Inferring Somatic Signatures from Single Nucleotide Variant Calls

1. Motivation: The Concept Behind Mutational Signatures
2. Methodology: From Mutations to Somatic Signatures
3. Workflow and Implementation: Analysis with the SomaticSignatures Package
4. Use case: Estimating Somatic Signatures from TCGA WES Studies
5. Alternatives: Inferring Somatic Signatures with Different Approaches
6. Frequently Asked Questions
7. References
8. Session Information

Julian Gehring, EMBL Heidelberg

1 Motivation: The Concept Behind Mutational Signatures

Recent publications introduced the concept of identifying mutational signatures from cancer sequencing studies and linked them to potential mutation generation processes [11,2,3]. Conceptually, this relates somatically occurring single nucleotide variants (SNVs) to the surrounding sequence which will be referred to as mutational or somatic motifs in the following. Based on the frequency of the motifs occurring in multiple samples, these can be decomposed mathematically into so called mutational signatures. In case of the investigation of tumors, the term somatic signatures will be used here to distinguish them from germline mutations and their generating processes.

The SomaticSignatures package provides an efficient and user-friendly implementation for the extraction of somatic motifs based on a list of somatically mutated genomic sites and the estimation of somatic signatures with different matrix decomposition algorithms. Methodologically, this is based on the work of Nik-Zainal and colleagues [11]. If you use SomaticSignatures in your research, please cite it as:

Gehring, Julian S., Bernd Fischer, Michael Lawrence, and Wolfgang Huber.
SomaticSignatures: Inferring Mutational Signatures from Single Nucleotide Variants.
Bioinformatics, 2015, btv408. http://dx.doi.org/10.1093/bioinformatics/btv408

2 Methodology: From Mutations to Somatic Signatures

The basic idea of somatic signatures is composed of two parts:

Firstly, each somatic mutation is described in relation of the sequence context in which it occurs. As an example, consider a SNV, resulting in the alteration from A in the normal to G in the tumor sample, that is embedded in the sequence context TAC. Thus, the somatic motif can be written as TAC>TGC or T.C A>G. The frequency of these motifs across multiple samples is then represented as a matrix $M_{ij}$, where $i$ counts over the motifs and $j$ over the samples.

In a second step, the matrix $M$ is numerically decomposed into two matrices $W$ and $H$

$$M_{ij} \approx \sum_{k=1}^{r} W_{ik} H_{kj}$$

for a fixed number $r$ of signatures. While $W$ describes the composition of each signature in term of somatic motifs, $H$ shows the contribution of the signature to the alterations present in each sample.

3 Workflow and Implementation: Analysis with the SomaticSignatures Package

The SomaticSignatures package offers a framework for inferring signatures of SNVs in a user-friendly and efficient manner for large-scale data sets. A tight integration with standard data representations of the Bioconductor project [8] was a major design goal. Further, it extends the selection of multivariate statistical methods for the matrix decomposition and allows a simple visualization of the results.

For a typical workflow, a set of variant calls and the reference sequence are needed. Ideally, the SNVs are represented as a VRanges object with the genomic location as well as reference and alternative allele defined. The reference sequence can be, for example, a FaFile object, representing an indexed FASTA file, a BSgenome object, or a GmapGenome object. Alternatively, we provide functions to extract the relevant information from other sources of inputs. At the moment, this covers the MuTect [4] variant caller and the h5vc package [13,14].

Generally, the individual steps of the analysis can be summarized as:

The somatic motifs for each variant are retrieved from the reference sequence with the mutationContext function and converted to a matrix representation with the motifMatrix function.
Somatic signatures are estimated with a method of choice (the package provides with nmfDecomposition and pcaDecomposition two approaches for the NMF and PCA).
The somatic signatures and their representation in the samples are assessed with a set of accessor and plotting functions.

To decompose $M$, the SomaticSignatures package implements two methods:

Non-negative matrix factorization (NMF): The NMF decomposes $M$ with the constraint of positive components in $W$ and $H$ [7]. The method was used [11] for the identification of mutational signatures, and can be computationally expensive for large data sets.
Principal component analysis (PCA): The PCA employs the eigenvalue decomposition and is therefore suitable for large data sets [15]. While this is related to the NMF, no constraint on the sign of the elements of $W$ and $H$ exists.

Other methods can be supplied through the decomposition argument of the identifySignatures function.

