Search Results for "hidden"

Using RNA-seq, tens of thousands of novel transcripts and isoforms have been identified (Djebali, et al Nature, 2012 , Carbili et al, Gene & Development, 2011) The discovery of these hidden transcriptome rejuvenate the need of distinguishing coding and noncoding RNA. However, Most previous coding potential prediction methods heavily rely on alignment, either pairwise alignment to search for protein evidence or multiple alignments to calculate phylogenetic conservation score (such as CPC , PhyloCSF and RNACode ). This is because most previously identified transcripts including protein coding RNA and short, housekeeping/regulatory RNAs such as snRNAs, snoRNA and tRNA are highly conserved. ...

SnowyOwl is a gene prediction pipeline that uses RNA-Seq data to train and provide hints for the generation of Hidden Markov Model (HMM)-based gene predictions, and to evaluate the resulting models. The pipeline has been validated and streamlined by comparing its predictions to manually curated gene models in three fungal genomes, and its results show substantial increases in sensitivity and selectivity over previous gene predictions. Sensitivity is gained by repeatedly running the HMM gene predictor Augustus with varied input parameters, and selectivity by choosing the models with best homology to known proteins and best agreement to the RNA-Seq data. ...

The General Hidden Markov Model Library (GHMM) is a C library with additional Python bindings implementing a wide range of types of Hidden Markov Models and algorithms: discrete, continous emissions, basic training, HMM clustering, HMM mixtures.

RefineHMM refines an original hidden Markov model (HMM) to find an optimal fit against the evolutionary group that the HMM models, and it does this using through iterative database searches and incremental subsequent adaptation of the seed set.