Installation
IMPORTANT NOTE: geneOncoX requires that you have R version 4.1 or higher installed
if (!("remotes" %in% installed.packages())) {
install.packages("remotes")
}
remotes::install_github('sigven/geneOncoX')Get basic gene annotations
This shows how to retrieve basic gene and cancer relevant gene annotations, including how to retrieve tumor suppressor genes, proto-oncogenes, and predicted cancer driver genes.
library(geneOncoX)
## load the data
download_dir <- tempdir()
gene_basic <- get_basic(cache_dir = download_dir)
#> INFO [2024-04-24 14:22:24] Downloading remote dataset from Google Drive to cache_dir
#> INFO [2024-04-24 14:22:28] Reading from cache_dir = '/tmp/Rtmp89Ovrp', argument force_download = FALSE
#> INFO [2024-04-24 14:22:28] Object 'gene_basic' sucessfully loaded
#> INFO [2024-04-24 14:22:28] md5 checksum is valid: 727c7777e2705d1fe196fffaae302f00
#> INFO [2024-04-24 14:22:28] Retrieved 65000 records
## Number of records
nrow(gene_basic$records)
#> [1] 65000
## Show metadata for underlying resources
gene_basic$metadata
#> source
#> 1 CancerMine
#> 2 Network of Cancer Genes
#> 3 IntoGen
#> 4 Cancer Gene Census
#> 5 NCBI
#> 6 Bailey et al., Cell, 2018
#> 7 Sanchez-Vega et al., Cell, 2018
#> 8 F1CDx
#> 9 TSO500
#> 10 DNA repair gene database
#> 11 dbNSFP
#> source_description
#> 1 Predicted tumor suppressors/oncogenes/cancer drivers from text mining of literature
#> 2 Tumor suppressors/oncogenes
#> 3 Predicted cancer driver genes
#> 4 Collection of cancer-relevant genes (soma/germline), tumor suppressors/oncogene annotations
#> 5 Basic gene identifiers (symbol, entrez ID, HGNC ID, name, synonyms, function summary)
#> 6 Predicted cancer driver genes / likely false positive driver genes
#> 7 Collection of curated signalling pathway annotations
#> 8 Collection of genes covered by Foundation One's F1CDx cancer gene panel
#> 9 Collection of genes covered by Illumina's TSO500 cancer gene panel
#> 10 Collection of genes involved in DNA repair
#> 11 Gene indispensability prediction, loss-of-function intolerance predictions etc.
#> source_url
#> 1 http://bionlp.bcgsc.ca/cancermine/
#> 2 http://ncg.kcl.ac.uk/
#> 3 https://www.intogen.org/download
#> 4 https://cancer.sanger.ac.uk/census
#> 5 https://www.ncbi.nlm.nih.gov/gene/
#> 6 https://pubmed.ncbi.nlm.nih.gov/29625053/
#> 7 https://pubmed.ncbi.nlm.nih.gov/29625050/
#> 8 https://www.foundationmedicine.com/test/foundationone-cdx
#> 9 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0260089
#> 10 https://panelapp.genomicsengland.co.uk/panels/256/
#> 11 https://sites.google.com/site/jpopgen/dbNSFP
#> source_citation source_version
#> 1 Lever et al., Nat Methods, 2019; 31110280 v50 (March 2023)
#> 2 Repana et al., Genome Biol, 2019; 30606230 v7.1
#> 3 Martínez-Jiménez et al., Nat Rev Cancer, 2020; 32778778 2023.05.31
#> 4 Sondka et al., Nat Rev Cancer, 2018; 30293088 v99
#> 5 Brown et al., Nucleic Acids Res, 2015; 25355515 2024-03-31
#> 6 Bailey et al., Cell, 2018; 29625053 <NA>
#> 7 Sanchez-Vega et al., Cell, 2018; 29625050 <NA>
#> 8 <NA> <NA>
#> 9 <NA> <NA>
#> 10 Woods et al., Science, 2001; 11181991 v1.1
#> 11 Liu et al., Genome Med, 2020; 33261662 v4.5
#> source_abbreviation
#> 1 cancermine
#> 2 ncg
#> 3 intogen
#> 4 cgc
#> 5 ncbi
#> 6 bailey2018
#> 7 sanchezvega2018
#> 8 foundation_one
#> 9 illumina
#> 10 woods_dnarepair
#> 11 dbnsfp
#> source_license
#> 1 CC0 1.0
#> 2 Free/open access
#> 3 CC0 1.0
#> 4 Free for non-commercial, academic use. Commercial use requires licensing
#> 5 NCBI data usage policies
#> 6 Free/open access
#> 7 Free/open access
#> 8 Free/open access
#> 9 Free/open access
#> 10 Free/open access
#> 11 Free for non-commercial, academic use.
