TOPMed-mCA

Overview

This repository houses the tools for Mosaic Chromosomal Alteration (mCA) analysis of TOPMed whole genome sequence data presented in "Mosaic chromosomal alterations in blood across ancestries via whole-genome sequencing", https://doi.org/10.1038/s41588-023-01553-1.

The project is a collaboration among the following institutions:

• University of Wisconsin -- Milwaukee
• Medical College of Wisconsin
• MD Anderson Cancer Center
• Fred Hutchinson Cancer Research Center
• University of Washington
• University of Michigan
• National Heart Lung and Blood Institute

The aim of this analysis is to process human genome data from the Sequence Read Archive (SRA) and the database of Genotypes and Phenotypes (dbGaP) through haplohseq, a tool that detects allelic imbalance in impure cell samples with potentially very low aberrant cell percentages, and MoCha, a similar tool for detecting such alterations.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5039922/

https://sites.google.com/site/integrativecancergenomics/software/haplohseq

https://software.broadinstitute.org/software/mocha/

Processing

Haplohseq takes as input a single-sample, multi-chromosome VCF (variant call format) file containing allele frequencies for each variant call. Haplohseq can also function using micro-array data.

Variant calls for 137,977 human genomes from TOPMed were available on dbGaP in the form of BCF (binary variant format, a compressed version of VCF) files. Unfortunately, these BCF files do not contain allele frequencies and are in multi-sample, single-chromosome format, not suitable for haplohseq.

Hence, this pipeline was developed in order to reformat and augment the VCF data for use with haplohseq. This includes the following high-level processing steps:

1. 1a-vcf-split.sbatch

   Split the multi-sample, single-chromosome dbGaP BCF files into single-sample, single-chromosome VCF files and combine the results into single-sample, multi-chromosome VCFs.

   This step is extremely I/O-intensive. Decoding the BCF files (using bcftools) is also CPU-limited. The largest of the multi-sample BCF files (for chromosome 2) is 83 gigabytes and takes approximately 2 days just to read using bcftools.

   bcftools view freeze.8.chr2.pass_only.phased.bcf > /dev/null
   

   Extracting the 137,977 samples one at a time would therefore take approximately 2 * 137,977 days = 750 years. To solve this issue, we developed vcf-split, which can extract several thousand samples from a multi-sample VCF stream simultaneously, while also performing some filtering functions. We found that some bcftools filtering options increase throughput while others slow it down, so filtering tasks are divided between bcftools and vcf-split. By balancing the workload between bcftools for decoding the BCF files and some of the filtering, and vcf-split to extract samples and perform the remaining filtering, we were able to generate all of the single-sample files in a few weeks on a small HPC cluster.

   Combining the resulting single-sample, single-chromosome VCF files into single-sample, multiple chromosome files is a simple matter of concatenating the single-chromosome files in the correct order.

2. 2a-ad2vcf.sbatch

   Augment the VCF files with allelic depth information.

   Unfortunately, the dbGaP BCF files do not contain allelic depth (allele frequencies) in each call. This is optional information, represented by the AD tag in the FORMAT field. It was omitted during the variant call process in order to save space, but is required for haplohseq's statistical model to detect imbalances.

   One option would be to simply rerun the variant calling pipeline and include allelic depth in the VCF output, but this would be enormously expensive.

   To solve this issue as quickly and efficiently as possible, we developed ad2vcf, a tool to extract allelic depth for each call in a VCF from Sequence Alignment Map (SAM) files and add it to the VCF.

   The alignment files corresponding to our dbGaP BCFs are available from the SRA in the form of CRAM (a highly compressed form of SAM format) files. The CRAM files for all 137,977 samples total roughly 2.7 petabytes (2,700 terabytes, 2,700,000 gigabytes). Using the fastest available file transfer tools, it would take well over a year to download this much data for processing on our own hardware.

   To resolve this issue, SRA files are directly available to virtual machine instances on Amazon Web Services (AWS) and Google Cloud Platform (GCP). The only tool available for accessing restricted data on AWS and GCP at the time of the analysis was Fusera, a fusefs wrapper with authentication features for accessing restricted SRA files. Fusera was already deprecated at the time of our analysis and no alternative was yet available. This step proved to be the most challenging due to reliability issues with the Fusera mounts, but with additional code features to automatically restart processing in the event of failures, we were able to complete it in a few months.

