Rapid dynamic programming algorithms for RNA secondary structure
Advances in Applied Mathematics
The least weight subsequence problem
SIAM Journal on Computing
Speeding up dynamic programming with application to molecular biology
Theoretical Computer Science
Sparse dynamic programming I: linear cost functions
Journal of the ACM (JACM)
Sparse dynamic programming II: convex and concave cost functions
Journal of the ACM (JACM)
Chaining multiple-alignment fragments in sub-quadratic time
Proceedings of the sixth annual ACM-SIAM symposium on Discrete algorithms
Speeding up dynamic programming
SFCS '88 Proceedings of the 29th Annual Symposium on Foundations of Computer Science
Notes on searching in multidimensional monotone arrays
SFCS '88 Proceedings of the 29th Annual Symposium on Foundations of Computer Science
WABI'05 Proceedings of the 5th International conference on Algorithms in Bioinformatics
Detecting MicroRNA targets by linking sequence, MicroRNA and gene expression data
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
A study of accessible motifs and RNA folding complexity
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
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Plant microRNAs (miRNAs) are short RNA sequences that bind to target mRNAs and change their expression levels by influencing their stabilities and marking them for cleavage. We present a high throughput approach for associating between microRNAs and conditions in which they act, using novel statistical and algorithmic measures. Our new prototype tool, miRNAXpress, computes a (binary) matrix T denoting the potential targets of microRNAs. Then, using T and an additional predefined matrix X indicating expression of genes under various conditions, it produces a new matrix that predicts associations between microRNAs and the conditions in which they act. The computational intensive part of miRNAXpress is the calculation of T. We provide a hybridization search algorithm which given a query microRNA, a text mRNA, and a predefined energy cutoff threshold, finds and reports all targets (putative binding sites) of the query in the text with binding energy below the predefined threshold. In order to speed it up, we utilize the sparsity of the search space without sacrificing the optimality of the results. Consequently, the time complexity of the search algorithm is almost linear in the size of a sparse set of locations where base-pairs are stacked at a height of three or more. We employed our tool to conduct a study, using the plant Arabidopsis thaliana as our model organism. By applying miRNAXpress to 98 microRNAs and 380 conditions, some biologically interesting and statistically strong relations were discovered. Further details, including figures and pseudo-code, can be found at: http://www.cs.technion.ac.il/~michalz/LinearRNA.ps