Wenzhong Zhao | PubMed Health

PubMed2003

NS2a-NS2b sequence analysis of DEN-4 virus strains.

To study the genetic variability of DEN-4 Chinese isolates, and to trace the origin of DV4 Chinese isolates, we cloned and sequenced the NS2a-NS2b junction of 5 isolates and prototype DV4 (H-241). Our results show that isolates from the 1990 Guangdong epidemic, which were isolated in the early, middle, and late periods of the epidemic, share the same sequence in the NS2a-NS2b junction. The sequence similarity between isolates from the Guangdong epidemic in 1990 and DV4 H-241 is 96%; these isolates can be grouped into genotype I. The sequence similarity between the isolate from the Guangdong epidemic in 1987 and Dominica strain 814669 is 96%; this isolate can be grouped into genotype II. For the first time, our results show that there are also 2 DV4 genotypes in the Guangdong area of southern China, and these isolates perhaps were introduced from other epidemic areas outside of China.

Base Sequence↗

PubMedDate not supplied

Efficient RNAi-based gene family knockdown via set cover optimization.

OBJECTIVE: We address the problem of selecting an efficient set of initiator molecules (siRNAs) for RNA interference (RNAi)-based gene family knockdown experiments. Our goal is to select a minimal set of siRNAs that (a) cover a targeted gene family or a specified subset of it, (b) do not cover any untargeted genes, and (c) are individually highly effective at inducing knockdown. METHODS AND MATERIAL: We give two formalizations of the gene family knockdown problem. First, we show that the problem, minimizing the number of siRNAs required to knock down a family of genes, is NP-Hard via a reduction to the set cover problem. Second, we generalize the basic problem to incorporate additional biological constraints and optimality criteria. To solve the resulting set-cover variants, we modify the classical branch-and-bound algorithm to include some of these biological criteria. We find that in many typical cases these constraints reduce the search space enough that we are able to compute exact minimal siRNA covers within reasonable time. For larger cases, we propose a probabilistic greedy algorithm for finding minimal siRNA covers efficiently. We apply our methods to two different gene families, the FREP genes from Biomphalaria glabrata and the olfactory genes from Caenorhabditis elegans. RESULTS AND CONCLUSION: Our computational results on real biological data show that the probabilistic greedy algorithm produces siRNA covers as good as the branch-and-bound algorithm in most cases. Both algorithms return minimal siRNA covers with high predicted probability that the selected siRNAs will be effective at inducing knockdown. We also examine the role of "off-target" interactions: the constraint of avoiding covering untargeted genes can, in some cases, substantially increase the complexity of the resulting solution. Overall, we find that in many common cases our approach significantly reduces the number of siRNAs required in gene family knockdown experiments, as compared to knocking down genes independently.

Algorithms↗