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UPDATE:

  • v3.4 allows you to include descriptions into the annotated files with -d parameter.
  • When annotating genomic change, HGVSg, three additional columns will be added. ONCOKB_HUGO_SYMBOL, ONCOKB_PROTEIN_CHANGE and ONCOKB_CONSEQUENCE
  • See Columns added section for more details

oncokb-annotator

API token required, please see OncoKB™ API section for more information

Status

Run all python tests Compare Annotation

Install dependencies

For python 3

pip install -r requirements/common.txt -r requirements/pip3.txt

For python 2.7

pip install -r requirements/common.txt -r requirements/pip2.7.txt

Usage

Example input files are under data. An example script is here: example.sh

MAF

Annotates variants in MAF(https://docs.gdc.cancer.gov/Data/File_Formats/MAF_Format/) with OncoKB™ annotation. Supports both python2 and python3.
Get more details on the command line using python MafAnnotator.py -h.

We recommend processing VCF files by vcf2maf before using the MafAnnotator here.

Atypical Alteration

You can still use MAF format to annotate atypical alterations, such as MSI-H, TMB-H, EGFR vIII. Please see more examples HERE.

Copy Number Alteration

Use GISTIC data format

We use GISTIC 2.0 format by default. For more information, please see https://docs.cbioportal.org/5.1-data-loading/data-loading/file-formats#discrete-copy-number-data, please see examples HERE.
Columns Locus ID and Cytoband are not required.

Individual CNA

You can also list copy number alteration individually by specifying -f individual, please see examples HERE.

Get more details on the command line using python CnaAnnotator.py -h.

Fusion

OncoKB™ offers to annotate functional fusions. The fusion format for intragenic deletion is GENE-intragenic or GENE-GENE. For other fusions, please use GENEA-GENEB or GENEA-GENEB Fusion.

Get more details on the command line using python FusionAnnotator.py -h.

Structural Variant

OncoKB™ offers to annotate structural variant. The types supported are DELETION, TRANSLOCATION, DUPLICATION, INSERTION, INVERSION, FUSION, UNKNOWN. All other types will be converted to UNKNOWN.

All structural variants with two different gene partners, they will be considered as functional fusions.

Get more details on the command line using python StructuralVariantAnnotator.py -h.

Clinical Data (Combine MAF+CNA+Fusion)

You can combine all annotation on sample/patient level using the clinical data annotator.

Get more details on the command line using python ClinicalDataAnnotator.py -h.

Annotate with HGVSp_Short, HGVSp, HGVSg or Genomic Change

OncoKB™ MafAnnotator supports annotating the alteration with HGVSp, HGVSp_Short, HGVSg or Genomic Change format. Please specify the query type with -q parameter. The acceptable values are HGVSp_Short, HGVSp, HGVSg and Genomic_Change(case-insensitive). Please see data/example.sh for examples.
If you do not specify query type, the MafAnnotator will try to figure out the query type based on the headers.

For HGVSp_Short

The annotator takes alteration from the column HGVSp_Short or Alteration

For HGVSp

The annotator takes alteration from the column HGVSp or Alteration

For HGVSg

The annotator takes alteration from the column HGVSg or Alteration

For Genomic_Change

The annotator takes genomic change from columns Chromosome, Start_Position, End_Position, Reference_Allele, Tumor_Seq_Allele1(Optional) and Tumor_Seq_Allele2.
Typically Tumor_Seq_Allele1 is the reference allele, Tumor_Seq_Allele2 is the variant allele. This is why Tumor_Seq_Allele1 is optional.
The annotator uses both if the value is different from Reference_Allele. Tumor_Seq_Allele2 has higher priority than Tumor_Seq_Allele1.

Annotation with Genomic_Change is relatively slow. We need to annotate the variant first with GenomeNexus(https://www.genomenexus.org/) then get annotation one by one. There is a plan to improve this method. If you are annotating a lot of data, please prioritize using other query type if applicable.

Annotate with different reference genomes (GRCh37, GRCh38)

OncoKB™ MafAnnotator supports annotating the alteration with reference genome GRCh37 and GRCh38.

The annotator will get the reference genome from MAF file column NCBI_Build or Reference_Genome.
If there is no reference genome specified in the file, we will use the default reference genome through -r parameter.

You can specify the default reference genome using -r parameter (This is only applicable to MafAnnotator.py).
The acceptable values are GRCh37, GRCh38 (case in-sensitive).

If both values are not specified, the annotator will use OncoKB™ default reference genome which is GRCh37.

Levels of Evidence

Introducing Simplified OncoKB™ Levels of Evidence:

  • New Level 2, defined as “Standard care biomarker recommended by the NCCN or other expert panels predictive of response to an FDA-approved drug in this indication” (formerly Level 2A).
  • Unified Level 3B, defined as “Standard care or investigational biomarker predictive of response to an FDA-approved or investigational drug in another indication” (combination of previous Levels 2B and 3B).