4 Use case: Estimating Somatic Signatures from TCGA WES Studies

In the following, the concept of somatic signatures and the steps for inferring these from an actual biological data set are shown. For the example, somatic variant calls from whole exome sequencing (WES) studies from The Cancer Genome Atlas (TCGA) project will be used, which are part of the SomaticCancerAlterations package.

library(SomaticSignatures)

library(SomaticCancerAlterations)
library(BSgenome.Hsapiens.UCSC.hg19)

4.1 Data: Preproccessing of the TCGA WES Studies

The SomaticCancerAlterations package provides the somatic SNV calls for eight WES studies, each investigating a different cancer type. The metadata summarizes the biological and experimental settings of each study.

sca_metadata = scaMetadata()

sca_metadata

             Cancer_Type        Center NCBI_Build Sequence_Source
   gbm_tcga          GBM broad.mit.edu         37             WXS
   hnsc_tcga        HNSC broad.mit.edu         37         Capture
   kirc_tcga        KIRC broad.mit.edu         37         Capture
   luad_tcga        LUAD broad.mit.edu         37             WXS
   lusc_tcga        LUSC broad.mit.edu         37             WXS
   ov_tcga            OV broad.mit.edu         37             WXS
   skcm_tcga        SKCM broad.mit.edu         37         Capture
   thca_tcga        THCA broad.mit.edu         37             WXS
             Sequencing_Phase      Sequencer Number_Samples
   gbm_tcga           Phase_I Illumina GAIIx            291
   hnsc_tcga          Phase_I Illumina GAIIx            319
   kirc_tcga          Phase_I Illumina GAIIx            297
   luad_tcga          Phase_I Illumina GAIIx            538
   lusc_tcga          Phase_I Illumina GAIIx            178
   ov_tcga            Phase_I Illumina GAIIx            142
   skcm_tcga          Phase_I Illumina GAIIx            266
   thca_tcga          Phase_I Illumina GAIIx            406
             Number_Patients                           Cancer_Name
   gbm_tcga              291               Glioblastoma multiforme
   hnsc_tcga             319 Head and Neck squamous cell carcinoma
   kirc_tcga             293                    Kidney Chromophobe
   luad_tcga             519                   Lung adenocarcinoma
   lusc_tcga             178          Lung squamous cell carcinoma
   ov_tcga               142     Ovarian serous cystadenocarcinoma
   skcm_tcga             264               Skin Cutaneous Melanoma
   thca_tcga             403                    Thyroid carcinoma

The starting point of the analysis is a VRanges object which describes the somatic variants in terms of their genomic locations as well as reference and alternative alleles. For more details about this class and how to construct it, please see the documentation of the VariantAnnotation package [12]. Since the genomic positions are given in the NCBI notation and the references used later are in UCSC notation, the functions ucsc and ncbi are used to easily switch between the two notations. In this example, all mutational calls of a study will be pooled together, in order to find signatures related to a specific cancer type.

sca_data = unlist(scaLoadDatasets())

sca_data$study = factor(gsub("(.*)_(.*)", "\\1", toupper(names(sca_data))))
sca_data = unname(subset(sca_data, Variant_Type %in% "SNP"))
sca_data = keepSeqlevels(sca_data, hsAutosomes())

sca_vr = VRanges(
    seqnames = seqnames(sca_data),
    ranges = ranges(sca_data),
    ref = sca_data$Reference_Allele,
    alt = sca_data$Tumor_Seq_Allele2,
    sampleNames = sca_data$Patient_ID,
    seqinfo = seqinfo(sca_data),
    study = sca_data$study)
sca_vr = ucsc(sca_vr)

sca_vr

   VRanges object with 594607 ranges and 1 metadata column:
              seqnames               ranges strand         ref
                 <Rle>            <IRanges>  <Rle> <character>
          [1]     chr1     [887446, 887446]      +           G
          [2]     chr1     [909247, 909247]      +           C
          [3]     chr1     [978952, 978952]      +           C
          [4]     chr1     [981607, 981607]      +           G
          [5]     chr1     [985841, 985841]      +           C
          ...      ...                  ...    ...         ...
     [594603]    chr22 [50961303, 50961303]      +           G
     [594604]    chr22 [50967746, 50967746]      +           C
     [594605]    chr22 [50967746, 50967746]      +           C
     [594606]    chr22 [51044090, 51044090]      +           C
     [594607]    chr22 [51044095, 51044095]      +           G
                           alt     totalDepth       refDepth
              <characterOrRle> <integerOrRle> <integerOrRle>
          [1]                A           <NA>           <NA>
          [2]                T           <NA>           <NA>
          [3]                T           <NA>           <NA>
          [4]                A           <NA>           <NA>
          [5]                T           <NA>           <NA>
          ...              ...            ...            ...
     [594603]                T           <NA>           <NA>
     [594604]                A           <NA>           <NA>
     [594605]                A           <NA>           <NA>
     [594606]                T           <NA>           <NA>
     [594607]                A           <NA>           <NA>
                    altDepth   sampleNames softFilterMatrix   |    study
              <integerOrRle> <factorOrRle>         <matrix>   | <factor>
          [1]           <NA>  TCGA-06-5858                    |      GBM
          [2]           <NA>  TCGA-32-1977                    |      GBM
          [3]           <NA>  TCGA-06-0237                    |      GBM
          [4]           <NA>  TCGA-06-0875                    |      GBM
          [5]           <NA>  TCGA-06-6693                    |      GBM
          ...            ...           ...              ... ...      ...
     [594603]           <NA>  TCGA-BJ-A0Z0                    |     THCA
     [594604]           <NA>  TCGA-BJ-A2NA                    |     THCA
     [594605]           <NA>  TCGA-BJ-A2NA                    |     THCA
     [594606]           <NA>  TCGA-EM-A3FK                    |     THCA
     [594607]           <NA>  TCGA-EL-A3T0                    |     THCA
     -------
     seqinfo: 22 sequences from an unspecified genome
     hardFilters: NULL

