geng-lee / wgs_snv_analysis

WGS_SNV_analysis

WGS variant discovery and quality control

Before executing this pipeline, you need to create a comma-separated file to specify the sample name and fastq file path (Sample ID, Fastq1, Fastq2). Headers are unnecessary. An example is /Variant_Discovery/cohort_info.txt. You also need a config file to specify path of tools and data, like /Variant_Discovery/myConfigFile.config

Step0 Check the quality of the sequence data

sh /Variant_Discovery/run_00_fastqc.sh myConfigFile.config

Step1 Perform sequence alignment and SNV detection on individual samples

In this step, sentieon is used to perform sequence alignment and variant detection, which is a commercial software used as an alternative to the bwa+gatk process.

sh /Variant_Discovery/run_01_sentieon.sh myConfigFile.config

Step2 Perform cohort-level variant detection

sh /Variant_Discovery/run_02_joint_calling.sh myConfigFile.config

Step3 Quality control to exclude low quality variants and samples

sh /Variant_Discovery/run_03_QC.sh myConfigFile.config

Step4 Variant annotations for prioritization

sh /Variant_Discovery/run_04_Annotation.sh myConfigFile.config

Variant prioritization

Rare variants often lack statistical validity, so we recommend performing a cascade-based filtering strategy. In this pipeline, we will filter to keep rare and likely deleterious variants based on annotation results. However, the next analyses are personalized, and examining loci in known disease-causative genes may be a good start.

python ./Variant_Priorization/Main.py