We have implemented these changes for 2 reasons:

OncoKB™ API

When you run MafAnnotator.py, FusionAnnotator.py and CnaAnnotator.py, you need a token before accessing the OncoKB™ data via its web API. Please visit OncoKB™ Data Access Page for more information about how to register an account and get an OncoKB™ API token.
With the token listed under OncoKB™ Account Settings Page, you could use it in the following format.

python ${FILE_NAME.py} -i ${INPUT_FILE} -o ${OUTPUT_FILE} -b ${ONCOKB_API_TOKEN}

Columns added

MafAnnotator/CnaAnnotator/StructuralVariantAnnotator/FusionAnnotator

Column Conditions Possible Values Description
ANNOTATED True, False Whether the variant is annotated by OncoKB successfully.
ONCOKB_HUGO_SYMBOL Only added when annotating genomic change or HGVSg When annotating genomic change, we obtained gene hugo symbol from GenomeNexus. This can be cross-referenced with your own gene name.
ONCOKB_PROTEIN_CHANGE Only added when annotating genomic change or HGVSg When annotating genomic change, we obtained alteration protein change from GenomeNexus. This can be cross-referenced with your own protein change.
ONCOKB_CONSEQUENCE Only added when annotating genomic change or HGVSg When annotating genomic change, we obtained alteration consequence from GenomeNexus. This can be cross-referenced with your own consequence/Variant Class.
GENE_IN_ONCOKB True, False Whether the gene has been curated by the OncoKB Team.
VARIANT_IN_ONCOKB True, False Whether the variant has been curated by the OncoKB Team. Note: when a variant does not exist, it may still have annotations.
MUTATION_EFFECT Gain-of-function, Likely Gain-of-function, Loss-of-function, Likely Loss-of-function, Switch-of-function, Likely Switch-of-function, Neutral, Likely Neutral, Inconclusive, Unknown The biological effect of a mutation/alteration on the protein function that gives rise to changes in the biological properties of cells expressing the mutant/altered protein compared to cells expressing the wildtype protein.
MUTATION_EFFECT_CITATIONS PMID, Abstract, Website link All citations related to the biological effect.
ONCOGENIC Oncogenic, Likely Oncogenic, Likely Neutral, Inconclusive, Unknown, Resistance In OncoKB™, “oncogenic” is defined as “referring to the ability to induce or cause cancer” as described in the second edition of The Biology of Cancer by Robert Weinberg (2014).
LEVEL_* Therapeutic implications The leveled therapeutic implications.
HIGHEST_LEVEL LEVEL_1, LEVEL_2, LEVEL_3A, LEVEL_3B, LEVEL_4, LEVEL_R1, LEVEL_R2 The highest level of evidence for therapeutic implications. Order: LEVEL_R1 > LEVEL_1 > LEVEL_2 > LEVEL_3A > LEVEL_3B > LEVEL_4 > LEVEL_R2
HIGHEST_SENSITIVE_LEVEL LEVEL_1, LEVEL_2, LEVEL_3A, LEVEL_3B, LEVEL_4 The highest sensitive level of evidence for therapeutic implications. Order: LEVEL_1 > LEVEL_2 > LEVEL_3A > LEVEL_3B > LEVEL_4
HIGHEST_RESISTANCE_LEVEL LEVEL_R1, LEVEL_R2 The highest resistance level of evidence for therapeutic implications. Order: LEVEL_R1 > LEVEL_R2
TX_CITATIONS PMID, Abstract, Website link All citations related to therapeutic implications.
LEVEL_Dx* Tumor type the level of evidence is assigned to The leveled diagnostic implications.
HIGHEST_DX_LEVEL LEVEL_Dx1, LEVEL_Dx2, LEVEL_Dx3 The highest level of evidence for diagnostic implications.
DX_CITATIONS PMID, Abstract, Website link All citations related to diagnostic implications.
LEVEL_Px* Tumor type the level of evidence is assigned to The leveled prognostic implications.
HIGHEST_PX_LEVEL LEVEL_Px1, LEVEL_Px2, LEVEL_Px3 The highest level of evidence for prognostic implications.
PX_CITATIONS PMID, Abstract, Website link All citations related to prognostic implications.
GENE_SUMMARY Only when parameter -d is specified Brief overview of the gene and its role in cancer
VARIANT_SUMMARY Only when parameter -d is specified Variant summary describes the variant oncogenicity, last review if it is VUS
TUMOR_TYPE_SUMMARY Only when parameter -d is specified Tumor type summary describes the therapeutic implication that applies to the indication
DIAGNOSTIC_SUMMARY Only when parameter -d is specified Diagnostic summary that applies to the indication, for hematologic malignancies only
PROGNOSTIC_SUMMARY Only when parameter -d is specified Prognostic summary that applies to the indication, for hematologic malignancies only
MUTATION_EFFECT_DESCRIPTION Only when parameter -d is specified The mutation effect description provides a brief overview of the biological and oncogenic effect of the VPS and includes appropriate references to peer-reviewed literature.

ClinicalDataAnnotator

Please see description above for columns LEVEL_*, HIGHEST_LEVEL, HIGHEST_SENSITIVE_LEVEL, HIGHEST_RESISTANCE_LEVEL, LEVEL_Dx*, HIGHEST_DX_LEVEL, LEVEL_Px*, HIGHEST_PX_LEVEL.
Beside these columsn, the following columns will also be added.

Column Description
ONCOGENIC_MUTATIONS The list of mutations that are Oncogenic or Likely Oncogenic.
#ONCOGENIC_MUTATIONS Number of oncogenic mutations.
RESISTANCE_MUTATIONS The list of resistance mutations.
#RESISTANCE_MUTATIONS Number of resistance mutations.
#MUTATIONS_WITH_SENSITIVE_THERAPEUTIC_IMPLICATIONS Number of mutations in the sample with sensitive therapeutic implications.
#MUTATIONS_WITH_RESISTANCE_THERAPEUTIC_IMPLICATIONS Number of mutations in the sample with resistance therapeutic implications.
#MUTATIONS_WITH_DIAGNOSTIC_IMPLICATIONS Number of mutations in the sample with diagnostic implications.
#MUTATIONS_WITH_PROGNOSTIC_IMPLICATIONS Number of mutations in the sample with prognostic implications.
#MUTATIONS Number of mutations in the sample.

Questions?

The best way is to email [email protected], so all our team members can help.