README.md
In UMCUGenetics/mutSigExtractor: Extracts SNV, indel, DBS, and SV signatures from vcf files.
mutSigExtractor
Description
mutSigExtractor is an R package for extracting mutation contexts from
vcf files. Extraction can be performed for the following mutation types:
- SNV: 96 trinucleotide contexts, consisting of the point mutation
and the 5’ and 3’ flanking nucleotides
- DBS: 78 possible double base substitution types
- Indel: Includes indels within repeat regions, indels with
flanking microhomology; and other indels. Each category is further
stratified by the repeat unit length, the number of bases in the
indel sequence that are homologous, and the indel sequence length,
respectively. Alternatively, the PCAWG indel contexts can also be
extracted
- SV: structural variants stratified by type (deletions,
duplications, inversions, translocations) and length (0 to >10Mb)
Signatures can also be extracted from these contexts for SNVs, indels,
DBSs (COSMIC/PCAWG), as well as for SVs (Nik-Zainal et
al. 2016).
Installation
mutSigExtractor requires some bioconductor packages to first be
installed.
## Bioconductor packages required by mutSigExtractor
install.packages('BiocManager')
BiocManager::install('BSgenome') ## Install genome parser
BiocManager::install('BSgenome.Hsapiens.UCSC.hg19') ## Install the default genome
BiocManager::install('GenomeInfoDb')
## Install mutSigExtractor directly from github using devtools
install.packages("devtools")
devtools::install_github('https://github.com/UMCUGenetics/mutSigExtractor/')
Other genomes (e.g. for hg38: BSgenome.Hsapiens.UCSC.hg38) can also be
used. Please see the below tutorial for details.
Tutorial
The COLO829v003T.purple.somatic.vcf.gz and
COLO829v003T.purple.sv.vcf.gz files in doc/vcf/ will be used to
demonstrate how to use mutSigExtractor.
setwd('/path/to/mutSigExtractor')
vcf_snv <- 'doc/vcf/COLO829v003T.purple.somatic.vcf.gz'
vcf_sv <- 'doc/vcf/COLO829v003T.purple.sv.vcf.gz'
The main functions for extracting signatures are:
extractSigsSnv()
extractSigsDbs()
extractSigsIndel()
extractSigsSv()
Note that SNVs, DBSs and indels are often reported in the same vcf file.
Therefore, extractSigsSnv(),extractSigsDbs(), and
extractSigsIndel() will automatically select the relevant mutation
types.
Extracting contexts and signatures
SNVs
Here, extraction of SNV contexts and signatures on one sample using
extractSigsSnv() will be demonstrated. The same concepts shown here
can be applied to the other mutation type functions.
With the below code we can extract the 96 trinucleotide contexts. This
returns a single column matrix of mutation counts per context.
Why is the output not simply a vector? This is so that the output can be
written to a txt file, which is useful when processing a large number of
samples on an HPC. When processing multiple samples locally, one can
simply use cbind() combine the context counts from all samples into
one matrix.
Note that, it is recommended that the vcf.filter argument is set to
‘PASS’ (or ‘.’ for certain vcf files) to remove low quality variants.
contexts_snv <- extractSigsSnv(vcf.file=vcf_snv, vcf.filter='PASS', output='contexts')
head(contexts_snv)
## COLO829v003T.purple.somatic.vcf.gz
## A[C>A]A 132
## A[C>A]C 62
## A[C>A]G 17
## A[C>A]T 57
## C[C>A]A 1892
## C[C>A]C 237
To extract signatures, we can then fit these contexts to e.g. the COSMIC
or PCAWG signature profiles. These are included in the package as
SBS_SIGNATURE_PROFILES_V2 and SBS_SIGNATURE_PROFILES_V3
respectively. For this example we will use the PCAWG profiles
(SBS_SIGNATURE_PROFILES_V3).
