CHIPS | Snakemake pipeline for quality control | BPM library

Create Project Directory As explained above, the PROJECT directory is simply a directory to contain an entire Chips run. It can be named anything, but for this section, we'll simply call it 'PROJECT'. Create Data Directory As explained above, creating a data directory is a place to gather all of your raw data files (.fastq, .fastq.gz, .bam). It is optional, but highly recommended. And in 'data', copy over or make symbolic links to your raw data files.
Create Project Directory As explained above, the PROJECT directory is simply a directory to contain an entire Chips run. It can be named anything, but for this section, we'll simply call it 'PROJECT' $ mkdir PROJECT $ cd PROJECT
Create Data Directory As explained above, creating a data directory is a place to gather all of your raw data files (.fastq, .fastq.gz, .bam). It is optional, but highly recommended. $ mkdir data And in 'data', copy over or make symbolic links to your raw data files
Clone CHIPS Repository In your PROJECT directory: $ mv CHIPS/ PROJECT/
Create config.yaml and metasheet.csv a. copy chips/config.yaml and chips/metasheet.csv into the PROJECT dir: In the PROJECT directory: $ cp CHIPS/config.yaml . $ cp CHIPS/metasheet.csv . b. setup config.yaml The config.yaml is where you define Chips run parameters and the ChIP-seq samples for analysis. genes_to_plot: If set, genomic region and TSS will be displayed in Genome Trackview figure. Multiple genes should be separated by space (default: GAPDH ACTB TP53). upstream/downstream: Upstream and Downstream of the genome region can be extended to have a better view of peaks. output_path: Directory to save all the output files (default: analysis). assembly: typically hg19/hg38 for human or mm9/mm10 for mouse (default: hg19) Choose the motif software: choose either homer or MDSeqPos (default: homer) Contamination Panel:The contamination panel is a panel that Chips will check for "cross-species" contamination. Out of the box, the config.yaml has hg19 and mm9 as assemblies to check. IF you would like to add other species/assemblies, simply add as many BWA indices as you would like cnv_analysis: Set to 'true' to enable copy number variation analysis samples: The most important part of the config file is to define the samples for Chips analysis. Each sample is given an arbitrary name, e.g. MCF7_ER, MCF7_input, etc. Sample names, however, can not start with a number, and cannot contain '.', '-' (dash--use underscores instead) (POSSIBLY others). For each sample, define the path to the raw data file (.fastq, .fastq.gz, .bam). For paired-end samples, simply add another line to define the path to the second pair. c. setup metasheet.csv: The metasheet.csv is where you group the samples (defined in config.yaml) into Treatment, Control (and if applicable, replicates). For Chips, each of these groupings is called a run. Open metasheet.csv in Excel or in a text-editor.You will notice the first (uncommented) line is: RunName,Treat1,Cont1,Treat2,Cont2 RunName- arbitrary name for the run, e.g. MCF7_ER_run Treat1- The sample name that corresponds to treatment sample. It must exactly match the sample name used in config.yaml Cont1- (optional) The input-control sample that should be paired with Treat1. Treat2- (optional) if you have replicates, define the treatment of the replicate here. Cont2- (optional) the input-control, if available, for Treat2
Set Up Refs A pre-built ref_files can be downloaded from the link. makesure in config.yaml, ref: "CHIPS/ref.yaml" linking to static refs. copying ref.yaml