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TR-CS-2005-23
A Practical Approach to Significance Assessment of siRNA Off-target
Effects in RNA Interference
Wenzhong Zhao and Terran Lane
Detection of potential cross-hybridization (or cross-reaction) between a short oligonucleotide sequence and a longer (unintended) sequence is crucial for many biological applications, such as selecting PCR primers, microarray nucleotide probes or short interfering RNAs (siRNAs). In this paper, we propose a flexible framework for estimating the significance of siRNA off-target effects on untargeted transcripts (messager RNA, or mRNA) in the RNA interference (RNAi) process. The framework can also be extended to other applications with minor changes.
We have developed and implemented a new homology sequence search framework -- siRNA Off-target Search (SOS). SOS uses a hybrid, q-gram based approach, combining two filtering techniques using overlapping and non-overlapping q-grams. Our approach considers three types of imperfect matches based on biological experiments, namely G:U wobbles, mismatches, and bulges. The three main improvements over existing methods are: the introduction of a more general cost model (an affine bulge cost model) for siRNA-mRNA off-target alignment; the use of separate searches for alignments with and without bulges, that enables efficient discovery of potential off-target candidates in the filtration phase; and the use of position-preserving and order-preserving hit-processing techniques, that further improves the filtration efficiency. Overall, SOS achieves better performance, in terms of speed and recall/precision, than BLAST in detecting potential siRNA off-targets.
TR-CS-2005-09
FINDMODEL: A tool to select the best-fit model of nucleotide substitution
Tao, N., Bruno, W.J., Abfalterer, W., Moret, B.M.E., Leitner, T., and Kuiken, C.
Choosing a model of sequence evolution is a crucial step when using DNA sequence data to reconstruct phylogenies: using a mismatched model will reduce accuracy and may lead to erroneous conclusions. FINDMODEL is a web-based tool for selecting a model of DNA (nucleotide) evolution; it is designed to be easy to use by researchers who do some sequencing and may not have access to phylogenetic packages. FINDMODEL can analyze 28 models or a restricted subset of 12 models. It creates a guide tree using Weighbor, optimizes branch lengths, calculates the likelihood for every chosen model (using baseml from the PAML package), and computes the Akaike information criterion (AIC). The model with the smallest AIC score is considered to be the best-fit model. Because of server limitations, the FINDMODEL web server processes inputs above a certain size in non-interactive mode, sending an email to the user when it has analyzed that user's data and providing a down-loadable file with the results. To test the performance of FINDMODEL, we generated simulated DNA sequences with Seq-Gen under four different models of nucleotide substitution of different complexity and compared the inferred model with the true model. We used 17 different configurations, with 5 instances for each set of parameter values. FINDMODEL returned the correct model for 75% of our test instances and also returned the correct model, but with variable site-specific rates instead of homogeneous rates for another 8%. Moreover, on all tests where FINDMODEL did not return the correct model, the normalized AIC error between the correct and the predicted models was below 0.002 (and the actual AIC difference was below 7).
TR-CS-2005-08
Inferring Ancestral Chloroplast Genomes with Duplications
Liying Cui, Jijun Tang, Bernard M. E. Moret, and Claude dePamphilis
Motivation: Genome evolution is shaped not only by nucleotide substitutions but also by structural changes including gene and genome duplications, insertions and deletions, and gene-order rearrangements. Reconstruction of phylogeny based on gene-order changes has been limited to cases where equal gene content or few deletions can be assumed. Since conserved duplicated regions are present in many genomes, the inference of duplication is needed in ancestral genome reconstruction.
Results: We apply GRAPPA-IR, a modified GRAPPA algorithm,to reconstruct ancestral chloroplast genomes containing duplicated genes. A test of GRAPPA-IR using six divergent chloroplast genomes from land plants and green algae recovers the phylogeny congruent with prior studies, while analyses that do not consider the duplications fail to obtain the accepted topology. The ancestral genome structure suggests that genome rearrangement in chloroplasts is probably limited by inverted repeats with a conserved core region. In addition, the boundaries of inverted repeats are hot spots for gene duplications or deletions.
TR-CS-2005-06
Designing Nucleotide Sequences for Computation: A Survey of Constraints
Jennifer Sager and Darko Stefanovic
We survey the biochemical constraints useful for the design of DNA code words for DNA computation. We define the DNA/RNA Code Constraint problem and cover biochemistry topics relevant to DNA libraries. We examine which biochemical constraints are best suited for DNA word design.
TR-CS-2004-33
Building the components for a biomolecular computer
Clint Morgan, Darko Stefanovic, Cristopher Moore and Milan N. Stojanovic
We propose a new method for amorphous bio-compatible computing using deoxyribozyme logic gates in which oligonucleotides act as enzymes on other oligonucleotides, yielding oligonucleotide products. Moreover, these reactions can be controlled by inputs that are also oligonucleotides. We interpret these reactions as logic gates, and the concentrations of chemical species as signals. Since these reactions are homogeneous, i.e., they use oligonucleotides as both inputs and outputs, we can compose them to construct complex logic circuits. Thus, our system for chemical computation offers functionality similar to conventional electronic circuits with the potential for deployment inside of living cells. Previously, this technology was demonstrated in closed-system batch reactions, which limited its computational ability to simple feed-forward circuits. In this work, we go beyond closed systems, and show how to use thermodynamically open reactors to build biomolecular circuits with feedback. The behavior of an open chemical system is determined both by its chemical reaction network and by the influx and efflux of chemical species. This motivates a change in design process from that used with closed systems. Rather than focusing solely on the stoichiometry of the chemical reactions, we must carefully examine their kinetics. Systems of differential equations and the theory of dynamical systems become the appropriate tools for designing and analyzing such systems. Using these tools, we present an inverter. Next, by introducing feedback into the reaction network, we construct devices with a sense of state. We show how a combination of analytical approximation techniques and numerical methods allows us to tune the dynamics of these systems. We demonstrate a flip-flop which exhibits behavior similar to the RS flip-flop of electronic computation. It has two states in which the concentration of one oligonucleotide is high and the other is low or vice versa. We describe how to control the state of the flip-flop by varying the concentration of the substrates. Moreover, there are large regions of parameter space in which this behavior is robust, and we show how to tune the influx rates as a function of the chemical reaction rates in a way that ensures bistability.
TR-CS-2004-25
Advances in Phylogeny Reconstruction from Gene Order and Content Data
Bernard M.E. Moret and Tandy Warnow
Genomes can be viewed in terms of their gene content and the order in which the genes appear along each chromosome. Evolutionary events that affect the gene order or content are ``rare genomic events" (rarer than events that affect the composition of the nucleotide sequences) and have been advocated by systematists for inferring deep evolutionary histories. This chapter surveys recent developments in the reconstruction of phylogenies from gene order and content, focusing on their performance under various stochastic models of evolution. Because such methods are currently quite restricted in the type of data they can analyze, we also present current research aimed at handling the full range of whole-genome data.
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