SimRAD-package: Simulations to predict the number of loci expected in RAD and...

In SimRAD: Simulations to Predict the Number of RAD and GBS Loci

Description

SimRAD provides a number of functions to simulate restriction enzyme digestion, library construction and fragments size selection to predict the number of loci expected from most of the Restriction site Associated DNA (RAD) and Genotyping By Sequencing (GBS) approaches. SimRAD aims to provide an estimation of the number of loci expected from a given genome depending on protocol type and parameters allowing to assess feasibility, multiplexing capacity and the amount of sequencing required.

Details

Package:	SimRAD
Type:	Package
Version:	0.96
Date:	2015-11-04
License:	GPL-2

This package provides a number of functions to simulate restriction enzyme digestion (insilico.digest), library construction (adapt.select) and fragments size selection (size.select) to predict the number of loci expected from most of the Restriction site Associated DNA (RAD) and Genotyping By Sequencing (GBS) approaches.
Built with non-model species in mind, a function can simulate DNA sequence when no reference genome sequence is available (sim.DNAseq) providing genome size and CG content is known. Alternatively, reference genomic sequences can be used as an input (ref.DNAseq). SimRAD can accommodate various Reduced Representation Libraries methods that may combine single or double restriction enzyme digestion (RAD and GBS), fragment size selection (ddRAD and ezRAD) and restriction site based fragment exclusion (RESTseq).

Author(s)

Olivier Lepais and Jason Weir

Maintainer: Olivier Lepais <[e-mail]>

References

Lepais O & Weir JT. 2014. SimRAD: an R package for simulation-based prediction of the number of loci expected in RADseq and similar genotyping by sequencing approaches. Molecular Ecology Resources, 14, 1314-1321. DOI: 10.1111/1755-0998.12273.

Examples

### Example 1: a single digestion (RAD)
simseq <-  sim.DNAseq(size=100000, GCfreq=0.51)
#Restriction Enzyme 1
#SbfI
cs_5p1 <- "CCTGCA"
cs_3p1 <- "GG"
simseq.dig <- insilico.digest(simseq, cs_5p1, cs_3p1, verbose=TRUE)

### Example 2: a single digestion (GBS)
simseq <-  sim.DNAseq(size=100000, GCfreq=0.51)
#Restriction Enzyme 1
# ApeKI : G|CWGC  which is equivalent of either G|CAGC or  G|CTGC
cs_5p1 <- "G"
cs_3p1 <- "CAGC"
cs_5p2 <- "G"
cs_3p2 <- "CTGC"
simseq.dig <- insilico.digest(simseq, cs_5p1, cs_3p1, cs_5p1, cs_3p1, verbose=TRUE)

### Example3: a double digestion (ddRAD)
# simulating some sequence:
simseq <-  sim.DNAseq(size=1000000, GCfreq=0.433)

#Restriction Enzyme 1
#TaqI
cs_5p1 <- "T"
cs_3p1 <- "CGA"
#Restriction Enzyme 2
#MseI #
cs_5p2 <- "T"
cs_3p2 <- "TAA"

simseq.dig <- insilico.digest(simseq, cs_5p1, cs_3p1, cs_5p2, cs_3p2, verbose=TRUE)
simseq.sel <- adapt.select(simseq.dig, type="AB+BA", cs_5p1, cs_3p1, cs_5p2, cs_3p2)

# wide size selection (200-270):
wid.simseq <- size.select(simseq.sel,  min.size = 200, max.size = 270, graph=TRUE, verbose=TRUE)

# narrow size selection (210-260):
nar.simseq <- size.select(simseq.sel,  min.size = 210, max.size = 260, graph=TRUE, verbose=TRUE)

#the resulting fragment characteristics can be further examined:
boxplot(list(width(simseq.sel), width(wid.simseq), width(nar.simseq)), names=c("All fragments",
        "Wide size selection", "Narrow size selection"), ylab="Locus size (bp)")

SimRAD documentation built on May 1, 2019, 10:16 p.m.