An investigation into the practical implications of grammar ambiguity in RNA secondary structure prediction
نویسنده
چکیده
A context-free grammar G (henceforth abbreviated to “grammar”) is a 4-tuple (N,V, P, S) consisting of the following components: a finite set N of non-terminal variables, a finite set V of terminal variables that is disjoint from N , a finite set P of production rules, mapping non-terminal variables to a series of non-terminals and terminals, and a distinguished symbol S ∈ N that is the start symbol. Beginning with the start symbol, following production rules, a ‘string’ of terminal variables is produced (if this exists). A grammar might be represented as follows.
منابع مشابه
In silico investigation of lactoferrin protein characterizations for the prediction of anti-microbial properties
Lactoferrin (Lf) is an iron-binding multi-functional glycoprotein which has numerous physiological functions such as iron transportation, anti-microbial activity and immune response. In this study, different in silico approaches were exploited to investigate Lf protein properties in a number of mammalian species. Results showed that the iron-binding site, DNA and RNA-binding sites, signal pepti...
متن کاملPreRkTAG: Prediction of RNA Knotted Structures Using Tree Adjoining Grammars
Background: RNA molecules play many important regulatory, catalytic and structural <span style="font-variant: normal; font-style: norma...
متن کاملCombining Hidden Markov Models and Stochastic-Context Free Grammars
Currently, gene finding and RNA secondary structure prediction are performed separately. This is only valid if, for any DNA sequence the location of genes and corresponding RNA secondary structure can assumed to be independent. I develop methods that take a hidden Markov model for gene finding and a stochastic context-free grammar for RNA secondary structure prediction and from these, construct...
متن کاملCombining Hidden Markov Models and Stochastic-Context Free Grammars
Currently, gene finding and RNA secondary structure prediction are performed separately. This is only valid if, for any DNA sequence the location of genes and corresponding RNA secondary structure can assumed to be independent. I develop methods that take a hidden Markov model for gene finding and a stochastic context-free grammar for RNA secondary structure prediction and from these, construct...
متن کاملStochastic Context-Free Grammars and RNA Secondary Structure Prediction
This thesis focus on the prediction of RNA secondary structure using stochastic context-free grammars (SCFG). The RNA secondary structure prediction problem consists of predicting a 2-dimensional structure from a 1-dimensional nucleotide sequence. The theory behind SCFG is explained and an overview of the research literature on various methods in the field of secondary structure prediction is g...
متن کامل