A Computational model for compressed sensing RNAi cellular screening

BackgroundRNA interference (RNAi) becomes an increasingly important and effective genetic tool to study the function of target genes by suppressing specific genes of interest. This system approach helps identify signaling pathways and cellular phase types by tracking intensity and/or morphological changes of cells. The traditional RNAi screening scheme, in which one siRNA is designed to knockdown one specific mRNA target, needs a large library of siRNAs and turns out to be time-consuming and expensive.ResultsIn this paper, we propose a conceptual model, called compressed sensing RNAi (csRNAi), which employs a unique combination of group of small interfering RNAs (siRNAs) to knockdown a much larger size of genes. This strategy is based on the fact that one gene can be partially bound with several small interfering RNAs (siRNAs) and conversely, one siRNA can bind to a few genes with distinct binding affinity. This model constructs a multi-to-multi correspondence between siRNAs and their targets, with siRNAs much fewer than mRNA targets, compared with the conventional scheme. Mathematically this problem involves an underdetermined system of equations (linear or nonlinear), which is ill-posed in general. However, the recently developed compressed sensing (CS) theory can solve this problem. We present a mathematical model to describe the csRNAi system based on both CS theory and biological concerns. To build this model, we first search nucleotide motifs in a target gene set. Then we propose a machine learning based method to find the effective siRNAs with novel features, such as image features and speech features to describe an siRNA sequence. Numerical simulations show that we can reduce the siRNA library to one third of that in the conventional scheme. In addition, the features to describe siRNAs outperform the existing ones substantially.ConclusionsThis csRNAi system is very promising in saving both time and cost for large-scale RNAi screening experiments which may benefit the biological research with respect to cellular processes and pathways.

[1]  A. Reynolds,et al.  Rational siRNA design for RNA interference , 2004, Nature Biotechnology.

[2]  H. Erfle,et al.  High-throughput RNAi screening by time-lapse imaging of live human cells , 2006, Nature Methods.

[3]  P. Zamore,et al.  Kinetic analysis of the RNAi enzyme complex , 2004, Nature Structural &Molecular Biology.

[4]  Stefan L Ameres,et al.  Cleavage of the siRNA passenger strand during RISC assembly in human cells , 2006, EMBO reports.

[5]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[6]  Thomas Tuschl,et al.  siRNAs: applications in functional genomics and potential as therapeutics , 2004, Nature Reviews Drug Discovery.

[7]  B. Cullen.  Enhancing and confirming the specificity of RNAi experiments , 2006, Nature Methods.

[8]  T. Tuschl,et al.  On the art of identifying effective and specific siRNAs , 2006, Nature Methods.

[9]  H. Himmelbauer,et al.  An endoribonuclease-prepared siRNA screen in human cells identifies genes essential for cell division , 2004, Nature.

[10]  D. Galbraith,et al.  Microarray-based analysis of gene expression in very large gene families: the cytochrome P450 gene superfamily of Arabidopsis thaliana. , 2001, Gene.

[11]  Meinard Müller,et al.  Information retrieval for music and motion , 2007 .

[12]  Terran Lane,et al.  A computational study of off-target effects of RNA interference , 2005, Nucleic acids research.

[13]  Paul Ahlquist,et al.  RNA-Dependent RNA Polymerases, Viruses, and RNA Silencing , 2002, Science.

[14]  Mikiko C. Siomi,et al.  The Discovery of Rna Interference (rnai) Biogenesis of Small Rnas on the Road to Reading the Rna-interference Code Insight Review , 2022 .

[15]  Xiaobo Zhou,et al.  Automatic Segmentation of High-Throughput RNAi Fluorescent Cellular Images , 2008, IEEE Transactions on Information Technology in Biomedicine.

[16]  Reuven Agami,et al.  A large-scale RNAi screen in human cells identifies new components of the p53 pathway , 2004, Nature.

[17]  Wilfred W. Li,et al.  MEME: discovering and analyzing DNA and protein sequence motifs , 2006, Nucleic Acids Res..

[18]  Alexander Gammerman,et al.  Machine learning classification with confidence: Application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression , 2011, NeuroImage.

[19]  R. Lehmann,et al.  Targeted mRNA degradation by double-stranded RNA in vitro. , 1999, Genes & development.

[20]  Thorsten Joachims,et al.  Making large-scale support vector machine learning practical , 1999 .

[21]  T. Tuschl,et al.  Mechanisms of gene silencing by double-stranded RNA , 2004, Nature.

[22]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[23]  D. Botstein,et al.  Genome-wide characterization of the Zap1p zinc-responsive regulon in yeast. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[24]  K. Ui-Tei,et al.  Guidelines for the selection of highly effective siRNA sequences for mammalian and chick RNA interference. , 2004, Nucleic acids research.

[25]  M. Amarzguioui,et al.  An algorithm for selection of functional siRNA sequences. , 2004, Biochemical and biophysical research communications.

[26]  Dieter Huesken,et al.  Design of a genome-wide siRNA library using an artificial neural network , 2005, Nature Biotechnology.

[27]  Mark A Behlke,et al.  Design of active small interfering RNAs. , 2007, Current opinion in molecular therapeutics.

[28]  John G. Proakis,et al.  Digital Signal Processing: Principles, Algorithms, and Applications , 1992 .

[29]  E. Candès,et al.  Sparsity and incoherence in compressive sampling , 2006, math/0611957.

[30]  Richard G. Baraniuk,et al.  Compressive Sensing DNA Microarrays , 2008, EURASIP J. Bioinform. Syst. Biol..

[31]  T. Tuschl,et al.  Analysis of gene function in somatic mammalian cells using small interfering RNAs. , 2002, Methods.

[32]  A. Kerlavage,et al.  Complementary DNA sequencing: expressed sequence tags and human genome project , 1991, Science.

[33]  T. Tuschl,et al.  RNA interference is mediated by 21- and 22-nucleotide RNAs. , 2001, Genes & development.

[34]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[35]  D. Donoho.  For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution , 2006 .

[36]  Kazunari Taira,et al.  siRNA becomes smart and intelligent , 2005, Nature Biotechnology.

[37]  Xiaobo Zhou,et al.  An image score inference system for RNAi genome-wide screening based on fuzzy mixture regression modeling , 2009, J. Biomed. Informatics.