simulateSV: Structural Variant Simulation
In Budheimer/RSVSim: RSVSim: an R/Bioconductor package for the simulation of structural variations
Description
Usage
Arguments
Details
Value
Author(s)
References
See Also
Examples
Description
A tool for simulating deletions, insertions, inversions, tandem duplications and translocations in any genome available as FASTA-file or BSgenome data package. Structural variations (SVs) are placed within the given genome, or only a subset of it, in a random, non-overlapping manner or at given genomic coordinates. SV breakpoints can be positioned uniformly or with a bias towards repeat regions and regions of high homology.
Usage
1
simulateSV(output=".", genome, chrs, dels=0, ins=0, invs=0, dups=0, trans=0, size, sizeDels=10, sizeIns=10, sizeInvs=10, sizeDups=10, regionsDels, regionsIns, regionsInvs, regionsDups, regionsTrans, maxDups=10, percCopiedIns=0, percBalancedTrans=1, bpFlankSize=20, percSNPs=0, indelProb=0, maxIndelSize=10, repeatBias=FALSE, weightsMechanisms, weightsRepeats, repeatMaskerFile, bpSeqSize=100, random=TRUE, seed, verbose=TRUE)
Arguments
output
Output directory for the rearranged genome and SV lists; turn this off by passing NA (default: current directory)
genome
The genome as DNAStringSet or as filename pointing to a FASTA-file containing the genome sequence
chrs
Restrict simulation to certain chromosomes only (default: all chromosomes available)
dels
Number of deletions
ins
Number of insertions
invs
Number of inversions
dups
Number of tandem duplications
trans
Number of translocations
size
Size of SVs in bp (a single numeric value); a quick way to set a size, which is applied to all simulated SVs
sizeDels
Size of deletions: Either a single number for all deletions or a vector with a length for every single deletion
sizeIns
Size of insertions: Either a single number for all insertions or a vector with a length for every single insertion
sizeInvs
Size of inversions: Either a single number for all inversions or a vector with a length for every single inversion
sizeDups
Size of tandem duplications: Either a single number for all tandem duplications or a vector with a length for every single tandem duplication
regionsDels
GRanges object with regions within the genome where to place the deletions
regionsIns
GRanges object with regions within the genome where to place the Insertions
regionsInvs
GRanges object with regions within the genome where to place the inversions
regionsDups
GRanges object with regions within the genome where to place the tandem duplications
regionsTrans
GRanges object with regions within the genome where to place the translocations
maxDups
Maximum number of repeats for tandem duplications
percCopiedIns
Percentage of copy-and-paste-like insertions (default: 0, i.e. no inserted sequences are duplicated)
percBalancedTrans
Percentage of balanced translocations (default: 1, i.e. all translocations are balanced)
bpFlankSize
Size of the each breakpoint's flanking regions, which may contain additional SNPs and/or indels
percSNPs
Percentage of SNPs within a breakpoint's flanking region
indelProb
Probability for an indel within a breakpoint's flanking region
maxIndelSize
Maximum size of an indel
repeatBias
If TRUE, the breakpoint positioning is biased towards repeat regions instead of a uniform distribution; turned off by default (see details below)
weightsMechanisms
Weights for SV formation mechanisms (see details and examples below)
weightsRepeats
Weights for repeat regions (see details and examples below)
repeatMaskerFile
Filename of a RepeatMasker output file
bpSeqSize
Length of the breakpoint sequences in the output
random
If TRUE, the SVs will be placed randomly within the genome or the given regions; otherwise, the given regions will be used as SV coordinates (random can also be a vector of five elements with TRUE/FALSE for every SV in the following order: deletions, insertions, inversions, duplications, translocations)
seed
Fixed seed for generation of random SV positions
verbose
If TRUE, some messages about the progress of the simulation will be printed into the R console
Details
About the supported SV types:
Deletions: A segment is cut out from the genome.
Insertions: A segment is cut or copied (see parameter percCopiedIns) from one chromosome A and inserted into another chromosome B.
Inversions: A segment is cut out from one chromosome and its reverse complement is inserted at the same place without loss or a shift of sequence.
Tandem duplication: A segment is duplicated one after the other at most maxDups times.
