We are very proud that our work on "Extracting Intuitive Sequence-Structure Motifs from High-Throughput RNA-Binding Protein Data" was accepted for publication in Nucleic Acids Research (NAR). NAR is one of the top journals in the field of molecular biology with an impact factor of 10. The article is the result of a Master's thesis by HPI student David Heller who was jointly supervised by Dr. Ralf Krestel from HPI and Prof. Annalisa Marsico from Max Planck Institute for Molecular Genetics in Berlin. The article describes ssHMM, an RNA motif finder based on a hidden Markov model (HMM) and Gibbs sampling which fully captures the relationship between RNA sequence and secondary structure preference of a given RNA-binding protein. The work combines the development of a novel machine learning methods to model complex biological processes.
Online version: https://doi.org/10.1093/nar/gkx756