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

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

Additional file 3: of An integrated clinical and genomic information system for cancer precision medicine

Galaxy workflow file (json data format) for WES data processing, it can be imported to another Galaxy server. (GA 53 kb

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

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 7: of An integrated clinical and genomic information system for cancer precision medicine

Figure S3. Cohort explorer for the whole TCGA LUAD cohort and our patient (1) Significant driver genes identified by MutSigCV [22]. Each horizontal bar represents total count of mutations on the corresponding gene in the cohort. Color scheme indicates the coding properties of mutations. (2) The gray bar represents –log10(p-values) of each driver gene. (3) Sample-wise count of mutations with coding properties color-coded. (4) Clinical features of samples. (5) Mutations found in our patient are plotted at left-most side (i.e. the first column). (PNG 120 kb

Additional file 5: of An integrated clinical and genomic information system for cancer precision medicine

Instruction for users to upload their own FASTQ files into our BioCloud system so that they can process the NGS data and get the various reports described in main script. (PDF 1060 kb

A High-Dimensional, Deep-Sequencing Study of Lung Adenocarcinoma in Female Never-Smokers

<div><h3>Background</h3><p>Deep sequencing techniques provide a remarkable opportunity for comprehensive understanding of tumorigenesis at the molecular level. As omics studies become popular, integrative approaches need to be developed to move from a simple cataloguing of mutations and changes in gene expression to dissecting the molecular nature of carcinogenesis at the systemic level and understanding the complex networks that lead to cancer development.</p> <h3>Results</h3><p>Here, we describe a high-throughput, multi-dimensional sequencing study of primary lung adenocarcinoma tumors and adjacent normal tissues of six Korean female never-smoker patients. Our data encompass results from exome-seq, RNA-seq, small RNA-seq, and MeDIP-seq. We identified and validated novel genetic aberrations, including 47 somatic mutations and 19 fusion transcripts. One of the fusions involves the <em>c-RET</em> gene, which was recently reported to form fusion genes that may function as drivers of carcinogenesis in lung cancer patients. We also characterized gene expression profiles, which we integrated with genomic aberrations and gene regulations into functional networks. The most prominent gene network module that emerged indicates that disturbances in G2/M transition and mitotic progression are causally linked to tumorigenesis in these patients. Also, results from the analysis strongly suggest that several novel microRNA-target interactions represent key regulatory elements of the gene network.</p> <h3>Conclusions</h3><p>Our study not only provides an overview of the alterations occurring in lung adenocarcinoma at multiple levels from genome to transcriptome and epigenome, but also offers a model for integrative genomics analysis and proposes potential target pathways for the control of lung adenocarcinoma.</p> </div

Circos plot of somatic mutations, copy number variations, transcriptome expression, and structural variations.

<p>From inside to out, structural variations (purple and orange), copy number variations (gain in dark red, loss in dark blue, mRNA expression (up in gold, down in olive), differentially expressed microRNAs (up in red, down in green), DNA methylation with sky-blue background (up in dark orange, down in chartreuse), somatic mutations with a gene symbols, and chromosomal cytobands.</p