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How to tune GATK for low-coverage data?

Learn how to optimize GATK for low-coverage sequencing data with essential setup, pre-processing, variant calling adjustments, and quality control steps.

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How to tune GATK for low-coverage data?

 

Introduction to GATK and Low-Coverage Data

 

  • GATK (Genome Analysis Toolkit) is a toolkit developed to manage next-generation sequencing data.
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  • Low-coverage data refers to sequencing data that has insufficient read coverage across the genome.

 

Initial Setup and Environment

 

  • Ensure that your environment has the appropriate version of GATK installed with all necessary dependencies.
  •  

  • Prepare your dataset. For low-coverage data, ensure quality control steps such as adapter removal and base quality trimming have been thoroughly conducted.

 

Pre-Processing Steps

 

  • Align the low-coverage reads to a reference genome using an aligner like BWA-MEM.
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  • Sort and index the aligned BAM files using tools like Samtools.
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  • Mark duplicates in your BAM files using GATK's `MarkDuplicates` to reduce variability caused by PCR duplication.

 

Handling Low-Coverage in GATK

 

  • Use `HaplotypeCaller` in the `ERC` (GVCF) mode to call variants. This method allows joint genotyping for more accurate variant calling.
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  • Adjust the `--min-base-quality-score` if necessary, based on the quality of your reads, to ensure low-quality bases do not skew results.
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  • Tune `--standard-min-confidence-threshold-for-calling` to manage the confidence level for calling variants in low-coverage regions.
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  • Review the `--min-pruning` parameter which helps omit branches in the assembly process, especially useful in sparse data.

 

Post-Procesing and Analysis

 

  • Realign around indels to correct misalignments caused by insertion or deletion polymorphisms using `RealignerTargetCreator` and `IndelRealigner`.
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  • Perform base quality score recalibration with `BaseRecalibrator` and `ApplyBQSR` for a more accurate base quality assessment.
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  • Conduct variant quality score recalibration (VQSR) if sufficient variant data is available or use hard filtering for very low-coverage data sets.

 

Validation and Quality Control

 

  • Compare variant calls against a validated dataset if available, or use tools such as `VerifyBamID` to estimate contamination levels.
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  • Use `VariantEval` to assess the quality of the variant calls by analyzing metrics such as Ti/Tv Ratio, Depth of Coverage, etc.
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  • Consider integrating externally available tools or benchmarks like Genome in a Bottle (GIAB) for accuracy assessment.

 

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