Translocation: A segment from the 5' or 3' end of one chromosome A is exchanged with the 5' or 3' end of another chromosome B. If it is not balanced (see parameter percBalancedTrans), the segment from chromosome B will be lost, what results in a duplicated sequence from chromosome A. Segments translocated between two different ends (5'<->3' or 3'<->5') are always inverted.
About SV sizes and predefined regions:
The region arguments (regionsDels,regionsIns, regionsInvs,regionsDups,regionsTrans) can be used in two ways: 1. as subsets of the genome where to place the SVs randomly or 2. as coordinates of SVs that shall be implemented at these exact positions. The latter can be useful to implement a predefined set of previously detected or known SVs. Set the parameter random to FALSE accordingly. In this case, the rownames of the region arguments will be used to name the SVs in the output.
In case of insertions and translocations, where two genomic regions are involved, add columns "chrB" and "startB" for insertions and "chrB", "startB", "endB" for translocations to the GRanges objects in regionsTrans and regionsIns, respectively.
It is recommended to set the size of an SV individually for every deletion, insertion, inversion or tandem duplication. See the function estimateSVSizes to estimate beta-distributed sizes from a training set of SVs (defaults for deletions, insertions, inversions and tandem duplications are available). Using the argument size to set the size for all SVs overrides all other size arguments.
There is no size argument for translocations. After random generation of the breakpoint, the translocation spans the chromosome until the closest of both chromosome ends.
About biases towards SV formation mechanisms and repeat regions:
When using the default genome hg19 and setting repeatBias=TRUE, RSVSim simulates a bias of breakpoint positioning towards certain kinds of repeat regions and regions of high homology. If repeatBias=FALSE (the default), the breakpoints will be placed uniformly across the whole genome.
This is done in two steps: 1. Weighting SV formation mechanisms (here: NAHR, NHR, VNTR, TEI, Other) for each SV type. 2. Weighting each SV formation mechanism for each kind of repeat (supported: LINE/L1, LINE/L2, SINE/Alu, SINE/MIR, segmental duplications (SD), tandem repeats (TR), Random). The default weights were chosen from studies with SVs >1.000bp by Mills et al., Pang et al., Ou et al. and Hu et al. It is possible to change these weights by using the arguments weightsMechanisms and weightsRepeats, which need to have a certain data.frame structure (see vignette and examples below for details).
For the mechanism NAHR, both breakpoints will lie within a repeat region, while for NHR, VNTR, TEI and Other, the repeat must make up for at least 75% of the SV region.
This feature requires the coordinates of repeat regions for hg19. This can be handled in two ways: 1. When using this feature the first time, RSVSim downloads the coordinates once automatically from the UCSC Browser's RepeatMasker track (which may take up to 45 Minutes!) 2. The user may specify the filename of a RepeatMasker output file downloaded from their homepage: http://www.repeatmasker.org/species/homSap.html (e.g. hg19.fa.out.gz). In both cases, RSVSim saves the coordinates as RData object to the RSVSim installation directory for a faster access in the future.
This feature is turned off automatically, when the user specifies his own genome. Then, breakpoints will be placed uniformly across the genome.
About additional breakpoint mutations:
RSVSim allows to randomly generate additional SNPs and indels within the flanking regions of each breakpoint.
By default, this feature is turned off. It is recommended to set the four arguments bpFlankSize, percSNPs, indelProb and maxIndelSize to use this feature.
Each flanking region may only contain one indel, while insertions and deletions are equally likely. SNPs can affect 0-100% of the region.
Misc:
By default, the human genome (hg19) will be used, which requires the package BSgenome.Hsapiens.UCSC.hg19.
SVs will not be placed within unannotated regions or assembly gaps denoted by the letter "N". These regions are detected and filtered automatically.
Value
The rerranged genome as a DNAStringSet. Its metadata slot contains a named list of data.frames with information about the simulated SVs:
deletions
The coordinates of the implemented deletions and the breakpoint sequence
insertions
The coordinates of the origin (chrA) and destination (chrB) of the inserted sequence and the breakpoints sequences at both ends (5' and 3'). If the sequence is cut out from the original chromosome A, the sequence of this breakpoint is given as well.
inversions
The coordinates of the implemented inversions and the breakpoint sequences at both ends (5' and 3')
tandemDuplications
The coordinates of the duplicated sequence and the breakpoint sequence
translocations
The coordinates of the translocated sequences and the two breakpoint sequences (if balanced)
The coordinates in the tables refer to the "normal" reference genome.
