7 research outputs found
Additional file 1: of An integrated clinical and genomic information system for cancer precision medicine
Figure S1. Galaxy workflow for WES data processing. (PNG 221Â 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 111Â 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 âlog10(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 120Â 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 1060Â 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 53Â 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 23Â 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