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Picard Specifications

Process Details

  • Name: Picard
  • Process UUID: 20bdcdc2-ed05-11e8-9ce4-85221b21564c
  • Process Group: QC

Overview

This quality control process runs Picard's CollectMultipleMetrics tool to generate comprehensive quality metrics for aligned sequencing data. It executes multiple metrics collection modules simultaneously to efficiently assess alignment quality, insert size distribution, base quality scores, GC bias, and other important sequencing characteristics. The process consolidates all generated metrics into both text and PDF format outputs for easy review and reporting.

This process is implemented in Bash, which invokes Picard Tools for metrics collection and Java-based PDF processing.

Key Functionality

  • Multiple Metrics Collection: Runs several Picard metrics modules simultaneously including CollectAlignmentSummaryMetrics, CollectInsertSizeMetrics, QualityScoreDistribution, MeanQualityByCycle, CollectBaseDistributionByCycle, CollectGcBiasMetrics, RnaSeqMetrics, CollectSequencingArtifactMetrics, and CollectQualityYieldMetrics
  • Efficient Processing: Reduces processing time by reading input files once and generating multiple metrics reports in a single run
  • Consolidated Reporting: Merges all generated PDF reports into a single comprehensive quality metrics document

Input/Output Specification

Inputs

Required Inputs

  • Mapped Reads
    • Description: Aligned sequencing reads for quality assessment
    • Format: BAM

Outputs

  • Output File

    • Description: Text-based quality metrics results from all collection modules
    • Format: OUT

  • Output PDF

    • Description: Consolidated PDF report containing visualizations and summaries of all quality metrics
    • Format: PDF

Parameters & Settings

This process uses conditional execution based on the run_Picard_CollectMultipleMetrics parameter, which is configured at the workflow level.

References & Resources

  • Tool Documentation: Picard Tools official documentation (https://broadinstitute.github.io/picard/)
  • Related Papers: Van der Auwera GA, Carneiro MO, Hartl C, et al. From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline. Curr Protoc Bioinformatics. 2013;43:11.10.1-11.10.33.