All the list items can also be written to the specified output directory (which is the current directory by default). The genome will be saved in FASTA format and the SVs data.frames as CSV tables.
Author(s)
Christoph Bartenhagen
References
Chen W. et al., Mapping translocation breakpoints by next-generation sequencing, 2008, Genome Res, 18(7), 1143-1149.
Lam H.Y. et al., Nucleotide-resolution analysis of structural variants using BreakSeq and a breakpoint library, 2010, Nat Biotechnol, 28(1), 47-55.
Mills R.E. et al., Mapping copy number variation by population-scale genome sequencing, 2011, Nature, 470(7332), 59-65
Ou Z. et al., Observation and prediction of recurrent human translocations mediated by NAHR between nonhomologous chromosomes, 2011, Genome Res, 21(1), 33-46.
Pang A.W. et al., Mechanisms of Formation of Structural Variation in a Fully Sequenced Human Genome, 2013, Hum Mutat, 34(2), 345-354.
Smit et al., RepeatMasker Open-3.0., 1996-2010, <http://www.repeatmasker.org>
See Also
Examples
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## Toy example: Artificial genome with two chromosomes
genome = DNAStringSet(c("AAAAAAAAAAAAAAAAAAAATTTTTTTTTTTTTTTTTTTT", "GGGGGGGGGGGGGGGGGGGGCCCCCCCCCCCCCCCCCCCC"))
names(genome) = c("chr1","chr2")
## Three deletions of sizes 10bp each
sim = simulateSV(output=NA, genome=genome, dels=3, sizeDels=10, bpSeqSize=10)
sim
metadata(sim)
## Three insertions of 5bp each; all cut-and-paste-like (default)
sim = simulateSV(output=NA, genome=genome, ins=3, sizeIns=5, bpSeqSize=10)
sim
metadata(sim)
## Three insertions of 5bp each; all copy-and-paste-like (note the parameter \code{percCopiedIns})
sim = simulateSV(output=NA, genome=genome, ins=3, sizeIns=5, percCopiedIns=1, bpSeqSize=10)
sim
metadata(sim)
## Three inversions of sizes 2bp, 4bp and 6bp
sim = simulateSV(output=NA, genome=genome, invs=3, sizeInvs=c(2,4,6), bpSeqSize=10)
sim
metadata(sim)
## A tandem duplication of 4bp with at most ten duplications
## The duplication shall be placed somewhere within chr4:18-40
library(GenomicRanges)
region = GRanges(IRanges(10,30),seqnames="chr1")
sim = simulateSV(output=NA, genome=genome, dups=1, sizeDups=4, regionsDups=region, maxDups=10, bpSeqSize=10)
sim
metadata(sim)
## A balanced translocation (default)
sim = simulateSV(output=NA, genome=genome,trans=1, bpSeqSize=6, seed=246)
sim
metadata(sim)
## Another translocation, but unbalanced (note the parameter \code{percBalancedTrans})
sim = simulateSV(output=NA, genome=genome, trans=1, percBalancedTrans=0, bpSeqSize=6)
sim
metadata(sim)
## Simulate all four SV types at once:
## 2 deletions (5bp), 2 insertions (5bp),2 inversions (3bp), 1 tandem duplication (4bp), 1 translocations
sim = simulateSV(output=NA, genome=genome, dels=2, ins=2, invs=2, dups=1, trans=1, sizeDels=5, sizeIns=5, sizeInvs=3, sizeDups=4, maxDups=3, percCopiedIns=0.5, bpSeqSize=10)
sim
metadata(sim)
## Avoid random generation of coordinates and implement a given deletion of 10bp on chr2:16-25
knownDeletion = GRanges(IRanges(16,25), seqnames="chr2")
names(knownDeletion) = "myDeletion"
knownDeletion
sim = simulateSV(output=NA, genome=genome, regionsDels=knownDeletion, bpSeqSize=10, random=FALSE)
sim
metadata(sim)
## Avoid random generation of coordinates and implement a given insertion from chr1:16:25 at chr2:26
knownInsertion = GRanges(IRanges(16,25), seqnames="chr1", chrB="chr2", startB=26)
names(knownInsertion) = "myInsertion"
knownInsertion
sim = simulateSV(output=NA, genome=genome, regionsIns=knownInsertion, bpSeqSize=10, random=FALSE)
sim
metadata(sim)
