python pcgr.py --input_vcf examples/tumor_sample.COAD.vcf.gz --input_cna_segments
-examples/tumor_sample.COAD.cna.tsv ~/pcgr-0.3 ~/pcgr-0.3/examples tumor_sample.COAD
+examples/tumor_sample.COAD.cna.tsv ~/pcgr-0.3.1 ~/pcgr-0.3.1/examples tumor_sample.COAD
This command will run the Docker-based PCGR workflow and produce the
following output files in the examples folder:
diff --git a/docs/_build/html/searchindex.js b/docs/_build/html/searchindex.js
index efe27231..713fb367 100644
--- a/docs/_build/html/searchindex.js
+++ b/docs/_build/html/searchindex.js
@@ -1 +1 @@
-Search.setIndex({docnames:["about","annotation_resources","getting_started","index","output"],envversion:50,filenames:["about.rst","annotation_resources.rst","getting_started.rst","index.rst","output.rst"],objects:{},objnames:{},objtypes:{},terms:{"1000g":4,"1000genom":1,"12th":2,"140453136a":1,"16gb":[],"17gb":2,"17th":[],"1gb":2,"1kg":[],"2016_03":[],"2016_09":1,"2020plu":2,"28th":[],"2gb":[],"5gb":2,"5th":1,"8th":1,"abstract":1,"case":[1,2,4],"class":4,"default":2,"function":[2,3,4],"import":[0,2,4],"new":2,"public":4,"short":[1,4],CDS:4,EAS:4,For:[1,2,4],IDs:4,POS:[],SAS:4,The:[0,1,2,4],There:0,These:[],_strong:[],abber:3,aberr:[0,2,4],about:3,abov:4,accept:4,acceptor:4,access:[0,4],accompani:2,accord:[2,4],acid:4,acquir:0,across:4,action:4,actual:1,ada:[],adapt:4,add:4,adddit:4,addit:[0,4],adenoma:1,adjust:4,advanc:2,af_norm:[],af_tumor:[],affect:4,affected_spl:[],affili:0,afr:4,afr_af_1kg:4,afr_af_exac:4,afr_af_gnomad:4,african:4,after:4,aggreg:4,aid:0,algorithm:4,align:4,all:[0,1],allel:4,allele_num:4,alon:0,alreadi:[1,2],also:1,alt:4,alter:[2,4],altern:4,american:4,amino:4,amino_acid:4,among:[],amplif:2,amr:4,amr_af_1kg:4,amr_af_exac:4,amr_af_gnomad:4,analys:2,analysi:0,analyz:4,ani:[2,4],annot:[0,2,3],annotation_resourc:[],antineoplast:[1,4],antineoplastic_drug_interact:4,antineoplastic_drugs_dgidb:4,appli:4,applic:[0,4],appri:4,approv:4,approx:2,april:[1,2],argument:2,asian:4,assembl:4,assign:2,associ:2,attach:[0,4],aug:[],b147:1,base:[2,3,4],basic:[0,2,3,4],been:[0,1,2,4],below:4,benign:[],best:4,betweeen:1,bgzip:4,bind:4,biocomput:4,bioconductor:4,biologi:0,biomark:[0,1,2],biotyp:4,block:4,block_substitut:[],bm_citat:4,bm_clinical_signific:4,bm_disease_nam:4,bm_drug_nam:4,bm_evidence_direct:4,bm_evidence_level:4,bm_evidence_typ:4,bm_rate:4,bool:[],boost:4,both:[0,4],braf:1,breast:4,browser:4,build:4,bundl:[0,1,2],cadd:4,call:2,call_confid:[],caller:4,can:[0,2,4],cancer:4,cancer_census_germlin:4,cancer_census_somat:4,cancer_mutation_hotspot:4,cancer_typ:4,cancerhotspot:4,candid:4,canon:4,cap:4,caption:[],care:0,carri:4,catalog:[1,4],catalogu:1,categori:4,caus:4,causal:4,cbmdb:1,cbmdb_id:4,ccd:4,cdna:4,cdna_posit:4,cds:[],cds_chang:4,cds_end_nf:4,cds_posit:4,cds_start_nf:4,cell:4,cell_typ:4,cellular:[],cencu:1,censu:4,challeng:0,chang:[2,4],check:2,chr1:4,chr7:1,chr:4,chrom:4,chrome:4,chromosom:4,citat:[],cite:4,civic:[1,4],civic_id:4,civic_id_2:4,classif:4,clin:[],clin_sig:4,clinic:[0,3],clinvar:[1,4],clinvar_msid:4,clinvar_pmid:4,clinvar_sig:4,clinvar_variant_origin:4,cluster:4,cna:2,cna_seg:[2,4],cnminor:[],