To get a first impression of the data, we count the number of somatic variants per study.

sort(table(sca_vr$study), decreasing = TRUE)

   
     LUAD   SKCM   HNSC   LUSC   KIRC    GBM   THCA     OV 
   208724 200589  67125  61485  24158  19938   6716   5872

4.2 Motifs: Extracting the Sequence Context of Somatic Variants

In a first step, the sequence motif for each variant is extracted based on the genomic sequence. Here, the BSgenomes object of the human hg19 reference is used for all samples. However, personalized genomes or other sources for sequences, for example an indexed FASTA file, can be used naturally. Additionally, we transform all motifs to have a pyrimidine base (C or T) as a reference base [2]. The resulting VRanges object then contains the new columns context and alteration which specify the sequence content and the base substitution.

sca_motifs = mutationContext(sca_vr, BSgenome.Hsapiens.UCSC.hg19, unify = TRUE)
head(sca_motifs)

   VRanges object with 6 ranges and 3 metadata columns:
         seqnames             ranges strand         ref              alt
            <Rle>          <IRanges>  <Rle> <character> <characterOrRle>
     [1]     chr1 [ 887446,  887446]      +           G                A
     [2]     chr1 [ 909247,  909247]      +           C                T
     [3]     chr1 [ 978952,  978952]      +           C                T
     [4]     chr1 [ 981607,  981607]      +           G                A
     [5]     chr1 [ 985841,  985841]      +           C                T
     [6]     chr1 [1120451, 1120451]      +           C                T
             totalDepth       refDepth       altDepth   sampleNames
         <integerOrRle> <integerOrRle> <integerOrRle> <factorOrRle>
     [1]           <NA>           <NA>           <NA>  TCGA-06-5858
     [2]           <NA>           <NA>           <NA>  TCGA-32-1977
     [3]           <NA>           <NA>           <NA>  TCGA-06-0237
     [4]           <NA>           <NA>           <NA>  TCGA-06-0875
     [5]           <NA>           <NA>           <NA>  TCGA-06-6693
     [6]           <NA>           <NA>           <NA>  TCGA-26-1439
         softFilterMatrix |    study     alteration        context
                 <matrix> | <factor> <DNAStringSet> <DNAStringSet>
     [1]                  |      GBM             CT            G.G
     [2]                  |      GBM             CT            A.G
     [3]                  |      GBM             CT            G.G
     [4]                  |      GBM             CT            G.C
     [5]                  |      GBM             CT            A.G
     [6]                  |      GBM             CT            C.G
     -------
     seqinfo: 22 sequences from an unspecified genome
     hardFilters: NULL

To continue with the estimation of the somatic signatures, the matrix $M$ of the form {motifs × studies} is constructed. The normalize argument specifies that frequencies rather than the actual counts are returned.

sca_mm = motifMatrix(sca_motifs, group = "study", normalize = TRUE)

head(round(sca_mm, 4))

             GBM   HNSC   KIRC   LUAD   LUSC     OV   SKCM   THCA
   CA A.A 0.0083 0.0098 0.0126 0.0200 0.0165 0.0126 0.0014 0.0077
   CA A.C 0.0093 0.0082 0.0121 0.0217 0.0156 0.0192 0.0009 0.0068
   CA A.G 0.0026 0.0061 0.0046 0.0144 0.0121 0.0060 0.0004 0.0048
   CA A.T 0.0057 0.0051 0.0070 0.0134 0.0100 0.0092 0.0007 0.0067
   CA C.A 0.0075 0.0143 0.0215 0.0414 0.0390 0.0128 0.0060 0.0112
   CA C.C 0.0075 0.0111 0.0138 0.0415 0.0275 0.0143 0.0018 0.0063

The observed occurrence of the motifs, also termed somatic spectrum, can be visualized across studies, which gives a first impression of the data. The distribution of the motifs clearly varies between the studies.

plotMutationSpectrum(sca_motifs, "study")

Mutation spectrum over studies

4.3 Decomposition: Inferring Somatic Signatures

The somatic signatures can be estimated with each of the statistical methods implemented in the package. Here, we will use the NMF and PCA, and compare the results. Prior to the estimation, the number $r$ of signatures to obtain has to be fixed; in this example, the data will be decomposed into 5 signatures.

n_sigs = 5

sigs_nmf = identifySignatures(sca_mm, n_sigs, nmfDecomposition)

sigs_pca = identifySignatures(sca_mm, n_sigs, pcaDecomposition)

sigs_nmf

   MutationalSignatures:
     Samples (8): GBM, HNSC, ..., SKCM, THCA
     Signatures (5): S1, S2, S3, S4, S5
     Motifs (96): CA A.A, CA A.C, ..., TG T.G, TG T.T

sigs_pca

   MutationalSignatures:
     Samples (8): GBM, HNSC, ..., SKCM, THCA
     Signatures (5): S1, S2, S3, S4, S5
     Motifs (96): CA A.A, CA A.C, ..., TG T.G, TG T.T

The individual matrices can be further inspected through the accessors signatures, samples, observed and fitted.

