For studies using high-throughput sequencing (HTS) data, simulations can be vital for planning sampling design and testing bioinformatic tools. However, most HTS sequencing tools provide only very simple ways of adding deviations from a reference genome. For HTS studies that focus on patterns of genomic variation among individuals, populations, or species, having a tool that can simulate realistic patterns of molecular evolution and generate HTS data from those simulations would be quite useful.
jackalope simply and efficiently simulates (i) haplotypes from reference genomes and (ii) reads from both Illumina and Pacific Biosciences (PacBio) platforms. It can either read reference genomes from FASTA files or simulate new ones. Variant haplotypes can be simulated using summary statistics, phylogenies, Variant Call Format (VCF) files, and coalescent simulations—the latter of which can include selection, recombination, and demographic fluctuations.
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. All outputs can be written to standard file formats.
jackalope, you should update the packages
zlibbioc. Since both of these are on Bioconductor, you should update
# To install the latest stable version from CRAN: install.packages("jackalope")
# install.packages("devtools") remotes::install_github("lucasnell/jackalope")
To use multithreading in
jackalope, you’ll need to compile it from source using the proper flags. If you’ve enabled OpenMP properly, running
jackalope:::using_openmp() in R should return
The first step is to add the following to the
.R/Makevars.win on Windows) file inside the home directory:
Then, you should be able to install
jackalope by running the following in R:
install.packages("jackalope", type = "source") ## Or, for development version: # remotes::install_github("lucasnell/jackalope")
Follow the directions here to install R compiler tools: https://thecoatlessprofessor.com/programming/cpp/r-compiler-tools-for-rcpp-on-macos/.
Check your version of
gcc --version in the Terminal. Then, check the table at https://mac.r-project.org/openmp/ to see which version of the runtime OpenMP downloads you need. For LLVM version 9.0.1, you run the following in the Terminal:
For the next step of actually installing
jackalope, one option is to add the following to your
This might not be desirable since it affects all package installations. An alternative method is to use the package
Add the following to the
.R/Makevars file inside the home directory:
Next, go to https://cran.r-project.org/bin/macosx/tools/ and download the newest versions of (1) the
clang compiler (version 8 at the time of writing) and (2) GNU Fortran (version 6.1 at the time of writing). The downloads will have the
.pkg extension. Next, install
gfortran by opening these
.pkg files and following the directions.
After this, add the following to your
~/.R/Makevars file (replacing
clang8 with your version of the clang compiler):
Now you should be able to install
jackalope by running
install.packages("jackalope", type = "source") in R.
For more information, please see https://thecoatlessprofessor.com/programming/openmp-in-r-on-os-x/.
Below shows how to simulate a 10kb genome, then create haplotypes from that genome using a phylogenetic tree:
library(jackalope) reference <- create_genome(n_chroms = 10, len_mean = 1000) tr <- ape::rcoal(5) ref_haplotypes <- create_haplotypes(reference, haps_phylo(tr), sub_JC69(0.1)) ref_haplotypes #> << haplotypes object >> #> # Haplotypes: 5 #> # Mutations: 16,870 #> #> << Reference genome info: >> #> < Set of 10 chromosomes > #> # Total size: 10,000 bp #> name chromosome length #> chrom0 CTGGCATTGAATCATATGAGGTGGCCAT...ACGTTGCACGATTGATTAAATTCCTGAA 1000 #> chrom1 CACTCCGTCGCACACTAGGTTTCGAGAT...GTGAGCTCGCGTACATGGAGCATTCTGT 1000 #> chrom2 CTTAGCCGGAGCGACTCGGAGCAACTGC...TGGCGTAATATGCCAGGTCCCGCGTGGC 1000 #> chrom3 CGCCTTCCATTTAGGACTTGTATTGGTG...GCTAAACTCCATGTGACTGTAATGTCAG 1000 #> chrom4 GGGTGATATGGTGTGCATGCTGAATTCG...AGAGTCTAGAGTCTCTGGGAGGTCAGGT 1000 #> chrom5 TTCGTTGGTGGGTGTCCTATGCTACGAT...CGCCCGCCGGTTTGACTTACTCGATTGG 1000 #> chrom6 GCATGGACAGATGTGATCTGAGTATACG...CAGACCCCATAAGGCCTGGGACACTGTG 1000 #> chrom7 TCGTTTCAACGTCCTTAAGTGTAGTATC...GGCTCGTTAGCTCTCCGAGGAGACGAGG 1000 #> chrom8 CAGGTAAGTTATCAAAGAACCTTCCTGG...ACGCATCACCTCGCAAGGAGACTCGTTA 1000 #> chrom9 GGTAGTAATTAGGCTTAAAATAGCAGTG...ATAACAAATGTTCGGCATACGATCTACG 1000
Below simulates 500 million paired-end, 100 bp reads from the haplotypes:
illumina(ref_haplotypes, out_prefix = "illumina", n_reads = 500e6, paired = TRUE, read_length = 100)
Below simulates 500 thousand PacBio reads from the reference genome:
pacbio(ref, out_prefix = "pacbio", n_reads = 500e3)