cntotal:[],cnv:[],coad:2,code:3,codon:[1,4],codon_numb:4,cohort:1,collect:1,colorect:[2,4],column:4,com:[],come:1,command:2,common:4,complet:[2,4],complex:0,composit:4,comprehens:0,compress:4,comput:2,concern:3,confer:1,confid:[],confirmed_somat:1,consensu:4,consequ:3,consid:4,consortium:[0,1,4],constitut:4,contain:[0,2,4],content:[3,4],context:0,contribut:[2,4],convent:4,coordin:4,copi:[0,2,3],correctli:4,correspond:1,cosmic:[1,4],cosmic_cancer_type_al:4,cosmic_cancer_type_gw:4,cosmic_codon_count_gw:4,cosmic_codon_frac_gw:4,cosmic_consequ:4,cosmic_count_gw:4,cosmic_drug_resist:4,cosmic_fathmm_pr:4,cosmic_mutation_id:4,cosmic_sample_sourc:4,cosmic_site_histolog:4,cosmic_vartyp:4,count:4,cover:4,cpu:2,creat:[],criteria:1,csq:4,curat:[1,4],current:4,damag:[],data:[0,2,3,4],databas:3,databundl:2,dataset:3,date:0,dbnsfp:[1,4],dbnsfp_consensus_lr:4,dbnsfp_consensus_svm:4,dbsnp:[1,4],dbsnp_mappingstatu:4,dbsnp_submiss:4,dbsnp_valid:4,dbsnpbuildid:4,dbsnprsid:4,dec:1,decompos:4,decompress:2,deconstructsig:4,dedic:[],defin:4,delet:[2,4],delin:4,denot:4,depend:0,depth:4,deriv:4,describ:4,descript:4,determin:[],develop:[0,4],dgidb:1,diagnosi:2,diagnost:[0,4],differ:[2,4],direct:[],directori:2,discov:1,diseas:4,distanc:4,distribut:4,dna:4,doc:[],docker:3,dockerhub:2,docm:1,docm_diseas:4,docm_pmid:4,document:[],domain:[3,4],done:4,donor:4,download:[],downstream:2,dp_normal:[],dp_tumor:[],drive:2,driver:[1,4],drug:[1,2,4],dure:2,each:4,eas_af_1kg:4,eas_af_exac:4,eas_af_gnomad:4,east:4,effect:[0,3],effect_predict:4,either:[1,4],emerg:4,end:4,engin:2,ensembl:[0,4],ensembl_gene_id:4,ensembl_transcript_id:4,ensp:4,entrez:4,entrez_id:4,error:2,estim:2,etc:[1,4],etiolog:[2,4],eur:4,eur_af_1kg:4,european:4,event:4,evid:[2,4],exac:[1,4],exampl:[2,4],exist:[2,4],existing_vari:4,exit:2,exom:[1,4],exon:[1,4],experi:4,experienc:4,experiment:4,expert:0,explor:4,extend:0,facet:[],factor:4,fail:2,fall:4,fals:2,famili:1,fathmm:4,fathmm_mkl:4,fda:1,featur:[3,4],feature_typ:4,feb:[],februai:1,februari:1,figur:0,file:[2,4],fin:[],fin_af_exac:4,fin_af_gnomad:4,find:[0,4],finnish:4,firefox:4,first:4,flag:[2,4],flag_pick_allel:4,flank:4,flexibl:0,focu:[],folder:2,follow:[1,2,4],forc:2,force_overwrit:2,fork:2,form:0,format:0,found:[1,2,4],four:4,frac:[],fraction:[],fraction_mut:4,frameshift:4,frequenc:3,from:[0,1,2,4],g12:1,gain:4,gencod:[1,4],gencode_tag:4,gencode_transcript_typ:4,gencode_v19:4,gene:[0,2,3],gene_biotyp:4,gen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\ No newline at end of file
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todo:[],tool:[0,2],toolbar:2,total:4,trait:4,transcript:[2,4],transcript_end:4,transcript_overlap_perc:4,transcript_start:4,transvar:1,treatment:4,trembl:4,treshold:2,trial:[1,4],trust:4,tsgene:[1,4],tsgene_oncogen:4,tsl:4,tsv:2,tumor:[0,1,2,4],tumor_sampl:2,tumor_suppressor:4,tumor_typ:4,tumorigenesi:4,two:[2,4],type:[2,4],unambigu:1,unannot:4,underli:[2,4],uniform:4,uniparc:4,uniprot:[1,4],uniprot_featur:4,uniprot_id:4,uniprotkb:4,uniqu:4,univers:0,unix:2,unpack:2,untar:2,upcom:4,upon:[],upper:4,uri:4,usag:2,use:[2,3],used:[1,2],user:4,using:[0,2,4],util:3,v15:[],