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Nucleo Quality Control

Workflows that generates, aggregates, and visualizes quality control files for CMO-CH.

Introduction

This section aims to provide an introduction to the Nucleo Quality Control process along with installation and running instructions. The Nucleo QC process generated QC metrics for all type of BAM files generated by Nucleo, including collapsed, uncollapsed, duplex and simplex BAM files. The main outputs of the workflow are the MultiQC report and coverage report. Thorough intepretation of the output files can be found in this gitbook section.

Workflow description

Nucleo QC tools, subworkflows and main workflows organization

The workflows for nucleo quality control are written in the common workflow language (CWL). Below, figure 1 illustrates the organization level, all tools used in the bigger workflows are listed first, this tools are then called in the subworkflows listed second, then the subworfklows are called to make bigger main workflows.

For instance, in the qc_collapsed subworkflows we are using multiple command line tools such as all the biometrics tools. The main workflow to generate all the QC outputs is nucleo_qc seen at the bottom in the high-level CWL workflow section. This workflow calls both the nucleo_qc_generator.cwl that generates the QC results for each BAM file (collapsed, uncollapsed, duplex and simplex) by calling all the subworkflows and nucleo_aggregate_visualize.cwl that aggregates the QC results into different folders and calls MultiQC to generate the html QC report.

For individuals/groups that do not require a multiqc report and would like to generate their own QC reports, you may use generate_aggregate.cwl instead of nucleo_qc.cql as this will generate all QC results apart from the MultiQC html report.

Requirements

Before installing the pipeline, make sure your system supports these requirements

The following are the requirements for running the workflow:

A system with either or configured.

Installation and Usage

You must have run the main Nucleo workflow first before running the Nucleo QC workflows.

Step 1: Create a virtual environment.

Option (A) - if using cwltool

If you are using cwltool only, please proceed using python 3.9 as done below:

Here we can use either or . Here we will use conda. The

Option (B) - recommended for Juno HPC cluster

If you are using toil, python 3 is required. Please install using Python 3.6 as done below:

Here we can use either or . Here we will use conda.

Step 2: Clone the repository

Clone the repository and checkout the develop branch for the most up-to-date version:

Replace release/1.0.0 with the latest stable release of the pipeline

Step 3: Install requirements using pip

We have already specified the version of cwltool and other packages in the requirements.txt file. Please use this to install.

Step 4: Load singularity and nodejs for HPC

For HPC normally singularity is used for containers. Therefore, please make sure that it is loaded. For JUNO/SELENE, you can do the following:

We also need to make sure nodejs is installed, this can be installed using conda:

If you are having issues with the initial set-up (venv/conda/node.js) please refer to the

Step 5: Generate an inputs file

Next you must generate a proper input file in either or format.For details on how to create this file, please follow this example (there is a minimal example of what needs to be filled in in the workflow inputs ):

It's also possible to create and fill in a "template" inputs file using this command:

Note: To see help for the inputs for cwl workflow you can use:

Once we have successfully installed the requirements we can now run the workflow using cwltool/toil .

Step 5: Run the workflow

Given the output files from , there are workflows to generate the quality control files, aggregate the files across many samples, and visualize them using MultQC. You can choose to run these workflows whether you have just one or hundreds of samples. Depending on your use case, there are two main options:

Option A:

Generate a single inputs.yml file for all your samples and run nucleo_qc.cwl. This will generate a single MultiQC report with all the samples. The samples you selected do run together may be from a single batch.

Here we show how to use to run the workflow on a single machine, such as a laptop

Run the workflow with a given set of input using on single machine

Here we show how to run the workflow using using single machine interface

Once we have successfully installed the requirements we can now run the workflow using cwltool if you have proper input file generated either in

Option B:

Generate multiple yaml files for each sample or sample batches and run nucleo_qc.cwl seperately on each. This will generate multiple outputs and multiple single sample MultiQC reports. In order to rejoin all samples to a single MultiQC report you must generate nucleo_aggregate_visualize.yml file () and then run nucleo_aggregate_visualize.cwl (Figure 4).

See workflow sections

Workflow inputs

Description of Nucleo QC workflow inputs

Parameter Used by Tools

Common Parameters Across Tools

Input for nucleo_qc.cwl

Option 1: Input.yml example to run workflow enable mosdepth/athena to create coverage reports

Option 2: Input.yml example to run workflow without mosdepth/athena

Input for nucleo_aggregate_visualize.cwl

Workflow outputs

The workflow outputs multiple QC metric files that are merged into a single MultiQC report. The workflow also outputs the athena coverage report. For further interpretation please use the link to and .

