Santa Cruz Protein - Coding Gene Structure Prediction Using Generalized Hidden Markov Models
نویسندگان
چکیده
Protein-coding Gene Structure Prediction Using Generalized Hidden Markov
منابع مشابه
A generalization of Profile Hidden Markov Model (PHMM) using one-by-one dependency between sequences
The Profile Hidden Markov Model (PHMM) can be poor at capturing dependency between observations because of the statistical assumptions it makes. To overcome this limitation, the dependency between residues in a multiple sequence alignment (MSA) which is the representative of a PHMM can be combined with the PHMM. Based on the fact that sequences appearing in the final MSA are written based on th...
متن کاملA Metastate HMM with Application to Gene Structure Identification in Eukaryotes
We introduce a generalized-clique hidden Markov model (HMM) and apply it to gene finding in eukaryotes (C. elegans). We demonstrate a HMM structure identification platform that is novel and robustly-performing in a number of ways. The generalized clique HMM begins by enlarging the primitive hidden states associated with the individual base labels (as exon, intron, or junk) to substrings of prim...
متن کاملLecture 6 : CRFs for Computational Gene Prediction
One of the fundamental problems in computational biology is to identify genes in very long genome sequences. As we know DNA is a sequence of nucleotide molecules (a.k.a. bases) which encode instructions for generation of proteins. However not all of these bases are responsible for protein generation. As an example shown in the 4th slide on page 1 of [1], in the eukaryotic gene structure, only e...
متن کاملProtein Structure Analysis Using Continuous Density Hidden Markov Models
Hidden Markov models [2] (HMMs) have been successfully applied to Bioinformatics such as gene finding, remote homology detection and secondary structure prediction. On the other hand, continuous density HMMs have been widely used in the field of speech recognition. Though continuous density HMMs were not applied to Bioinformatics so far, they may also be useful in Bioinformatics. Currently, we ...
متن کاملToPS: A Framework to Manipulate Probabilistic Models of Sequence Data
Discrete Markovian models can be used to characterize patterns in sequences of values and have many applications in biological sequence analysis, including gene prediction, CpG island detection, alignment, and protein profiling. We present ToPS, a computational framework that can be used to implement different applications in bioinformatics analysis by combining eight kinds of models: (i) indep...
متن کامل