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We avoided over-optimistic performance estimates by removing sequence similarity between proteins used for method development (training/cross-training) and testing.
After removing sequence duplicates, we created a multiple alignment of the sequences using MUSCLE [ 32] (using the following parameters: -maxiters 2 and -diags).
After prefiltering the raw data by removing sequence adaptors and low quality reads, the tags were mapped to the human genome (hg19) by Bowtie software.
After removing sequence reads containing low-quality sequences (reads with ambiguous bases 'N'), adapter sequences, and reads with more than 10%Q<200 bases, mRNA transcriptome de novo assembly was performed using the Trinity program [ 26].
By removing sequence redundancies and alternative splice forms of the same gene, we identified 11 putative OSCA genes and named them OsOSCA1.1 to OsOSCA4.1, in accordance with Arabidopsis orthologues (Additional file 1: Table S1).
In the meantime, many other studies [ 52 56] found that the single biggest cause of the over-estimation problem is the sequence errors or noise, so new methods such as SLP [ 52], PyroNoise [ 54], Denoiser [ 55] and Ampliconnoise [ 56] focus at identifying and removing sequence noise.
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We subsequently improved the software by removing sequences where doing so led to fully complementary electrostatic pairings.
Quality control included removing sequences with ambiguous base calls, or ones that did not match the primers perfectly [90].
Filtering for sequence ambiguity was performed by removing sequences with homopolymers and removing sequences with a high rate of re-sampling.
This could include removing sequences with a low average quality, removing sequences with too many low-quality individual bases, or masking low-quality positions with Ns.
Removing sequences exceeding 400 nt in length and similar sequences using blastclust leaves 363 bacterial ncRNAs.
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