In the help menu —
python ~/git/oncokb-annotator/MafAnnotator.py -h
Hugo_Symbol instead of Amino_Acid_Change

$ python /data/MoCha/patidarr/oncokb-annotator-1.1.0/ClinicalDataAnnotator.py -i 283228/20170910/oncoKB/283228.clinical.txt -o 283228/20170910/oncoKB/283228.oncoKB.clinical.txt -a 283228/20170910/oncoKB/283228.maf,283228/20170910/oncoKB/283228.cnv
annotating 283228/20170910/oncoKB/283228.clinical.txt...
Traceback (most recent call last):
  File "/data/MoCha/patidarr/oncokb-annotator-1.1.0/ClinicalDataAnnotator.py", line 53, in <module>
    main(sys.argv[1:])
  File "/data/MoCha/patidarr/oncokb-annotator-1.1.0/ClinicalDataAnnotator.py", line 40, in main
    processclinicaldata(annotatedalterationfiles, inputclinicalfile, outputclinicalfile)
  File "/gpfs/gsfs6/users/MoCha/patidarr/oncokb-annotator-1.1.0/AnnotatorCore.py", line 495, in processclinicaldata
    il = headers[l]
KeyError: 'LEVEL_R2'

but when I ran the same command but version oncokb-annotator-1.0.3, it worked...

with msk-override

https://github.com/genome-nexus/genome-nexus-annotation-pipeline

e.g.

java -jar annotationPipeline-1.0.0.jar --filename $MAF --output-filename $MAF_GN --isoform-override mskcc --replace-symbol-entrez true

This line, when executed on Windows (including 64-bit Windows) fails

csv.field_size_limit(sys.maxsize) # for reading large files

The error is

OverflowError: Python int too large to convert to C long

caused by the Windows C long being 32 bits.
The following fixes this, and should be OK for Python 3 and Python 2, on any CPU / OS.

import ctypes as ct
csv.field_size_limit(int(ct.c_ulong(-1).value // 2))

See _csv.Error: field larger than field limit (131072)

If the last column in a MAF is all empty strings (which usually is the result of cmo_maf2maf with Caller being the last column ), MafAnnotator.py output is shifted by one column.

Hi guys,

What is the source of the gene symbols you use?

I'm using http://oncokb.org/api/v1/genes to pull down genes and subset on tumor-suppressor genes. This is one of the entries:

{
    "entrezGeneId":84142,
    "hugoSymbol":"FAM175A",
    "name":"family with sequence similarity 175 member A",
    "oncogene":false,
    "curatedIsoform":"ENST00000321945",
    "curatedRefSeq":"NM_139076.2",
    "geneAliases":["ABRA1","CCDC98"],
    "tsg":true
}

The "official" symbol for this gene, however, seems to be ABRAXAS1: https://www.ncbi.nlm.nih.gov/gene/84142. That is also the primary symbol associated with the gene in the internal instance of the IMPACT series.

with the latest version of annotator, I got the following errors with example code. My token is not Expired (174 days left)

ERROR:AnnotatorCore:error when processing https://www.oncokb.org/api/v1/utils/allCuratedGenes.json 
reason: Forbidden
INFO:MafAnnotator:annotating data/example_maf.txt ...
INFO:MafAnnotator:done!

any suggestions?
Thanks

Change the pull_mutation_info arguments to a list of class which includes hugo, protein_change, consequence, start, end, cancer_type.
Add additional method to support post to oncokb api.

This is the step after getting back from oncokb api and processed the result of the queries response.
At this moment, you are able to make call to oncokb, then after getting the processed result, we want to include the original rows and newly processed annotation to the file.

• Store the original rows to a list. Similar to queries, let's create another list calls rows. At the place you push Query to queries, we should push row to rows
• pull_mutation_info should return you a list of annotations based on the list of queries you send in. Loop through the list of annotation, get the annotation from the annotation list.
• get the row from the rows, the index of the row is the same of the annotation
• append the annotation after row
• write the row to the file

We should have the branch ready but not merge to master yet unless we have the data published.

Dear developers,

Thank you for the very useful tool!
I found that highly recurrent MYCN P44L mutation is annotated as Unknown, while it is present in oncoKB (https://www.oncokb.org/gene/MYCN). Please find attached my annotated MAF file.