## This example simulates a translocation t(9;22) leading to the BCR-ABL fusion gene.
## It uses simple breakpoints within 9q34.1 and 22q11.2 for demonstration
## Take care to add coordinates of both chromosomes to the GRanges object:
trans_BCR_ABL = GRanges(IRanges(133000000,141213431), seqnames="chr9", chrB="chr22", startB=23000000, endB=51304566, reciprocal=TRUE)
names(trans_BCR_ABL) = "BCR_ABL"
trans_BCR_ABL
## This example requires the \pkg{BSgenome.Hsapiens.UCSC.hg19} which is used by default (hence, no genome argument)
## Not run: sim = simulateSV(output=NA, chrs=c("chr9", "chr22"), regionsTrans=trans_BCR_ABL, bpSeqSize=30, random=FALSE)
## Add additional SNPs and indels at the flanking regions of each SV breakpoint:
## One deletion and 10% SNPs, 100% indel probability within 10bp up-/downstream of the breakpoint
sim = simulateSV(output=NA, genome=genome, dels=1, sizeDels=5, bpFlankSize=10, percSNPs=0.25, indelProb=1, maxIndelSize=3, bpSeqSize=10);
sim
metadata(sim)
## Setting the weights for SV formation mechanism and repeat biases demands a given data.frame structure
## The following weights are the default settings
## Please make sure your data.frames have the same row and column names, when setting your own weights
data(weightsMechanisms, package="RSVSim")
weightsMechanisms
data(weightsRepeats, package="RSVSim")
weightsRepeats
## The weights take effect, when no genome argument has been specified (i.e. the default genome hg19 will be used) and the argument repeatBias has been set to TRUE
## Not run: sim = simulateSV(output=NA, dels=10, invs=10, ins=10, dups=10, trans=10, repeatBias = TRUE, weightsMechanisms=weightsMechanisms, weightsRepeats=weightsRepeats)
## If weightsMechanisms and weightsRepeats were omitted, RSVSim loads the default weights automatically (see details section above for more info)
## Not run: sim = simulateSV(output=NA, dels=10, invs=10, ins=10, dups=10, trans=10, repeatBias = TRUE)
Budheimer/RSVSim documentation built on May 30, 2019, 1:26 p.m.
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Description Usage Arguments Details Value Author(s) References See Also Examples
Description
A tool for simulating deletions, insertions, inversions, tandem duplications and translocations in any genome available as FASTA-file or BSgenome data package. Structural variations (SVs) are placed within the given genome, or only a subset of it, in a random, non-overlapping manner or at given genomic coordinates. SV breakpoints can be positioned uniformly or with a bias towards repeat regions and regions of high homology.
Usage
1 | simulateSV(output=".", genome, chrs, dels=0, ins=0, invs=0, dups=0, trans=0, size, sizeDels=10, sizeIns=10, sizeInvs=10, sizeDups=10, regionsDels, regionsIns, regionsInvs, regionsDups, regionsTrans, maxDups=10, percCopiedIns=0, percBalancedTrans=1, bpFlankSize=20, percSNPs=0, indelProb=0, maxIndelSize=10, repeatBias=FALSE, weightsMechanisms, weightsRepeats, repeatMaskerFile, bpSeqSize=100, random=TRUE, seed, verbose=TRUE)
|
Arguments
output |
Output directory for the rearranged genome and SV lists; turn this off by passing |
genome |
The genome as |
chrs |
Restrict simulation to certain chromosomes only (default: all chromosomes available) |
dels |
Number of deletions |
ins |
Number of insertions |
invs |
Number of inversions |
dups |
Number of tandem duplications |
trans |
Number of translocations |
size |
Size of SVs in bp (a single numeric value); a quick way to set a size, which is applied to all simulated SVs |
sizeDels |
Size of deletions: Either a single number for all deletions or a vector with a length for every single deletion |
sizeIns |
Size of insertions: Either a single number for all insertions or a vector with a length for every single insertion |
sizeInvs |
Size of inversions: Either a single number for all inversions or a vector with a length for every single inversion |
sizeDups |
Size of tandem duplications: Either a single number for all tandem duplications or a vector with a length for every single tandem duplication |
regionsDels |
|
regionsIns |
|
regionsInvs |
|
regionsDups |
|
regionsTrans |
|
maxDups |
Maximum number of repeats for tandem duplications |
percCopiedIns |
Percentage of copy-and-paste-like insertions (default: 0, i.e. no inserted sequences are duplicated) |
percBalancedTrans |
