MosaicSim: Simulation of Mosaic Variants in Sequencing Data

Hackathon team: Lead: Fritz Sedlazeck - Developers: Erik Stricker, Xinchang Zheng, Michal Izydorczyk, Chi-Lam Poon, Philippe Sanio, Farhang Jaryani, Joyjit Daw, Divya Kalra, Adam Alexander - Writers: Erik Stricker, Sontosh Deb

We provide two simulation workflows which output sequencing read files with artificial mosaic variants and a ground truth mosaic variant annotation file for the validation of mosaic variant callers.

Table of Contents

1. Background 2. Installation 3. Dependencies 4. Tests 5. How to Use It 6. Example Implementation 7. Method Description 8. Contributers 9. References

Background

In the context of individual genome comparison, mutations that appear within a small fraction of the population are considered rare variants¹. When assessing a population of cells from a tissue of the same individual in turn, rare variants only present in a small fraction of the cells are defined as mosaic variants (MVs)². Recent studies have shown that there is potential disease associations of for certain MVs². However, MVs are challenging to detect because they are mixed in with data from the non-mutated cells and present in the same sequencing file. Therefore, several pipelines have been developed or adjusted to extract mosaic single nucleotide, structural or indel variants from whole genome sequencing data such as Sniffles³, DeepMosaic⁴, Mutect2⁵, DeepVariant⁶. To benchmark and validate the efficiency and accuracy of these methods, sequencing files with known MVs are necessary. We developed two simulation workflows called SpikeVar (Spike in Variants from a second individual) and TweakVar (Tweak Variants within existing reads of one individual), which output sequencing read files with artificial MVs and a ground truth annotation file for the MVs. SpikeVar accomplishes this by spiking in real reads from a sample at user-defined ratio into the sequencing file from a second sample. In contrast, TweakVar creates a list of random mutations and modifies a fraction of existing reads to match the user-defined MV frequency.

Installation

These instructions are valid for Linux x86. For other platforms (e.g. MacOS), instructions will need to be adapted.

Load conda, e.g.

module load miniconda/3

or

module load anaconda3 ##sometimes python has to be loaded before starting an environment)
module load python

Installing two conda environments with python 3.10

conda create -n MosaicSim python=3.10
conda init
conda activate MosaicSim

Unload python if previously loaded so that python 3.10 from conda environment will be used

module unload python

Obtain the MosaicSim from github (replace $HOME with your preferred installation director)

cd  $HOME
git clone https://github.com/erikstricker/MosaicSim.git

To install the relevant python dependencies, run

REPO_ROOT="$HOME/MosaicSim"

pip install -r $REPO_ROOT/requirements.txt

Ensure to also load mosdepth≥0.3.2 (for SpikeVar), samtools ≥1.17, and bcftools≥1.17

Once the requirements are installed, please install or load the following additional packages. If you work in a cluster with the packages already installed, you can load the path or module directly.

Installation

conda install -c bioconda samtools bcftools mosdepth

Loading (e.g.)

export PATH=/path/to/software/mosdepth/mosdepth-0.3.2/bin:$PATH
export PATH=/path/to/software/samtools/samtools-1.17/bin:$PATH
export PATH=/path/to/software/bcftools/bcftools-1.17/bin:$PATH

or

module load mosdepth
module load samtools
module load bcftools

Dependencies

SpikeVar

• mosdepth 0.3.2
• samtools ≥1.17
• bcftools ≥1.17
• Python 3.10

TweakVar

• pysam 0.21.0)
• numpy ≥1.24.3
• biopython 1.81
• samtools ≥1.17
• bcftools ≥1.17

Contributers

Erik Stricker	Chi-Lam Poon	Philippe Sanio	Xinchang Zheng	Farhang Jaryani

Joyjit Daw	Michal Izydorczyk	Sontosh Deb	Fritz Sedlazeck	Adam Alexander

Divya Kalra

References

[1] Sariya S, Lee JH, Mayeux R, Vardarajan BN, Reyes-Dumeyer D, Manly JJ, Brickman AM, Lantigua R, Medrano M, Jimenez-Velazquez IZ, Tosto G. Rare Variants Imputation in Admixed Populations: Comparison Across Reference Panels and Bioinformatics Tools. Front Genet. 2019;10:239. Epub 20190403. doi: 10.3389/fgene.2019.00239. PubMed PMID: 31001313; PMCID: PMC6456789.

[2] Miller CR, Lee K, Pfau RB, Reshmi SC, Corsmeier DJ, Hashimoto S, Dave-Wala A, Jayaraman V, Koboldt D, Matthews T, Mouhlas D, Stein M, McKinney A, Grossman T, Kelly BJ, White P, Magrini V, Wilson RK, Mardis ER, Cottrell CE. Disease-associated mosaic variation in clinical exome sequencing: a two-year pediatric tertiary care experience. Cold Spring Harb Mol Case Stud. 2020;6(3). Epub 20200612. doi: 10.1101/mcs.a005231. PubMed PMID: 32371413; PMCID: PMC7304353.

[3] Sedlazeck FJ, Rescheneder P, Smolka M, Fang H, Nattestad M, von Haeseler A, Schatz MC. Accurate detection of complex structural variations using single-molecule sequencing. Nat Methods. 2018;15(6):461-8. Epub 20180430. doi: 10.1038/s41592-018-0001-7. PubMed PMID: 29713083; PMCID: PMC5990442.

[4] Yang X, Xu X, Breuss MW, Antaki D, Ball LL, Chung C, Shen J, Li C, George RD, Wang Y, Bae T, Cheng Y, Abyzov A, Wei L, Alexandrov LB, Sebat JL, Network NBSM, Gleeson JG. Control-independent mosaic single nucleotide variant detection with DeepMosaic. Nat Biotechnol. 2023;41(6):870-7. Epub 20230102. doi: 10.1038/s41587-022-01559-w. PubMed PMID: 36593400; PMCID: PMC10314968.

[5] McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297-303. Epub 20100719. doi: 10.1101/gr.107524.110. PubMed PMID: 20644199; PMCID: PMC2928508.

[6] Poplin R, Chang PC, Alexander D, Schwartz S, Colthurst T, Ku A, Newburger D, Dijamco J, Nguyen N, Afshar PT, Gross SS, Dorfman L, McLean CY, DePristo MA. A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol. 2018;36(10):983-7. Epub 20180924. doi: 10.1038/nbt.4235. PubMed PMID: 30247488.