Quality of life of patients with oesophageal cancer in Taiwan: validation and application of the Taiwan Chinese (Mandarin) version of the EORTC QLQ-OES18: a brief communication

[[abstract]]Purpose: The aim of this study was to examine the reliability and validity, and the application of the Taiwan Chinese Version of the EORTC QLQ-OES18. Methods: The authors translated the questionnaire according to the guideline of the EORTC. Ninety-five patients with oesophageal cancer in National Taiwan University Hospital were interviewed using the questionnaire and the EORTC QLQ-C30 between October 2002 and September 2007. Answer distribution and psychometric properties of the EORTC QLQ-OES18 were examined. Results: The mean age of the patients was 60?years (SD 12?years). Most of the patients were in advanced stages of disease, with two-thirds off-treatment. The Cronbach's alpha coefficients were satisfactory (0.77-0.82) or near-satisfactory (pain: 0.67). The item-to-own and item-to-other scale correlations showed satisfactory results. Patients who were on-treatment versus off-treatment had significantly poorer quality of life scores in dysphagia, dry mouth, and taste, and a borderline poorer score in cough. Opposite situations were seen in the scales of reflux and choking. Conclusions: The EORTC QLQ-OES18 is a valid instrument to assess quality of life issues in patients with oesophageal cancer in Taiwan

Additional file 6: of VIPER: Visualization Pipeline for RNA-seq, a Snakemake workflow for efficient and complete RNA-seq analysis

Figure S4. (a) Code snippet from the config.yaml file demonstrating the addition of a boolean flag indicating whether or not to run the genome wide SNP scan. (b) Code snippet from the snp.snakefile demonstrating the addition of rules built off of existing output (aligned STAR BAM files) and yielding additional output (genome-wide SNP scans). (PDF 104 kb

Additional file 3: of VIPER: Visualization Pipeline for RNA-seq, a Snakemake workflow for efficient and complete RNA-seq analysis

Figure S2. (a) Example of the VIPER project folder. The main components are VIPER, DATA, and ANALYSIS with the input files config.yaml and metasheet.csv. (b) Expanded ANALYSIS folder illustrating the output of VIPER. The plots folder here is expanded to illustrate how the output assumes a simple hierarchical structure, and that each of the clustering figures are associated with a text file containing the underlying information. (PDF 212 kb

Additional file 8: of VIPER: Visualization Pipeline for RNA-seq, a Snakemake workflow for efficient and complete RNA-seq analysis

Figure S3. VIPER was run on a dataset (12 samples; single end data; 36.7 M reads on average) and finished in 24 h. VIPER performance during this run is captured using Ganglia on a 96GB RAM 6 processor Intel Xeon machine. (a) System usage and (b) CPU load captured showing how VIPER is parallelized across 6 processors with (c) ~35G memory utilized for the alignment part of the pipeline. (PDF 79 kb

Additional file 1: of VIPER: Visualization Pipeline for RNA-seq, a Snakemake workflow for efficient and complete RNA-seq analysis

Config Example (YAML 6 kb

Additional file 4: of VIPER: Visualization Pipeline for RNA-seq, a Snakemake workflow for efficient and complete RNA-seq analysis

Figure S1. Graphical overview of the computational steps performed by VIPER processing a single fastq file. The nodes of the graph represent the execution of a rule and a directed edge between node A and B means that the rule underlying node B needs the output of node A as an input. A path in the graph represents a sequence of jobs that have to be executed serially, but disjoint paths can be run in parallel. This specific directed acyclic graph (DAG) was automatically generated by VIPER based on the directive to run the rule named ‘target’, using a single fastq file as input. (PDF 436 kb

Additional file 1: of VIPER: Visualization Pipeline for RNA-seq, a Snakemake workflow for efficient and complete RNA-seq analysis

Config Example (YAML 6 kb

Additional file 2: of VIPER: Visualization Pipeline for RNA-seq, a Snakemake workflow for efficient and complete RNA-seq analysis

Metasheet Example (CSV 600 bytes

Additional file 1: of Cross-site comparison of ribosomal depletion kits for Illumina RNAseq library construction

Figure S1. BioAnalyzer traces for samples used in the study. Left: Intact UHR, Right: Heat degraded UHR RNA. (PPTX 127 kb

Additional file 4: of Cross-site comparison of ribosomal depletion kits for Illumina RNAseq library construction

Figure S3. Insert size distribution for RNAseq libraries from intact RNA. The insert size for each library passing the 50% rRNA filter was calculated for reads with convergent reads that were separated by < 1000 bp. Kit abbreviations: RZ = RiboZero Gold, LX = Lexogen RiboCop, NE = NEBNext rRNA Depletion, K=Kapa RiboErase, CR = Clontech Ribogone, CZ = SMARTer Pico total RNA. Top: length of the 90th percentile of inserts reads. Middle: length of the median insert read. Bottom: length of the 10th percentile of inserts read. (PPTX 82 kb