Best regards,
Mycn_example.txt

Andrey

Is there any way to use specific versions of OncoKB itself (not the annotator)?

Say for example I wanted to stick to the release from May 9, 2019 here: https://oncokb.org/news.

Hi,

I like this program. I am using it to a current project. I have a few questions about MafAnnotator.py.

1. Should the input MAF should be pre-filtered to include only "functional mutations"? I noticed that your example MAF only has functional mutations.
2. For elements of the MAF that don't have annotated protein positions (e.g. intronic mutations), I seem to be getting the messages of the form "position wrong at lineN -/M" where M and N are integers. Why is this? I looked at the logic in the program. It seems to be checking the HGVSp columns against the Protein_position column which are both unpopulated in the case of mutations that don't cause an amino acid coding change.
3. Should the read depth columns have any effect on the results?
4. For a specific example related to all of these questions, consider the attached file which contains the output file where the input files only differ in that the columns t_depth (368), t_ref_count (164), t_alt_count (86), n_depth (359), n_ref_count (249) and n_alt_count (1) have NA in one file and they have integers in parentheses above in the other. Please note the sums don't add up because the allele-specific read counting was capped at 250 in the generating program.
5. I was not expecting the KIT intronic mutation in the attachment would be actionable, so my sense is that the result with the allele counts populated is correct. But, I would like to verify that this is the desired results. In other words, should this intron KIT mutations be considered to be an actionable mutation?

Thanks,
Pete Vedell
Informatics Specialist
Mayo Clinic
[email protected]
example_unexpected_result.maf.transposed.txt

Replace current GET method with POST, so we can decrease the workload
https://www.oncokb.org/swagger-ui/index.html

• Understand how function makeoncokbrequest and pulloncokb works in AnnotatorCore.py

• Understand why we want to change GET to POST

• Use pull_mutation_info as example where uses pulloncokb: we should call pull_mutation_info after reading 50 rows instead of calling it every row.

• Understand how method processalterationevents works.
  It takes a file and reads the row on line 269.
  It processes the row and gathers all necessary information before calling pull_mutation_info

• Change the pull_mutation_info arguments to a list of class which includes hugo, protein_change, consequence, start, end, cancer_type
  From last step, you understand how the processalterationevents works. Instead of reading one row then call pull_mutation_info, we want to read 50 rows and put processed data into a list. Then past the list to pull_mutation_info.

• Process 50 rows in pulloncokb and return 50rows response and append the result to the file

• If the api failed, we should try to resend the api again.(Sometimes the system may not be stable for overwhelming requests, but there should always be a service available)

• Have a dependency file
• Add test coverage
• Work with #21

Information related to this topic

• https://docs.python.org/3/howto/pyporting.html

hi @jjgao,

I want to use a local version of oncokb to make sure the results I am generating today are what I will get down the road on a sample (if I reran a sample).
I setup the oncokB, and I can browse the site but when I oncokb-annotator I am getting error.
python /data/MoCha/patidarr/oncokb-annotator-1.1.0/MafAnnotator.py -i tmp.maf -o tm2.maf -t MEL -u https://mocha-cbioportal.ncifcrf.gov/cbioportal-dev

annotating tmp.maf...
error when processing https://mocha-cbioportal.ncifcrf.gov/cbioportal-dev/annotate/mutations/byProteinChange?hugoSymbol=FANCD2&alteration=X426_splice&tumorType=MEL&consequence=splice_region_variant
error when processing https://mocha-cbioportal.ncifcrf.gov/cbioportal-dev/annotate/mutations/byProteinChange?hugoSymbol=OPA1&alteration=I24V&tumorType=MEL&consequence=missense_variant&proteinStart=24&proteinEnd=24
error when processing https://mocha-cbioportal.ncifcrf.gov/cbioportal-dev/annotate/mutations/byProteinChange?hugoSymbol=SDHA&alteration=L649Efs*4&tumorType=MEL&consequence=frameshift_variant
error when processing https://mocha-cbioportal.ncifcrf.gov/cbioportal-dev/annotate/mutations/byProteinChange?hugoSymbol=BRAF&alteration=V600K&tumorType=MEL&consequence=missense_variant&proteinStart=600&proteinEnd=600
error when processing https://mocha-cbioportal.ncifcrf.gov/cbioportal-dev/annotate/mutations/byProteinChange?hugoSymbol=PPP6C&alteration=T120Nfs*32&tumorType=MEL&consequence=frameshift_variant
done!