   The SRA Toolkit has reportedly been updated to provide authenticated access to restricted files, though we were not able to test it before our access expired. Nonetheless, anyone wishing to access restricted SRA files in the future should plan to use the SRA Toolkit. For running ad2vcf, this would involve using "sratools sam-dump" in place of Fusera mounts and "samtools view" in our scripts.

3. 3d-filter-sites.sh, 3d-filter-sites.sbatch

   Filter VCF calls for minimum separation of calls (1000 bases for our analysis), minor allele frequency (MAF), and structural variants (79852, freeze1).

   To reduce noise in the haplohseq and MoCha analyses the resulting AD-enhanced VCF files were filtered to keep only sites with a minor allele frequency (MAF) of 0.05 and a minimum separation of 1000 nucleotides. Other MAF values such as 0.01 were also tested and did not prove to have a significant impact on haplohseq results. These filters were implemented by a simple awk script.

   Additionally, known germ line structural variants were removed in order to focus on acquired mutations. Structural variants are provided by dbGaP in the form of BCF files. We used "bedtools subtract" to remove these variants.

4. go-haploh, 4a-haplohseq.sh, 4a-haplohseq.sbatch

   Run haplohseq on the filtered VCF files.

   The AD-enhanced, filtered VCF files were then processed through haploseq to detect mosaic chromosomal alteration events. Haplohseq takes many parameters. We experimented with several combinations of the following and settled on the values shown below:

   1. Event prevalence = 0.01
   2. Minimum event size = 30 megabases
   3. Minimum depth (coverage of the variant site) = 10

5. 5-mocha.sh, 5-mocha.sbatch

   Run MoCha on the filtered VCF files.

   MoCha is another tool for detecting mosaic chromosomal alterations. We ran MoCha on the same filtered VCF files in order to compare the sensitivities of the tools and determine the strengths and weaknesses of each.

6. Examine results and analyze in GWAS (Genome Wide Association) pipeline to identify potentially related phenotypes.

General Notes

Most computational analysis was performed on a FreeBSD HPC cluster managed by SPCM and running the SLURM resource manager.

Code development and testing was performed on a FreeBSD workstation configured with Desktop-installer and using the OpenZFS file system with lz4 compression. The compression capabilities of ZFS significantly reduce disk usage and read/write time for uncompressed temporary files such as raw VCFs. This is especially helpful when splitting the multisample VCFs, since we cannot simultaneously pipe the thousands of VCF streams written by vcf-split through a compressor such as gzip, bzip2, or xz. We must write raw VCFs and compress them afterward, limiting parallelism to the number of available CPUs.

All long-term output files were subsequently compressed with xz, which provides significantly better compression ratios than lz4, gzip, or bzip2. (Typically about 40% smaller than gzip).

Filtering reads based on MAPQ in ad2vcf resulted in no noticeable improvement to the haplohseq results. Reads for the 4 groups of CRAMs processed (whi, bjm, group3, and group4) were filtered only for unmapped reads.

Repository Organization

• Scripts in the top-level directory are part of the primary analysis pipeline.
• Scripts used for exploration are found under Utils.
• Primary data are stored under Data.
• Analysis logs are under Logs.
• Metadata to be saved for replicating the analysis are under Save.

Software Requirements

All software required for this analysis can be installed on FreeBSD with the following command:

pkg install mca-calling

Mca-calling is a "metaport" which does not install any files of its own, but installs other ports/packages (e.g. bcftools, haplohseq, vcf-split, ad2vcf, mawk, etc.) as dependencies.

General tools such as awk, sed, sort, xz, etc. are included in the FreeBSD base installation. Mawk outperforms BSD and GNU awk and may be used as a drop-in replacement to speed up some scripts, though this will have only a minor impact on overall analysis time as awk is generally not a bottleneck.

Users of other Unix-like systems such as Linux and macOS can deploy all of the necessary tools using pkgsrc, a package manager with stringent quality assurance that supports virtually any POSIX platform.. The auto-pkgsrc-setup script at https://NetBSD.org/~bacon will help you get started. Installing pkgsrc using auto-pkgsrc-setup takes about 10 minutes, and provides easy installation of about 20,000 software packages, including nearly 1,000 scientific programs. Installing the biology/mca-calling meta-package will install all of the tools required for this analysis.

Most FreeBSD ports are also available in dports on Dragonfly BSD.

For more information on package managers, see the Repology website.