BackgroundTranslation is a central process of life, and its regulation is crucial for cell growth. In this article, focusing on two model organisms, Escherichia coli and Saccharomyces cerevisiae, we study how three major local features of a gene's coding sequence (its adaptation to the tRNA pool, its amino acid charge, and its mRNA folding energy) affect its translation elongation.ResultsWe find that each of these three different features has a non-negligible distinct correlation with the speed of translation elongation. In addition, each of these features might contribute independently to slowing down ribosomal speed at the beginning of genes, which was suggested in previous studies to improve ribosomal allocation and the cost of translation, and to decrease ribosomal jamming. Remarkably, a model of ribosomal translation based on these three basic features highly correlated with the genomic profile of ribosomal density. The robustness to transcription errors in terms of the values of these features is higher at the beginnings of genes, suggesting that this region is important for translation.ConclusionsThe reported results support the conjecture that translation elongation speed is affected by the three coding sequence determinants mentioned above, and not only by adaptation to the tRNA pool; thus, evolution shapes all these determinants along the coding sequences and across genes to improve the organism's translation efficiency.

MetaPathwayHunter is a pathway alignment tool that, given a query pathway and a collection of pathways, finds and reports all approximate occurrences of the query in the collection, ranked by similarity and statistical significance. It is based on a novel, efficient graph matching algorithm that extends the functionality of known techniques. The program also supports a visualization interface with which the alignment of two homologous pathways can be graphically displayed. We employed this tool to study the similarities and differences in the metabolic networks of the bacterium Escherichia coli and the yeast Saccharomyces cerevisiae, as represented in highly curated databases. We reaffirmed that most known metabolic pathways common to both the species are conserved. Furthermore, we discovered a few intriguing relationships between pathways that provide insight into the evolution of metabolic pathways. We conclude with a description of biologically meaningful meta-queries, demonstrating the power and flexibility of our new tool in the analysis of metabolic pathways.

International audienceGiven two strings of size n over a constant alphabet, the classical algorithm for computing the similarity between two sequences [D. Sankoff and J. B. Kruskal, eds., Time Warps, String Edits, and Macromolecules; Addison-Wesley, Reading, MA, 1983; T. F. Smith and M. S. Waterman, J. Molec. Biol., 147 (1981), pp. 195-197] uses a dynamic programming matrix and compares the two strings in O(n²) time. We address the challenge of computing the similarity of two strings in subquadratic time for metrics which use a scoring matrix of unrestricted weights. Our algorithm applies to both local and global similarity computations. The speed-up is achieved by dividing the dynamic programming matrix into variable sized blocks, as induced by Lempel-Ziv parsing of both strings, and utilizing the inherent periodic nature of both strings. This leads to an O(n² / log n) algorithm for an input of constant alphabet size. For most texts, the time complexity is actually O(h n² / log n), where h≤1 is the entropy of the text. We also present an algorithm for comparing two run-length encoded strings of length m and n, compressed into m' and n' runs, respectively, in O(m'n+n'm) complexity. This result extends to all distance or similarity scoring schemes that use an additive gap penalty

mRNA molecules are folded in the cells and therefore many of their substrings may actually be inaccessible to protein and microRNA binding. The need to apply an accessibility criterion to the task of genome-wide mRNA motif discovery raises the challenge of overcoming the core O(n(3)) factor imposed by the time complexity of the currently best known algorithms for RNA secondary structure prediction. We speed up the dynamic programming algorithms that are standard for RNA folding prediction. Our new approach significantly reduces the computations without sacrificing the optimality of the results, yielding an expected time complexity of O(n(2) psi(n)), where psi(n) is shown to be constant on average under standard polymer folding models. A benchmark analysis confirms that in practice the runtime ratio between the previous approach and the new algorithm indeed grows linearly with increasing sequence size. The fast new RNA folding algorithm is utilized for genome-wide discovery of accessible cis-regulatory motifs in data sets of ribosomal densities and decay rates of S. cerevisiae genes and to the mining of exposed binding sites of tissue-specific microRNAs in A. thaliana.

The currently fastest algorithm for RNA Single Strand Folding requires O (nZ) time and Θ(n 2 ) space, where n denotes the length of the input string and Z is a sparsity parameter satisfying n Z < n 2 . We show how to reduce the time and space complexities of this algorithm in the sparse case. The space reduction is based on the observation that some solutions for sub-instances are not examined after a certain stage of the algorithm, and may be discarded from memory. The running time speed up is achieved by combining two independent sparsification criteria, which restrict the number of expressions that need to be examined in bottleneck computations of the algorithm. This yields an O (n 2 + P Z) time and Θ(Z ) space algorithm, where P is a sparsity parameter satisfying P < n Z n(P + 1). For the base-pairing maximization variant, the time complexity is further reduced to O (L Z), where L denotes the maximum number of base-pairs in a folding of the input string and satisfies L n\2. The presented techniques also extend to the related RNA Simultaneous Alignment and Folding problem. For an input composed of two strings of lengths n and m, the time and space complexities are reduced from O (nmZ ) and Θ(n 2 m 2 ) down to O (n 2 m 2 +PZ ) and Θ(nm 2 +Z ) respectively, whereZ andP are sparsity parameters satisfyingP < nm Z < nm(P + 3).A preliminary extended abstract of this work previously appeared in Backofen et al. (2009) [5]. Code implementations (in Java) may be downloaded from: http://www.cs.bgu.ac.il/ zakovs/RNAfold/SparseFold.zip.