Copy Number Variations Detection - TD. Using Sequenza under Galaxy

Transcription

1 Copy Number Variations Detection - TD Using Sequenza under Galaxy I. Data loading We will analyze the copy number variations of a human tumor (parotid gland carcinoma), limited to the chr17, from a WES (whole-exome sequencing) experiment. All genomic coordinates correspond to the 2009 build of the reference human genome (hg19 / GRC37). Upload of required data files The required data files to upload with Upload data > Upload file are : a. Pileup file corresponding to the patient s normal DNA (whole blood) : 70.N.rdx17.pup.gz File type : pileup. b. Pileup file corresponding to the patient s tumoral DNA : 70.T1.rdx17.pup.gz File type : pileup. c. The local GC% content corresponding to the chr17 (computed on non-overlapping 50 bases wide windows from the human reference genome) : 9.gc50b.chr17.txt.gz File type : tabular. NOTA : All three files can be uploaded at once by pasting the three links in the URL/text box. 1

2 Upload completed NOTA : Pileup files were previously generated using samtools from the alignment of original fastq files with bwa mem, both time-consuming steps we could not perform during this 105 minutes workshop. The local GC% content file was previously generated using the command-line version of Sequenza, and limited to chr17. II. CNV detection using Sequenza a. Select the Sequenza tool in the left panel : CNV Analysis > Sequenza, name your sample and select Pileup in the File Format selection box : Switching Sequenza to pileup file format b. You ll observe that the content of the middle panel automatically has changed, consequently to your selection of the pileup file format as data input. Now please select the file M370.N.rdx17.pup as the Normal pileup file, and check that M370.T1.rdx17.pup is selected as the Tumor pileup file, as well as the hg19.gc50b.chr17.txt as the GC content file. 2

3 Selecting the normal pileup file c. Now you have the possibility to whether tell Sequenza the tumoral cellularity (% of cells evaluated as tumoral in the tumor biopsy, as tumor biopsies of solid tumors are most of the time contaminated by normal cells from the local tissue, or lymphocytes infiltrations, among others) and ploidy, when it is known. This step is optional, as Sequenza has an option to evaluate by itself these two parameters. Here you have the possibility to let Sequenza in blind mode by letting the No value, or set 80 as the Cellularity value, and 4 as the Ploidy value (these values were determined by an anatomopathologist). d. You can now execute Sequenza. Sequenza with defined tumoral cellularity and ploidy values NOTA : Sequenza can also run directly on bam files. In this case, it will perform the generation of the pileup files itself. *** BREAK : THEORETICAL COURSES *** 3

4 III. Sequenza output a. Sequenza output in Galaxy are available as an HTML page featuring several plots (PDF format) and links to downloadable data. Sequenza output in Galaxy b. We ll detail the different outputs together. IV. Segments (minimal) annotation a. The segmentation results (available after the File (best solution or user solution) sentence, under the first plot) needs to be polished to be used for further steps. Mainly, we have to remove double quotes ( ) in the first column and comment the header. i. Download the segments results file M370_segments.txt (if you stated M370 as the sample name). ii. In your favorite text editor, remove double quotes throughout the file (replacing it with no character. Typically through seeking Edit > Find (& Replace), or with keyboard shortcuts like Ctrl + H, Ctrl + F or Ctrl + R). iii. Just add a # as the very 1 st character of the file, at the start of the first line. iv. Save your file with a.bed extension. v. Upload this file to your current history. b. Or just upload this file into your current history : 70.seg.bed c. Also upload this file, too, which is a BED file containing the name and genomic positions of human genes known to have a role in cancer : 9_cancer.symbols.bed 4

5 d. We will then perform a selection of the segmented regions based on their high copy number identified by Sequenza. For this, select the Filter and Sort > Filter tool. Select the segments table as input, and specify this condition : c10>=15. Filtering a tabular file on a column content e. If done correctly, you should obtain a new interval file with a selection of a single segment. We will then annotate it, crossing it with the list of human genes involved in cancer we uploaded previously. For this, select the Join, Subtract and Group > Join two Datasets tool. Select the file hg19_cancer.symbols.bed as the first file, and the result of your filtering as the second file, then execute the tool. Joining two interval files according to their genomic positions overlap NOTA : Several alternative tools are available under Galaxy to join/cross/intersect interval and/or bed files. An alternative tool that performs the same operation on this Galaxy instance (GenoToul) is available as the BED Tools > Bed intersect tool. 5