Procrustes analysis of non-invasive measures and wound tissue metabolome.

(A) Procrustes rotated plot with a Procrustes distance of 0.6817; P<0.001. (B) Procrustes rotated loadings plot highlighting metabolites mostly correlated to the non-invasive measures. 139–1-methyladenosine; 225—linolenic acid; 242—D-(+)-galactose; 253—myo-inositol; 264 –xanthine; 295—linolenic acid; 303 and 304—unknown; 325 and 339—glycerol; 340—shikimic acid; 341—D-(+)-maltose.</p

Analysis of variance–simultaneous component analysis (ASCA) on wound headspace metabolome.

(A) ASCA scores plot for site. (B) ASCA scores plot for time.</p

Procrustes analysis of non-invasive measures and wound headspace metabolome.

(A) Procrustes rotated plot with a Procrustes distance of 0.7803; P = 0.0016. (B) Procrustes rotated loadings plot highlighting metabolites mostly correlated to the non-invasive measures. 8, 25, 30, 69, 75, 92—unknown; 105–1,5-dimethyl-2-oxabicyclo[3.2.1] nonan-7-one; 112–1,3-bis(1,1-dimethylethyl) benzene; 121, 126 –unknown; 129—isobutyl-2,2,4-trimethyl-3-hydroxypentanoate.</p

Analysis of variance–simultaneous component analysis (ASCA) loadings plot for wound headspace metabolome.

(A) ASCA loadings plot for site: 10, 11, 12, 25, 53, 59, 90, 94, 108 –unknown; 111–1,3-bis(1,1-dimethylethyl)benzene. (B) ASCA loadings plot for time: 21, 35, 56, 92, 94—unknown; 105–1,5-dimethyl-2-oxabicyclo[3.2.1] nonan-7-one; 108—unknown; 112–1,3-bis(1,1-dimethylethyl) benzene; 124 –unknown; 129—isobutyl-2,2,4-trimethyl-3-hydroxypentanoate.</p

Wound microbiome.

(A) Mean relative abundances of bacterial taxa between time points. Relative abundance is shown on the Y-axis. Taxa are filtered to those with a mean abundance greater than 1%. (B and C) Boxplots showing the relative abundances of Staphylococcus and Propionibacterium across time points. * denotes P < 0.05, as determined by Wilcoxon’s signed ranks test.</p

Principal component analysis (PCA) and PCA loadings on wound tissue metabolome.

(A) PCA scores plot of principal component (PC) 1 vs. PC2. The total explained variance of PC1 is 36.62% and for PC2 is 18.23%. (B) PCA-loadings plot: 99—unknown; 220—l-glutamine; 277–1,3-dihydroxyacetone dimer; 295 and 296—linolenic acid; 303 and 304—unknown; 325 and 339—glycerol; 335—adenosine.</p

Boxplots showing significantly varied metabolites as identified from multivariate analysis across time points.

Kruskal–Wallis test with accompanying Dunn-Bonferroni post hoc analyses were performed (n = 6). * P P P < 0.001.</p

Effect of k-mer filtering (k-mer) / mapper subtraction (Map) and lowcomplexity filtering (LC) on post-assembly contig numbers.

<p>Effect of k-mer filtering (k-mer) / mapper subtraction (Map) and lowcomplexity filtering (LC) on post-assembly contig numbers.</p

Effect of k-mer filtering (K-mer)/ mapper subtraction (Map) on post-assembly contig number using multiple optimized assemblers with the HCV 9x mean coverage Illumina read dataset.

<p>Effect of k-mer filtering (K-mer)/ mapper subtraction (Map) on post-assembly contig number using multiple optimized assemblers with the HCV 9x mean coverage Illumina read dataset.</p

Optimal word size for viral assembly with multiple assemblers.

<p>Optimal word size for viral assembly with multiple assemblers.</p