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The protein matches were filtered to pick the best-matching protein to each nucleotide sequence, at each position, as described previously [ 3].
Therefore, motif matches were filtered using all 4 sets of conserved non-coding sequences from De Witte et al. (http://bioinformatics.intec.ugent.be/blsspeller/) (De Witte et al. 2015) and DNaseI-hypersensitive sites downloaded from Zhang et al. (2012).
Finally all the matches were collected together and redundant matches were filtered out.
Once the matches were filtered, we began to reconstruct the TE copies, to obtain a true annotation of TE copies and not of TE fragments only.
For high confidence peptide identification, peptide matches were filtered with an expectation value of less than 0.05 in the Mascot search.
The peptide matches were filtered based on cross-correlation scores (Xcorr) of 1.9, 2.2 and 3.75 for charge states 1+, 2+, and 3+, respectively.
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Our method has the following characteristics: (1) the images are matched by leveraging sparse mesh based image synthesis; (2) the putative point matches are filtered by geometrical consistency check and geometrical model verification; and (3) the two point clouds are merged via bundle adjustment by linking the ground-to-aerial tracks.
Second, all envelope matches are filtered based on the number of missing peaks and the number of consecutive matched peaks.
To generate a list of cross-reactive probes, all matches are filtered based on one additional criterion, the total number of bases matched (47 bases for both Infinium I and II), derived from the sex methylation analysis.
Exons with no perfect match were filtered out of the training set.
Peptide spectral matches (PSMs) were filtered to an initial peptide-level false discovery rate (FDR) of 1% with subsequent filtering to attain a final protein-level FDR of 1%.
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