Nucleo_qc.cwl produces two main folders (Figure 1):

Sample folder: The sample folder contains sample-specific files regarding contamination.
MultiQC folder: The MultiQC folder contains the following:

Interpretation - MultiQC

Description for each section of the MultiQC report

This section will guide you in how to interpret the output from running the ACCESS QC workflow. The main output from running the ACCESS QC workflow is a MultiQC report (for both single sample and multiple samples). Each subsection explains certain parts of the MultiQC report.

Summary QC metrics

Summary QC metrics

Summary of all quality control metrics generated using the workflow

The first section of the MultiQC report is the Summary QC metrics table that displays the summary information per sample. The metrics presented are mainly lab sample quality metrics, alignment information, and quality of the target capture.

Interpretation

In the above table, the coloring represents the QC criteria of the values set by the Bioinformatics tram. The coloring schema is either red, yellow, or green, which indicates if the metric fails, is borderline, or passes the thresholds. This allows the Bioinformatics team to quickly glance at all samples to see where potential issues are. Below are the descriptions for each column and were the data was obtained from.

Coverage vs GC content

Awareness of possible loss of accuracy in downstream sequencing results due to coverage due to GC content bias.

The figure plots below represent the normalized coverage against the % GC content from the CMO-CH target regions. Each line is data from a single sample.

Methods

Tool used: BAM type: (1) collapsed BAM and (2) uncollapsed BAM.

The data used to produce this figure are the values under the normalized_coverage

Capture Metrics

Validating the efficacy of the bait sets.

Introduction

There are several sections displaying bait set capture efficiency. Each section corresponds to a separate BAM type and bait set combination. The tool used to produce the metrics is GATK-CollectHsMetrics. By default, only the mean bait coverage, mean target coverage, and % Usable bases on-target are displayed. However, there are many more metrics that can be toggled to display by clicking on the Configure Columns button.

Methods

Tool used: BAM type: (1) Uncollapsed BAM, (1) Collapsed BAM, (1) Duplex BAM, and (4) Simplex BAM

Interpretation

The aim is to have high coverage across the panel.

Insert Size

Confirmation of fragment length information for different sample types

Introduction

This figure below represents the insert size distribution from the CMO-CH target regions. Insert size is calculated from the start and stop positions of the reads after mapping to the reference genome.

Methods

Tool used: BAM type: Collapsed BAM

The data used to produce this figure are the values under the MODE_INSERT_SIZE column contained in the output file from CollectInsertSizeMetrics.

Interpretation

Usually this plot has a specific shape as Cell free DNA has distinctive features due to the natural processes behind its fragmentation. One such feature is the set of 10-11 bp fluctuations that indicate the preferential splicing of fragments due to the number of bases per turn of the DNA helix, which causes a unique pattern of binding to the surface of histones.

The more pronounced peak at 166 bp indicate complete wrapping of the DNA around the histones' circumference, and similarly the second more pronounced peak indicates two complete wraps.

However in the CMO-CH panel we are using Buffy coat samples. These samples are mechanically sheared and thus do not exhibit these distinctive features, hence the different shape for their distribution in comparison to other reports.

Target coverage distribution

Introduction

This figure shows the density plot of coverage values from the ACCESS target regions. Each line is data from one sample. Each sample is normalized by the median coverage value of that sample to align all peaks with one another and correct for sample-level differences.

Methods

Tool used: BAM type: Collapsed BAM

The data used to produce this figure are the values under the normalized_coverage column, which are in the *_per_target_coverage.txt output file from CollectHsMetrics. Then the gaussian_kde function from the python scipy package is used to produce the density plot.

Interpretation

Each distribution should be unimodal, apart from a second peak on the low end due to X chromosome mapping from male samples. Narrow peaks are indicative of evenly distributed coverage across all bait regions. Wider distributions indicate uneven read distribution and may be correlated with a large GC bias. Note that the provided bed file lists the start and stop coordinates of ACCESS design probes, not the actual genomic target regions.

Duplex Metrics

Duplex family sizes

Understanding the frequency of UMI families of different read counts

We investigate the number of Duplex families by plotting the count of the number of families with the ab and ba single-strand families of size ab_size and ba_size.

Simplex family sizes

We investigate the number of Simplex families by plotting the count of the number of families with the ab and ba single-strand families of size ab_size and ba_size.

Duplex yield metrics

Metrics produced by CollectDuplexSeqMetrics that are sampled at various levels of coverage, via random downsampling, during the construction of duplex metrics. The downsampling is done in such a way that the fractions are approximate, and not exact, therefore the fraction the field should only be interpreted as a guide, and the read_pairs field used to quantify how much data was used.