but when I paste the https://mocha-cbioportal.ncifcrf.gov/cbioportal-dev/annotate/mutations/byProteinChange?hugoSymbol=BRAF&alteration=V600K&tumorType=MEL&consequence=missense_variant&proteinStart=600&proteinEnd=600 in browser I get following:

{"query":{"id":null,"type":"regular","hugoSymbol":"BRAF","entrezGeneId":673,"alteration":"V600K","alterationType":null,"svType":null,"tumorType":"MEL","consequence":"missense_variant","proteinStart":600,"proteinEnd":600,"hgvs":null},"geneExist":true,"variantExist":true,"alleleExist":true,"oncogenic":"Oncogenic","mutationEffect":{"knownEffect":"Gain-of-function","description":"","citations":{"pmids":["25417114","20179705","23833300","22535154","26091043","26343582","30630828","23922205","25079552","28783719","19251651","15035987"],"abstracts":[]}},"highestSensitiveLevel":"LEVEL_1","highestResistanceLevel":null,"highestDiagnosticImplicationLevel":null,"highestPrognosticImplicationLevel":null,"otherSignificantSensitiveLevels":[],"otherSignificantResistanceLevels":[],"hotspot":true,"geneSummary":"BRAF, an intracellular kinase, is frequently mutated in melanoma, thyroid and lung cancers among others.","variantSummary":"The BRAF V600K mutation is known to be oncogenic.","tumorTypeSummary":"The RAF-inhibitors encorafenib, dabrafenib and vemurafenib alone or in combination with the MEK-inhibitors binimetinib, trametinib and cobimetinib, respectively, are FDA-approved for the treatment of patients with BRAF V600E/K mutant melanoma.","prognosticSummary":"","diagnosticSummary":"","diagnosticImplications":[],"prognosticImplications":[],"treatments":[{"drugs":[{"ncitCode":"C82386","drugName":"Dabrafenib","uuid":"939cd40b-b515-499d-b099-fd29027c0d17","synonyms":["DABRAFENIB","GSK-2118436","GSK-2118436A","BRAF Inhibitor GSK2118436","Dabrafenib","GSK2118436","Benzenesulfonamide, N-(3-(5-(2-amino-4-pyrimidinyl)-2-(1,1-dimethylethyl)-4-thiazolyl)-2-fluorophenyl)-2,6-difluoro-"]}],"approvedIndications":["Dabrafenib is FDA-approved for BRAF V600E mutant unresectable or metastatic melanoma."],"fdaApproved":null,"level":"LEVEL_1","pmids":["22608338","23051966","22735384"],"abstracts":[]},{"drugs":[{"ncitCode":"C82386","drugName":"Dabrafenib","uuid":"939cd40b-b515-499d-b099-fd29027c0d17","synonyms":["DABRAFENIB","GSK-2118436","GSK-2118436A","BRAF Inhibitor GSK2118436","Dabrafenib","GSK2118436","Benzenesulfonamide, N-(3-(5-(2-amino-4-pyrimidinyl)-2-(1,1-dimethylethyl)-4-thiazolyl)-2-fluorophenyl)-2,6-difluoro-"]},{"ncitCode":"C77908","drugName":"Trametinib","uuid":"fb2bb01c-c0ec-4641-abf7-87f486075022","synonyms":["Mekinist","TRAMETINIB","JTP-74057","MEK Inhibitor GSK1120212","N-(3-{3-cyclopropyl-5-[(2-fluoro-4-iodophenyl)amino]-6,8-dimethyl-2,4,7-trioxo-3,4,6,7-tetrahydropyrido[4,3-d]pyrimidin-1(2H)-yl}phenyl)acetamide","GSK1120212","Trametinib"]}],"approvedIndications":["Dabrafenib + Trametinib is FDA-approved for BRAF V600E or V600K mutant unresectable or metastatic melanoma"],"fdaApproved":null,"level":"LEVEL_1","pmids":["28891408","25265492","25287827","29361468","28991513","23020132","25399551"],"abstracts":[]},{"drugs":[{"ncitCode":"C98283","drugName":"Encorafenib","uuid":"001e534f-3e63-432f-90a6-d1af1759e4e2","synonyms":["LGX-818","Encorafenib","LGX818","LGX 818","ENCORAFENIB","Braftovi"]},{"ncitCode":"C84865","drugName":"Binimetinib","uuid":"feb9f4a3-e374-4c75-8a3b-0f1fbcdbf677","synonyms":["ARRY-438162","Mektovi","Binimetinib","ARRY-162","MEK162","BINIMETINIB"]}],"approvedIndications":["In combination for patients with unresectable or metastatic melanoma with a BRAF V600E or V600K mutation"],"fdaApproved":null,"level":"LEVEL_1","pmids":["29573941"],"abstracts":[]},{"drugs":[{"ncitCode":"C77908","drugName":"Trametinib","uuid":"fb2bb01c-c0ec-4641-abf7-87f486075022","synonyms":["Mekinist","TRAMETINIB","JTP-74057","MEK Inhibitor GSK1120212","N-(3-{3-cyclopropyl-5-[(2-fluoro-4-iodophenyl)amino]-6,8-dimethyl-2,4,7-trioxo-3,4,6,7-tetrahydropyrido[4,3-d]pyrimidin-1(2H)-yl}phenyl)acetamide","GSK1120212","Trametinib"]}],"approvedIndications":["Trametinib is FDA-approved for BRAF V600E or V600K mutant unresectable or metastatic melanoma"],"fdaApproved":null,"level":"LEVEL_1","pmids":["29361468","25399551","22663011","25265492"],"abstracts":[]},{"drugs":[{"ncitCode":"C64768","drugName":"Vemurafenib","uuid":"4e91da20-6cf0-4e07-995f-7f7db4c7c077","synonyms":["Vemurafenib","BRAF(V600E) Kinase Inhibitor RO5185426","RO 5185426","PLX4032","PLX-4032","1-propanesulfonamide, n-(3-((5-(4-chlorophenyl)-1h-pyrrolo(2,3-b)pyridin-3-yl)carbonyl)-2,4-difluorophenyl)-","RG 7204","Zelboraf","BRAF (V600E) kinase inhibitor RO5185426","RG7204","VEMURAFENIB"]}],"approvedIndications":["Vemurafenib is FDA-approved for BRAF V600E mutant unresectable or metastatic melanoma"],"fdaApproved":null,"level":"LEVEL_1","pmids":["28961848","24508103","25399551"],"abstracts":[]},{"drugs":[{"ncitCode":"C64768","drugName":"Vemurafenib","uuid":"4e91da20-6cf0-4e07-995f-7f7db4c7c077","synonyms":["Vemurafenib","BRAF(V600E) Kinase Inhibitor RO5185426","RO 5185426","PLX4032","PLX-4032","1-propanesulfonamide, n-(3-((5-(4-chlorophenyl)-1h-pyrrolo(2,3-b)pyridin-3-yl)carbonyl)-2,4-difluorophenyl)-","RG 7204","Zelboraf","BRAF (V600E) kinase inhibitor RO5185426","RG7204","VEMURAFENIB"]},{"ncitCode":"C68923","drugName":"Cobimetinib","uuid":"eb357145-3b18-4aca-b75a-5e18dd2bf4f9","synonyms":["Cotellic","Cobimetinib","XL518","COBIMETINIB","GDC-0973","MEK Inhibitor GDC-0973"]}],"approvedIndications":["Cobimetinib is