4.4 Assessment: Number of Signatures

Up to now, we have performed the decomposition based on a known number $r$ of signatures. In many settings, prior biological knowledge or complementing experiments may allow to determine $r$ independently. If this is not the case, we can try to infer suitable values for $r$ from the data.

Using the assessNumberSignatures function, we can compute the residuals sum of squares (RSS) and the explained variance between the observed $M$ and fitted $WH$ mutational spectrum for different numbers of signatures. These measures are generally applicable to all kinds of decomposition methods, and can aid in choosing a likely number of signatures. The usage and arguments are analogous to the identifySignatures function.

n_sigs = 2:8

gof_nmf= assessNumberSignatures(sca_mm, n_sigs, nReplicates = 5)

gof_pca = assessNumberSignatures(sca_mm, n_sigs, pcaDecomposition)

The obtained statistics can further be visualized with the plotNumberSignatures. For each tested number of signatures, black crosses indicate the results of individual runs, while the red dot represents the average over all respective runs. Please note that having multiple runs is only relevant for randomly seeded decomposition methods, as the NMF in our example.

plotNumberSignatures(gof_nmf)

Summary statistics for selecting the number of signatures in the NMF decomposition.

plotNumberSignatures(gof_pca)

Summary statistics for selecting the number of signatures in the PCA decomposition.

$r$ can then be chosen such that increasing the number of signatures does not yield a significantly better approximation of the data, i.e. that the RSS and the explained variance do not change sufficiently for more complex models. The first inflection point of the RSS curve has also been proposed as a measure for the number of features in this context [9]. Judging from both statistics for our dataset, a total of 5 signatures seems to explain the characteristics of the observed mutational spectrum well. In practice, a combination of a statistical assessment paired with biological knowledge about the nature of the data will allow for the most reliable interpretation of the results.

4.5 Visualization: Exploration of Signatures and Samples

To explore the results for the TCGA data set, we will use the plotting functions. All figures are generated with the ggplot2 package, and thus, their properties and appearances can directly be modified, even at a later stage.

library(ggplot2)

Focusing on the results of the NMF first, the five somatic signatures (named S1 to S5) can be visualized either as a heatmap or as a barchart.

plotSignatureMap(sigs_nmf) + ggtitle("Somatic Signatures: NMF - Heatmap")

Composition of somatic signatures estimated with the NMF, represented as a heatmap.

plotSignatures(sigs_nmf) + ggtitle("Somatic Signatures: NMF - Barchart")

Composition of somatic signatures estimated with the NMF, represented as a barchart.

plotObservedSpectrum(sigs_nmf)

plotFittedSpectrum(sigs_nmf)

Each signature represents different properties of the somatic spectrum observed in the data. While signature S1 is mainly characterized by selective C>T alterations, others as S4 and S5 show a broad distribution across the motifs.

In addition, the contribution of the signatures in each study can be represented with the same sets of plots. Signature S1 and S3 are strongly represented in the GBM and SKCM study, respectively. Other signatures show a weaker association with a single cancer type.

plotSampleMap(sigs_nmf)

Occurrence of signatures estimated with the NMF, represented as a heatmap.

plotSamples(sigs_nmf)

Occurrence of signatures estimated with the NMF, represented as a barchart.

In the same way as before, the results of the PCA can be visualized. In contrast to the NMF, the signatures also contain negative values, indicating the depletion of a somatic motif.

Comparing the results of the two methods, we can see similar characteristics between the sets of signatures, for example S1 of the NMF and S2 of the PCA.

plotSignatureMap(sigs_pca) + ggtitle("Somatic Signatures: PCA - Heatmap")

Composition of somatic signatures estimated with the PCA, represented as a heatmap.

plotSignatures(sigs_pca) + ggtitle("Somatic Signatures: PCA - Barchart")

Composition of somatic signatures estimated with the PCA, represented as a barchart.

plotFittedSpectrum(sigs_pca)

Since the observed mutational spectrum is defined by the data alone, it is identical for both all decomposition methods.

plotObservedSpectrum(sigs_pca)

4.5.1 Customization: Changing Plot Properties

As elaborated before, since all plots are generated with the ggplot2 framework [17], we can change all their properties. To continue the example from before, we will visualize the relative contribution of the mutational signatures in the studies, and change the plot to fit our needs better.

library(ggplot2)

p = plotSamples(sigs_nmf)

## (re)move the legend
p = p + theme(legend.position = "none")
## (re)label the axis
p = p + xlab("Studies")
## add a title
p = p + ggtitle("Somatic Signatures in TGCA WES Data")
## change the color scale
p = p + scale_fill_brewer(palette = "Blues")
## decrease the size of x-axis labels
p = p + theme(axis.text.x = element_text(size = 9))

Occurrence of signatures estimated with the NMF, customized plot. See the original plot above for comparisons.