Mean Base Quality

Introduction

This figure below illustrates the mean base quality by cycle BaseQualityScoreRecalibration (BQSR). The sequencer uses the difference in intensity of the fluorescence of the bases to give an estimate of the quality of the base that has been read. The BQSR tool from GATK recalculates these values based on the empirical error rate of the reads themselves, which is a more accurate estimate of the original quality of the read.

Methods

Tool used: BAM type: Uncollapsed BAM.

Interpretation

It is normal to see a downwards trend in pre and post-recalibration base quality towards the ends of the reads. Average post-recalibration quality scores should be above 20. Spikes in quality may be indicative of a sequencer artifact.

Duplex Noise Metrics

Contamination

Introduction

Two metrics are used to estimate sample contamination: minor contamination and major contamination. Moreover, minor contamination is calculated separately for collapsed and duplex BAMs. Both contamination metrics are produced by the fingerprinting SNP set. However, minor contamination is calculated using just the homozygous sites, whereas the major contamination is via the ratio of heterozygous to homozygous sites. For each contamination-BAM type combination there is a table showing per-sample contamination values and any associated metrics.

Hotspot in Normals

The table below summarised the variants called in hotspot regions in the normal samples.

Major contamination

Major contamination is calculated by the Collapsed BAM and is calculated using the ratio of heterozygous to homozygous sites.

Minor contamination

Minor contamination is calculated separately for collapsed and duplex BAMs. Minor contamination is calculated using just the homozygous sites.

Contributing sites

Fingerprinting

Introduction

This section contains a table showing the samples clustered into groups, where each row in the table corresponds to one sample. The table will show whether your samples are grouping together in unexpected ways, which would indicate sample mislabelling.

Methods

Tool used: BAM type: Collapsed BAM Regions: MSK-ACCESS-v1_0-curatedSNPs.vcfIt is a two step process to produce the table: (1) extract SNP genotypes from each sample using biometrics extract command and (2) perform a pairwise comparison of all samples to determine sample relatedness using the biometrics genotype command. Please see the biometrics documentation for further documentation on the methods.

Interpretation

Below is a description of all the columns.

QC thresholds

QC thresholds are specified in the config file and I

CMO-CH QC thresholds

Metric

PASS

WARNING

FAIL

Insert size

athena_coverage_report_dir:
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collapsed_extraction_files:
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    path: /juno/work/access/production/runs/voyager/staging/cmo_ch_qc/cf19eabf-29da-4448-a42d-d72af47e75b6/C-JU5J5X-N001-d01/C-JU5J5X-N001-d01_collapsed.pickle
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config:
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  - class: Directory
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duplex_extraction_files:
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gatk_mean_quality_by_cycle_recal_dir:
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  - class: Directory
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samples-json:
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simplex_bam_stats_dir:
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  - class: Directory
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biometrics_extract_files_dir:
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- class: Directory
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athena_coverage_report_dir:
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collapsed_bam_duplex_metrics_dir:
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collapsed_extraction_files:
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config:
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duplex_bam_stats_dir:
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  - class: Directory
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duplex_extraction_files:
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    path: /juno/work/access/production/runs/voyager/staging/cmo_ch_qc/cf19eabf-29da-4448-a42d-d72af47e75b6/C-JU5J5X-N001-d01/C-JU5J5X-N001-d01_duplex.pickle
  - class: File
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gatk_mean_quality_by_cycle_recal_dir:
  - class: Directory
    path: /juno/work/access/production/runs/voyager/staging/cmo_ch_qc/cf19eabf-29da-4448-a42d-d72af47e75b6/multiqc_1.10.1.7/aggregate_parsed_stats/all_qc_files/gatk_mean_quality_by_cycle_recal_dir/C-JU5J5X-N001-d01/
  - class: Directory
    path: /juno/work/access/production/runs/voyager/staging/cmo_ch_qc/90cbad6f-3361-49f2-9ef1-eabcaf90d352/multiqc_1.10.1.7/aggregate_parsed_stats/all_qc_files/gatk_mean_quality_by_cycle_recal_dir/C-40R694-N001-d01
samples-json:
  class: File
  path: /work/bergerm1/bergerlab/charalk/projects/nucleo_qc/repos/agg_vis_3167_C/samples_3167_C_sub.json
simplex_bam_stats_dir:
  - class: Directory
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  - class: Directory
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uncollapsed_bam_stats_dir:
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    path: /juno/work/access/production/runs/voyager/staging/cmo_ch_qc/cf19eabf-29da-4448-a42d-d72af47e75b6/multiqc_1.10.1.7/aggregate_parsed_stats/all_qc_files/uncollapsed_bam_stats_dir/C-JU5J5X-N001-d01/
  - class: Directory
    path: /juno/work/access/production/runs/voyager/staging/cmo_ch_qc/90cbad6f-3361-49f2-9ef1-eabcaf90d352/multiqc_1.10.1.7/aggregate_parsed_stats/all_qc_files/uncollapsed_bam_stats_dir/C-40R694-N001-d01
biometrics_extract_files_dir:
- class: Directory
  path: test_data/all_qc_files/biometrics_extract_files_dir/Myeloid200-1-05500HJ_P20/
- class: Directory
  path: test_data/all_qc_files/biometrics_extract_files_dir/Myeloid200-2-05500HJ_P20/y