Percentage of balanced translocations (default: 1, i.e. all translocations are balanced) |
bpFlankSize |
Size of the each breakpoint's flanking regions, which may contain additional SNPs and/or indels |
percSNPs |
Percentage of SNPs within a breakpoint's flanking region |
indelProb |
Probability for an indel within a breakpoint's flanking region |
maxIndelSize |
Maximum size of an indel |
repeatBias |
If TRUE, the breakpoint positioning is biased towards repeat regions instead of a uniform distribution; turned off by default (see details below) |
weightsMechanisms |
Weights for SV formation mechanisms (see details and examples below) |
weightsRepeats |
Weights for repeat regions (see details and examples below) |
repeatMaskerFile |
Filename of a RepeatMasker output file |
bpSeqSize |
Length of the breakpoint sequences in the output |
random |
If TRUE, the SVs will be placed randomly within the genome or the given regions; otherwise, the given regions will be used as SV coordinates ( |
seed |
Fixed seed for generation of random SV positions |
verbose |
If TRUE, some messages about the progress of the simulation will be printed into the R console |
Details
About the supported SV types:
Deletions: A segment is cut out from the genome.
Insertions: A segment is cut or copied (see parameter
percCopiedIns) from one chromosome A and inserted into another chromosome B.Inversions: A segment is cut out from one chromosome and its reverse complement is inserted at the same place without loss or a shift of sequence.
Tandem duplication: A segment is duplicated one after the other at most
maxDupstimes.Translocation: A segment from the 5' or 3' end of one chromosome A is exchanged with the 5' or 3' end of another chromosome B. If it is not balanced (see parameter
percBalancedTrans), the segment from chromosome B will be lost, what results in a duplicated sequence from chromosome A. Segments translocated between two different ends (5'<->3' or 3'<->5') are always inverted.
About SV sizes and predefined regions:
The region arguments (
regionsDels,regionsIns,regionsInvs,regionsDups,regionsTrans) can be used in two ways: 1. as subsets of the genome where to place the SVs randomly or 2. as coordinates of SVs that shall be implemented at these exact positions. The latter can be useful to implement a predefined set of previously detected or known SVs. Set the parameterrandomto FALSE accordingly. In this case, the rownames of the region arguments will be used to name the SVs in the output.In case of insertions and translocations, where two genomic regions are involved, add columns "chrB" and "startB" for insertions and "chrB", "startB", "endB" for translocations to the
GRangesobjects inregionsTransandregionsIns, respectively.It is recommended to set the size of an SV individually for every deletion, insertion, inversion or tandem duplication. See the function
estimateSVSizesto estimate beta-distributed sizes from a training set of SVs (defaults for deletions, insertions, inversions and tandem duplications are available). Using the argumentsizeto set the size for all SVs overrides all other size arguments.There is no size argument for translocations. After random generation of the breakpoint, the translocation spans the chromosome until the closest of both chromosome ends.
About biases towards SV formation mechanisms and repeat regions:
When using the default genome hg19 and setting
repeatBias=TRUE, RSVSim simulates a bias of breakpoint positioning towards certain kinds of repeat regions and regions of high homology. IfrepeatBias=FALSE(the default), the breakpoints will be placed uniformly across the whole genome.This is done in two steps: 1. Weighting SV formation mechanisms (here: NAHR, NHR, VNTR, TEI, Other) for each SV type. 2. Weighting each SV formation mechanism for each kind of repeat (supported: LINE/L1, LINE/L2, SINE/Alu, SINE/MIR, segmental duplications (SD), tandem repeats (TR), Random). The default weights were chosen from studies with SVs >1.000bp by Mills et al., Pang et al., Ou et al. and Hu et al. It is possible to change these weights by using the arguments weightsMechanisms and weightsRepeats, which need to have a certain data.frame structure (see vignette and examples below for details).
For the mechanism NAHR, both breakpoints will lie within a repeat region, while for NHR, VNTR, TEI and Other, the repeat must make up for at least 75% of the SV region.
