samtools markdup in a pipe

Lussy04

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24-01-2025

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Next-generation sequencing (NGS) data analysis often involves numerous steps, from raw read alignment to variant calling. Efficiently chaining these steps together using pipelines significantly reduces processing time and minimizes errors. A crucial part of many NGS pipelines is duplicate read marking, typically handled by samtools markdup. This article explores how to seamlessly integrate samtools markdup within a larger pipeline, enhancing your NGS data processing workflow.

Understanding Duplicate Reads and samtools markdup

Duplicate reads in NGS data arise from PCR amplification during library preparation. These identical reads can skew downstream analyses, inflating variant allele frequencies and leading to false-positive results. samtools markdup is a powerful tool that identifies and marks these duplicates, allowing you to filter them out later in your analysis. It's a critical step for accurate variant calling and other downstream analyses.

How samtools markdup Works

samtools markdup operates on a sorted BAM file (Binary Alignment/Map). It identifies duplicate reads based on their start position, mapping quality, and read sequence. Duplicate reads are marked in the BAM file header and often contain a flag indicating their duplicate status. The original file remains unchanged; a new BAM file with the marked duplicates is created.

Integrating samtools markdup into your Pipeline

Integrating samtools markdup into a pipeline usually involves using a shell scripting language like Bash or a workflow management system like Snakemake or Nextflow. Here's how you might integrate it into a simple Bash pipeline:

# Assume your aligned BAM file is called aligned.bam

samtools sort -n aligned.bam -o aligned_sorted.bam
samtools markdup -r aligned_sorted.bam marked_duplicates.bam
samtools index marked_duplicates.bam

This simple pipeline performs the following steps:

1. Sorting: samtools sort -n aligned.bam -o aligned_sorted.bam sorts the input BAM file (aligned.bam) by read name (-n). This sorting is crucial because samtools markdup requires sorted input. The output is saved as aligned_sorted.bam.

2. Duplicate Marking: samtools markdup -r aligned_sorted.bam marked_duplicates.bam runs samtools markdup on the sorted BAM file. -r allows for random access to the BAM file, improving performance. The output, containing marked duplicates, is saved as marked_duplicates.bam.

3. Indexing: samtools index marked_duplicates.bam creates an index file for the output BAM, enabling faster random access and improved performance for downstream tools.

Advanced Pipeline Integration

For more complex pipelines, consider using workflow management systems like Snakemake or Nextflow. These systems allow for better handling of dependencies, parallel processing, and more robust error handling. Here's a simplified example using Snakemake:

# Snakemake rule for markdup
rule markdup:
    input:
        sorted_bam = "aligned_sorted.bam"
    output:
        marked_bam = "marked_duplicates.bam"
    shell:
        """
        samtools markdup -r {input.sorted_bam} {output.marked_bam}
        samtools index {output.marked_bam}
        """

This Snakemake rule defines the markdup step, specifying inputs and outputs. Snakemake handles dependency management and execution automatically.

Post-markdup Processing and Duplicate Removal

After running samtools markdup, you typically proceed with filtering out the marked duplicate reads. Several tools and approaches exist, depending on your downstream analysis. You can use samtools view with appropriate flags to filter based on the duplicate flag. For example:

samtools view -h -F 1024 marked_duplicates.bam > deduplicated.bam

This command filters out reads with the duplicate flag (bit 1024) and saves the deduplicated reads in deduplicated.bam. Remember to always check your BAM file specifications and flags to ensure accurate filtering.

Conclusion

Efficiently integrating samtools markdup into your NGS data processing pipeline is essential for accurate downstream analysis. Whether you use simple shell scripting or more advanced workflow management systems, incorporating this step will significantly improve the quality and reliability of your NGS data analysis. Remember to choose the method that best suits your needs and complexity of your pipeline. Proper duplicate marking is a cornerstone of high-quality NGS data analysis.