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Similarly, we removed variants that are nearly identical and possibly just the result of sequencing errors or natural genetic variation within a species.
A series of filtering steps removed variants that failed to pass filters and those with greater than 30% of samples missing data.
We removed variants that do not pass the default quality filter, including homozygous calls with quality scores less than 20, or heterozygous calls with quality scores less than 40.
At this stage we also removed variants that were only detected on one direction of sequence read (either forward or reverse), as these are likely to represent sequencing artifacts.
To remove those likely to be due to technical artifacts or incorrect calling of parental genotypes, we removed variants that had been seen before in WGS500, the 1000 Genomes Project or the NHLBI Exome Sequencing Project (ESP).
For example, neither of the two other studies removed variants that showed an excess of homozygous LOF variants in the population (the Hardy Weinberg filter), but we show that this filter is indeed a good indicator of an erroneous SNP call.
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We detected SNPs and indels using VarScan v.2.2.3 (Koboldt et al. 2009), removing variants that occurred on only one strand.
We further reduced the set by removing variants that are not consistent with the genetics of the phenotype segregation resulting in an average of 56 variants per mouse.
In practice, GWAS investigators may perform similar filtering on Cosmopolitan reference panels to remove variants that are underpowered in a particular study.
We also used this Perl script to remove variants that had a total read depth < 5 or < 2 alternative alleles in the dataset.
VCFtools was then used to filter out variants with fewer than 10 observations, followed by BEDtools to remove variants that fell outside of regions with corresponding assembly alignments [ 50].
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