We present a systematic treatment of alignment distance and local similarity algorithms on trees and forests. We build upon the tree alignment algorithm for ordered trees given by Jiang et. al (1995) and extend it to calculate local forest alignments, which is essential for finding local similar regions in RNA secondary structures. The time complexity of our algorithm is Motivation IntroductionRNA is a chain molecule, mathematically a string over a four letter alphabet. It is built from nucleotides containing the bases A(denine), C(ytosine), G(uanine), and U(racil). By folding back onto itself, an RNA molecule forms structure, stabilized by the forces of hydrogen bonds between certain pairs of bases (A-U, C-G, G-U), and dense stacking of neighbouring base pairs.The investigation of RNA secondary structures is a challenging task in molecular biology. RNA molecules have a large variety of functions in the cell which often depend on special structural properties. String edit distance [25] clearly is the most successful model in sequence comparison. It is used in document processing, file comparison, molecular sequence analysis, and numerous other applications of approximate string matching. The basic model is that one string is "edited" into another string by a sequence of edit operations, such as single character replacement (R), deletion (D) or insertion (I). The weights associated with the edit operations sum up to an overall score, and the edit sequence giving the minimal score defines the edit distance of the two strings. Equivalently, the editing process, ignoring the order of edit operations, can be represented as an alignment. This equivalence does not generalize to trees, as already mentioned in [1]. For each tree alignment one can construct a corresponding sequence of edit operations, but not vice versa. One can understand editing as finding a largest common sub-structure, while aligning means finding the smallest common superstructure (In fact, this depends on the scoring scheme.). Which model is favourable depends on the problem. Previous workThe first generalization of the edit model from strings to rooted ordered trees is due to [23], algorithmically improved in [31] and implemented and applied to computa-
To gain insight into the biogenesis of photosystem II (PSII) and to identify auxiliary factors required for this process, we characterized the mutant hcf173 of Arabidopsis thaliana. The mutant shows a high chlorophyll fluorescence phenotype (hcf) and is severely affected in the accumulation of PSII subunits. In vivo labeling experiments revealed a drastically decreased synthesis of the reaction center protein D1. Polysome association experiments suggest that this is primarily caused by reduced translation initiation of the corresponding psbA mRNA. Comparison of mRNA steady state levels indicated that the psbA mRNA is significantly reduced in hcf173. Furthermore, the determination of the psbA mRNA half-life revealed an impaired RNA stability. The HCF173 gene was identified by map-based cloning, and its identity was confirmed by complementation of the hcf phenotype. HCF173 encodes a protein with weak similarities to the superfamily of the short-chain dehydrogenases/reductases. The protein HCF173 is localized in the chloroplast, where it is mainly associated with the membrane system and is part of a higher molecular weight complex. Affinity chromatography of an HCF173 fusion protein uncovered the psbA mRNA as a component of this complex.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.