If you want to visualize a large number of samples or signatures, the default color palette may not provide a sufficient number of distinct colors. You can add a well-suited palette to your plot, as we have shown before with the scale_fill functions. For example, scale_fill_discrete will get you the default ggplot2 color scheme; while this supports many more colors, the individual levels may be hard to distinguish.

4.6 Clustering: Grouping by Motifs or Samples

An alternative approach to interpreting the mutational spectrum by decomposition is clustering. With the clusterSpectrum function, the clustering is computed, by grouping either by the sample or motif dimension of the spectrum. By default, the Euclidean distance is used; other distance measures, as for example cosine similarity, are implemented is the proxy package and can be passed as an optional argument.

clu_motif = clusterSpectrum(sca_mm, "motif")

library(ggdendro)

p = ggdendrogram(clu_motif, rotate = TRUE)
p

Hierachical clustering of the mutational spectrum, according to motif.

4.7 Extension: Correction for Batch Effects and Confounding Variables

When investigating somatic signatures between samples from different studies, corrections for technical confounding factors should be considered. In our use case of the TCGA WES studies, this is of minor influence due to similar sequencing technology and variant calling methods across the studies. Approaches for the identification of so termed batch effects have been proposed [10,16] and existing implementations can be used in identifying confounding variables as well as correcting for them. The best strategy in addressing technical effects depends strongly on the experimental design; we recommend reading the respective literature and software documentation for finding an optimal solution in complex settings.

From the metadata of the TCGA studies, we have noticed that two different sequencing approaches have been employed, constituting a potential technical batch effect. The ComBat function of the sva package allows us to adjust for this covariate, which yields a mutational spectrum corrected for contributions related to sequencing technology. We can then continue with the identification of somatic signatures as we have seen before.

library(sva)

sca_anno = as.data.frame(lapply(sca_metadata, unlist))

model_null = model.matrix(~ 1, sca_anno)

sca_mm_batch = ComBat(sca_mm, batch = sca_anno$Sequence_Source, mod = model_null)

   Found 2 batches
   Adjusting for 0 covariate(s) or covariate level(s)
   Standardizing Data across genes
   Fitting L/S model and finding priors
   Finding parametric adjustments
   Adjusting the Data

4.8 Extension: Normalization of Sequence Motif Frequencies

If comparisons are performed across samples or studies with different capture targets, for example by comparing whole exome with whole genome sequencing, further corrections for the frequency of sequence motifs can be taken into account [11]. The kmerFrequency function provides the basis for calculating the occurrence of k-mers over a set of ranges of a reference sequence.

As an example, we compute the frequency of 3-mers for the human toplevel chromosomes, based on a sample of 10'000 locations.

k = 3
n = 1e4

hs_chrs = as(seqinfo(BSgenome.Hsapiens.UCSC.hg19), "GRanges")
hs_chrs = keepStandardChromosomes(hs_chrs)

k3_hs_chrs = kmerFrequency(BSgenome.Hsapiens.UCSC.hg19, n, k, hs_chrs)
k3_hs_chrs

Analogously, the k-mer occurrence across a set of enriched regions, such as in exome or targeted sequencing, can be obtained easily. The following outlines how to apply the approach to the human exome.

library(TxDb.Hsapiens.UCSC.hg19.knownGene)

k = 3
n = 1e4

hs_exons = reduce(exons(TxDb.Hsapiens.UCSC.hg19.knownGene))
hs_exons = keepStandardChromosomes(hs_exons)

k3_exons = kmerFrequency(BSgenome.Hsapiens.UCSC.hg19, n, k, hs_exons)

With the normalizeMotifs function, the frequency of motifs can be adjusted. Here, we will transform our results of the TCGA WES studies to have the same motif distribution as of a whole-genome analysis. The kmers dataset contains the estimated frequency of 3-mers across the human genome and exome.

data(kmers)
norms = k3wg / k3we
head(norms)

   seq
         AAA       AAC       AAG       AAT       ACA       ACC 
   1.4519030 1.1022862 1.0248394 1.5075775 1.1089220 0.8910534

sca_mm_norm = normalizeMotifs(sca_mm, norms)

4.9 Extension: Motifs from Non-Reference Genomes

When we determine the sequence context for each alteration, we typically use one of the reference BSgenome packages in Bioconductor. But we are not restricted to those, and derive the somatic motifs from different types of sequence sources, for example 2bit and FASTA files. More precisely, the mutationContext function will work on any object for which a getSeq method is defined. You can get the full list available on your system, the results may vary depending on which packages you have loaded.

showMethods("getSeq")

   Function: getSeq (package Biostrings)
   x="BSgenome"
   x="FaFile"
   x="FaFileList"
   x="TwoBitFile"

This allows us to perform our analysis also on non-standard organisms and genomes, for which a BSgenome package is not available, for example the 1000genomes human reference sequence. Or we can generate genomic references for specific populations, by updating the standard genomes with a set of known variants; see the documentation of the BSgenome package and the injectSNPs function in particular for this.

