oncodriveclustl 1.2.0

OncodriveCLUSTL is a clustering method to identify cancer drivers

OncodriveCLUSTL

OncodriveCLUSTL is a sequence-based clustering method to identify significant clustering signals in nucleotide sequence.

One of the main goals of cancer research is the identification of the genomic elements that drive tumorigenesis. OncodriveCLUSTL is a new nucleotide sequence-based clustering algorithm to detect significant clustering signals across genomic regions. OncodriveCLUSTL is based on a local background model derived from the nucleotide context mutational probabilities of the cohort under study. Our method is able to identify well-known cancer drivers in coding regions and it can be applied to non-coding regions and non-human data.

License

OncodriveCLUSTL is available to the general public subject to certain conditions described in its LICENSE.

Installation

OncodriveCLUSTL depends on Python 3.5 or above. We recommend to install it using the Anaconda Python distribution:

conda install -c bbglab oncodriveclustl

OncodriveCLUSTL can also be installed using pip:

pip install oncodriveclustl

You can obtain the latest code from the repository and install it for development with pip:

git clone [e-mail]:bbglab/oncodriveclustl.git
cd oncodriveclustl
python -m venv .venv
.venv/bin/pip install -e .
source .venv/bin/activate
oncodriveclustl --help

[!NOTE] The first time that you run OncodriveCLUSTL with a given reference genome, it will download it from our servers. By default the downloaded datasets go to ~/.bgdata. If you want to move these datasets to another folder you have to define the system environment variable BGDATA_LOCAL with an export command.

[!NOTE] If you install a modern build tool like uv, you can simply do this:

git clone https://github.com/bbglab/oncodriveclustl.git
cd oncodriveclustl
uv run oncodriveclustl --help

Input data

OncodriveCLUSTL only requires two main inputs, the mutations file and the annotations file.

Mutations file

TSV file containing SNVs (substitutions) mapped to a reference genome (e.g., human hg19 or mouse c3h). If other mutation types are present (insertions, deletions, double base substitutions, etc), they will be filtered out during the analysis. This file must contain, at least, the following 5 columns with header:

1. CHROMOSOME: 1, 2,..., X, Y
2. POSITION: Integer indicating the position of the mutation
3. REF: Reference nucleotide
4. ALT: Alternate nucleotide
5. SAMPLE: Identifier of the sample

Additional columns are:

6. CANCER_TYPE: Type of tumor. When specified, OncodriveCLUSTL will calculate one mutational profile for each cancer type and mutations will be randomized accordingly.
7. SIGNATURE: User-defined group to compute k-mer nucleotide mutational probabilities. When specified, OncodriveCLUSTL will calculate one mutational profile for each group and will randomize each mutation accordingly.

[!NOTE] OncodriveCLUSTL assumes all SNVs are mapped to the positive strand.

[!WARNING] When using the --signature-group option, please check that the number of mutations per group is sufficient for an accurate signatures calculation.

Annotations file

TSV file containing the coordinates of genomic elements (GEs). This file must contain, at least, the following 5 columns with header:

1. CHROMOSOME: 1, 2,..., X, Y
2. START: Starting position of the genomic region
3. END: Final position of the genomic region
4. ELEMENT: Identifier of the GE
5. SYMBOL: Symbol of the GE. OncodriveCLUSTL will analyze GEs as SYMBOL + ELEMENT.

Additional columns are:

6. STRAND: Strand of the GE coordinates ("+" or "-").

[!WARNING] Coordinates of a given GE cannot overlap.

You can check the input formats in the files provided in the example.

If you have a VCF file or directory of VCF files containing somatic mutations, you can run our VCF parser to obtain a tabular file compatible with OncodriveCLUSTL input format::

parse_vcf -i [INPUT_DIRECTORY] -o [OUTPUT_FILE]

Please, check parsers/vcf.py module for more details.

If you would like to run OncodriveCLUSTL using a per-calculated signature or mutational profile, you need to provide a dictionary containing the reference k-mer to alternate mutational probabilities in JSON format:

{
    "my_dataset": {
        "GCA>G": 0.02424271083094251,
        "AGC>A": 0.023005887103025254,
        "ACG>T": 0.037613802858829135,
        "CGA>C": 0.10691031051670515,
        "GAC>G": 0.017846071811001615,
        "TTC>A": 0.024003748061871697,
        "CTT>G": 0.024149863672267024,
        "GGA>T": 0.011178562948734577,
        "AGG>C": 0.010654720767868876,
        "GGG>C": 0.012031686292218055,
        "CAA>T": 0.014478959792844522,
        "TGA>A": 0.01255651801972085,
        "GGA>A": 0.011178562948734577,
        "CGA>A": 0.03563677017223505,
        "TCC>T": 0.011158347971568658,
        "GCC>A": 0.010952316565906438,
        // ...
    }
}

OncodriveCLUSTL requires non-collapsed k-mer probabilities (192 for tri-nucleotides, 3072 for penta-nucleotides).