This feature requires the coordinates of repeat regions for hg19. This can be handled in two ways: 1. When using this feature the first time, RSVSim downloads the coordinates once automatically from the UCSC Browser's RepeatMasker track (which may take up to 45 Minutes!) 2. The user may specify the filename of a RepeatMasker output file downloaded from their homepage: http://www.repeatmasker.org/species/homSap.html (e.g. hg19.fa.out.gz). In both cases, RSVSim saves the coordinates as RData object to the RSVSim installation directory for a faster access in the future.
This feature is turned off automatically, when the user specifies his own genome. Then, breakpoints will be placed uniformly across the genome.
About additional breakpoint mutations:
RSVSim allows to randomly generate additional SNPs and indels within the flanking regions of each breakpoint.
By default, this feature is turned off. It is recommended to set the four arguments bpFlankSize, percSNPs, indelProb and maxIndelSize to use this feature.
Each flanking region may only contain one indel, while insertions and deletions are equally likely. SNPs can affect 0-100% of the region.
Misc:
By default, the human genome (hg19) will be used, which requires the package BSgenome.Hsapiens.UCSC.hg19.
SVs will not be placed within unannotated regions or assembly gaps denoted by the letter "N". These regions are detected and filtered automatically.
Value
The rerranged genome as a DNAStringSet. Its metadata slot contains a named list of data.frames with information about the simulated SVs:
deletions |
The coordinates of the implemented deletions and the breakpoint sequence |
insertions |
The coordinates of the origin (chrA) and destination (chrB) of the inserted sequence and the breakpoints sequences at both ends (5' and 3'). If the sequence is cut out from the original chromosome A, the sequence of this breakpoint is given as well. |
inversions |
The coordinates of the implemented inversions and the breakpoint sequences at both ends (5' and 3') |
tandemDuplications |
The coordinates of the duplicated sequence and the breakpoint sequence |
translocations |
The coordinates of the translocated sequences and the two breakpoint sequences (if balanced) |
The coordinates in the tables refer to the "normal" reference genome.
All the list items can also be written to the specified output directory (which is the current directory by default). The genome will be saved in FASTA format and the SVs data.frames as CSV tables.
Author(s)
Christoph Bartenhagen
References
Chen W. et al., Mapping translocation breakpoints by next-generation sequencing, 2008, Genome Res, 18(7), 1143-1149. Lam H.Y. et al., Nucleotide-resolution analysis of structural variants using BreakSeq and a breakpoint library, 2010, Nat Biotechnol, 28(1), 47-55. Mills R.E. et al., Mapping copy number variation by population-scale genome sequencing, 2011, Nature, 470(7332), 59-65 Ou Z. et al., Observation and prediction of recurrent human translocations mediated by NAHR between nonhomologous chromosomes, 2011, Genome Res, 21(1), 33-46. Pang A.W. et al., Mechanisms of Formation of Structural Variation in a Fully Sequenced Human Genome, 2013, Hum Mutat, 34(2), 345-354. Smit et al., RepeatMasker Open-3.0., 1996-2010, <http://www.repeatmasker.org>
See Also
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | ## Toy example: Artificial genome with two chromosomes
genome = DNAStringSet(c("AAAAAAAAAAAAAAAAAAAATTTTTTTTTTTTTTTTTTTT", "GGGGGGGGGGGGGGGGGGGGCCCCCCCCCCCCCCCCCCCC"))
names(genome) = c("chr1","chr2")
## Three deletions of sizes 10bp each
sim = simulateSV(output=NA, genome=genome, dels=3, sizeDels=10, bpSeqSize=10)
sim
metadata(sim)
## Three insertions of 5bp each; all cut-and-paste-like (default)
sim = simulateSV(output=NA, genome=genome, ins=3, sizeIns=5, bpSeqSize=10)
sim
metadata(sim)