SBS_SIGNATURE_PROFILES_V3[1:5,1:5]
## SBS1 SBS2 SBS3 SBS4 SBS5
## A[C>A]A 0.000886 5.80e-07 0.02080 0.0422 0.01200
## A[C>A]C 0.002280 1.48e-04 0.01650 0.0333 0.00944
## A[C>A]G 0.000177 5.23e-05 0.00175 0.0156 0.00185
## A[C>A]T 0.001280 9.78e-05 0.01220 0.0295 0.00661
## C[C>A]A 0.000312 2.08e-04 0.02250 0.0807 0.00743
Signature fitting is done using fitToSignatures() This function uses
the non-negative linear least squares algorithm based on lsqnonneq()
from the pracma package. mut.context.counts can be a numeric vector,
matrix, or dataframe. If a matrix/dataframe, rows represent samples and
columns represent contexts.
sigs_snv <- fitToSignatures(
mut.context.counts=contexts_snv[,1],
signature.profiles=SBS_SIGNATURE_PROFILES_V3
)
head(sigs_snv)
## SBS1 SBS2 SBS3 SBS4 SBS5 SBS6
## 71.02426 770.66129 0.00000 0.00000 0.00000 0.00000
Alternatively, signatures can be extracted directly from the vcf by
specifying output='signatures' in extractSigsSnv()
sig_snv_2 <- extractSigsSnv(
vcf.file=vcf_snv, vcf.filter='PASS', output='signatures',
signature.profiles=SBS_SIGNATURE_PROFILES_V3
)
head(sig_snv_2)
## COLO829v003T.purple.somatic.vcf.gz
## SBS1 71.02426
## SBS2 770.66129
## SBS3 0.00000
## SBS4 0.00000
## SBS5 0.00000
## SBS6 0.00000
Output contexts for DBSs, indels, and SVs
The context extractions for mutation types other than SNVs are shown
below.
DBSs
contexts_dbs <- extractSigsDbs(vcf.file=vcf_snv, vcf.filter='PASS', output='contexts')
head(contexts_dbs)
## COLO829v003T.purple.somatic.vcf.gz
## AC>CA 0
## AC>CG 0
## AC>CT 5
## AC>GA 3
## AC>GG 0
## AC>GT 1
Indels
For indels, extractSigsIndel() defaults to method='CHORD' which is
used by Classifier of HOmologous Recombination Deficiency (CHORD). When
method='CHORD', contexts are always extracted (i.e. no output
argument).
contexts_indel <- extractSigsIndel(vcf.file=vcf_snv, vcf.filter='PASS')
head(contexts_indel)
## COLO829v003T.purple.somatic.vcf.gz
## del.rep.len.1 155
## del.rep.len.2 24
## del.rep.len.3 11
## del.rep.len.4 13
## del.rep.len.5 24
## ins.rep.len.1 121
However, the PCAWG indel contexts can also be extracted by setting
method='PCAWG'. Here, output can be 'signatures' to directly
extract the PCAWG indel signatures.
contexts_indel <- extractSigsIndel(vcf.file=vcf_snv, vcf.filter='PASS', method='PCAWG', output='contexts')
head(contexts_indel)
## COLO829v003T.purple.somatic.vcf.gz
## del.1.C.1 25
## del.1.C.2 20
## del.1.C.3 8
## del.1.C.4 6
## del.1.C.5 1
## del.1.C.6+ 4
SVs
SV vcf files generally do not adhere to one standard. extractSigsSv()
currently supports SV vcf parsing of GRIDSS (conforms to vcf spec 4.2)
and Manta vcfs. You can specify the vcf type with sv.caller='gridss'
(default) or sv.caller='manta'.
contexts_sv <- extractSigsSv(vcf.file=vcf_sv, vcf.filter='PASS', output='contexts', sv.caller='gridss')
head(contexts_sv)
## COLO829v003T.purple.sv.vcf.gz
## DEL_0e00_1e03_bp 6
## DEL_1e03_1e04_bp 8
## DEL_1e04_1e05_bp 11
## DEL_1e05_1e06_bp 12
## DEL_1e06_1e07_bp 1
## DEL_1e07_Inf_bp 0
In case you want to use SV vcfs from other callers, you can provide a
dataframe as input to extractSigsSv() containing SV type and length
info. See below for more info.
Dataframes as input
Alternatively, dataframes can be used as input, which is handy if you
want to parse the vcfs yourself, or have inputs in tabular format.
For SNVs and indels, a dataframe with the columns: chrom, pos, ref, alt.
