PubMed · 16617250
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Richard Kellermayer. 2006. Probability in gene expression.. https://doi.org/10.1097/01.gim.0000214454.70349.6f
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STALARD (selective target amplification for low-abundance RNA detection) enables isoform-level quantification of low-abundance RNAs using conventional laboratory equipment. Here, we describe steps for RNA isolation, primer design, reverse transcription, selective target amplification, and downstream analysis. The protocol couples selective pre-amplification with a quantitative reverse-transcription PCR (RT-qPCR) readout and optional nanopore sequencing. Using 1 μg input RNA and 12 pre-amplification cycles, STALARD reduces Cq values by approximately 10-12 cycles, bringing the target into a reliably quantifiable range. For complete details on the use and execution of this protocol, please refer to Jeong et al.1.
A beta-glucosidase gene (bglA) from Butyrivibrio fibrisolvens H17c was cloned into the binary vector pGA482 under the control of the 35S Cauliflower Mosaic Virus (CaMV) promoter. A second construct was generated for accumulation of the bglA gene product in the vacuole of transformed tobacco plants. Reverse transcription-polymerase chain reaction analysis demonstrated that the bglA gene was expressed in 71% of cytosol-targeted and 67% of vacuole-targeted transgenic tobacco T(1) plants. T(1) transgenic plants (pGLU100 and pGLU200) exhibited elevated levels of free salicylic acid (SA) with a concomitant significant decrease in the level of glucosylsalicylic acid (GSA) compared to the untransformed tobacco plants and tobacco plants transformed with the empty vector (pGA482). Following inoculation with Tobacco Mosaic Virus (TMV), lesion area was 51% smaller in pGLU100 plants and 60% smaller in pGLU200 plants compared to inoculated untransformed and negative control plants.
BACKGROUND: Microarray has been widely used to measure the relative amounts of every mRNA transcript from the genome in a single scan. Biologists have been accustomed to reading their experimental data directly from tables. However, microarray data are quite large and are stored in a series of files in a machine-readable format, so direct reading of the full data set is not feasible. The challenge is to design a user interface that allows biologists to usefully view large tables of raw microarray-based gene expression data. This paper presents one such interface--an electronic table (E-table) that uses fisheye distortion technology. RESULTS: The Fisheye Viewer for microarray-based gene expression data has been successfully developed to view MIAME data stored in the MAGE-ML format. The viewer can be downloaded from the project web site http://polaris.imt.uwm.edu:7777/fisheye/. The fisheye viewer was implemented in Java so that it could run on multiple platforms. We implemented the E-table by adapting JTable, a default table implementation in the Java Swing user interface library. Fisheye views use variable magnification to balance magnification for easy viewing and compression for maximizing the amount of data on the screen. CONCLUSION: This Fisheye Viewer is a lightweight but useful tool for biologists to quickly overview the raw microarray-based gene expression data in an E-table.