Quick Start

1. Specific the path of references (.fasta) and samples (.fastq) in a configure file (.YAML).

   For example, write down and save the following block into a text file and named it as data.yaml.

   reference:
     contamination:
       fa: ./ref/contamination.fa
     genes:
       fa: ./ref/genes.fa
     genome:
       fa: /data/reference/genome/Mus_musculus/GRCm39.fa
       star: /data/reference/genome/Mus_musculus/star/GRCm39.release108
   
   samples:
     mESCWT-rep1-input:
       data:
         - R1: ./test/IP16.fastq.gz
       group: mESCWT
       treated: false
     mESCWT-rep1-treated:
       data:
         - R1: ./test/IP4.fastq.gz
       group: mESCWT
       treated: true
     mESCWT-rep2-treated:
       data:
         - R1: ./test/IP5.fastq.gz
       group: mESCWT
       treated: true
   

   You can also copy and edit from this template.

   Read the more details on how to customize.

2. Run all the analysis by one command:

   apptainer run docker://y9ch/bidseq
   

   default

   The pipeline will load configure file named data.yaml under the current directory.

   How to run apptainer on computation nodes without internet acess?

   1. (On the login node with internet connection) Run module load apptainer to mount the apptainer utils, if it is not installed by default.

   2. (On the login node with internet connection) Build the bidseq_latest.sif file using the command apptainer pull docker://y9ch/bidseq.

   3. (On the computation node) Run apptainer run bidseq_lastest.sif -c data.yaml to start the pipeline. Note that most HPC systems mount directories in a complex manner. Therefore, you need to find out the actual path by executing realpath ./ and specify this output into apptainer using apptainer run -B /the/real/path ...

   If your configure file is not named as data.yaml, add -c your_file_name.yaml arg after the command to customize.

3. View the analytics reports and filtered sites.

   default

   3 folder will be created in the working directory (default: workspace),

   • trimming, mapping, deduping reports are in report_reads folder, with key numbers in all the steps reported in one webpage^(example).
   • filtered sites for Ψ sites detection are in filter_sites folder. These sites are only passed the simplest filtering, you can apply customized threshold into them based your data type and quality.
   • processed mapping results (.bam) are in align_bam folder. You can zoom into location that you interested in IGV.