Michal Ziv-Ukelson

Composite effects of gene determinants on the translation speed and density of ribosomes

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 genes 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 organisms translation efficiency.

A study of accessible motifs and RNA folding complexity.

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.

Sparse RNA folding: Time and space efficient algorithms

The currently fastest algorithm for RNA Single Strand Folding requires O(nZ) time and @Q(n^2) space, where n denotes the length of the input string and Z is a sparsity parameter satisfying n=

On the Common Substring Alignment Problem

The Common Substring Alignment Problem is defined as follows: Given a set of one or more strings S1,S2?Sc and a target string T, Y is a common substring of all strings Si, that is, Si=BiYFi. The goal is to compute the similarity of all strings Si with T, without computing the part of Y again and again. Using the classical dynamic programming tables, each appearance of Y in a source string would require the computation of all the values in a dynamic programming table of size O(n?) where ? is the size of Y. Here we describe an algorithm which is composed of an encoding stage and an alignment stage. During the first stage, a data structure is constructed which encodes the comparison of Y with T. Then, during the alignment stage, for each comparison of a source Si with T, the pre-compiled data structure is used to speed up the part of Y. We show how to reduce the O(n?) alignment work, for each appearance of the common substring Y in a source string, to O(n)-at the cost of O(n?) encoding work, which is executed only once.

Speeding Up HMM Decoding and Training by Exploiting Sequence Repetitions

Abstract We present a method to speed up the dynamic program algorithms used for solving the HMM decoding and training problems for discrete time-independent HMMs. We discuss the application of our method to Viterbi’s decoding and training algorithms (IEEE Trans. Inform. Theory IT-13:260–269, 1967), as well as to the forward-backward and Baum-Welch (Inequalities 3:1–8, 1972) algorithms. Our approach is based on identifying repeated substrings in the observed input sequence. Initially, we show how to exploit repetitions of all sufficiently small substrings (this is similar to the Four Russians method). Then, we describe four algorithms based alternatively on run length encoding (RLE), Lempel-Ziv (LZ78) parsing, grammar-based compression (SLP), and byte pair encoding (BPE). Compared to Viterbi’s algorithm, we achieve speedups of Θ(log n) using the Four Russians method,

Approximate labelled subtree homeomorphism

\Omega(\frac{r}{\log r})

Rich parameterization improves RNA structure prediction

using RLE,

The microRNA Transcriptome of Human Cytomegalovirus (HCMV)

\Omega(\frac{\log n}{k})

A Faster Algorithm for RNA Co-folding

using LZ78,

Sparse RNA Folding: Time and Space Efficient Algorithms

\Omega(\frac{r}{k})