Variant-Calling-from-RNA-seq-Data-Using-the-GATK

In brief, the key modifications made to the DNAseq Best Practices focus on handling splice junctions correctly, which involves specific mapping and pre-processing procedures, as well as some new functionality in the HaplotypeCaller. Here is a detailed overview:

Workflow

RNA-Seq variant calling using GATK (Genome Analysis Toolkit) involves several key steps. Below is a step-by-step guide to perform variant calling on RNA-Seq data:

1. Quality Control (QC)

• Tools: FastQC, MultiQC
• Steps:
  1. Run FastQC on the raw FASTQ files to assess the quality of the sequencing reads.
  2. Use MultiQC to aggregate and visualize the results from multiple FastQC reports.

2. Read Trimming (Optional)

• Tools: Trimmomatic, Cutadapt
• Steps:
  • Trim adapters and low-quality bases from the reads if necessary.

3. Alignment to Reference Genome

• Tools: STAR, HISAT2
• Steps:
  1. Align the RNA-Seq reads to the reference genome using STAR or HISAT2.
  2. Generate a BAM file sorted by coordinates.

4. Mark Duplicates

• Tools: GATK (MarkDuplicates), Picard
• Steps:
  1. Use GATK MarkDuplicates or Picard MarkDuplicates to identify and mark duplicate reads in the BAM file.

5. Split'N'Trim and Reassign Mapping Qualities

• Tools: GATK
• Steps:
  1. Use GATK SplitNCigarReads to split reads into exonic segments and hard-clip any overhanging portions.
  2. Reassign mapping qualities from STAR (MAPQ=255) to a lower value (e.g., 60) using SplitNCigarReads.

6. Base Quality Score Recalibration (BQSR)

• Tools: GATK
• Steps:
  1. Perform Base Quality Score Recalibration (BQSR) using GATK BaseRecalibrator.
  2. Generate recalibrated BAM files.

7. Variant Calling

• Tools: GATK HaplotypeCaller
• Steps:
  1. Run GATK HaplotypeCaller with the RNA-Seq-specific settings (-ERC GVCF mode recommended) to call variants.

8. Post-Processing

• Steps:
  1. Combine GVCFs (if working with multiple samples) using GATK CombineGVCFs.
  2. Genotype GVCFs using GATK GenotypeGVCFs.

9. Variant Filtering

• Tools: GATK VariantFiltration
• Steps:
  1. Filter variants using GATK VariantFiltration with appropriate RNA-Seq-specific filters.
  2. For SNPs and indels, you may apply the following filters:
     • SNPs: QD < 2.0 || FS > 30.0 || MQ < 40.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0
     • Indels: QD < 2.0 || FS > 200.0 || ReadPosRankSum < -20.0

10. Annotation

• Tools: ANNOVAR, SnpEff, VEP
• Steps:
  1. Annotate the variants to determine their potential impact using SnpEff, VEP, or ANNOVAR.

11. Visualization and Interpretation

• Tools: IGV (Integrative Genomics Viewer)
• Steps:
  1. Visualize the aligned reads and called variants in IGV to ensure accuracy.
  2. Interpret the variants in the context of the biological question being studied.

This workflow should cover the essentials of RNA-Seq variant calling using GATK. Each tool has specific parameters that may need to be adjusted depending on the dataset and research question.