Skip to content

Commit

Permalink
Update README.md
Browse files Browse the repository at this point in the history
  • Loading branch information
@zhx828
zhx828 committed Jun 2, 2022
1 parent 50e6baf commit c92363f
Showing 1 changed file with 28 additions and 5 deletions.
33 changes: 28 additions & 5 deletions README.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -33,8 +33,10 @@ We recommend processing VCF files by [vcf2maf](https://github.com/mskcc/vcf2maf/
You can still use MAF format to annotate atypical alterations, such as MSI-H, TMB-H, EGFR vIII. Please see more examples [HERE](data/example_atypical_alterations.txt).

### Copy Number Alteration
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](data/example_cna.txt).

#### 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](data/example_cna.txt).
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](data/example_individual_cna.txt).

Get more details on the command line using `python CnaAnnotator.py -h`.
Expand Down Expand Up @@ -103,11 +105,12 @@ python ${FILE_NAME.py} -i ${INPUT_FILE} -o ${OUTPUT_FILE} -b ${ONCOKB_API_TOKEN}
```


## Columns added in the annotation files
## Columns added in the annotation files using MafAnnotator/CnaAnnotator/StructuralVariantAnnotator/FusionAnnotator
| Column | Possible Values | Description |
|---------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 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. |
| ANNOTATED | True, False | Whether the variant is annotated by OncoKB successfully |
| 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). |
Expand All @@ -123,5 +126,25 @@ python ${FILE_NAME.py} -i ${INPUT_FILE} -o ${OUTPUT_FILE} -b ${ONCOKB_API_TOKEN}
| 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 |

## Columns added in the files using ClinicalDataAnnotator
| Column | Possible Values | Description |
|-----------------------------------------------------|-------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------|
| 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 |
| 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 |
| 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 |
| 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 contact@oncokb.org so all our team members can help.

0 comments on commit c92363f

Please sign in to comment.