The Picard Pipeline Sequencing Pipeline Informatics at Broad

• What is Picard?

• Picard in context

• What Picard produces and how to access it

• Metrics, Metrics, Metrics

•  A set of tools for processing and analyzing next generation sequencing data

– Many of which are released publicly

•  A set of pipelines that process all Illumina sequence data generated at Broad

Extract Illumina Data to Standard

Format

Align reads with BWA or

MAQ

Mark Duplicate

Reads

Re-align reads around known indels

Calibrate Quality Scores

Collect Metrics about Libraries and

Run

Verify Sample Identity Run Triage

•  Adapter trimming/marking happens during data extraction from Illumina (information is used during alignment)

•  Indexed runs are de-multiplexed during extraction and each index/sample processed independently

•  Recalibration only performed for references with dbSNP

Aggregation Pipeline

Merge all data for each

library

Mark Duplicate Reads per

library

Collect Metrics per

library

Merge all libraries for a

sample

Collect Metrics about the Sample

•  A single BAM file is created per Sample (within the context of a project)

•  Aggregations are started after data is processed or re-processed through the run-level pipeline (after a 12 hour “quiet period”)

•  Outdated aggregations are kept for 2 weeks after newer aggregations are completed

Downstream pipelines and

analysts

Run Level Pipeline

What Where Pipeline Outputs /seq/picard/{flowcell} Aggregation Outputs /seq/picard_aggregation/{project}/{sample} Picard Binaries /seq/software/picard/current/bin Metrics Documentation http://iwww/~picard/picard_metric_definitions.html Source Code https://svn.broadinstitute.org/picard/trunk

https://picard.svn.sourceforge.net/svnroot/picard/trunk

•  And coming soon – BASS

•  Programmatic access to BAM files in BASS available

•  Web page to access BAM files in BASS under construction

•  All primary data is delivered in BAM format, which includes basecalls (the reads), quality scores, alignment data, etc.

•  BAM files processed through Picard always contain all reads, including:

–  All unaligned reads (marked as unmapped)

–  All duplicate reads (marked as duplicates)

–  All “non-PF” reads (marked as failing vendor quality)

•  The pipeline generates tons of metrics!

•  And we have tools to generate even more than run in the pipeline

•  Please check before re-inventing the wheel

Internal Control Metrics Quality Calibration Data Alignment Summary Metrics

GC Bias Metrics Quality By Cycle Quality Distribution

Duplication Metrics Insert Size Metrics Low Pass Concordance

Hybrid Selection Metrics SNP Fingerprint Jumping Library Metrics

dbSNP Concordance Quality/Yield Metrics Barcode Metrics

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20AL7ABXX.1 GC Bias Plot Total clusters: 124,301,048, Aligned reads: 206,073,969

GC% of 100 base windows

Frac

tion

of n

orm

aliz

ed c

over

age

010

2030

40

Mea

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se q

ualit

y

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Normalized CoverageWindows at GC%Base Quality at GC%

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20AL7ABXX.1.aligned.duplicates_marked.bam Insert Size Histogram

Insert Size

Cou

nt

FR

0 20 40 60 80 100 120 140

010

2030

4050

209KJABXX.1.aligned.duplicates_marked.bam Quality By Cycle

Cycle

Mea

n Q

uality

Mean QualityMean Original Quality

0 10 20 30 40

0.0e

+00

5.0e

+08

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+09

1.5e

+09

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+09

209KJABXX.1.aligned.duplicates_marked.bam Quality Score Distribution

Quality Score

Obs

erva

tions

Quality ScoresOriginal Quality Scores

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209KJABXX.1.unmapped.bam Total (n=481735) IC Error Rate by Cycle

Cycle

Erro

r Rat

e (lo

g ax

is)

0.00

10.

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Read 1Read 2

•  Integrate GATK Unified Genotyper in single-sample mode

•  Customized pipeline for cDNA/RNA sequencing

•  Yet more sample identity/validity checking