v19:[1,4],v22:[],v23:1,v30:[],v31:1,v600e:1,v78:[],v80:1,v85:1,valid:4,valu:2,variabl:4,variant:[0,2,3],variant_class:4,variat:4,variou:4,vartyp:[],vcf:2,vcf_sample_id:4,vcfanno:[0,2],vcfbreakmulti:4,vcflib:4,vcftool:4,vector:4,vep:[0,1,2],vep_all_consequ:4,veri:2,version:[2,4],view:4,virtual:2,weak:[],weak_mutect:[],weak_strelka:[],weight:4,what:3,whenev:1,where:4,whether:4,which:[0,1,2,4],why:3,wide:[1,4],window:2,within:[1,2,4],work:4,workflow:[0,2,4],working_directori:[],wtsi:4,xvf:2,you:2,your:2,yyyymmdd:2},titles:["About","Annotation 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\ No newline at end of file
diff --git a/docs/getting_started.md b/docs/getting_started.md
index 9399eae5..3e800218 100644
--- a/docs/getting_started.md
+++ b/docs/getting_started.md
@@ -23,18 +23,18 @@ An installation of Python (version 2.7.13) is required to run PCGR. Check that P
#### Download PCGR
-__April 12th 2017__: New release (v0.3)
+__April 14th 2017__: New release (0.3.1)
-* Download and unpack the [latest release (v0.3)](https://github.com/sigven/pcgr/releases/latest)
+* Download and unpack the [latest release (0.3.1)](https://github.com/sigven/pcgr/releases/latest)
* Download and unpack the data bundle (approx. 17Gb) in the PCGR directory
- * Download [the latest data bundle](https://drive.google.com/file/d/0B8aYD2TJ472mQjZOMmg4djZfT1k/) from Google Drive to `~/pcgr-X.X` (replace _X.X_ with the version number, e.g. `~/pcgr-0.3`)
+ * Download [the latest data bundle](https://drive.google.com/file/d/0B8aYD2TJ472mQjZOMmg4djZfT1k/) from Google Drive to `~/pcgr-X.X` (replace _X.X_ with the version number, e.g. `~/pcgr-0.3.1`)
* Decompress and untar the bundle, e.g. through the following Unix command: `gzip -dc pcgr.databundle.GRCh37.YYYYMMDD.tgz | tar xvf -`
A _data/_ folder within the _pcgr-X.X_ software folder should now have been produced
-* Pull the [PCGR Docker image - v0.3](https://hub.docker.com/r/sigven/pcgr/) from DockerHub (3.1Gb) :
- * `docker pull sigven/pcgr:0.3` (PCGR annotation engine)
+* Pull the [PCGR Docker image - 0.3.1](https://hub.docker.com/r/sigven/pcgr/) from DockerHub (3.1Gb) :
+ * `docker pull sigven/pcgr:0.3.1` (PCGR annotation engine)
### Run test - generation of clinical report for a cancer genome
@@ -90,7 +90,7 @@ A tumor sample report is generated by calling the Python script __pcgr.py__, whi
The _examples_ folder contain input files from two tumor samples sequenced within TCGA. A report for a colorectal tumor case can be generated by running the following command in your terminal window:
`python pcgr.py --input_vcf examples/tumor_sample.COAD.vcf.gz --input_cna_segments `
-`examples/tumor_sample.COAD.cna.tsv ~/pcgr-0.3 ~/pcgr-0.3/examples tumor_sample.COAD`
+`examples/tumor_sample.COAD.cna.tsv ~/pcgr-0.3.1 ~/pcgr-0.3.1/examples tumor_sample.COAD`
This command will run the Docker-based PCGR workflow and produce the following output files in the _examples_ folder:
diff --git a/docs/getting_started.rst b/docs/getting_started.rst
index 4faca462..35ddf1c0 100644
--- a/docs/getting_started.rst
+++ b/docs/getting_started.rst
@@ -42,10 +42,10 @@ terminal window.