## CHROM POS REF ALT
## 1 1 16145827 A C
## 2 1 16492085 G C
## 3 1 17890303 C G
## 4 1 18877885 G A
## 5 1 18919776 T C
For SVs, a dataframe with the columns: sv_type, sv_len. For sv_type,
values must be DEL, DUP, INV, TRA (deletions, duplications, inversions,
translocations). For translocations, sv_len information is discarded.
The column names themselves do not matter, as long as the columns are in
the aforementioned order.
## sv_type sv_len
## 1 TRA NA
## 2 DEL 1696
## 3 DEL 22644
## 4 DUP 1703
## 5 DEL 1789
## 6 DEL 49256
These dataframe can be provided to the extractSigs* functions using the
argument df. For example:
extractSigsSv(df=sv_dataframe, vcf.filter='PASS', output='contexts', sv.caller='gridss')
Available signature profiles
Below is a summary of the signature profiles pre-loaded within
mutSigExtractor
SBS_SIGNATURE_PROFILES_V2: Original 30 SNV signaturesSBS_SIGNATURE_PROFILES_V3: PCAWG SNV signaturesINDEL_SIGNATURE_PROFILES: PCAWG indel signaturesDBS_SIGNATURE_PROFILES: PCAWG DBS signaturesSV_SIGNATURE_PROFILES: SV signatures from Nik-Zainal et
al. 2016 with
clustered SV information removed
Using other reference genomes
A different reference genome than the default
(BSgenome.Hsapiens.UCSC.hg19) can be used. Genomes should be BSgenomes.
The variable name (i.e. no quotes) of the BSgenome object is
specified to ref.genome.
## Make sure to install and load the desired ref genome first
install.packages('BiocManager')
BiocManager::install('BSgenome.Hsapiens.UCSC.hg38')
## Non-default genomes need to be explicitly loaded. The default (BSgenome.Hsapiens.UCSC.hg19)
## is automatically loaded.
library(BSgenome.Hsapiens.UCSC.hg38)
## Specify the name of the BSgenome object to the ref.genome argument
extractSigsSnv(
vcf.file='/path/to/vcf/with/snvs_and_indels/', vcf.filter='PASS', output='contexts',
ref.genome=BSgenome.Hsapiens.UCSC.hg38
)
UMCUGenetics/mutSigExtractor documentation built on Aug. 30, 2024, 2:12 p.m.
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mutSigExtractor
Description
mutSigExtractor is an R package for extracting mutation contexts from vcf files. Extraction can be performed for the following mutation types:
- SNV: 96 trinucleotide contexts, consisting of the point mutation and the 5’ and 3’ flanking nucleotides
- DBS: 78 possible double base substitution types
- Indel: Includes indels within repeat regions, indels with flanking microhomology; and other indels. Each category is further stratified by the repeat unit length, the number of bases in the indel sequence that are homologous, and the indel sequence length, respectively. Alternatively, the PCAWG indel contexts can also be extracted
- SV: structural variants stratified by type (deletions, duplications, inversions, translocations) and length (0 to >10Mb)
Signatures can also be extracted from these contexts for SNVs, indels, DBSs (COSMIC/PCAWG), as well as for SVs (Nik-Zainal et al. 2016).
Installation
mutSigExtractor requires some bioconductor packages to first be installed.
## Bioconductor packages required by mutSigExtractor
install.packages('BiocManager')
BiocManager::install('BSgenome') ## Install genome parser
BiocManager::install('BSgenome.Hsapiens.UCSC.hg19') ## Install the default genome
BiocManager::install('GenomeInfoDb')
## Install mutSigExtractor directly from github using devtools
install.packages("devtools")
devtools::install_github('https://github.com/UMCUGenetics/mutSigExtractor/')
Other genomes (e.g. for hg38: BSgenome.Hsapiens.UCSC.hg38) can also be
used. Please see the below tutorial for details.
Tutorial
The COLO829v003T.purple.somatic.vcf.gz and
COLO829v003T.purple.sv.vcf.gz files in doc/vcf/ will be used to
demonstrate how to use mutSigExtractor.
setwd('/path/to/mutSigExtractor')
vcf_snv <- 'doc/vcf/COLO829v003T.purple.somatic.vcf.gz'
vcf_sv <- 'doc/vcf/COLO829v003T.purple.sv.vcf.gz'
The main functions for extracting signatures are:
extractSigsSnv()
extractSigsDbs()
extractSigsIndel()
extractSigsSv()
Note that SNVs, DBSs and indels are often reported in the same vcf file.
