jackalope: A swift, versatile phylogenomic and high‐throughput sequencing simulator

Abstract

High-throughput sequencing (HTS) is central to the study of population genomics and has an increasingly important role in constructing phylogenies. Choices in research design for sequencing projects can include a wide range of factors, such as sequencing platform, depth of coverage and bioinformatic tools. Simulating HTS data better informs these decisions, as users can validate software by comparing output to the known simulation parameters. However, current standalone HTS simulators cannot generate variant haplotypes under even somewhat complex evolutionary scenarios, such as recombination or demographic change. This greatly reduces their usefulness for fields such as population genomics and phylogenomics. Here I present the R package jackalope that simply and efficiently simulates (i) sets of variant haplotypes from a reference genome and (ii) reads from both Illumina and Pacific Biosciences platforms. Haplotypes can be simulated using phylogenies, gene trees, coalescent-simulation output, population-genomic summary statistics, and Variant Call Format (VCF) files. jackalope can simulate single, paired-end or mate-pair Illumina reads, as well as reads from Pacific Biosciences. These simulations include sequencing errors, mapping qualities, multiplexing and optical/PCR duplicates. It can read reference genomes from fasta files and can simulate new ones, and all outputs can be written to standard file formats. jackalope is available for Mac, Windows and Linux systems.

Editorial 2021

“…Top resources published in Molecular Ecology Resources in 2020 include key contributions across multiple subject areas of the journal. Three separate but complementary computer programs, which are at the frontier of their respective fields, were published: RADinitio, which allows users to simulate RAD (restriction site‐associated DNA) data to aid experimental design (Rivera‐Colón et al, 2020); Jackalope, which enables users to generate high‐throughput sequencing simulations that can take complex evolutionary scenarios into account (Nell, 2020); and a new module of the landscape genetics programs CDPOP and CDMetaPOP, which enables users to simulate multilocus adaption in complex environments in order to better evaluate genotype–environment associations (Landguth et al., 2020). To ensure that all researchers have access to the latest tools in their field, computer programs continue to be published as ‘free access’.…”

Sandy: A user-friendly and versatile NGS simulator to facilitate sequencing assay design and optimization

“…Curiously, most of these tools have been designed to simulate either genomic (Huang et al 2012) or transcriptomic data (Griebel et al 2012), but not both. More recently published algorithms that simulate whole-genome sequencing (WGS) data address the efficiency problem, add more complexity to simulations, and cover more sequencing platforms in response to the increasing complexity of genomic analysis (Henriksen et al 2023;Yu et al 2020;Nell 2020). Furthermore, highly specialized tools for simulating a specific type of data or sequencing platform are available (Li et al 2018;Ono et al 2013;Stöcker et al 2016;Balzer et al 2010).…”

Benchmarking the topological accuracy of bacterial phylogenomic workflows using in silico evolution

“…This approach provides a good grasp of the true phylogeny and allows the sampling of ancestral strains, but the method is costly and time-consuming, and evolutionary parameters cannot be easily controlled. Finally, some studies have used in silico evolution to produce realistic sequencing data together with an a priori defined true phylogeny [3,4,[6][7][8][9]. This approach offers the possibility to increase or decrease the rate of a range of evolutionary events, such as point mutations, indels, gene duplication, gene loss, gene translocation and lateral gene transfer.…”

“…Several in silico evolution frameworks have been developed, with differing goals and strengths [3,[7][8][9][10][11][12][13]. In the current study, we aimed to select a simulation strategy producing complete, haploid bacterial genomes.…”

“…As we aimed to compare against a true tree, the in silico evolution was guided by a user-provided phylogenetic tree. We surveyed the Genetic Data Simulator database (https://surveillance.cancer.gov/genetic-simulation-resources/) and previously published manuscripts [3,4,[7][8][9][10][11]13]. The workflow used by Lees et al (2018) [3] was used as it satisfied all our criteria.…”