Glycerol Reduces Cross Hybridization on Nitrocellulose Membrane

Lateral flow assay (LFD) based nucleic acid lateral flow (NALF) method has been developed recently. The method met point of care testing (POCT) as simple and rapid procedures, less equipment, and can be performance by less skilled technician. NALF based on nucleic acid hybridizationis more economical then immunochromatography assay which use antibody-antigen recognition. Cross hybridization has issued while used to differentiate organism with high GC content and high homology as high similarity genome. Some techniques has applied to give high stringency condition avoid cross hybridization reaction but need more procedure to apply. We found glycerol applied to buffer assay could reduce cross hybridization on nitrocellulose membrane. The study used 2 kinds of high stringency buffer as PBS and SSC bases and high concentration of ssDNA amplicon as sample. Without glycerol ingredient gave cross hybridization signal on test line. But used glycerol could reduce those even omitted with PBS based buffer assay. Beside those, glycerol could significantly increased hybridization signal in SSC based buffer assay (p<0.05).

Northern Blot Analysis

The whole workflow for the quantification of specific transcripts by Northern blot analysis is described in detail, including RNA isolation, probe generation via labelling with Dig-dUTP, hybridization, signal visualization and quantification.

Correction of Spatial Bias in Oligonucleotide Array Data

Background. Oligonucleotide microarrays allow for high-throughput gene expression profiling assays. The technology relies on the fundamental assumption that observed hybridization signal intensities (HSIs) for each intended target, on average, correlate with their target’s true concentration in the sample. However, systematic, nonbiological variation from several sources undermines this hypothesis. Background hybridization signal has been previously identified as one such important source, one manifestation of which appears in the form of spatial autocorrelation. Results. We propose an algorithm, pyn, for the elimination of spatial autocorrelation in HSIs, exploiting the duality of desirable mutual information shared by probes in a common probe set and undesirable mutual information shared by spatially proximate probes. We show that this correction procedure reduces spatial autocorrelation in HSIs; increases HSI reproducibility across replicate arrays; increases differentially expressed gene detection power; and performs better than previously published methods. Conclusions. The proposed algorithm increases both precision and accuracy, while requiring virtually no changes to users’ current analysis pipelines: the correction consists merely of a transformation of raw HSIs (e.g., CEL files for Affymetrix arrays). A free, open-source implementation is provided as an R package, compatible with standard Bioconductor tools. The approach may also be tailored to other platform types and other sources of bias.