Chromosomal alignment of genes.

<p>a) Reads aligning to coding genes were mapped back to the human chromosomes and represented as the % reads aligning to each chromosome. All chromosomes of the human genome are represented within the urinary microvesicles including the mitochondrial chromosome (M). Solid bars – +DNase sample, lined bars – -DNase sample. b) Correlation of reads aligning to coding genes in the +DNase and -DNase samples suggests little if any DNA was present (R² = 0.9664).</p

Top 10 expressed ncRNA in microvesicles (+DNase).

<p>The most abundantly expressed ncRNA as defined by the RNAdb <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096094#pone.0096094-Pang1" target="_blank">[14]</a> are listed by number of reads present in the microvesicle population, the RNAdb ID, ncRNA name and accession ID number as identified in the DNase treated sample.</p

Analysis of the top 50 genes found in urinary exosomes.

<p>The top 50 most highly expressed genes were determined and grouped in terms of function or name. a) In the +DNase sample 76% of genes were related to ribosomal proteins and a further 6% related to translation regulation. Other genes related to ferritin, prostate specific genes, cell regulation and novel genes were also featured. A similar distribution was also seen for the -DNase sample (b). In both instances the TPT1 gene was the most abundantly expressed gene.</p

Mapping of coding genes to the genitourinary epithelium.

<p>a) Cartoon of the genitourinary system, highlighting specialized regions including the glomerulus (1), proximal tubules (2), medullary thick ascending limb (3), distal convoluted tubule (4), collecting duct (5), bladder (6) and prostate (7). b) The number of deep sequencing reads normalized to gene length were graphed to produce a transcriptional profile for each of the sub-regions of the genitourinary tract (solid bars – +DNase, lined bars – -DNase).</p

Urinary microvesicle RNA integrity and alignment to the genome.

<p>a) RNA isolated from urinary microvesicles was shown to be of high integrity with prominent 18S and 28S rRNA peaks when analyzed using the Agilent Bioanalyzer. Red trace 1.7 ng RNA with DNase, Blue trace 2.2 ng RNA without DNase. b) Flow chart outlining sample processing. An initial RNase and DNase digestion was carried out to remove extraneous nucleic acids co-isolating with the microvesicle pellet. To determine the proportion of potential DNA inside the microvesicles the extracted RNA was divided into two groups; No DNase digestion (-DNase), which yields RNA+DNA and DNase digested (+DNase) which yields RNA. c) Both the -DNase and the +DNase samples showed a similar trend in read distribution with ∼88% of reads mapping to rRNA, ∼4% mapping to genes and ∼6% mapping to ncRNA. A smaller proportion of reads (<0.1%) mapped to mitochondrial genome. Approximately 2% of reads failed to hit the human genome. (mito – mitochondrial).</p

Top 10 expressed ncRNA in microvesicles (-DNase).

<p>The most abundantly expressed ncRNA as defined by the RNAdb <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096094#pone.0096094-Pang1" target="_blank">[14]</a> are listed by number of reads present in the microvesicle population, the RNAdb ID, ncRNA name and accession ID number as identified in the sample without DNase treatment.</p

Alignment of microvesicle non-coding RNA to repeat regions.

<p>Repeat class was defined by the UCSC repeatMasker dataset <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0096094#pone.0096094-GuduricFuchs1" target="_blank">[12]</a> and the number of known repeats listed under ‘# known’. All repeat classes were found in microvesicles and the number of hits and percentage hits are shown for each sample, with DNase treatment (+DNase) (RNA sample) and without DNase treatment (-DNase) (RNA+DNA sample). SINE-Short interspersed nuclear elements (which include ALUs), LINE-Long interspersed nuclear elements, LTR-Long terminal repeat elements (which include retrotransposons), DNA repeat elements, Simple repeats (micro-satellites), Low complexity repeats, Satellite repeats, RNA repeats (which includes RNA, rRNA – ribosomal RNA, scRNA – small cytoplasmic RNA, snRNA – small nuclear RNA, srpRNA – signal recognition particle RNA, tRNA – transfer RNA), Other repeats (including Rolling Circle (RC)) were detected within the non-ORF reads of microvesicles and presented as hits and % hits detected.</p

Phase information increased sensitivity, and base quality scores increased specificity.

<p>We compared <i>V-Phaser</i> to alternate versions of <i>V-Phaser</i> with specific components disabled. In the No Phase version, <i>V-Phaser</i> called variants without phase information. In the Uniform Errors version, <i>V-Phaser</i> estimated uniform error rates within homopolymer and nonhomopolymer regions without regard to assigned base qualities. In the No Filtering version, <i>V-Phaser</i> did not filter out low quality bases. (<b>A</b>) Phase information increased sensitivity. The version without phase information attained a sensitivity of 90%, but all other versions of <i>V-Phaser</i> used phase information and attained a sensitivity of 97% or more. We calculated sensitivity as the percentage of known variants correctly identified. Data are from WNV mixed population control dataset. (<b>B</b>) Individual base quality scores increased specificity. Among loci with mismatches, the Uniform Errors version had only 91% specificity, but all other versions incorporated base quality scores in their probability model and attained 97% specificity or more. We calculated specificity as the percentage of loci in the control sample correctly identified as having no variants among loci that had at least one candidate variant. Data are from infectious clone (HIV NL4-3) control dataset.</p

Phase increased sensitivity to detect variants.

<p>Phase increased sensitivity to detect variants, as seen over a range of error rates at coverages of 100-fold, 250-fold, and 500-fold. The <i>phased variant detection threshold frequency (VDTF)</i> is the lowest frequency of reads with variants at two specific loci that <i>V-Phaser</i> can distinguish from error among reads that span both loci. The <i>unphased VDTF</i> is the lowest frequency of one variant that <i>V-Phaser</i> can distinguish from error among reads that cover that locus. 100-fold <i>phased</i> sequence coverage achieves comparable detection thresholds as 500-fold <i>unphased</i>. We use Equation 7 to calculate the <i>phased</i> and <i>unphased VDTFs</i>. (See the <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002417#s4" target="_blank">Materials and Methods</a> section for Equation 7 and its derivation.)</p

NQS filtering improves fit of probability model to data.

<p>(<b>A</b>) Quantile-quantile (q-q) plots under NQS filtering show good fit of the probability model to the observed distribution of errors. Since the probability model is discrete, p values are projected onto a uniform distribution, and the distribution of projected p values is compared with the expected null distribution. See <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002417#s4" target="_blank">Materials and Methods</a> section for details. (<b>B</b>) In contrast, q-q plots under no filtering show that no filtering skews the calibration of the probability model used by <i>V-Phaser</i>. Q-q plots of models based on subsets of the reads demonstrate that this effect becomes more pronounced with increasing coverage (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002417#pcbi.1002417.s001" target="_blank">Figure S1</a>). Q-q plots are scaled to fit curve, so y = x line is not at a 45 degree angle.</p