Download PCGR
^^^^^^^^^^^^^
-**April 12th 2017**: New release (v0.3)
+**April 14th 2017**: New release (0.3.1)
- Download and unpack the `latest release
- (v0.3) `__
+ (0.3.1) `__
- Download and unpack the data bundle (approx. 17Gb) in the PCGR
directory
@@ -53,7 +53,7 @@ Download PCGR
- Download `the latest data
bundle `__
from Google Drive to ``~/pcgr-X.X`` (replace *X.X* with the
- version number, e.g. ``~/pcgr-0.3``)
+ version number, e.g. ``~/pcgr-0.3.1``)
- Decompress and untar the bundle, e.g. through the following Unix
command:
``gzip -dc pcgr.databundle.GRCh37.YYYYMMDD.tgz | tar xvf -``
@@ -62,10 +62,10 @@ Download PCGR
have been produced
- Pull the `PCGR Docker image -
- v0.3 `__ from DockerHub
+ 0.3.1 `__ from DockerHub
(3.1Gb) :
- - ``docker pull sigven/pcgr:0.3`` (PCGR annotation engine)
+ - ``docker pull sigven/pcgr:0.3.1`` (PCGR annotation engine)
Run test - generation of clinical report for a cancer genome
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
@@ -125,7 +125,7 @@ sequenced within TCGA. A report for a colorectal tumor case can be
generated by running the following command in your terminal window:
``python pcgr.py --input_vcf examples/tumor_sample.COAD.vcf.gz --input_cna_segments``
-``examples/tumor_sample.COAD.cna.tsv ~/pcgr-0.3 ~/pcgr-0.3/examples tumor_sample.COAD``
+``examples/tumor_sample.COAD.cna.tsv ~/pcgr-0.3.1 ~/pcgr-0.3.1/examples tumor_sample.COAD``
This command will run the Docker-based PCGR workflow and produce the
following output files in the *examples* folder:
diff --git a/pcgr.py b/pcgr.py
index 6e7ad23d..7b1167e7 100755
--- a/pcgr.py
+++ b/pcgr.py
@@ -18,12 +18,12 @@ def __main__():
parser.add_argument('--num_vcfanno_processes', dest = "num_vcfanno_processes", default=4, type=int, help='Number of processes used during vcfanno annotation')
parser.add_argument('--num_vep_forks', dest = "num_vep_forks", default=4, type=int, help='Number of forks (--forks option in VEP) used during VEP annotation')
parser.add_argument('--force_overwrite', action = "store_true", help='By default, the script will fail with an error if any output file already exists. You can force the overwrite of existing result files by using this flag')
- parser.add_argument('--version', action='version', version='%(prog)s 0.3')
- parser.add_argument('pcgr_dir',help='PCGR base directory with accompanying data directory, e.g. ~/pcgr-0.3')
+ parser.add_argument('--version', action='version', version='%(prog)s 0.3.1')
+ parser.add_argument('pcgr_dir',help='PCGR base directory with accompanying data directory, e.g. ~/pcgr-0.3.1')
parser.add_argument('output_dir',help='Output directory')
parser.add_argument('sample_id',help="Tumor sample/cancer genome identifier - prefix for output files")
- docker_image_version = 'sigven/pcgr:0.3'
+ docker_image_version = 'sigven/pcgr:0.3.1'
args = parser.parse_args()
overwrite = 0
diff --git a/src/Dockerfile b/src/Dockerfile
index 601812a5..abcf74e5 100755
--- a/src/Dockerfile
+++ b/src/Dockerfile
@@ -15,7 +15,7 @@ ENV PACKAGE_BIO="tabix samtools libhts1 bedtools"
