The genotype analysis shown in Figures1and2includes 193 non-human non-avian influenza strains. All data was downloaded from the NCBI influenza 4SC-202 price whole genome database [30]. Finding markers tied to function Figure4shows the frequency distribution for the size of amino acid combinations (combinations up to size 10 were checked) that distinguish avian and human strains at the different accuracy thresholds. The highest accuracy threshold of 99.5% (red bar in Figure4) requires using more mutations per combination to accurately discriminate host type. For example, a minimum of 3 amino acid positions are required,

with most combinations using 4 or more amino acid positions. By contrast,

at the lowest accuracy thresholds, only single or pairs of amino acids are needed. Figure 4 Mutation combination sizes. Relative frequency JQ-EZ-05 of mutation combination sizes for different classification accuracy thresholds. Red is the highest accuracy cut off, followed by blue, orange and green. In Chen et al. (2006) functional significance was calibrated to detect the 627 PB2 mutation. A feature of the 627 PB2 mutation is that the human variant (Lysine) was found in 1% of the background avian flu and 23% of the H5N1 avian flu (~5% total) suggesting less human specific selective pressure. Thus distinguishing at the minimal accuracy threshold (set at 98.3%) using 627 PB2 required at least one additional marker. From the combinations of amino acid positions used for discrimination, an individual marker’s functional significance was determined by two Acyl CoA dehydrogenase criteria. The marker must be part of a combination of mutations that separates the two phenotype classes with the same degree of accuracy (at one of the four confidence thresholds) that was achieved using the complete proteome alignment as input. Second the marker’s individual contribution to the combination’s classification accuracy must be above

a minimal threshold defined by the distribution of observed contribution values. A mutation’s contribution value was measured by the maximal increase in classification accuracy gained by adding the marker as a feature to one of the classifiers that met the minimal accuracy requirements. For example, mutation 627 PB2 could be combined with IWR 1 several additional mutations to make an accurate classifier. The classification accuracy of each of the additional mutations was measured without including 627 PB2 and compared to the accuracy when including 627 PB2, with the maximal difference being 627 PB2′s contribution value. Figure5plots the contribution values for each candidate marker’s maximal contribution to classification accuracy for the 4 different accuracy thresholds.