Co-transcriptional effect on RNA folding agucacaaacgcu agugcuaguuua uaugcagucuua RNA secondary structure prediction is an area of bioinformatics that is the subject of significant amounts of research; it is the process of turning a sequence of bases on an RNA strand, for example, AUGUAGCAUC into a

Abstract. RNA secondary structure prediction is an important problem studied extensively in the past three dacades. However, pseudoknots are usually excluded in RNA secondary structure prediction due to the hardness of examining all possible structures efficiently and model the energy correctly

: : s n is an RNA sequence and 1 i < j n, then i j denotes the base-pairing of s i with s j . Definition 16.1: A secondary structure of s = s 1 s 2 : : : s n is a set S of base pairs such that each base is paired at most once. More precisely, for all i j 2 S and i 0 j 0 2 S, i = i 0

The aim of this project was to predict, as accurately as possible, the secondary structure of RNA, given its primary sequence. We have imple-mented two of the basic algorithms that accomplish that task. The pro-grams consist of two parts: One generates data about where the residues pair. The other

The prediction of RNA structure is useful for understanding evolution for both in silico and in vitro studies. Physical methods like NMR studies to predict RNA secondary structure are expensive and difficult. Computational RNA secondary structure prediction is easier. Comparative sequence analysis

We present an analysis of currently used free energy functions for internal loop stability in RNA secondary structure. This analysis enables us to present an O(|s|&sup3;) algorithm for evaluating internal loops thus improving the overall complexity of RNA secondary structure prediction from O

energy V (i; j) of the compound structure closed by i j, plus the energy W (i 1) of the remainder s 1 ; s 2 ; : : : ; s i 1 . 17.1.2. V (i; j) V (i; j) = ( +1; for i j min(eH(i; j); eS(i; j) +V (i + 1; j 1); VBI (i; j); VM (i; j)); for i < j The terms in the second equation correspond

Our recently created Statistical Alignment package, StatAlign (Novak et al. 2008), does multiple sequence alignment and phylogenetic tree reconstruction using a statistical alignment (Hein et al. 2000) strategy for joint Bayesian estimations of alignment and phylogeny. There are several other implementations of statistical alignment (Bradley et al. 2009, Satija et al. 2009), and for reviews on

RNA secondary structures are important in many biological processes and efficient structure prediction can give vital directions for experimental investigations. Many available programs for RNA secondary structure prediction only use a single sequence at a time. This may be sufficient in some

Though not as abundant in known biological processes as proteins, RNA molecules serve as more than mere intermediaries between DNA and proteins, e.g. as catalytic molecules. Furthermore, RNA secondary structure prediction based on free energy rules for stacking and loop formation remains one