Additional file 1: of EMSAR: estimation of transcript abundance from RNA-seq data by mappability-based segmentation and reclustering

Supplementary Material. (PDF 250 kb

Additional file 1: of MONGKIE: an integrated tool for network analysis and visualization for multi-omics data

Supplementary text, figures, and data files. All text and materials were formated as a small self-contained website (1 HTML file with necessary figures and data files). Data files include input and result files of the case study including the fold change of expression values between tumor vs. normal conditions (in log2FC), average expression value of each gene in 4 GBM subtypes, GBM-altered subnetworks (nodes and edges) weighted by expression correlations between each pair of genes, and gene sets in 2 critical modules and their functional annotations. (ZIP 4315kb

Variants Affecting Exon Skipping Contribute to Complex Traits

<div><p>DNA variants that affect alternative splicing and the relative quantities of different gene transcripts have been shown to be risk alleles for some Mendelian diseases. However, for complex traits characterized by a low odds ratio for any single contributing variant, very few studies have investigated the contribution of splicing variants. The overarching goal of this study is to discover and characterize the role that variants affecting alternative splicing may play in the genetic etiology of complex traits, which include a significant number of the common human diseases. Specifically, we hypothesize that single nucleotide polymorphisms (SNPs) in splicing regulatory elements can be characterized <em>in silico</em> to identify variants affecting splicing, and that these variants may contribute to the etiology of complex diseases as well as the inter-individual variability in the ratios of alternative transcripts. We leverage high-throughput expression profiling to 1) experimentally validate our <em>in silico</em> predictions of skipped exons and 2) characterize the molecular role of intronic genetic variations in alternative splicing events in the context of complex human traits and diseases. We propose that intronic SNPs play a role as genetic regulators within splicing regulatory elements and show that their associated exon skipping events can affect protein domains and structure. We find that SNPs we would predict to affect exon skipping are enriched among the set of SNPs reported to be associated with complex human traits.</p> </div

Additional file 1: of An integrated clinical and genomic information system for cancer precision medicine

Figure S1. Galaxy workflow for WES data processing. (PNG 221 kb

Distance to the Nearest Exon of Intronic SNPs Tends to Be Smaller Than the Distance to the Skipped Exon of ISE SNPs.

<p>The upper 3 rows show the general distribution of the distance to the nearest exon of all intronic SNPs and the distance to the skipped exon of the ISE SNPs (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002998#pgen.1002998.s003" target="_blank">Figure S3</a>). The bottom 3 rows show the distribution of the distance to the nearest exon of trait-associated intronic SNPs and distance to the nearest exon of trait-associated ISE SNPs (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1002998#pgen-1002998-g007" target="_blank">Figure 7</a>).</p

Additional file 4: of An integrated clinical and genomic information system for cancer precision medicine

Galaxy workflow file (json data format) for WTS data processing, it can be imported to another Galaxy server. (GA 23 kb

Additional file 6: of An integrated clinical and genomic information system for cancer precision medicine

Figure S4. An example of filtering process to select a patient cohort based on clinical information or properties. A. Selection of female and lifelong never-smoker patients in the TCGA LUAD cohort. (“Cohort Selection” menu is located in left-top side of the page) B. Driver genes were sorted by mutation frequency by clicking the “# Mutations” label at the bottom. The sorting result confirmed that EGFR is the most frequently mutated gene among these patients, whereas TP53 mutation was prevalent in other patients as shown in Additional file 7: Figure S3. (PNG 179 kb

Additional file 2: of An integrated clinical and genomic information system for cancer precision medicine

Figure S2. Galaxy workflow for WTS data processing. (PNG 111 kb

Structural Alignment of Pairs of Transcript Isoforms.

<p>(A) <i>SLC25A15</i> (B) <i>RNF8</i>. <i>SLC25A15</i> and <i>RNF8</i> show differences in the 3D structures for the protein with and without the skipped exon. The structural difference is quantified by RMSD and TM-score.</p

Protein Domains for Skipped Exons.

a<p>Affymetrix Exon Identifier.</p