RNA sequence structure alignement

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Efficient Parameterized Algorithm for Biopolymer Structure-Sequence Alignment: 

Efficient Parameterized Algorithm for Biopolymer Structure-Sequence Alignment by Christian Ehrlich Song1, C. Liu1, X. Huang2, R.L. Malmberg3, Y. Xu4, and L.Cai1 1 Dept. of Computer Science, Univ. of Georgia, USA 2 Dept. of Computer Science, Arkansas State Univ., USA 3 Dept. of Plant Bio., Univ. of Georgia, USA 4 Dept. of Biochemistry and Molecular Bio., Univ. of Georgia, USA WABI2005, LNBI3692, pp. 376-388, 2005.

Introduction Graph generation Alignment Results: 

Introduction Graph generation Alignment Results Structure-sequence alignment http://legionella.cu-genome.org http://www.sanger.ac.uk

Introduction Graph generation Alignment Results: 

Introduction Graph generation Alignment Results Motivation non-coding (nc)RNA - gene regulation - chromosome replication - RNA modification Why not simply search for the consensus sequence? structure search is way more accurate !!!

Introduction Graph generation Alignment Results: 

Introduction Graph generation Alignment Results Existing methods for structure-sequence alignment Integer Linear Programming (ILP) (Lenhof, Reinert, Vingron; 1998) huge number of linear constrains Divide-and-conquer based on ‘open links’ (Xu, Xu, Uberbacher; 1998) only useable for very small RNAs Standard DP extended to handle pseudoknots (Umura et al.; 1999) memory overhead, computational intractable

Introduction Graph generation Alignment Results: 

Introduction Graph generation Alignment Results Main concept (Song et al.; 2005) 1 2 3 4 5 6 RNA structure 1 2 3 4 5 6 C N Structure graph a b c d e f Genomic sequence N C a b c d e f Sequence graph Tree-decomposition Dynamic programming Struc-Seq aligned!

Introduction Graph generation Alignment Results: 

Introduction Graph generation Alignment Results Constructing the structure graph H 1 2 3 4 5 6 RNA consensus structure Obtain RNA consensus structure from multiple alignment or DB (e.g. Rfam) Identify base-pairing regions Each base-pairing region is modeled as vertex. Each structural interaction is modeled as undirected edge. Each loop is modeled as directed edge. N- and C-terminus are modeled as source and sink, respectively.. done!

Introduction Graph generation Alignment Results: 

Introduction Graph generation Alignment Results Constructing the sequence graph G Genomic sequence Identify candidate region in the sequence (take the k best) For each set of candidates construct a structure graph Connect all pairing candidate regions Add N- and C-terminus done!

Introduction Graph generation Alignment Results: 

Introduction Graph generation Alignment Results Tree-decomposition 1 2 3 4 5 6 C N Structure graph H 1 2 3 1 2 6 1 6 5 N 1 C 4 5 6 3 4 5 2 3 4 Tree-decomposition T ‘all‘ tree properties are conserved! u v

Introduction Graph generation Alignment Results: 

Introduction Graph generation Alignment Results Dynamic programming approach (bottom up) 1 2 6 1 6 5 4 5 6 Tree-decomposition g h f A mapping is valid if for any set of overlapping candidates, at most one can be selected in the subgraph and 1. 2. d1 d2 d3

Introduction Graph generation Alignment Results: 

Introduction Graph generation Alignment Results Objective function S1 - the alignment between a structural unit u and its candidate S2 - the alignment between the interaction of two structural units (u,v) and the interaction of the coresponding candidates f(u),f(v) S3 - the alignment between a loop and the coresponding candidate loop Dynamic programming

Introduction Graph generation Alignment Results: 

Introduction Graph generation Alignment Results Sensitivity/Selectivity

Introduction Graph generation Alignment Results: 

Introduction Graph generation Alignment Results Performance

Take this massage home!: 

Take this massage home! Main concept 1 2 3 4 5 6 RNA structure 1 2 3 4 5 6 C N Structure graph a b c d e f Genomic sequence N C a b c d e f Sequence graph Tree-decomposition Dynamic programming Struc-Seq aligned!

Take this massage home!: 

Take this massage home! Why use struc.-seq. alignment based on tree-decomposition? Sensitivity and selectivity comparable to other struc-seq alignment models (e.g. CMs) Speed-up of about 30 to 40 times over other methods Applicable in large sequences (e.g. virus/bacteria genome) Handling of pseudoknot-structures is very easy

Introduction Graph generation Alignment Results: 

Introduction Graph generation Alignment Results Referenzes Yinglei Song et al.: Efficient Parameterized Algorithm for Biopolymer Structure-Sequence Alignment. WABI2005, LNBI3692, pp. 376-388, 2005. Yinglei Song et al.: Tree Decomposition Based Fast Search of RNA Structures Including Pseudoknots in Genomes. unpublished Yinglei Song et al.: A Tree-Decomposition Approach to Protein Structure Prediction. unpublished H.L. Bodlaende: A Tourist Guide through Treewidth. Acta Cybernetica, 11:1–21, 1193. A. Ehrenfeucht et al.: An O(n2) Divide-and-Concer Algorithm for the Prime Tree Decomposition of Two-Structures and Modular Decomposition of Graphs. Journal of Algorithms 16, 283-294 (1994)

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