PubMed · 8650313
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H Mukai. 1996. [PCR primer].. https://pubmed.ncbi.nlm.nih.gov/8650313/
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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.
Blocks are ungapped multiple sequence alignments representing conserved protein regions, and the Blocks Database consists of blocks from documented protein families. World Wide Web (http://www. blocks.fhcrc.org) and Email (blocks@blocks.fhcrc.org) servers provide tools for homology searching and for analyzing protein family relationships. New enhancements include a multiple alignment processor that extends the use of these tools to imported multiple alignments of families not present in the database and a PCR primer designer that implements a new strategy for gene isolation.
The diagnosis of hepatitis C is based on serological testing for antibodies against various epitopes of the hepatitis C virus (HCV) and detection of HCV RNA in serum, because anti-HCV antibodies alone cannot discriminate patients who are infectious from those who have resolved the infection. If HCV RNA is not detected, which is the case in at least 20% of enzyme immunoassay (EIA)-positive patients, diagnosis remains unclear in a state of disease possibly well suited for therapeutic intervention. Therefore, we investigated if detection of HCV antigens or HCV RNA in routinely processed, formalin-fixed and paraffin-embedded (ffpe) liver biopsy specimens of patients positive for anti-HCV, but negative for HCV RNA in serum, could confirm diagnosis in this serological constellation. We detected HCV RNA by reverse-transcription polymerase chain reaction (RT-PCR) in 27 (61%) of 44 ffpe liver biopsies from EIA-positive, but HCV-RNA-seronegative, patients. Testing of 18 of these biopsies by a panel of polyclonal antibodies against structural and nonstructural HCV proteins revealed positive immunostaining in 6 cases (33%), which were also positive by RT-PCR. Most biopsies showed necroinflammation compatible with chronic hepatitis C, and the detection of tissue HCV RNA correlated significantly with a higher grade of inflammatory activity. Detectability of HCV RNA did not correlate with HCV subtype. In conclusion, the search for HCV RNA by RT-PCR within the liver biopsy specimen can establish rapid and unequivocal diagnosis of hepatitis C in at least 60% of anti-HCV antibody-positive patients who are seronegative for HCV RNA, and thus may help to avoid repeated testing and delayed therapy. Tissue RT-PCR may also be more efficient than serological testing for surveillance of interferon therapy response, because ongoing chronic active hepatitis C is clearly demonstrated in the absence of detectable serum HCV RNA.