STAN

Bidirectional HMMs are a new type of software tool to aid in characterizing directed genomic states. These states are essential for understanding DNA replication, transcription, and repair. They involve the recruitment of protein complexes that change their composition as they progress along the genome in a directed or strand-specific manner.

In the past, Chromatin Immunoprecipitation (ChIP) in conjunction with Hidden Markov Models (HMMs) has been instrumental in understanding these processes. However, current HMM-based approaches cannot assign forward or reverse direction to states or properly integrate strand-specific (e.g., RNA expression) with non-strand-specific (e.g., ChIP) data, which is indispensable to accurately characterize directed processes.

To overcome these limitations, the authors developed bidirectional HMMs to infer directed genomic states from occupancy profiles de novo. This means they can determine the direction of genomic states and integrate strand-specific and non-strand-specific data to more accurately characterize directed processes.

The application of bidirectional HMMs to RNA polymerase II-associated factors in yeast and chromatin modifications in human T cells has yielded promising results. They have recovered the majority of transcribed loci, revealed gene-specific variations in the yeast transcription cycle, and indicated the existence of directed chromatin state patterns at transcribed, but not at repressed, regions in the human genome.

In yeast, bidirectional HMMs have identified 32 new transcribed loci, a regulated initiation-elongation transition, and the absence of elongation factors Ctk1 and Paf1 from a class of genes, a distinct transcription mechanism for highly expressed genes, and novel DNA sequence motifs associated with transcription termination.