The number of position weight matrices for select organisms before and after homology mapping.

<p>The number of matrices that are initially associated with each organism is compared to the number following mapping of transcription factors with completely-identical sequences, as well as the increase following identical DNA binding domain-level mapping for the (A) JASPAR, (B) TRANSFAC, and (C) JASPAR & TRANSFAC databases. The JASPAR and TRANSFAC databases initially contained PWMs from 124 different species, compared to 1578 species following domain-level homology mapping. In particular, significantly increased PWM coverage is possible through domain-level mappings for the open-access JASPAR database.</p

Self and cross Spearman correlation coefficients (

<p><b>) between position weight matrix-based scores and experimental PBM fluorescence intensities.</b> The blue points are the completely-identical and domain-identical transcription factor pairs of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042779#pone-0042779-g001" target="_blank">Figure 1</a>. The alignment of blue points along the gray diagonal line demonstrates the comparable performance of PWMs derived from completely-identical and domain-identical transcription factor pairs, whereas the magnitude of is an indication of how well the PWM captures the DNA binding properties of the transcription factor. As a point of comparison, the correlation coefficients for all other pairwise sets of transcription factors were calculated. The green points below the gray diagonal are indicative of PWMs from other transcription factors that failed to capture the DNA binding properties in the PBM data. Green points near the diagonal resulted from other transcription factors within the same domain family (<i>e.g.</i>, homeodomain) that have similar PWMs and, therefore, DNA binding properties. UniPROBE identifiers UP00017 and UP00389 were significantly outperformed by other PWMs in the data set (see text for details).</p

Average precision curves, calculated as the number of top <i>n</i> position weight matrix-based scores and experimental PBM fluorescence intensities in common.

<p>Precision curves were generate for cross scoring of completely-identical pairs (CCI), cross scoring of domain-identical pairs (CDI), self scoring of completely-identical pairs (SCI), and self scoring of domain-identical pairs (SDI) listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042779#pone-0042779-g001" target="_blank">Figure 1</a>. The average precision is nearly exactly overlaying for CCI and SCI, as well as CDI and SDI, owing to the ability of self and cross PWM scans to equivalently capture the DNA binding properties in the PBM data. As with the Spearman correlation coefficients in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042779#pone-0042779-g004" target="_blank">Figure 4</a>, the average precision for the domain-identical data set actually outperformed the completely-identical transcription factor pair scoring, reflecting the more challenging nature of the completely-identical data set (see text for details).</p

Spearman correlation coefficients (

<p><b>) for position weight matrix (PWM) scanning of transcription factor pairs and their accompanying experimental protein binding microarray (PBM) fluorescence intensities.</b> Transcription factor pair groupings, as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042779#pone-0042779-g001" target="_blank">Figure 1</a>, were cross scans of completely-identical pairs (CCI), cross scans of domain-identical pairs (CDI), self scans of completely-identical pairs (SCI), and self scans of domain-identical pairs (SDI). Each point represents a PWM:PBM pairing as described in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042779#s2" target="_blank">Methods</a>. The transcription factor Elf3 (UniPROBE identifiers UP00090 and UP00407) was an outlier with the lowest correlation coefficients. The lower correlation coefficients for these identifiers is likely due to the transcription factor Elf3 having two different DNA binding domains.</p

The distribution of Spearman correlation coefficients for the domain-identical PWM and all other PWMs from the same homeodomain family for each TF from the test set in <b>Figure 1</b>.

<p>In each case, the correlation coefficient for the domain-identical PWM either clearly outperforms or is in the cluster of top performing PWMs, demonstrating that domain-identical PWMs capture the DNA sequence affinity and specificity of transcription factors better than considering the TF family alone.</p

Differential Pathway Associations for PTEN clusters in Uterine Corpus Endometrial Carcinoma.

<p>Clusters without significant pathway associations are omitted for clarity. A false discovery rate of 1% was used to filter for significance.</p

Clusters Highlighted in Protein Structures.

<p><b>A)</b> PIK3CA (gray) bound to PIK3R1 (orange). PIK3CA has two clusters (539–547 in green and 1043–1049 in blue) with very different global gene expression association significance levels in Breast Cancer (BRCA) discussed in the text. <b>B)</b> Residues 30–40 of CTNNB1 (blue) bound to BTRC (gray). This region of Beta-catenin is inside the 25–45 cluster which contains degradation regulating phosphorylated amino acids and is strongly associated with global gene expression changes in uterine corpus endometrial carcinoma (UCEC) and liver hepatocellular carcinoma (LIHC). Bottom bars in both plots show linear protein sequences with additional clusters in dark gray and PFAM protein domains in light gray. Mutation count histograms are shown for specific tumor types above the sequence with green dots representing synonymous mutations, blue dots representing missense mutations, and yellow dots nonsense mutations. Protein images created using UCSF Chimera [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1005347#pcbi.1005347.ref026" target="_blank">26</a>].</p

Cluster Statistics Comparing Multiscale Clusters from M²C with the DBSCAN based method OncodriveCLUST and Pfam domains.

<p><b>A)</b> Cluster length histogram. <b>B)</b> Cluster mutation count histogram using all mutation types. <b>C)</b> Coverage with competing method histogram and Pfam. Cluster X is said to overlap cluster Y if over 50% of cluster X is covered by cluster Y. <b>D)</b> Cross-validation of mixture models: each circle shows the log-likelihood of the mixture model trained from partition 1 to generate the data from partition 2 for a single gene (red circles). The opposite analysis, using partition 2’s mixture model to generate data from partition 1, is also shown (purple x’s).</p

Gene Expression Association Statistics.

<p><b>A)</b> The number of cluster features (meaning clusters in the context of a tumor type) analyzed for gene expression association broken down by tumor type and by clustering method. A single cluster can be analyzed multiple times if it is present with greater than 4 mutations in multiple tumor types. <b>B)</b> The number of cluster features with significant global gene expression associations (solid) and pathway gene expression associations (hatched). Two significance levels (1% is lighter colors and 10% darker colors) are shown for each method. Here a cluster feature with multiple associations is only counted once. <b>C)</b> The number of significant global gene expression associations found by each method at different significance levels (1% is lighter colors and 10% darker colors). Hatched bars indicate associations with lower P-values than the corresponding any non-synonymous feature for the same gene. Here cluster features with multiple associations are counted multiple times. <b>D)</b> Same as (C) but for pathway gene expression associations.</p

Science, conscience et environnement : penser le monde complexe

<p><b>A)</b> Violin plot of the response of mTOR inhibitor Temsirolimus (IC50s, y-axis) across cancer cell lines. The cell lines are grouped (x-axis) as wild-type PTEN (WT), depicted with gray markers and gray violin outline, cell lines with any non-synonymous mutation in PTEN (Any), depicted in black, and cell lines with a mutation in one of the hotspots, depicted in blue. Mutation clusters and any non-synonymous mutation features that are significantly associated with drug response (FDR<10%) are depicted in red. <b>B)</b> Violin plot of the response of a PI3Kb inhibitor for mutation clusters in PIK3CA.</p