Your English writing platform
Discover LudwigExact(2)
Custom written script was used for additional sorting and filtering of the pileup output based on a nucleotide depth cutoff of 10 for each SNP.
Next, to reduce the identification of false positive SNPs, we filtered potential SNPs using a stringent nucleotide depth cutoff of 10 [e.g., at least 10 adenines (A) in one genotype vs. at least 10 guanines (G) in the other genotype] for each genotype (see Methods for details).
Similar(58)
The 17-nucleotide depth distribution suggests a genome size of 323 Mb, larger than peach (220 Mb, http://www.rosaceae.org/peach/genome), but close to our estimate of 250 Mb from flow cytometry using rice as the reference (date not shown).
This increased transcriptome nucleotide coverage depth facilitated de novo assembly, enhanced the sequencing accuracy, and avoided possible contamination.
These nucleotide read depth thresholds allowed us to examine 90.99% of the mappable genome for SNPs [Additional File 1: Supplementary Table S2].
Consensus contigs were defined as contiguous regions with a per nucleotide read depth greater than or equal to 3 for all lanes within a particular experiment (Additional File 4, 5).
For SNP-calling, we settled on a approach that required a nucleotide read depth ≥6× per position, with ≥80% base-calls supporting a SNP in the evolved genome data and ≥5× read depth, with ≥70% base-calls supporting a different base in the parental genome data.
To detect copy-number polymorphisms (CNPs), we averaged the per-nucleotide read depth data across 25 bp bins across the unique nuclear genome and normalized by the total nuclear bases acquired.
By clicking on a block, a window of detailed information about nucleotide variations and depth pops up (Fig. 1l).
Note that, with next generation sequencing data at low depth, nucleotide diversity cannot be simply computed dividing the number of SNPs called by the length of sequence assembled.
This standardized NGS pipeline demonstrated 100% sensitivity and 100% specificity, uniformity, and high-depth nucleotide coverage per sample (approximately 7000 reads per nucleotide).
Write better and faster with AI suggestions while staying true to your unique style.
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