ENV PACKAGE_DEV="perl debconf-utils build-essential gfortran python-dev python-pip gcc-multilib autoconf zlib1g-dev git libncurses5-dev libblas-dev liblapack-dev cpanminus libcurl4-gnutls-dev libssh2-1-dev libxml2-dev vim libssl-dev openssl libcairo2-dev"
ENV PYTHON_MODULES="numpy cython scipy transvar bx-python pyvcf cyvcf cyvcf2 biopython crossmap pandas"
-ENV R_BASE_VERSION 3.3.2
+ENV R_BASE_VERSION 3.3.3
USER root
WORKDIR /
diff --git a/src/R/pcgrr2/R/utils.R b/src/R/pcgrr2/R/utils.R
index 6fee795c..1291b1e8 100644
--- a/src/R/pcgrr2/R/utils.R
+++ b/src/R/pcgrr2/R/utils.R
@@ -311,8 +311,8 @@ cna_segment_annotation <- function(cna_file, logR_threshold_amplification, logR_
names(civic_cna_biomarkers) <- toupper(names(civic_cna_biomarkers))
civic_cna_biomarkers <- dplyr::rename(civic_cna_biomarkers, GENE = GENESYMBOL, CNA_TYPE = CIVIC_CONSEQUENCE, DESCRIPTION = EVIDENCE_DESCRIPTION, CITATION = PUBMED_HTML_LINK)
- cna_biomarkers <- NULL
- cna_biomarker_segments <- NULL
+ cna_biomarkers <- data.frame()
+ cna_biomarker_segments <- data.frame()
if(!is.null(tsgene_homozygous_deletion)){
if(nrow(tsgene_homozygous_deletion) > 0){
civic_biomarker_hits1 <- dplyr::inner_join(tsgene_homozygous_deletion, civic_cna_biomarkers, by=c("GENE","CNA_TYPE"))
@@ -327,9 +327,11 @@ cna_segment_annotation <- function(cna_file, logR_threshold_amplification, logR_
}
if(!is.null(cna_biomarkers)){
- cna_biomarkers <- cna_biomarkers[c("CHROMOSOME","GENE","CNA_TYPE","EVIDENCE_LEVEL","CLINICAL_SIGNIFICANCE","EVIDENCE_TYPE","DESCRIPTION","DISEASE_NAME","EVIDENCE_DIRECTION","DRUG_NAMES","CITATION","RATING","GENE_NAME","CANCER_CENSUS_SOMATIC","KEGG_PATHWAY","ANTINEOPLASTIC_DRUG_INTERACTIONS","SEGMENT_LENGTH", "SEGMENT","LogR")]
- cna_biomarkers <- cna_biomarkers %>% dplyr::arrange(EVIDENCE_LEVEL,RATING)
- cna_biomarker_segments <- dplyr::select(cna_biomarkers, SEGMENT, LogR) %>% dplyr::distinct()
+ if(nrow(cna_biomarkers) > 0){
+ cna_biomarkers <- cna_biomarkers[c("CHROMOSOME","GENE","CNA_TYPE","EVIDENCE_LEVEL","CLINICAL_SIGNIFICANCE","EVIDENCE_TYPE","DESCRIPTION","DISEASE_NAME","EVIDENCE_DIRECTION","DRUG_NAMES","CITATION","RATING","GENE_NAME","CANCER_CENSUS_SOMATIC","KEGG_PATHWAY","ANTINEOPLASTIC_DRUG_INTERACTIONS","SEGMENT_LENGTH", "SEGMENT","LogR")]
+ cna_biomarkers <- cna_biomarkers %>% dplyr::arrange(EVIDENCE_LEVEL,RATING)
+ cna_biomarker_segments <- dplyr::select(cna_biomarkers, SEGMENT, LogR) %>% dplyr::distinct()
+ }
}
cna_data <- list(ranked_segments = cna_segments_filtered, oncogene_amplified = oncogene_amplified, tsgene_homozygous_deletion = tsgene_homozygous_deletion,cna_df_for_print = df_print_sorted, cna_biomarkers = cna_biomarkers, cna_biomarker_segments = cna_biomarker_segments)
diff --git a/src/R/pcgrr2_0.1.0.tar.gz b/src/R/pcgrr2_0.1.0.tar.gz
index e7a20197..e14a4d29 100644
Binary files a/src/R/pcgrr2_0.1.0.tar.gz and b/src/R/pcgrr2_0.1.0.tar.gz differ
diff --git a/src/pcgr.tgz b/src/pcgr.tgz
index e31de265..6b96cce1 100644
Binary files a/src/pcgr.tgz and b/src/pcgr.tgz differ