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The actual level of polymorphism detected by automated fragment analysis using fluorescent dye labeled primers was much higher than that based on in silico analysis.
These markers are amenable to high throughput genotyping due to multiplexing and efficient resolution of amplicons by automated fragment analysis [ 2, 3].
The actual level of polymorphism detected by automated fragment analysis using 47 fluorescent dye labeled primers was much higher than that based on in silico analysis although the trend was maintained.
PCR products were visualized on agarose gel stained with ethidium bromide and compared to a control with 30 CGGs; PCR products that appeared larger were further analyzed by automated fragment analysis in a 3730 xl DNA Analyzer (Applied Biosystems, Foster City, CA) for assessment of repeat number.
The primary aim of the present study was, therefore, to improve the efficiency of MLVA as a typing scheme for discrimination of F. noatunensis isolates, through development of a single tube multiplex PCR amplification followed by automated fragment analysis using capillary electrophoresis.
The level of polymorphism detected by automated fragment analysis using 50 of each fluorescent dye-labelled TFGMS (39.7% polymorphism, PIC 0.53 and 1 8 alleles) and TFFDMS markers (34.3%, 0.43 and 1 6) was much higher than based on metaphor agarose gel with the normal unlabelled markers.
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Precise allele sizing in automated fragment analysis system has encouraging implications to various high-throughput genotyping applications in sugarcane.
Molecular weights of PCR-generated fragments for loci 1982 and 3232 for some isolates exceeded 1 kb, and results of automated fragment analysis were inconsistent.
For automated fragment analysis, the amplified FAM-labelled PCR products along with ABI GeneScan-600 LIZ size standard (Applied Biosystems, IL, USA) were resolved in automated 96 capillary ABI 3730xl DNA Analyzer.
However, genotyping using the automated fragment analysis system in this study revealed comparable level of polymorphism with the genic and genomic microsatellite markers in sugarcane.
This background knowledge in the form of binding relevant substructures can either be derived by hand selection or by automated fragment-based data mining methods.
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