Taking further, we can base our analysis on the personalized genomic sequence for each individual, in case it is available. If we imagined that we had a set of somatic variant calls as VCF files and the personalized genomic sequence as FASTA files for two individuals A and B at hand, here a simple outline on how our analysis could work.

## Somatic variant calls
vr_A = readVcfAsVRanges(vcf_A_path, "GenomeA")
vr_B = readVcfAsVRanges(vcf_B_path, "GenomeB")

## Genomic sequences
fa_A = FaFile(fasta_A_path)
fa_B = FaFile(fasta_B_path)

## Somatic motifs
vr_A = mutationContext(vr_A, fa_A)
vr_B = mutationContext(vr_B, fa_B)

## Combine for further analysis
vr = c(vr_A, vr_B)

5 Alternatives: Inferring Somatic Signatures with Different Approaches

For the identification of somatic signatures, other methods and implementations exist. The original framework [11,3] proposed for this is based on the NMF and available for the Matlab programming language [1]. In extension, a probabilistic approach based on Poisson processes has been proposed [6] and implemented [5].

6 Frequently Asked Questions

6.1 Citing SomaticSignatures

If you use the SomaticSignatures package in your work, please cite it:

citation("SomaticSignatures")

   
     Julian S. Gehring, Bernd Fischer, Michael Lawrence, Wolfgang
     Huber (2015): SomaticSignatures: Inferring Mutational
     Signatures from Single Nucleotide Variants. Bioinformatics
     July 10, 2015, btv408. doi:10.1093/bioinformatics/btv408
   
   A BibTeX entry for LaTeX users is
   
     @Article{,
       title = {SomaticSignatures: Inferring Mutational Signatures from Single Nucleotide Variants.},
       author = {Julian S. Gehring and Bernd Fischer and Michael Lawrence and Wolfgang Huber},
       year = {2015},
       journal = {Bioinformatics},
       doi = {10.1093/bioinformatics/btv408},
       url = {http://dx.doi.org/10.1093/bioinformatics/btv408},
     }

6.2 Getting Help

We welcome questions or suggestions about our software, and want to ensure that we eliminate issues if and when they appear. We have a few requests to optimize the process:

All questions and follow-ups should take place over the Bioconductor support site, which serves as a repository of information. First search the site for past threads which might have answered your question.
The subject line should contain SomaticSignatures and a few words describing the problem.
If you have a question about the behavior of a function, read the sections of the manual page for this function by typing a question mark and the function name, e.g. ?mutationContext. Additionally, read through the vignette to understand the interplay between different functions of the package. We spend a lot of time documenting individual functions and the exact steps that the software is performing.
Include all of your R code and its output related to the question you are asking.
Include complete warning or error messages, and conclude your message with the full output of sessionInfo().

6.3 Installing and Upgrading

Before you want to install the SomaticSignatures package, please ensure that you have the latest version of R and Bioconductor installed. For details on this, please have a look at the help packages for R and Bioconductor. Then you can open R and run the following commands which will install the latest release version of SomaticSignatures:

source("http://bioconductor.org/biocLite.R")
biocLite("SomaticSignatures")

Over time, the packages may also receive updates with bug fixes. These installed packages can be updated with:

source("http://bioconductor.org/biocLite.R")
biocLite()

6.4 Working with VRanges

A central object in the workflow of SomaticSignatures is the VRanges class which is part of the VariantAnnotation package. It builds upon the commonly used GRanges class of the GenomicRanges package. Essentially, each row represents a variant in terms of its genomic location as well as its reference and alternative allele.

library(VariantAnnotation)

There are multiple ways of converting its own variant calls into a VRanges object. One can for example import them from a VCF file with the readVcf function or employ the readMutect function for importing variant calls from the MuTect caller directly. Further, one can also construct it from any other format in the form of:

vr = VRanges(
    seqnames = "chr1",
    ranges = IRanges(start = 1000, width = 1),
    ref = "A",
    alt = "C")

vr

   VRanges object with 1 range and 0 metadata columns:
         seqnames       ranges strand         ref              alt
            <Rle>    <IRanges>  <Rle> <character> <characterOrRle>
     [1]     chr1 [1000, 1000]      +           A                C
             totalDepth       refDepth       altDepth   sampleNames
         <integerOrRle> <integerOrRle> <integerOrRle> <factorOrRle>
     [1]           <NA>           <NA>           <NA>          <NA>
         softFilterMatrix
                 <matrix>
     [1]                 
     -------
     seqinfo: 1 sequence from an unspecified genome; no seqlengths
     hardFilters: NULL

