Guillaume Rizk

Bioinformatic prediction, deep sequencing of microRNAs and expression analysis during phenotypic plasticity in the pea aphid, Acyrthosiphon pisum.

BackgroundPost-transcriptional regulation in eukaryotes can be operated through microRNA (miRNAs) mediated gene silencing. MiRNAs are small (18-25 nucleotides) non-coding RNAs that play crucial role in regulation of gene expression in eukaryotes. In insects, miRNAs have been shown to be involved in multiple mechanisms such as embryonic development, tissue differentiation, metamorphosis or circadian rhythm. Insect miRNAs have been identified in different species belonging to five orders: Coleoptera, Diptera, Hymenoptera, Lepidoptera and Orthoptera.ResultsWe developed high throughput Solexa sequencing and bioinformatic analyses of the genome of the pea aphid Acyrthosiphon pisum in order to identify the first miRNAs from a hemipteran insect. By combining these methods we identified 149 miRNAs including 55 conserved and 94 new miRNAs. Moreover, we investigated the regulation of these miRNAs in different alternative morphs of the pea aphid by analysing the expression of miRNAs across the switch of reproduction mode. Pea aphid microRNA sequences have been posted to miRBase: http://microrna.sanger.ac.uk/sequences/ConclusionsOur study has identified candidates as putative regulators involved in reproductive polyphenism in aphids and opens new avenues for further functional analyses.

GPU Accelerated RNA Folding Algorithm

Many bioinformatics studies require the analysis of RNA or DNA structures. More specifically, extensive work is done to elaborate efficient algorithms able to predict the 2-D folding structures of RNA or DNA sequences. However, the high computational complexity of the algorithms, combined with the rapid increase of genomic data, triggers the need of faster methods. Current approaches focus on parallelizing these algorithms on multiprocessor systems or on clusters, yielding to good performance but at a relatively high cost. Here, we explore the use of computer graphics hardware to speed up these algorithms which, theoretically, provide both high performance and low cost. We use the CUDA programming language to harness the power of NVIDIA graphic cards for general computation with a C-like environment. Performances on recent graphic cards achieve a ×17 speed-up.

Chapter 14 – GPU Accelerated RNA Folding Algorithm*

Publisher Summary This chapter presents an implementation of the main kernel in the widely used RNA folding package Unafold. Its key computation is a dynamic programming algorithm with complex dependency patterns, making it an a priori bad match for GPU computing. This study shows that reordering computations in such a way to enable tiled computations and good data reuse can significantly improve GPU performance and yields good speedup compared with optimized CPU implementation that also uses the same approach to tile and vectorize the code. RNA, or ribonucleic acid, is a single-stranded chain of nucleotide units. Because RNA is single stranded, it does not have the double-helix structure of DNA. Rather, all the base pairs of a sequence force the nucleotide chain to fold in “on itself” into a system of different recognizable domains like hairpin loops, bulges, interior loops, or stacked regions. This 2D space conformation of RNA sequences is called the secondary structure, and many bioinformatics studies require detailed knowledge of this. Algorithms computing this 2D folding runs in O(n3) complexity, which means computation time quickly becomes prohibitive when dealing with large datasets of long sequences. The goal is to write a GPU efficient algorithm with the same usage and results as the one in the Unafold implementation.