FDA-approved for the treatment of patients with unresectable or metastatic melanoma with a BRAF V600E or V600K mutation, in combination with vemurafenib. Cobimetinib is not indicated for treatment of patients with wild-type BRAF melanoma."],"fdaApproved":null,"level":"LEVEL_1","pmids":["27480103","25265494"],"abstracts":[]},{"drugs":[{"ncitCode":"C82386","drugName":"Dabrafenib","uuid":"939cd40b-b515-499d-b099-fd29027c0d17","synonyms":["DABRAFENIB","GSK-2118436","GSK-2118436A","BRAF Inhibitor GSK2118436","Dabrafenib","GSK2118436","Benzenesulfonamide, N-(3-(5-(2-amino-4-pyrimidinyl)-2-(1,1-dimethylethyl)-4-thiazolyl)-2-fluorophenyl)-2,6-difluoro-"]},{"ncitCode":"C1857","drugName":"Panitumumab","uuid":"d7b1d12a-e942-4801-bb64-916c9bdfaaf3","synonyms":["ABX-EGF, Clone E7.6.3","PANITUMUMAB","Vectibix","Panitumumab","MoAb ABX-EGF","ABX-EGF","ABX-EGF Monoclonal Antibody","Monoclonal Antibody ABX-EGF","panitumumab"]},{"ncitCode":"C77908","drugName":"Trametinib","uuid":"fb2bb01c-c0ec-4641-abf7-87f486075022","synonyms":["Mekinist","TRAMETINIB","JTP-74057","MEK Inhibitor GSK1120212","N-(3-{3-cyclopropyl-5-[(2-fluoro-4-iodophenyl)amino]-6,8-dimethyl-2,4,7-trioxo-3,4,6,7-tetrahydropyrido[4,3-d]pyrimidin-1(2H)-yl}phenyl)acetamide","GSK1120212","Trametinib"]}],"approvedIndications":[],"fdaApproved":null,"level":"LEVEL_2B","pmids":["29431699"],"abstracts":[{"link":"http://ascopubs.org/doi/abs/10.1200/JCO.2018.36.4_suppl.627","abstract":"Cutsem et al. Abstract# 627, ASCO 2018"},{"link":"https://academic.oup.com/annonc/article/29/suppl_5/mdy149.026/5039436","abstract":"Cutsem et al. Abstract# O-027, ESMO 2018"}]},{"drugs":[{"ncitCode":"C98283","drugName":"Encorafenib","uuid":"001e534f-3e63-432f-90a6-d1af1759e4e2","synonyms":["LGX-818","Encorafenib","LGX818","LGX 818","ENCORAFENIB","Braftovi"]},{"ncitCode":"C1723","drugName":"Cetuximab","uuid":"5fce3074-e420-4c36-9603-2423daf20118","synonyms":["CETUXIMAB","Cetuximab Biosimilar CMAB009","IMC-C225","Chimeric Anti-EGFR Monoclonal Antibody","Cetuximab Biosimilar KL 140","Cetuximab Biosimilar CDP-1","cetuximab","Chimeric Monoclonal Antibody C225","Cetuximab","Chimeric MoAb C225","Erbitux"]},{"ncitCode":"C84865","drugName":"Binimetinib","uuid":"feb9f4a3-e374-4c75-8a3b-0f1fbcdbf677","synonyms":["ARRY-438162","Mektovi","Binimetinib","ARRY-162","MEK162","BINIMETINIB"]}],"approvedIndications":[],"fdaApproved":null,"level":"LEVEL_2B","pmids":["29431699"],"abstracts":[{"link":"http://ascopubs.org/doi/abs/10.1200/JCO.2018.36.4_suppl.627","abstract":"Cutsem et al. Abstract# 627, ASCO 2018"},{"link":"https://academic.oup.com/annonc/article/29/suppl_5/mdy149.026/5039436","abstract":"Cutsem et al. Abstract# O-027, ESMO 2018"}]}],"dataVersion":"","lastUpdate":"06/19/2019","vus":false}