7 References

References

[1]	L. Alexandrov. WTSI Mutational Signature Framework, Oct. 2012. [ http ]
[2]	L. B. Alexandrov, S. Nik-Zainal, D. C. Wedge, S. A. J. R. Aparicio, S. Behjati, A. V. Biankin, G. R. Bignell, N. Bolli, A. Borg, A.-L. Børresen-Dale, S. Boyault, B. Burkhardt, A. P. Butler, C. Caldas, H. R. Davies, C. Desmedt, R. Eils, J. E. Eyfjörd, J. A. Foekens, M. Greaves, F. Hosoda, B. Hutter, T. Ilicic, S. Imbeaud, M. Imielinsk, N. Jäger, D. T. W. Jones, D. Jones, S. Knappskog, M. Kool, S. R. Lakhani, C. López-Otín, S. Martin, N. C. Munshi, H. Nakamura, P. A. Northcott, M. Pajic, E. Papaemmanuil, A. Paradiso, J. V. Pearson, X. S. Puente, K. Raine, M. Ramakrishna, A. L. Richardson, J. Richter, P. Rosenstiel, M. Schlesner, T. N. Schumacher, P. N. Span, J. W. Teague, Y. Totoki, A. N. J. Tutt, R. Valdés-Mas, M. M. van Buuren, L. v. . Veer, A. Vincent-Salomon, N. Waddell, L. R. Yates, Australian Pancreatic Cancer Genome Initiative, ICGC Breast Cancer Consortium, ICGC MMML-Seq Consortium, Icgc PedBrain, J. Zucman-Rossi, P. Andrew Futreal, U. McDermott, P. Lichter, M. Meyerson, S. M. Grimmond, R. Siebert, E. Campo, T. Shibata, S. M. Pfister, P. J. Campbell, and M. R. Stratton. Signatures of mutational processes in human cancer. Nature, 500(7463):415-421, Aug. 2013. [ DOI \| .html ]
[3]	L. B. Alexandrov, S. Nik-Zainal, D. C. Wedge, P. J. Campbell, and M. R. Stratton. Deciphering Signatures of Mutational Processes Operative in Human Cancer. Cell Reports, 3(1):246-259, Jan. 2013. [ DOI \| http ]
[4]	K. Cibulskis, M. S. Lawrence, S. L. Carter, A. Sivachenko, D. Jaffe, C. Sougnez, S. Gabriel, M. Meyerson, E. S. Lander, and G. Getz. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nature Biotechnology, advance online publication, Feb. 2013. [ DOI \| .html ]
[5]	A. Fischer, C. J. Illingworth, P. J. Campbell, and V. Mustonen. EMu: probabilistic inference of mutational processes and their localization in the cancer genome. Genome biology, 14(4):R39, Apr. 2013. [ DOI ]
[6]	A. Fischer. EMu: Expectation-Maximisation inference of mutational signatures, 2013. [ http ]
[7]	R. Gaujoux and C. Seoighe. A flexible R package for nonnegative matrix factorization. BMC Bioinformatics, 11(1):367, July 2010. [ DOI \| http ]
[8]	R. C. Gentleman, V. J. Carey, D. M. Bates, B. Bolstad, M. Dettling, S. Dudoit, B. Ellis, L. Gautier, Y. Ge, J. Gentry, K. Hornik, T. Hothorn, W. Huber, S. Iacus, R. Irizarry, F. Leisch, C. Li, M. Maechler, A. J. Rossini, G. Sawitzki, C. Smith, G. Smyth, L. Tierney, J. Y. Yang, and J. Zhang. Bioconductor: open software development for computational biology and bioinformatics. Genome Biology, 5(10):R80, Sept. 2004. [ DOI \| http ]
[9]	L. N. Hutchins, S. M. Murphy, P. Singh, and J. H. Graber. Position-dependent motif characterization using non-negative matrix factorization. Bioinformatics, 24(23):2684-2690, Dec. 2008. [ DOI \| http ]
[10]	J. T. Leek and J. D. Storey. Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis. PLoS Genet, 3(9):e161, Sept. 2007. [ DOI \| http ]
[11]	S. Nik-Zainal, L. B. Alexandrov, D. C. Wedge, P. Van Loo, C. D. Greenman, K. Raine, D. Jones, J. Hinton, J. Marshall, L. A. Stebbings, A. Menzies, S. Martin, K. Leung, L. Chen, C. Leroy, M. Ramakrishna, R. Rance, K. W. Lau, L. J. Mudie, I. Varela, D. J. McBride, G. R. Bignell, S. L. Cooke, A. Shlien, J. Gamble, I. Whitmore, M. Maddison, P. S. Tarpey, H. R. Davies, E. Papaemmanuil, P. J. Stephens, S. McLaren, A. P. Butler, J. W. Teague, G. Jönsson, J. E. Garber, D. Silver, P. Miron, A. Fatima, S. Boyault, A. Langerød, A. Tutt, J. W. Martens, S. A. Aparicio, A. Borg, A. V. Salomon, G. Thomas, A.-L. Børresen-Dale, A. L. Richardson, M. S. Neuberger, P. A. Futreal, P. J. Campbell, and M. R. Stratton. Mutational Processes Molding the Genomes of 21 Breast Cancers. Cell, 149(5):979-993, May 2012. [ DOI \| http ]
[12]	V. Obenchain, M. Morgan, and M. Lawrence. VariantAnnotation: Annotation of Genetic Variants, 2011. [ .html ]
[13]	P. T. Pyl, J. Gehring, B. Fischer, and W. Huber. h5vc: scalable nucleotide tallies with HDF5. Bioinformatics, 30(10):1464-1466, May 2014. [ DOI \| http ]
[14]	P. T. Pyl. h5vc: Managing alignment tallies using a hdf5 backend, 2013. [ .html ]
[15]	W. Stacklies, H. Redestig, M. Scholz, D. Walther, and J. Selbig. pcaMethodsa bioconductor package providing PCA methods for incomplete data. Bioinformatics, 23(9):1164-1167, May 2007. [ DOI \| http ]
[16]	Y. Sun, N. R. Zhang, and A. B. Owen. Multiple hypothesis testing adjusted for latent variables, with an application to the AGEMAP gene expression data. The Annals of Applied Statistics, 6(4):1664-1688, Dec. 2012. Zentralblatt MATH identifier: 06141543; Mathematical Reviews number (MathSciNet): MR3058679. [ DOI \| http ]
[17]	H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer, New York, 1st ed. 2009. corr. 3rd printing 2010 edition edition, Feb. 2010. [ http ]