omaf: true output: null reference: path: /juno/work/cch/production/resources/reference/versions/hg19/Homo_sapiens_assembly19.fasta class: File secondaryFiles: - path: /work/cch/production/resources/reference/versions/hg19/Homo_sapiens_assembly19.fasta.fai class: File - path: /work/cch/production/resources/reference/versions/hg19/Homo_sapiens_assembly19.dict class: File duplex_bam: - path: /juno/work/access/production/runs/voyager/staging/cmo_ch_nucelo/07cc97f0-c83b-41a2-a308-debdd6404968/C-T496P0-N001-d01_collapsed_duplex.bam class: File secondaryFiles: - path: /juno/work/access/production/runs/voyager/staging/cmo_ch_nucelo/07cc97f0-c83b-41a2-a308-debdd6404968/C-T496P0-N001-d01_collapsed_duplex.bai class: File sample_sex: - unknown sample_name: - C-T496P0-N001-d01 simplex_bam: - path: /juno/work/access/production/runs/voyager/staging/cmo_ch_nucelo/07cc97f0-c83b-41a2-a308-debdd6404968/C-T496P0-N001-d01_collapsed_simplex.bam class: File secondaryFiles: - path: /juno/work/access/production/runs/voyager/staging/cmo_ch_nucelo/07cc97f0-c83b-41a2-a308-debdd6404968/C-T496P0-N001-d01_collapsed_simplex.bai class: File hotspots_maf: path: /juno/work/cch/production/resources/cmo-ch/versions/v1.0/regions_of_interest/versions/v1.0/hotspot-list-union-v1-v2_with_TERT.maf class: File sample_group: - C-T496P0 samples-json: path: /juno/work/ci/temp/897c0b7d-8631-4e55-8f86-5a435a84b9e1/samples_json.json class: File collapsed_bam: - path: /juno/work/access/production/runs/voyager/staging/cmo_ch_nucelo/07cc97f0-c83b-41a2-a308-debdd6404968/C-T496P0-N001-d01_collapsed_FM.bam class: File secondaryFiles: - path: /juno/work/access/production/runs/voyager/staging/cmo_ch_nucelo/07cc97f0-c83b-41a2-a308-debdd6404968/C-T496P0-N001-d01_collapsed_FM.bai class: File bait_intervals: path: /juno/work/cch/production/resources/cmo-ch/versions/v1.0/regions_of_interest/versions/v1.0/picard_baits.interval_list class: File fragment_count: 1 multiqc_config: path: /work/cch/production/resources/cmo-ch/versions/v1.0/multiqc_config/versions/v1.0/config_ch.yaml class: File noise_sites_bed: path: /juno/work/cch/production/resources/cmo-ch/versions/v1.0/regions_of_interest/versions/v1.0/picard_baits.bed class: File filter_duplicate: 0 generic_counting: true target_intervals: path: /juno/work/cch/production/resources/cmo-ch/versions/v1.0/regions_of_interest/versions/v1.0/picard_baits.interval_list class: File biometrics_vcf_file: path: /juno/work/cch/production/resources/cmo-ch/versions/v1.0/regions_of_interest/versions/v1.0/fp_tiling_snps.vcf class: File group_reads_by_umi_bam: - path: /juno/work/access/production/runs/voyager/staging/cmo_ch_nucelo/07cc97f0-c83b-41a2-a308-debdd6404968/C-T496P0-N001-d01_collapsed_grouped.bam class: File uncollapsed_bam_base_recal: - path: /juno/work/access/production/runs/voyager/staging/cmo_ch_nucelo/07cc97f0-c83b-41a2-a308-debdd6404968/C-T496P0-N001-d01_uncollapsed_BR.bam class: File secondaryFiles: - path: /juno/work/access/production/runs/voyager/staging/cmo_ch_nucelo/07cc97f0-c83b-41a2-a308-debdd6404968/C-T496P0-N001-d01_uncollapsed_BR.bai class: File