Could you please let me know what I am doing wrong here?

Thanks,
@patidarr

Hi,

Is it possible to give MSI Status as input to OncoKBPlots.py?

Thanks,
Rajesh

Hi,
I ran oncokb-annotator on 04/03/2020 on a subset of the MSK IMPACT cohort and some of the archer fusion annotations are inconsistent with cBioPortal. For example the oncogenic column for P-0022485-T01-IM6 NR4A3-EWSR1 fusion - Archer was blank when I ran the annotator, but on the portal it shows this fusion as likely oncogenic. P-0024067-T01-IM6 FLI1-EWSR1 fusion - Archer is another example of this, the annotator result is blank but it's level 4 on the portal. No errors showed up when running FusionAnnotator.py and I cloned the repo right before running it. Attached is a list of the IMPACT samples with the discrepancy in my cohort and the output of the oncogenic column from the annotator. Adjacent is the annotation from the portal that I manually curated. Do you know what could be causing the discrepancy? I'd really appreciate any help.
Thanks!

• Run the project and run the example.sh successfully

• Create a method to Transfer 3-letter to 1 letter: https://www.hgvs.org/mutnomen/codon.html

• Create test cases for the method, follow https://github.com/oncokb/oncokb-annotator/blob/master/test_AnnotatorCore.py

• Index HGVSP when running ihgvs = geIndexOfHeader() similar to HGVSP_SHORT

• Add examples for HGVSP. In the example_maf.txt, add a HGVSp Column, add two more records with HGVSp specified instead of HGVSp_Short. You could use two existing examples for comparison.

The OncoKB API classifies (some?) mutations defined as Splice_Region and Intron variants by VEP as likely oncogenic.

Currently, the method pulloncokb makes the call to and process the result from OncoKB. It should not be the case while you are trying to support GET/POST at the same time considering we are only making changes for the pull_mutation_info. Lets' change the logic of the pulloncokb to take the response from oncokb instead.

• rename pulloncokb to process_oncokb_annotation.
• The parameter should be only one and named annotation. Remember, this is annotation for one query.
• Process annotation. So the basic logic of this method would not change. The only difference is that instead of fetching oncokb and process, we only process the result.

Hi,

I tried running your example data, and the results (example_maf.onckkb.txt) do not match what I see on the OncoKB website.

For example, the PIK3CA E542K variant was annotated as level 3B and 4, but here:

http://oncokb.org/#/gene/PIK3CA/variant/E542K

...it says 3A. None of the levels in the output match what I see online.

Am I doing something wrong?

thanks for your help,
Greg.

Hi,

I have a basic question.

Why only 'Amplified' or 'Deleted' CNAs are processed by AnnotatorCore.py, i.e. why CNAs showing 'Loss' or 'Gain' are filtered out?

Thanks,

Francesco

Removing errors like:
'position wrong at line361: -/5430

This will prevent the changes causing the annotation mismatch.

• use the oncokb bot token from secret

I'm a Chinese and normally the unicode is UTF-8.
When I run MafAnnotator.py, the error occurred as follows:

#####################################################################
########################### parting line ################################
annotating data/example_maf.txt...
Traceback (most recent call last):
File "MafAnnotator.py", line 81, in
main(sys.argv[1:])
File "MafAnnotator.py", line 68, in main
processalterationevents(inputmaffile, outputmaffile, previousresultfile, defaultcancertype, cancertypemap, False)
File "/Users/peiyuchen/Biosoft/oncokb-annotator-1.0.6/AnnotatorCore.py", line 129, in processalterationevents
inithotspots()
File "/Users/peiyuchen/Biosoft/oncokb-annotator-1.0.6/AnnotatorCore.py", line 120, in inithotspots
missensesinglehotspots = gethotspots(cancerhotspotsbaseurl+"/api/hotspots/single", "single residue")
File "/Users/peiyuchen/Biosoft/oncokb-annotator-1.0.6/AnnotatorCore.py", line 97, in gethotspots
hotspotsjson = json.load(urllib.urlopen(url))
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/json/init.py", line 290, in load
**kw)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/json/init.py", line 338, in loads
return _default_decoder.decode(s)
File "/System/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/json/decoder.py", line 369, in decode
raise ValueError(errmsg("Extra data", s, end, len(s)))
ValueError: Extra data: line 2 column 1 - line 33 column 1 (char 6 - 477818)
############################ parting line ####################################
#########################################################################

I wonder if it resulted from my Chinese system unicode, so I shifted my mac in English system. However, nothing changed, same error as before.
Just a little python background so I cannot solve this problem by myself, could anyone help me to figure out what's wrong with my MafAnnotator.py?

Thanks a lot for helping me.

For annotated MAF, CNA, and fusion files, let's add these two columns:

• Gene annotated by OncoKB
• Variant annotated by OncoKB

This is only true for CnaAnnotator.py I think.

