TY - GEN
T1 - RNA tree comparisons via unrooted unordered alignments
AU - Milo, Nimrod
AU - Zakov, Shay
AU - Katzenelson, Erez
AU - Bachmat, Eitan
AU - Dinitz, Yefim
AU - Ziv-Ukelson, Michal
PY - 2012/10/1
Y1 - 2012/10/1
N2 - We generalize some current approaches for RNA tree alignment, which are traditionally confined to ordered rooted mappings, to also consider unordered unrooted mappings. We define the Homeomorphic Subtree Alignment problem, and present a new algorithm which applies to several modes, including global or local, ordered or unordered, and rooted or unrooted tree alignments. Our algorithm generalizes previous algorithms that either solved the problem in an asymmetric manner, or were restricted to the rooted and/or ordered cases. Focusing here on the most general unrooted unordered case, we show that our algorithm has an O(n T n S min (d T, d S)) time complexity, where n T and n S are the number of nodes and d T and d S are the maximum node degrees in the input trees T and S, respectively. This maintains (and slightly improves) the time complexity of previous, less general algorithms for the problem. Supplemental materials, source code, and web-interface for our tool are found in http://www.cs.bgu.ac.il/~negevcb/FRUUT.
AB - We generalize some current approaches for RNA tree alignment, which are traditionally confined to ordered rooted mappings, to also consider unordered unrooted mappings. We define the Homeomorphic Subtree Alignment problem, and present a new algorithm which applies to several modes, including global or local, ordered or unordered, and rooted or unrooted tree alignments. Our algorithm generalizes previous algorithms that either solved the problem in an asymmetric manner, or were restricted to the rooted and/or ordered cases. Focusing here on the most general unrooted unordered case, we show that our algorithm has an O(n T n S min (d T, d S)) time complexity, where n T and n S are the number of nodes and d T and d S are the maximum node degrees in the input trees T and S, respectively. This maintains (and slightly improves) the time complexity of previous, less general algorithms for the problem. Supplemental materials, source code, and web-interface for our tool are found in http://www.cs.bgu.ac.il/~negevcb/FRUUT.
UR - https://www.scopus.com/pages/publications/84866655407
U2 - 10.1007/978-3-642-33122-0_11
DO - 10.1007/978-3-642-33122-0_11
M3 - Conference contribution
AN - SCOPUS:84866655407
SN - 9783642331213
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 135
EP - 148
BT - Algorithms in Bioinformatics - 12th International Workshop, WABI 2012, Proceedings
T2 - 12th International Workshop on Algorithms in Bioinformatics, WABI 2012
Y2 - 10 September 2012 through 12 September 2012
ER -