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As a result, the neutral pH was selected as the appropriate pH for per run.
The RNA was then checked for RNA integrity and run as per manufacturer's guidelines on the Human Gene 1.0 ST chip (Affymetrix).
The video above is one of our system's better runs (loosely defined as achieving a good per run extinguishing success rate in a short amount of system uptime).
The structure was solved using 800 simulated annealing runs of 2.5 × 10 moves per run as implemented in DASH.
As error rate varies per run, we estimated per run error rate based on sequencing errors from Roche's internal homopolymer controls, which were kindly provided by the Genome Quebec Innovation Centre (McGill University).
The structure was solved using 200 simulated annealing runs of 2.5 × 10 moves per run as implemented in DASH, allowing 7 degrees of freedom (6 external and 1 internal).
The high density in the average depth of sequences by NGS suggests that an increase in the DNA sample numbers for sequencing beyond 46 per run as described here is likely to contribute to even lower costs.
The first 25% of samples were discarded as burnin, leaving 75,001 trees per run.
As reagent cost is ~$5.00 per run ($0.10/SNP), the method is cost-effective for targeted genotyping of up to 500 to maximally 1000 candidate SNPs.
As you can equip multiple cards per run, there are many thousands of potential "load-outs" with which to fine-tune the balance between skill and risk.
The initial 25% of the samples were discarded as burn-in, leaving 1501 trees per run, when we summarized the trees using TreeAnnotator implemented in the BEAST v1.5.4 package.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com