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Use individual CNA for testing
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@zhx828
zhx828 committed Jun 2, 2022
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13 changes: 5 additions & 8 deletions .github/workflows/compare-annotation.yml
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Expand Up @@ -43,7 +43,7 @@ jobs:
MUTATION_DATA_NAME=data_mutations_mskcc.txt
CLINICAL_DATA_NAME=data_clinical_sample.txt
FUSION_DATA_NAME=data_fusions.txt
CNA_DATA_NAME=data_CNA.txt
INDIVIDUAL_CNA_DATA_NAME=data_individual_CNA.txt
cd data || exit
curl -s -H "Authorization: token ${ONCOKB_OAUTH_TOKEN}" https://api.github.com/repos/knowledgesystems/oncokb-data/contents/annotation/annotator-test/data | jq -r '.[] | .download_url + " " + .name' | while IFS=' ' read -r downloadurl name; do
Expand All @@ -60,19 +60,16 @@ jobs:
IMAF=data/"$MUTATION_DATA_NAME"
OMAF=compare/"$PREFIX"_"$MUTATION_DATA_NAME"
IC=data/"$CLINICAL_DATA_NAME"
OC=compare/"$PREFIX"_"$CLINICAL_DATA_NAME"
IF=data/"$FUSION_DATA_NAME"
OF=compare/"$PREFIX"_"$FUSION_DATA_NAME"
ICNA=data/"$CNA_DATA_NAME"
OCNA=compare/"$PREFIX"_"$CNA_DATA_NAME"
IICNA=data/"$INDIVIDUAL_CNA_DATA_NAME"
OICNA=compare/"$PREFIX"_"$INDIVIDUAL_CNA_DATA_NAME"
python MafAnnotator.py -i "$IMAF" -o "$OMAF" -c "$IC" -b "$ONCOKB_API_TOKEN"
python FusionAnnotator.py -i "$IF" -o "$OF" -c "$IC" -b "$ONCOKB_API_TOKEN"
python CnaAnnotator.py -i "$ICNA" -o "$OCNA" -c "$IC" -b "$ONCOKB_API_TOKEN"
python ClinicalDataAnnotator.py -i "$IC" -o "$OC" -a "$OMAF,$OCNA,$OF"
python CnaAnnotator.py -i "$IICNA" -o "$OICNA" -c "$IC" -b "$ONCOKB_API_TOKEN" -f "individual"
python ClinicalDataAnnotator.py -i "$IC" -o "$OC" -a "$OMAF,$OICNA,$OF"
git config user.name oncokb-bot
git config user.email dev.oncokb@gmail.com
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11 changes: 5 additions & 6 deletions AnnotatorCore.py
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Expand Up @@ -168,7 +168,9 @@ def setsampleidsfileterfile(f):
HGVSP_SHORT_HEADER = 'HGVSP_SHORT'
HGVSP_HEADER = 'HGVSP'
HGVSG_HEADER = 'HGVSG'
HGVS_HEADERS = [ALTERATION_HEADER, HGVSP_SHORT_HEADER, HGVSP_HEADER, HGVSG_HEADER, 'AMINO_ACID_CHANGE', 'FUSION']
# columns for copy number alteration
CNA_HEADERS = [ALTERATION_HEADER, 'COPY_NUMBER_ALTERATION', 'CNA', 'GISTIC']
HGVS_HEADERS = [ALTERATION_HEADER, HGVSP_SHORT_HEADER, HGVSP_HEADER, HGVSG_HEADER, 'AMINO_ACID_CHANGE', 'FUSION'] + CNA_HEADERS
SAMPLE_HEADERS = ['SAMPLE_ID', 'TUMOR_SAMPLE_BARCODE']
PROTEIN_START_HEADERS = ['PROTEIN_START']
PROTEIN_END_HEADERS = ['PROTEIN_END']
Expand All @@ -186,9 +188,6 @@ def setsampleidsfileterfile(f):
GC_VAR_ALLELE_2_HEADER = 'TUMOR_SEQ_ALLELE2'
GENOMIC_CHANGE_HEADERS = [GC_CHROMOSOME_HEADER, GC_START_POSITION_HEADER, GC_END_POSITION_HEADER, GC_REF_ALLELE_HEADER, GC_VAR_ALLELE_1_HEADER, GC_VAR_ALLELE_2_HEADER]

# columns for copy number alteration
CNA_HEADER = ['COPY_NUMBER_ALTERATION', 'CNA', 'GISTIC']

# columns for structural variant annotation
SV_GENEA_HEADER = ['SITE1_GENE', 'GENEA', 'GENE1']
SV_GENEB_HEADER = ['SITE2_GENE', 'GENEB', 'GENE2']
Expand Down Expand Up @@ -976,7 +975,7 @@ def process_individual_cna_file(outf, cna_data_file, defaultCancerType, cancerTy
isample = geIndexOfHeader(headers, SAMPLE_HEADERS)
ihugo = geIndexOfHeader(headers, HUGO_HEADERS)
icancertype = geIndexOfHeader(headers, CANCER_TYPE_HEADERS)
icna = geIndexOfHeader(headers, CNA_HEADER)
icna = geIndexOfHeader(headers, CNA_HEADERS)

hugo = row[ihugo] if ihugo >= 0 else None
cna_type = get_cna(row[icna], annotate_gain_loss)
Expand Down Expand Up @@ -1097,7 +1096,7 @@ def process_clinical_data(annotatedmutfiles, clinicalfile, outfile):
ismutorcna = ihugo != -1 & ihgvs != -1

if not isfusion and not ismutorcna:
log.error("missing proper header")
log.error("file " + annotatedmutfile + " missing proper header")
exit()

for row in reader:
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33 changes: 28 additions & 5 deletions README.md
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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.

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