PubMed · 10524297
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D Kumari, K Usdin. 1999. Sequencing errors in reactions using labeled terminators.. https://doi.org/10.2144/99274bm02
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Plant viruses pose a significant threat to global agriculture and require efficient tools for their timely detection. We present AutoPVPrimer, an innovative pipeline that integrates artificial intelligence (AI) and machine learning to accelerate the development of plant virus primers. The pipeline uses Biopython to automatically retrieve different genomic sequences from the NCBI database to increase the robustness of the subsequent primer design. The design_primers_with_tuning module uses a random forest classifier that optimizes parameters and provides flexibility for different experimental conditions. Quality control measures, including the evaluation of poly-X content and melting temperature, increase primer reliability. Unique to AutoPVPrimer is the visualize_primer_dimer module, which supports the visual evaluation of primer dimers-a feature missing in other tools. Primer specificity is validated via primer BLAST, which contributes to the overall efficiency of the pipeline. AutoPVPrimer has been successfully applied to the tomato mosaic virus, proving its adaptability and efficiency. The modular design allows customization by the user and extends the applicability to different plant viruses and experimental scenarios. The pipeline represents a significant advance in primer design and provides researchers with an effective tool to accelerate molecular biology experiments. Future developments aim to extend compatibility and incorporate user feedback to consolidate AutoPVPrimer as an innovative contribution to the bioinformatics toolbox and a promising resource for the advancement of plant virology research.
A highly sensitive and specific assay method was developed for the detection of viable Escherichia coli as an indicator organism in water, using nucleic acid sequence-based amplification (NASBA) and electrochemiluminescence (ECL) analysis. Viable E. coli were identified via a 200-nt-long target sequence from mRNA (clpB) coding for a heat shock protein. In the detection assay, a heat shock was applied to the cells prior to disruption to induce the synthesis of clpB mRNA and the mRNA was extracted, purified, and finally amplified using NASBA. The amplified mRNA was quantified with an ECL detection system after hybridization with specific DNA probes. Several disruption methods were investigated to maximize total RNA extracted from viable cells. Optimization was also carried out regarding the design of NASBA primer pairs and detection probes, as well as reaction and detection conditions. Finally, the assay was tested regarding sensitivity and specificity. Analysis of samples revealed that as few as 40 E. coli cells/mL can be detected, with no false positive signals resulting from other microorganisms or nonviable E. coli cells. Also, it was shown that a quantification of E. coli cells was possible with our assay method.
Gene disruptions are a vital tool for understanding Saccharomyces cerevisiae gene function. An arrayed library of gene disruption strains has been produced by a consortium of yeast laboratories; however their use is limited to a single genetic background. Since the yeast research community works with several different strain backgrounds, disruption libraries in other common laboratory strains are desirable. We have developed simple PCR-based methods that allow transfer of gene disruptions from the S288C-derived strain library into any Saccharomyces strain. One method transfers the unique sequence tags that flank each of the disrupted genes and replaces the kanamycin resistance marker with a recyclable URA3 gene from Kluyveromyces lactis. All gene-specific PCR amplifications for this method are performed using a pre-existing set of primers that are commercially available. We have also extended this PCR technique to develop a second general gene disruption method suitable for any transformable strain of Saccharomyces.