MOESM3 of Transcriptional network analysis on brains reveals a potential regulatory role of PPP1R3F in autism spectrum disorders

Additional file 2. The constructed networks from the Parikshak et al. dataset [19]

MOESM1 of Transcriptional network analysis on brains reveals a potential regulatory role of PPP1R3F in autism spectrum disorders

Additional file 1. Detailed explanation of the methods being used in this study

MOESM5 of Transcriptional network analysis on brains reveals a potential regulatory role of PPP1R3F in autism spectrum disorders

Additional file 5. The list of the combined set of target genes of PPP1R3F

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Additional file 3. The constructed networks from the Gupta et al. dataset [17]

MOESM2 of Transcriptional network analysis on brains reveals a potential regulatory role of PPP1R3F in autism spectrum disorders

Additional file 4. Supplementary figures

Distribution of demographic and clinical variables of 101 lung adenocarcinoma patients.

<p>Distribution of demographic and clinical variables of 101 lung adenocarcinoma patients.</p

Mutual exclusivity of driver genes detected in 825 patients combining TCGA, Broad Institute, and EAGLE WES of lung adenocarcinoma.

<p><b>(A)</b> A MEGS with six genes covering 60.3% of patients. Samples without nonsynonymous mutations in these six genes are not shown. Samples labelled as blue carry a nonsynonymous mutation in the gene region, while samples labelled as gray do not carry a synonymous mutation in the gene region. <b>(B)</b> A MEGS with four genes covering 33.3% of patients. Samples without nonsynonymous mutations in these four genes are not shown.</p

Somatic mutations in three LUAD candidate driver genes (<i>POU4F2</i>, <i>ZKSCAN1</i>, and <i>ASEF</i>) in EAGLE, TCGA and Broad Institute studies.

<p>The protein sequences from these three genes are schematically described using grey bars along with their respective structural and functional domains in color-coded blocks. Each mallet represents an independent nonsilent mutation with potential functional relevance in the three studies (the complete list of mutations is reported in <a href="http://www.plosmedicine.org/article/info:doi/10.1371/journal.pmed.1002162#pmed.1002162.s007" target="_blank">S1 Table</a>). Numbers below each sequence representation mark the total length of the transcript, the domain ranges, and the locations of mutations.</p

Somatic mutations of lung adenocarcinoma in EAGLE data.

<p><b>(A)</b> Distribution of point somatic mutations across nine mutation types. <b>(B)</b> The top panel shows the number of nonsilent mutations detected by whole-exome analysis for 101 EAGLE samples. Tumor samples were arranged from left to right by the number of nonsilent mutations. The middle panel shows the mutations for previously reported significantly mutated genes based on the TCGA data, reported in the TumorPortal website. The next panel shows the mutations for the three new driver genes. The bottom panels show smoking status. The right panel shows the frequency of nonsilent mutations in EAGLE data for each driver gene. Each column represents one patient.</p

Association between genomic features and clinical outcomes.

<p><b>(A)</b> The mutational status of TP53 and KRAS and the time of developing distant metastasis. <i>p</i>-values were two-sided. Red: mutated; blue: not mutated. <b>(B)</b> The association between the fraction of nine point mutation types and overall transversions and the time of developing distant metastasis after initial diagnosis. Relative risks and their 95% confidence intervals were estimated based on a Cox regression model adjusted for age, sex, and disease stage. <i>p</i>-values were two-sided. <b>(C)</b> Cancer-free survival was not associated with the mutational status of TP53 or KRAS. <i>p</i>-values were two-sided. Red: mutated; blue: not mutated.</p