Therefore, extractSigsSnv(),extractSigsDbs(), and
extractSigsIndel() will automatically select the relevant mutation
types.
Extracting contexts and signatures
SNVs
Here, extraction of SNV contexts and signatures on one sample using
extractSigsSnv() will be demonstrated. The same concepts shown here
can be applied to the other mutation type functions.
With the below code we can extract the 96 trinucleotide contexts. This returns a single column matrix of mutation counts per context.
Why is the output not simply a vector? This is so that the output can be
written to a txt file, which is useful when processing a large number of
samples on an HPC. When processing multiple samples locally, one can
simply use cbind() combine the context counts from all samples into
one matrix.
Note that, it is recommended that the vcf.filter argument is set to
‘PASS’ (or ‘.’ for certain vcf files) to remove low quality variants.
contexts_snv <- extractSigsSnv(vcf.file=vcf_snv, vcf.filter='PASS', output='contexts')
head(contexts_snv)
## COLO829v003T.purple.somatic.vcf.gz
## A[C>A]A 132
## A[C>A]C 62
## A[C>A]G 17
## A[C>A]T 57
## C[C>A]A 1892
## C[C>A]C 237
To extract signatures, we can then fit these contexts to e.g. the COSMIC
or PCAWG signature profiles. These are included in the package as
SBS_SIGNATURE_PROFILES_V2 and SBS_SIGNATURE_PROFILES_V3
respectively. For this example we will use the PCAWG profiles
(SBS_SIGNATURE_PROFILES_V3).
SBS_SIGNATURE_PROFILES_V3[1:5,1:5]
## SBS1 SBS2 SBS3 SBS4 SBS5
## A[C>A]A 0.000886 5.80e-07 0.02080 0.0422 0.01200
## A[C>A]C 0.002280 1.48e-04 0.01650 0.0333 0.00944
## A[C>A]G 0.000177 5.23e-05 0.00175 0.0156 0.00185
## A[C>A]T 0.001280 9.78e-05 0.01220 0.0295 0.00661
## C[C>A]A 0.000312 2.08e-04 0.02250 0.0807 0.00743
Signature fitting is done using fitToSignatures() This function uses
the non-negative linear least squares algorithm based on lsqnonneq()
from the pracma package. mut.context.counts can be a numeric vector,
matrix, or dataframe. If a matrix/dataframe, rows represent samples and
columns represent contexts.
sigs_snv <- fitToSignatures(
mut.context.counts=contexts_snv[,1],
signature.profiles=SBS_SIGNATURE_PROFILES_V3
)
head(sigs_snv)
## SBS1 SBS2 SBS3 SBS4 SBS5 SBS6
## 71.02426 770.66129 0.00000 0.00000 0.00000 0.00000
Alternatively, signatures can be extracted directly from the vcf by
specifying output='signatures' in extractSigsSnv()
sig_snv_2 <- extractSigsSnv(
vcf.file=vcf_snv, vcf.filter='PASS', output='signatures',
signature.profiles=SBS_SIGNATURE_PROFILES_V3
)
head(sig_snv_2)
## COLO829v003T.purple.somatic.vcf.gz
## SBS1 71.02426
## SBS2 770.66129
## SBS3 0.00000
## SBS4 0.00000
## SBS5 0.00000
## SBS6 0.00000
Output contexts for DBSs, indels, and SVs
The context extractions for mutation types other than SNVs are shown below.
DBSs
contexts_dbs <- extractSigsDbs(vcf.file=vcf_snv, vcf.filter='PASS', output='contexts')
head(contexts_dbs)
## COLO829v003T.purple.somatic.vcf.gz
## AC>CA 0
## AC>CG 0
## AC>CT 5
## AC>GA 3
## AC>GG 0
## AC>GT 1
Indels
For indels, extractSigsIndel() defaults to method='CHORD' which is
used by Classifier of HOmologous Recombination Deficiency (CHORD). When
method='CHORD', contexts are always extracted (i.e. no output
argument).
