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Comparison results from applying both filterY and trim steps (filterY&trim) with those from performing trim step alone also revealed the useless of filterY step on improving SNP calling performance (Table 2 and Figure 1E).
Results from applying both filterY and trim steps (filterY&trim) compared with those from performing filterY step alone also revealed that trim step would increase the number of false positives (Table 2 and Figure 1C).
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Application of the trim step might even introduce false positives, especially for high-coverage data.
Compared with low coverage data, the problem of introducing false positives caused by the trim step is more serious for high coverage data (Additional file 1).
The trim step obtained the largest number of mapped reads, while the filterY produced the fewest number of mapped reads resulting from lots of low-quality reads being discarded.
The novel variants unique to the trim call set had a much lower Ti/Tv ratio (0.98) compared with the Ti/Tv ratio (1.49) of those unique to the raw call set, which suggested that more false positive variants were introduced by the trim step.
Although the trim step helped align more reads and identify slightly more variants (1.6%, ~651 k vs. ~641 k), it obtained a lower dbSNP rate (77.21%) and a lower novel transition/transversion ratio (Ti/Tv ratio) (1.58) compared with those using raw sequencing data (dbSNP: 77.91%, novel Ti/Tv ratio: 1.65) (Table 2).
This is supported by our previous findings where Acinetobacter and Pseudomonas spp. were detected on Pangasius samples collected at the filleting and trimming steps of processing in Vietnamese companies (Tong Thi et al. 2013).
Of the total of 5 300 ESTs, 770 were removed during the quality trimming steps.
Unpaired-end reads resulting from the previous trimming steps were discarded.
These trimming steps were achieved using internal software based on the FastX package [ 29].
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