Sentence examples for scoring peaks from inspiring English sources

It is interesting that, when using only the top scoring peaks from each peak-caller, the results, in terms of motif containing regions, are fairly equal regardless of the type of data used: statistical background, experimental background or normalized data.

These scoring peaks can be filtered out by setting a threshold on the minimum number of sequences present in the multi-species alignment (see Additional file 1: Figure S5B-D).

Alleles were scored using Genemarker v2.4.0 (SoftGenetics, USA) with manual editing of the automatically scored peaks.

To score the peaks, we used the percent of the total area contributed by each peak and then scaled the scored peaks to add up to 100% in each individual.

One locus (Mm09) did not amplify and another one did not yield easily scored peaks (Mm119), hence only 8 of 10 loci were eventually used for parentage assignment and genetic analyses.

Only peptides possessing tryptic termini and exhibiting a score of ≥10.5, and a scored peak intensity of ≥70%, were considered legitimate identifications.

Peptides were validated by fixed thresholds of forward reverse scores >8 and scored peak intensities >70.

We also lowered the scored peak intensity percent thresholds for the ETD validation.

Protein identifications were validated with the following filters: protein score >11, peptides score >6 and scored peak intensity (SPI) >60%%.

For peaks matched to multiple peptides, a single peptide was selected based on the highest Spectrum Mill score and percent scored peak intensity (% SPI) and the lowest Spectrum Mill reverse score.

A Spectrum Mill auto-validation was first performed in the protein details, followed by peptide mode using default values [Minimum scores, minimum scored peak intensity (SPI), forward minus reversed score threshold, and rank 1 minus rank 2 score threshold].