contexts_indel <- extractSigsIndel(vcf.file=vcf_snv, vcf.filter='PASS')
head(contexts_indel)
## COLO829v003T.purple.somatic.vcf.gz
## del.rep.len.1 155
## del.rep.len.2 24
## del.rep.len.3 11
## del.rep.len.4 13
## del.rep.len.5 24
## ins.rep.len.1 121
However, the PCAWG indel contexts can also be extracted by setting
method='PCAWG'. Here, output can be 'signatures' to directly
extract the PCAWG indel signatures.
contexts_indel <- extractSigsIndel(vcf.file=vcf_snv, vcf.filter='PASS', method='PCAWG', output='contexts')
head(contexts_indel)
## COLO829v003T.purple.somatic.vcf.gz
## del.1.C.1 25
## del.1.C.2 20
## del.1.C.3 8
## del.1.C.4 6
## del.1.C.5 1
## del.1.C.6+ 4
SVs
SV vcf files generally do not adhere to one standard. extractSigsSv()
currently supports SV vcf parsing of GRIDSS (conforms to vcf spec 4.2)
and Manta vcfs. You can specify the vcf type with sv.caller='gridss'
(default) or sv.caller='manta'.
contexts_sv <- extractSigsSv(vcf.file=vcf_sv, vcf.filter='PASS', output='contexts', sv.caller='gridss')
head(contexts_sv)
## COLO829v003T.purple.sv.vcf.gz
## DEL_0e00_1e03_bp 6
## DEL_1e03_1e04_bp 8
## DEL_1e04_1e05_bp 11
## DEL_1e05_1e06_bp 12
## DEL_1e06_1e07_bp 1
## DEL_1e07_Inf_bp 0
In case you want to use SV vcfs from other callers, you can provide a
dataframe as input to extractSigsSv() containing SV type and length
info. See below for more info.
Dataframes as input
Alternatively, dataframes can be used as input, which is handy if you want to parse the vcfs yourself, or have inputs in tabular format.
For SNVs and indels, a dataframe with the columns: chrom, pos, ref, alt.
## CHROM POS REF ALT
## 1 1 16145827 A C
## 2 1 16492085 G C
## 3 1 17890303 C G
## 4 1 18877885 G A
## 5 1 18919776 T C
For SVs, a dataframe with the columns: sv_type, sv_len. For sv_type, values must be DEL, DUP, INV, TRA (deletions, duplications, inversions, translocations). For translocations, sv_len information is discarded. The column names themselves do not matter, as long as the columns are in the aforementioned order.
## sv_type sv_len
## 1 TRA NA
## 2 DEL 1696
## 3 DEL 22644
## 4 DUP 1703
## 5 DEL 1789
## 6 DEL 49256
These dataframe can be provided to the extractSigs* functions using the
argument df. For example:
extractSigsSv(df=sv_dataframe, vcf.filter='PASS', output='contexts', sv.caller='gridss')
Available signature profiles
Below is a summary of the signature profiles pre-loaded within mutSigExtractor
SBS_SIGNATURE_PROFILES_V2: Original 30 SNV signaturesSBS_SIGNATURE_PROFILES_V3: PCAWG SNV signaturesINDEL_SIGNATURE_PROFILES: PCAWG indel signaturesDBS_SIGNATURE_PROFILES: PCAWG DBS signaturesSV_SIGNATURE_PROFILES: SV signatures from Nik-Zainal et al. 2016 with clustered SV information removed
Using other reference genomes
A different reference genome than the default
(BSgenome.Hsapiens.UCSC.hg19) can be used. Genomes should be BSgenomes.
The variable name (i.e. no quotes) of the BSgenome object is
specified to ref.genome.
## Make sure to install and load the desired ref genome first
install.packages('BiocManager')
BiocManager::install('BSgenome.Hsapiens.UCSC.hg38')
## Non-default genomes need to be explicitly loaded. The default (BSgenome.Hsapiens.UCSC.hg19)
## is automatically loaded.
library(BSgenome.Hsapiens.UCSC.hg38)
## Specify the name of the BSgenome object to the ref.genome argument
extractSigsSnv(
vcf.file='/path/to/vcf/with/snvs_and_indels/', vcf.filter='PASS', output='contexts',
ref.genome=BSgenome.Hsapiens.UCSC.hg38
)
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