An efficient algorithm for the stochastic simulation of the hybridization of DNA to microarrays

<p>Abstract</p> <p>Background</p> <p>Although oligonucleotide microarray technology is ubiquitous in genomic research, reproducibility and standardization of expression measurements still concern many researchers. Cross-hybridization between microarray probes and non-target ssDNA has been implicated as a primary factor in sensitivity and selectivity loss. Since hybridization is a chemical process, it may be modeled at a population-level using a combination of material balance equations and thermodynamics. However, the hybridization reaction network may be exceptionally large for commercial arrays, which often possess at least one reporter per transcript. Quantification of the kinetics and equilibrium of exceptionally large chemical systems of this type is numerically infeasible with customary approaches.</p> <p>Results</p> <p>In this paper, we present a robust and computationally efficient algorithm for the simulation of hybridization processes underlying microarray assays. Our method may be utilized to identify the extent to which nucleic acid targets (e.g. cDNA) will cross-hybridize with probes, and by extension, characterize probe robustnessusing the information specified by MAGE-TAB. Using this algorithm, we characterize cross-hybridization in a modified commercial microarray assay.</p> <p>Conclusions</p> <p>By integrating stochastic simulation with thermodynamic prediction tools for DNA hybridization, one may robustly and rapidly characterize of the selectivity of a proposed microarray design at the probe and "system" levels. Our code is available at <url>http://www.laurenzi.net</url>.</p

Transcriptome Analysis of Medicinal Plant <i>Salvia miltiorrhiza</i> and Identification of Genes Related to Tanshinone Biosynthesis

<div><p><i>Salvia miltiorrhiza</i> Bunge, a perennial plant of Lamiaceae, accumulates abietane-type diterpenoids of tanshinones in root, which have been used as traditional Chinese medicine to treat neuroasthenic insomnia and cardiovascular diseases. However, to date the biosynthetic pathway of tanshinones is only partially elucidated and the mechanism for their root-specific accumulation remains unknown. To identify enzymes and transcriptional regulators involved in the biosynthesis of tanshinones, we conducted transcriptome profiling of <i>S. miltiorrhiza</i> root and leaf tissues using the 454 GS-FLX pyrosequencing platform, which generated 550,546 and 525,292 reads, respectively. RNA sequencing reads were assembled and clustered into 64,139 unigenes (29,883 isotigs and 34,256 singletons). NCBI non-redundant protein databases (NR) and Swiss-Prot database searches anchored 32,096 unigenes (50%) with functional annotations based on sequence similarities. Further assignments with Gene Ontology (GO) terms and KEGG biochemical pathways identified 168 unigenes referring to the terpenoid backbone biosynthesis (including 144 MEP and MVA pathway genes and 24 terpene synthases). Comparative analysis of the transcriptomes identified 2,863 unigenes that were highly expressed in roots, including those encoding enzymes of early steps of tanshinone biosynthetic pathway, such as copalyl diphosphate synthase (SmCPS), kaurene synthase-like (SmKSL) and CYP76AH1. Other differentially expressed unigenes predicted to be related to tanshinone biosynthesis fall into cytochrome P450 monooxygenases, dehydrogenases and reductases, as well as regulatory factors. In addition, 21 <i>P450</i> genes were selectively confirmed by real-time PCR. Thus we have generated a large unigene dataset which provides a valuable resource for further investigation of the radix development and biosynthesis of tanshinones.</p> </div