8 Session Information

   R version 3.2.2 (2015-08-14)
   Platform: x86_64-pc-linux-gnu (64-bit)
   Running under: Ubuntu 14.04.3 LTS
   
   locale:
    [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
    [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
    [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
    [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
    [9] LC_ADDRESS=C               LC_TELEPHONE=C            
   [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
   
   attached base packages:
   [1] stats4    parallel  stats     graphics  grDevices utils    
   [7] datasets  methods   base     
   
   other attached packages:
    [1] sva_3.14.0                       
    [2] genefilter_1.50.0                
    [3] mgcv_1.8-7                       
    [4] nlme_3.1-122                     
    [5] ggdendro_0.1-15                  
    [6] ggplot2_1.0.1                    
    [7] BSgenome.Hsapiens.UCSC.hg19_1.4.0
    [8] BSgenome_1.36.3                  
    [9] rtracklayer_1.28.10              
   [10] SomaticCancerAlterations_1.4.0   
   [11] SomaticSignatures_2.4.8          
   [12] bigmemory_4.4.6                  
   [13] BH_1.58.0-1                      
   [14] bigmemory.sri_0.1.3              
   [15] Biobase_2.28.0                   
   [16] VariantAnnotation_1.14.13        
   [17] Rsamtools_1.20.4                 
   [18] Biostrings_2.36.4                
   [19] XVector_0.8.0                    
   [20] GenomicRanges_1.20.6             
   [21] GenomeInfoDb_1.4.2               
   [22] IRanges_2.2.7                    
   [23] S4Vectors_0.6.5                  
   [24] BiocGenerics_0.14.0              
   [25] knitr_1.11                       
   
   loaded via a namespace (and not attached):
    [1] splines_3.2.2           foreach_1.4.2          
    [3] exomeCopy_1.14.0        Formula_1.2-1          
    [5] highr_0.5               latticeExtra_0.6-26    
    [7] RBGL_1.44.0             RSQLite_1.0.0          
    [9] lattice_0.20-33         biovizBase_1.16.0      
   [11] digest_0.6.8            RColorBrewer_1.1-2     
   [13] colorspace_1.2-6        ggbio_1.16.1           
   [15] Matrix_1.2-2            plyr_1.8.3             
   [17] OrganismDbi_1.10.0      XML_3.98-1.3           
   [19] biomaRt_2.24.0          zlibbioc_1.14.0        
   [21] xtable_1.7-4            scales_0.3.0           
   [23] BiocParallel_1.2.21     annotate_1.46.1        
   [25] proxy_0.4-15            pkgmaker_0.22          
   [27] GenomicFeatures_1.20.4  nnet_7.3-11            
   [29] proto_0.3-10            mime_0.4               
   [31] survival_2.38-3         magrittr_1.5           
   [33] evaluate_0.7.2          GGally_0.5.0           
   [35] doParallel_1.0.8        MASS_7.3-44            
   [37] NMF_0.20.6              foreign_0.8-66         
   [39] graph_1.46.0            tools_3.2.2            
   [41] registry_0.3            formatR_1.2            
   [43] gridBase_0.4-7          stringr_1.0.0          
   [45] munsell_0.4.2           cluster_2.0.3          
   [47] rngtools_1.2.4          AnnotationDbi_1.30.1   
   [49] lambda.r_1.1.7          pcaMethods_1.58.0      
   [51] futile.logger_1.4.1     grid_3.2.2             
   [53] RCurl_1.95-4.7          dichromat_2.0-0        
   [55] iterators_1.0.7         labeling_0.3           
   [57] bitops_1.0-6            gtable_0.1.2           
   [59] codetools_0.2-14        DBI_0.3.1              
   [61] reshape_0.8.5           markdown_0.7.7         
   [63] reshape2_1.4.1          GenomicAlignments_1.4.1
   [65] gridExtra_2.0.0         Hmisc_3.16-0           
   [67] futile.options_1.0.0    stringi_0.5-5          
   [69] Rcpp_0.12.0             rpart_4.1-10           
   [71] acepack_1.3-3.3