bcc15eda8acfe94c3f5e7185dc6a642c0c |