Output data

OncodriveCLUSTL generates three output files:

Elements results file ('elements_results.txt')

TSV file containing results of the analyzed elements:

1. SYMBOL: GE symbol #. ENSID: GE ID #. CGC: True if GE in the COSMIC Cancer Gene Census (CGC) list (Sondka et al., 2018)
2. CHROMOSOME: 1, 2,..., X, Y
3. STRAND: Strand of the GE ("+" or "-")
4. LENGTH: length (bp) of the GE
5. TOTAL_MUT: total substitutions observed in the GE
6. CLUSTERED_MUT: number of substitutions in a cluster
7. CLUSTERS: number of clusters
8. SIM_CLUSTERS: number of simulated clusters
9. SCORE: GE score
10. P_EMPIRICAL: empirical p-value of the GE
11. Q_EMPIRICAL: empirical q-value of the GE
12. P_ANALYTICAL: analytical p-value of the GE
13. Q_ANALYTICAL: analytical q-value of the GE
14. P_TOPCLUSTER: analytical p-value of the cluster with highest cluster score
15. Q_TOPCLUSTER: analytical q-value of the cluster with highest cluster score

Clusters results file ('clusters_results.tsv').

TSV file containing results of the clusters observed in the analyzed elements:

1. RANK: Position of the GE in the list of
2. SYMBOL: GE symbol
3. ENSID: GE ID
4. CGC: True if GE in the CGC list
5. CHROMOSOME: 1, 2,..., X, Y
6. STRAND: Strand of the GE ("+" or "-")
7. COORDINATES: genomic coordinates of the cluster. It can be 'coord1,coord2' for clusters inside a single region or 'coord1,coord2;coord3,coord4' for those spanning regions (--concatenate flag)
8. MAX_COORD: genomic position with the highest smoothing score inside the cluster
9. WIDTH: cluster's width (pb)
10. N_MUT: number of substitutions in the cluster
11. N_SAMPLES: number of samples with a mutation in the cluster
12. FRA_UNIQ_SAMPLES: proportion of unique samples mutated in the cluster out of the total of mutations in the cluster
13. SCORE: cluster score
14. P: analytical p-value of the cluster

Log file ('results.log')

TXT file containing OncodriveCLUSTL's run information.

Usage

OncodriveCLUSTL is meant to be used through the command line.

Usage: oncodriveclustl [OPTIONS]

Options:
  -i, --input-file PATH           File containing somatic mutations
                                  [required]
  -r, --regions-file PATH         File with the genomic regions to analyze
                                  [required]
  -o, --output-directory TEXT     Output directory to be created  [required]
  -sig, --input-signature PATH    File containing input context based
                                  mutational probabilities (signature)
  -ef, --elements-file PATH       File with the symbols of the elements to
                                  analyze
  -e, --elements TEXT             Symbol of the element(s) to analyze
  -g, --genome [hg38|hg19|mm10|c3h|car|cast|f344]
                                  Genome to use
  -emut, --element-mutations INTEGER
                                  Cutoff of element mutations. Default is 2
  -cmut, --cluster-mutations INTEGER
                                  Cutoff of cluster mutations. Default is 2
  -sw, --smooth-window INTEGER RANGE
                                  Smoothing window. Default is 11  [3<=x<=101]
  -cw, --cluster-window INTEGER RANGE
                                  Cluster window. Default is 11  [3<=x<=101]
  -kmer, --kmer [3|5]             K-mer nucleotide context
  -n, --n-simulations INTEGER     number of simulations. Default is 1000
  -sim, --simulation-mode [mutation_centered|region_restricted]
                                  Simulation mode
  -simw, --simulation-window INTEGER RANGE
                                  Simulation window. Default is 31
                                  [19<=x<=101]
  -sigcalc, --signature-calculation [frequencies|region_normalized]
                                  Signature calculation: mutation frequencies
                                  (default) or k-mer mutation counts
                                  normalized by k-mer region counts
  -siggroup, --signature-group [SIGNATURE|SAMPLE|CANCER_TYPE]
                                  Header of the column to group signatures
                                  calculation
  -c, --cores INTEGER RANGE       Number of cores to use in the computation.
                                  By default it will use all the available
                                  cores.  [1<=x<=10]
  --seed INTEGER                  Seed to use in the simulations
  --log-level [debug|info|warning|error|critical]
                                  Verbosity of the logger
  --concatenate                   Calculate clustering on concatenated genomic
                                  regions (e.g., exons in coding sequences)
  --clustplot                     Generate a needle plot with clusters for an
                                  element
  --qqplot                        Generate a quantile-quantile (Q-Q) plot for
                                  a dataset
  --gzip                          Gzip compress files
  -h, --help                      Show this message and exit.

[!NOTE] When using simulation mode 'mutation_centered', simulation windows can be simulated outside the GE.

[!NOTE] When using --signature-calculation region_normalized, k-mer mutation counts will be normalized by k-mer nucleotide counts in the genomic regions provided as input (--regions-file).

Run the example

If you run OncodriveCLUSTL from the source code, you can run an example of TCGA pancreatic adenocarcinomas (Ellrott et al. 2018) for coding regions (Mularoni et al., 2016) using 1000 simulations. First you need to download the example folder. Then you run OncodriveCLUSTL with default mode and parameters as:

oncodriveclustl -i example/PAAD.tsv.gz -r example/cds.hg19.regions.gz -o example/output

The results will be saved in a folder named output.

You can compute a more sophisticated analysis using non-default parameters and generate a quantile-quantile plot by typing:

oncodriveclustl -i example/PAAD.tsv.gz -r example/cds.hg19.regions.gz -o example/output -sw 15 -cw 15 -simw 35 -sim region_restricted --concatenate --qqplot

If you want to run a specific GE and generate a plot its observed clusters, you can type::

oncodriveclustl -i example/PAAD.tsv.gz -r example/cds.hg19.regions.gz -o example/output -sw 15 -cw 15 -simw 35 -sim region_restricted --concatenate --clustplot -e KRAS