The last target protein developed was based on c1wu0A, and the confidence level and coverage were 99.9%and98%8%, respectively.
Within each simulation, confidence intervals and coverage were assessed with respect to the true (fixed) value of μ1, and then averaged across simulations.
We demonstrate here that by using a simple similarity search valuable information with a high degree of confidence and high coverage can be obtained, even at low sequence identity, thanks to a strategy that carefully equates semantic similarity with shared and ranked biological features.
Table 1 summarizes the LOD and LOQ estimations of Cy3 and Cy5 based on weighted prediction interval at 99% confidence (i.e., α = β = 0.01) and weighted tolerance interval at 99% confidence and 99% coverage (i.e., α = β = 0.01 and P = 0.99).
Given the diversity and richness of the Internet, and the availability of data and information from other sources (for example, traditional public health, syndromic, and laboratory surveillance) of varying degrees of confidence and geographic coverage, how to quantify the payoff of including different sources in biosurveillance systems is unclear.
We achieved high confidence and deep coverage of the phosphoproteome by analyzing a large number of phosphopeptide-enriched fractions and by taking advantage of recent developments in MS hardware (Michalski et al., 2011) and software (Cox and Mann, 2008; Cox et al., 2011).
Phyre identified the merlin sequence specifically as moesin (the fourth part of the FERM domain) from the armyworm (PDB 2ILJA) as their first rated sequence, although the human merlin FERM domain was identified with 100% confidence and 100% coverage but presumably not given the top rating because the sequence was significantly longer than the PDB structure.
MS and MaxQuant identified both gag and gp120 proteins with an ID confidence ≥99% and coverage of 44% and 17%, respectively.
For a specific example of a three-stage drop-the-losers trial, we compare their performance against several alternative estimators in terms of bias, mean squared error, confidence interval width and coverage.
Taking the (slightly) lower number of peptides identified by the alternating CID/ETD approach (compared to CID-only) and the higher confidence and peptide sequence coverage into account the difference between these approaches can roughly be described as "quality versus quantity".