Hi @jjgao,

I can figure out what I am doing wrong to get this error:
[patidarr@cn3151 processedDATA]$ python /data/MoCha/patidarr/oncokb-annotator/MafAnnotator.py -i LG0520/20170910/LG0520.consensus.maf -o LG0520/20170910/oncoKB/LG0520.maf -t LUSC
annotating LG0520/20170910/LG0520.consensus.maf...
Traceback (most recent call last):
File "/data/MoCha/patidarr/oncokb-annotator/MafAnnotator.py", line 81, in
main(sys.argv[1:])
File "/data/MoCha/patidarr/oncokb-annotator/MafAnnotator.py", line 68, in main
processalterationevents(inputmaffile, outputmaffile, previousresultfile, defaultcancertype, cancertypemap, False)
File "/gpfs/gsfs6/users/MoCha/patidarr/oncokb-annotator/AnnotatorCore.py", line 127, in processalterationevents
inithotspots()
File "/gpfs/gsfs6/users/MoCha/patidarr/oncokb-annotator/AnnotatorCore.py", line 118, in inithotspots
missensesinglehotspots = gethotspots(cancerhotspotsbaseurl+"/api/hotspots/single", "single residue")
File "/gpfs/gsfs6/users/MoCha/patidarr/oncokb-annotator/AnnotatorCore.py", line 95, in gethotspots
hotspotsjson = json.load(urllib.urlopen(url))
File "/usr/local/Anaconda/envs/py2.7/lib/python2.7/json/init.py", line 291, in load
**kw)
File "/usr/local/Anaconda/envs/py2.7/lib/python2.7/json/init.py", line 339, in loads
return _default_decoder.decode(s)
File "/usr/local/Anaconda/envs/py2.7/lib/python2.7/json/decoder.py", line 364, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "/usr/local/Anaconda/envs/py2.7/lib/python2.7/json/decoder.py", line 382, in raw_decode
raise ValueError("No JSON object could be decoded")
ValueError: No JSON object could be decoded

I used vcf2maf to generate the MAF file and I have used the similar maf file on another patient and it has worked, there are some 70 patients its not working on.

Any insight would be very helpful.

Thanks,
Rajash

It necessarily requires one to run the code from inside the git/oncokb-annotator/ directory otherwise it complains of "IOError: [Errno 2] No such file or directory: 'data/curated_genes.txt’"

If for some reasons, the post api failed when fetching oncokb, we should change it to get call to get all queries annotated one by one so we would not lose most of the data.

• When API fails due to one of the oncokb services is not available

• When API takes too long to response

The issue is raised when we try to annotate a large CNA data file.

We support GRCh37 and GRCh38

the annotator returns all original data.
But sometimes it gives the following issue and stopped the analysis

UnicodeDecodeError: 'ascii' codec can't decode byte 0xe2 in position 0: ordinal not in range(128)

Hi, there,

I recently cloned this version of oncokb_annotator and run "CnaAnnotator.py" using the following command and got the following error:

[gongy@luna BRAF_Fusions]$ which python
/opt/common/CentOS_6-dev/python/python-2.7.10/bin/python
[gongy@luna BRAF_Fusions]$ python ~/utilities/pipelines/oncokb-annotator/CnaAnnotator.py -i /home/jonssonp/res/dmp/mskimpact/data_CNA.txt -o test
annotating /home/jonssonp/res/dmp/mskimpact/mskimpact/data_CNA.txt...
Traceback (most recent call last):
  File "/home/gongy/utilities/pipelines/oncokb-annotator/CnaAnnotator.py", line 72, in <module>
    main(sys.argv[1:])
  File "/home/gongy/utilities/pipelines/oncokb-annotator/CnaAnnotator.py", line 59, in main
    processcnagisticdata(inputcnafile, outputcnafile, previousresultfile, defaultcancertype, cancertypemap, False)
  File "/home/gongy/utilities/pipelines/oncokb-annotator/AnnotatorCore.py", line 400, in processcnagisticdata
    outf.write(oncokbinfo)
UnicodeEncodeError: 'ascii' codec can't encode character u'\xe9' in position 62: ordinal not in range(128)
[gongy@luna BRAF_Fusions]$ diff /home/bandlamc/ifs_work/git/oncokb-annotator/CnaAnnotator.py ~/utilities/pipelines/oncokb-annotator/CnaAnnotator.py

I tried to use an older version of "CnaAnnotator.py" from the following directory and it works well:

/home/bandlamc/ifs_work/git/oncokb-annotator/CnaAnnotator.py

Please let me know if you can't access to any of these files.

Thanks.
Yixiao Gong

... based on the data types, ie. copy number endpoint for copy number events, sv endpoint for SVs. For mutations, if already annotated with protein change, use byProteinChange by default, otherwise use byGenomicChange, and give an option to alway use byGenomicChange.

Please compare to the current results.