Nucleo Quality Control

hashtagIntroduction

hashtag

Workflow description

Requirements

hashtagBefore installing the pipeline, make sure your system supports these requirements

Installation and Usage

hashtagStep 1: Create a virtual environment.

hashtagOption (A) - if using cwltool

hashtagOption (B) - recommended for Juno HPC cluster

hashtagStep 2: Clone the repository

hashtagStep 3: Install requirements using pip

hashtagStep 4: Load singularity and nodejs for HPC

hashtagStep 5: Generate an inputs file

hashtagStep 5: Run the workflow

hashtagOption A:

hashtagRun the workflow with a given set of input using on single machine

hashtagOption B:

Workflow inputs

hashtagParameter Used by Tools

hashtagCommon Parameters Across Tools

Input for nucleo_qc.cwl

hashtagOption 1: Input.yml example to run workflow enable mosdepth/athena to create coverage reports

hashtagOption 2: Input.yml example to run workflow without mosdepth/athena

Input for nucleo_aggregate_visualize.cwl

Workflow outputs

Interpretation - MultiQC

hashtagTable of Contents

Summary QC metrics

hashtagInterpretation

Coverage vs GC content

hashtagMethods

Capture Metrics

hashtagIntroduction

hashtagMethods

hashtagInterpretation

Insert Size

hashtagIntroduction

hashtagMethods

hashtagInterpretation

Target coverage distribution

hashtagIntroduction

hashtagMethods

hashtagInterpretation

Duplex Metrics

Duplex family sizes

Simplex family sizes

Duplex yield metrics

Mean Base Quality

hashtagIntroduction

hashtagMethods

hashtagInterpretation

Duplex Noise Metrics

Contamination

hashtagIntroduction

Hotspot in Normals

Major contamination

Minor contamination

Contributing sites

Fingerprinting

hashtagIntroduction

hashtagMethods

hashtagInterpretation

QC thresholds

hashtagCMO-CH QC thresholds

Installation and Usage

hashtagStep 1: Create a virtual environment.

hashtagOption (A) - if using cwltool

hashtagOption (B) - recommended for Juno HPC cluster

hashtagStep 2: Clone the repository

hashtagStep 3: Install requirements using pip

hashtagStep 4: Load singularity and nodejs for HPC

hashtagStep 5: Generate an inputs file

hashtagStep 5: Run the workflow

hashtagOption A:

hashtagRun the workflow with a given set of input using on single machine

hashtagOption B:

Nucleo Quality Control

hashtagIntroduction

hashtag

Introduction

Before installing the pipeline, make sure your system supports these requirements

Step 1: Create a virtual environment.

Option (A) - if using cwltool

Option (B) - recommended for Juno HPC cluster

Step 2: Clone the repository

Step 3: Install requirements using pip

Step 4: Load singularity and nodejs for HPC

Step 5: Generate an inputs file

Step 5: Run the workflow

Option A:

Run the workflow with a given set of input using on single machine

Option B:

Parameter Used by Tools

Common Parameters Across Tools

Option 1: Input.yml example to run workflow enable mosdepth/athena to create coverage reports

Option 2: Input.yml example to run workflow without mosdepth/athena

Table of Contents

Interpretation

Methods

Introduction

Methods

Interpretation

Introduction

Methods

Interpretation

Introduction

Methods

Interpretation

Introduction

Methods

Interpretation

Introduction

Introduction

Methods

Interpretation

CMO-CH QC thresholds

Step 1: Create a virtual environment.

Option (A) - if using cwltool

Option (B) - recommended for Juno HPC cluster

Step 2: Clone the repository

Step 3: Install requirements using pip

Step 4: Load singularity and nodejs for HPC

Step 5: Generate an inputs file

Step 5: Run the workflow

Option A:

Run the workflow with a given set of input using on single machine

Option B:

Introduction

Before installing the pipeline, make sure your system supports these requirements

Parameter Used by Tools

Common Parameters Across Tools

Run the workflow with a given set of input using toil on single machine

Option 1: Input.yml example to run workflow enable mosdepth/athena to create coverage reports

Option 2: Input.yml example to run workflow without mosdepth/athena

Introduction

Methods

Interpretation

Interpretation

Methods

Interpretation

Introduction

Introduction

Methods

Interpretation

Introduction

Methods

Interpretation

Table of Contents

Introduction

Methods

Interpretation

Introduction

Methods

Interpretation

CMO-CH QC thresholds