## Three insertions of 5bp each; all copy-and-paste-like (note the parameter \code{percCopiedIns})
sim = simulateSV(output=NA, genome=genome, ins=3, sizeIns=5, percCopiedIns=1, bpSeqSize=10)
sim
metadata(sim)
## Three inversions of sizes 2bp, 4bp and 6bp
sim = simulateSV(output=NA, genome=genome, invs=3, sizeInvs=c(2,4,6), bpSeqSize=10)
sim
metadata(sim)
## A tandem duplication of 4bp with at most ten duplications
## The duplication shall be placed somewhere within chr4:18-40
library(GenomicRanges)
region = GRanges(IRanges(10,30),seqnames="chr1")
sim = simulateSV(output=NA, genome=genome, dups=1, sizeDups=4, regionsDups=region, maxDups=10, bpSeqSize=10)
sim
metadata(sim)
## A balanced translocation (default)
sim = simulateSV(output=NA, genome=genome,trans=1, bpSeqSize=6, seed=246)
sim
metadata(sim)
## Another translocation, but unbalanced (note the parameter \code{percBalancedTrans})
sim = simulateSV(output=NA, genome=genome, trans=1, percBalancedTrans=0, bpSeqSize=6)
sim
metadata(sim)
## Simulate all four SV types at once:
## 2 deletions (5bp), 2 insertions (5bp),2 inversions (3bp), 1 tandem duplication (4bp), 1 translocations
sim = simulateSV(output=NA, genome=genome, dels=2, ins=2, invs=2, dups=1, trans=1, sizeDels=5, sizeIns=5, sizeInvs=3, sizeDups=4, maxDups=3, percCopiedIns=0.5, bpSeqSize=10)
sim
metadata(sim)
## Avoid random generation of coordinates and implement a given deletion of 10bp on chr2:16-25
knownDeletion = GRanges(IRanges(16,25), seqnames="chr2")
names(knownDeletion) = "myDeletion"
knownDeletion
sim = simulateSV(output=NA, genome=genome, regionsDels=knownDeletion, bpSeqSize=10, random=FALSE)
sim
metadata(sim)
## Avoid random generation of coordinates and implement a given insertion from chr1:16:25 at chr2:26
knownInsertion = GRanges(IRanges(16,25), seqnames="chr1", chrB="chr2", startB=26)
names(knownInsertion) = "myInsertion"
knownInsertion
sim = simulateSV(output=NA, genome=genome, regionsIns=knownInsertion, bpSeqSize=10, random=FALSE)
sim
metadata(sim)
## This example simulates a translocation t(9;22) leading to the BCR-ABL fusion gene.
## It uses simple breakpoints within 9q34.1 and 22q11.2 for demonstration
## Take care to add coordinates of both chromosomes to the GRanges object:
trans_BCR_ABL = GRanges(IRanges(133000000,141213431), seqnames="chr9", chrB="chr22", startB=23000000, endB=51304566, reciprocal=TRUE)
names(trans_BCR_ABL) = "BCR_ABL"
trans_BCR_ABL
## This example requires the \pkg{BSgenome.Hsapiens.UCSC.hg19} which is used by default (hence, no genome argument)
## Not run: sim = simulateSV(output=NA, chrs=c("chr9", "chr22"), regionsTrans=trans_BCR_ABL, bpSeqSize=30, random=FALSE)
## Add additional SNPs and indels at the flanking regions of each SV breakpoint:
## One deletion and 10% SNPs, 100% indel probability within 10bp up-/downstream of the breakpoint
sim = simulateSV(output=NA, genome=genome, dels=1, sizeDels=5, bpFlankSize=10, percSNPs=0.25, indelProb=1, maxIndelSize=3, bpSeqSize=10);
sim
metadata(sim)
## Setting the weights for SV formation mechanism and repeat biases demands a given data.frame structure
## The following weights are the default settings
## Please make sure your data.frames have the same row and column names, when setting your own weights
data(weightsMechanisms, package="RSVSim")
weightsMechanisms
data(weightsRepeats, package="RSVSim")
weightsRepeats
## The weights take effect, when no genome argument has been specified (i.e. the default genome hg19 will be used) and the argument repeatBias has been set to TRUE
## Not run: sim = simulateSV(output=NA, dels=10, invs=10, ins=10, dups=10, trans=10, repeatBias = TRUE, weightsMechanisms=weightsMechanisms, weightsRepeats=weightsRepeats)
## If weightsMechanisms and weightsRepeats were omitted, RSVSim loads the default weights automatically (see details section above for more info)
## Not run: sim = simulateSV(output=NA, dels=10, invs=10, ins=10, dups=10, trans=10, repeatBias = TRUE)
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