SigProfiler
Background:
Cancer genomes evince somatic mutations, which are imprinted by different mutational processes, that give rise to diverse mutational signatures. Their analysis from single base substitutions and their immediate sequencing context, allows the classification of small mutational events (including substitutions, insertions, deletions, and doublet substitutions) for better understanding the mutational processes that have shaped a cancer genome.
In this sense, SigProfiler constitutes a Galaxy-based wrapper of a computational method developed by Ludmil B. Alexandrov, that allow the exploration and visualization of mutational patterns for all types of small mutational events. Specifically, the following actions can be performed using SigProfiler wrapper:
1. Identify and categorize the mutations based on possible single nucleotide variants (SNVs), double base substitutions (DBS), and insertions/deletions and provides further transcriptional strand bias categorization. Afterwards, the classification of these mutations are integrated into distinct matrices. SigProfiler provides matrix generation support for SBS-6, SBS-96, SBS-1536, DBS-78 and DBS-1248. In addition, the generation of mutational matrices of indels including ID-28 and ID-83 are procured. Besides, an ID-8628 matrix that extends the ID-83 classification is generated. SigProfiler examines transcriptional strand bias for single base substitutions, doublet base substitutions, and small indels. It is evaluated whether a mutation occurs on the transcribed or the non-transcribed strand of well-annotated protein coding genes of a reference genome. Mutations found in the transcribed regions of the genome are further subclassified as: (i) transcribed, (ii) un-transcribed, (iii) bi-directional, or (iv) unknown.
2. Generation of plots of all types of mutational signatures as well as all types of mutational patterns in cancer genomes.
Additional Information:
Classification of Single Base substitutions (SBSs): Single base substitutions (SBSs) are single DNA base-pairs substituted with another single DNA base-pairs. The most basic classification catalogues SBSs into six distinct categories, including: C:G > A:T, C:G > G:C, C:G > T:A, T:A > A:T, T:A > C:G, and T:A > G:C. In practice, a C:G > A:T substitution is denoted as either a C > A mutation using the pyrimidine base or as a G > T mutation using the purine base. In consequence, the most commonly used SBS-6 classification of single base substitutions can be written as: C > A, C > G, C > T, T > A, T > C, and T > G. Additionally, the SBS-6 classification can be further expanded by considering the base-pairs immediately adjacent 5′ and 3′ to the somatic mutation. Therefore, an extended classification for analysis of mutational signatures is SBS-96, where each of the classes in SBS-6 is further elaborated using one base adjacent at the 5′ of the mutation and one base adjacent at the 3′ of the mutation. Logically, SBS-96 can be further elaborated by including additional 5′ and 3′ adjacent context. Each of the six single base substitutions in SBS-6 has 256 possible pentanucleotides resulting in a classification with 1536 possible channels.
Classification of Doublet Base substitutions (DBSs): Doublet base substitutions (DBSs) are somatic mutations in which a set of two adjacent DNA base-pairs is simultaneously substituted with another set of two adjacent DNA base-pairs. An example of a DBS is a set of CT:GA base-pairs mutating to a set of AA:TT base-pairs, which is usually denoted as CT:GA > AA:TT. It should be noted that a CT:GA > AA:TT mutation can be equivalently written as either a CT > AA mutation. Overall, the basic classification catalogues DBSs into 78 distinct categories denoted as the DBS-78 matrix. Similarly, we can expand the characterization of DBS mutations by considering the 5′ and 3′ adjacent contexts. With seventy-eight possible DBS mutations having sixteen possible tetranucleotides each, this context expansion results in 1248 possible channels denoted as the DBS-1248 context.
Classification of small insertions and deletions (IDs): A somatic insertion is the incorporation of a set of base-pairs that lengthens a chromosome, while a somatic deletion is the removing of a set of existing base-pairs from a given location of a chromosome. Unfortunately, indel classification cannot be performed analogously to SBS or DBS classifications, where the immediate sequencing context flanking each mutation was utilized to subclassify these mutational events. Consequently, indels (IDs) are classified as single base-pair or longer events. They can be further subclassified as either a C:G or a T:A indel, while longer indels can also be subclassified based on their lengths: 2 bp, 3 bp, 4 bp, and 5 + bp.
Incorporation of transcription Strand Bias (TSB): The mutational classifications described above allow the characterization of mutational patterns of single base substitutions, doublet base substitutions, and small insertions and deletions. Nevertheless, these classifications can be further elaborated by incorporating strand bias. Mutations from the same type are expected to be equally distributed across the two DNA strands. However, in many cases an asymmetric number of mutations are observed due to either one of the strands being preferentially repaired or one of the strands having a higher propensity for being damaged. To sub-classify mutations based on their transcriptional strand bias, the pyrimidine orientation with respect to the locations of well-annotated protein coding genes on a genome is considered.
Running SigProfiler:
1. Reference Genomes: Before using SigProfiler, the installation of a reference genome is demanded. By default, the tool supports the following reference genomes:
Human: GRCh37 & GRCh38
Mouse: mm9 & mm10
Rat: rn6
Nematode: c_elegans
A right command line should look like:
sigprofiler -ig GRCh37
- Mutational signatures calculation:
After successful installation of a reference genome, SigProfiler can be applied to files containing somatic mutations in multiple formats, for transforming these mutational catalogues into mutational matrices. Specifically, the tool can read data formats such as Variant Calling Format (VCF) and Mutation Annotation Format (MAF) and the following parameters should be provided for generating the diverse matrices and plots:
—name | -n = Project name --genome | -g = Reference Genome -files | -f = Absolute path where the input mutation files are located
A right command line should look like:
sigprofiler -n MYPROJECT -g GRCh37 -f /path_to_folder_with_VCF_files/ -p
Options --version show program's version number and exit
-h, --help show this help message and exit —install_genome Install de novo any of the following reference genomes: 'GRCh37', 'GRCh38', 'mm9' or 'mm10'.
--name=APPENDIX Provide a project name —genome=NAME Provide a reference genome (ex: GRCh37, GRCh38, mm9 or mm10).
--files=Abs_path Path where the input vcf files are located —exome Use only the exome or not
--bed=FILE BED file containing the set of regions to be used in generating the matrices —chrom Create the matrices on a per chromosome basis
--plot Generate the plots for each context —tsb Performs a transcriptional strand bias test for the 24, 384, and 6144 contexts
--gs Performs a gene strand bias test For further info see: https://github.com/AlexandrovLab/SigProfilerMatrixGenerator