#> source_license_url
#> 1 https://creativecommons.org/publicdomain/zero/1.0/
#> 2 <NA>
#> 3 https://creativecommons.org/publicdomain/zero/1.0/
#> 4 https://cancer.sanger.ac.uk/cosmic/license
#> 5 https://www.ncbi.nlm.nih.gov/home/about/policies/#data
#> 6 <NA>
#> 7 <NA>
#> 8 <NA>
#> 9 <NA>
#> 10 <NA>
#> 11 <NA>Get classified tumor suppressor genes
## Get tumor suppressor genes - as indicated from either
## Cancer Gene Census (CGC) or Network of Cancer Genes (NCG)
## - show literature support from CancerMine
tsg <- gene_basic$records |>
dplyr::filter(cgc_tsg == TRUE |
ncg_tsg == TRUE) |>
dplyr::select(symbol, entrezgene, name, gene_biotype,
cgc_tsg, ncg_tsg, cancermine_cit_links_tsg,
ncbi_function_summary) |>
dplyr::rename(support_cancermine = cancermine_cit_links_tsg)
## Make as datatable
tsg_table <- DT::datatable(
tsg,
escape = FALSE,
extensions = c("Buttons", "Responsive"),
width = "100%",
options = list(
buttons = c("csv", "excel"), dom = "Bfrtip"))Get classified proto-oncogenes
## Get proto-oncogenes - as indicated from either
## Cancer Gene Census (CGC) or Network of Cancer Genes (NCG),
## - show literature support from CancerMine
oncogene <- gene_basic$records |>
dplyr::filter(cgc_oncogene == TRUE |
ncg_oncogene == TRUE) |>
dplyr::select(symbol, entrezgene,
name, gene_biotype,
cgc_tsg, ncg_tsg,
cancermine_cit_links_oncogene,
ncbi_function_summary) |>
dplyr::rename(support_cancermine =
cancermine_cit_links_oncogene)
## Make as datatable
oncogene_table <- DT::datatable(
oncogene,
escape = FALSE,
extensions = c("Buttons", "Responsive"),
width = "100%",
options = list(
buttons = c("csv", "excel"),
dom = "Bfrtip"))Get predicted cancer driver genes
## Get predicted cancer driver genes - as indicated from either
## - Cancer Gene Census (CGC) - tier1/tier2
## - Network of Cancer Genes (NCG) - canonical drivers
## - IntOGen mutational driver catalogue
## - TCGA's PanCancer driver prediction (Bailey et al., Cell, 2018)
##
## Rank hits by how many sources that contribute to classification
##
cancer_driver <- gene_basic$records |>
dplyr::filter(cgc_driver_tier1 == TRUE |
cgc_driver_tier2 == TRUE |
intogen_driver == TRUE |
ncg_driver == TRUE |
tcga_driver == TRUE) |>
dplyr::mutate(driver_score = 0) |>
dplyr::mutate(driver_score = dplyr::if_else(
cgc_driver_tier1 == T,
driver_score + 2,
as.numeric(driver_score))) |>
dplyr::mutate(driver_score = dplyr::if_else(
cgc_driver_tier2 == T,
driver_score + 1,
as.numeric(driver_score))) |>
dplyr::mutate(driver_score = dplyr::if_else(
intogen_driver == T,
driver_score + 1,
as.numeric(driver_score))) |>
dplyr::mutate(driver_score = dplyr::if_else(
ncg_driver == T,
driver_score + 1,
as.numeric(driver_score))) |>
dplyr::mutate(driver_score = dplyr::if_else(
tcga_driver == T,
driver_score + 1,
as.numeric(driver_score))) |>
dplyr::select(symbol, entrezgene,
name, gene_biotype,
cgc_driver_tier1,
cgc_driver_tier2,
intogen_driver,
#intogen_phenotype,
intogen_role,
ncg_driver,
ncg_phenotype,
tcga_driver,
driver_score,
cancermine_cit_links_driver,
ncbi_function_summary) |>
dplyr::rename(support_cancermine =
cancermine_cit_links_driver) |>
dplyr::arrange(dplyr::desc(driver_score), symbol)
## Make as datatable
driver_table <- DT::datatable(
cancer_driver,
escape = FALSE,
extensions = c("Buttons", "Responsive"),
width = "100%",
options = list(
buttons = c("csv", "excel"),
dom = "Bfrtip"))Get cancer predisposition genes
This show how to retrieve known cancer predisposition genes, utilizing multiple sources, including Cancer Gene Census, Genomics England PanelApp, TCGA’s pan-cancer study of germline variants, and other/user-curated entries.
## load the data
gene_predisposition <- get_predisposition(cache_dir = download_dir)
#> INFO [2024-04-24 14:22:29] Downloading remote dataset from Google Drive to cache_dir
#> INFO [2024-04-24 14:22:30] Reading from cache_dir = '/tmp/Rtmp89Ovrp', argument force_download = FALSE
#> INFO [2024-04-24 14:22:30] Object 'gene_predisposition' sucessfully loaded
#> INFO [2024-04-24 14:22:30] md5 checksum is valid: 0af770a658c13cde90520ed35492566e
#> INFO [2024-04-24 14:22:30] Retrieved 609 records
## Number of cancer predisposition genes
nrow(gene_predisposition$records |> dplyr::filter(
!stringr::str_detect(cpg_source, "^ACMG_SF$")
))
#> [1] 562
## Get statistics regarding how reference sources on
## cancer predisposition genes contribute
##
## CGC - Cancer Gene Census (germline)
## PANEL_APP - N = 43 gene panels for inherited cancer conditions/
## cancer syndromes (Genomics England PanelApp)
## CURATED_OTHER - curated/user-contributed genes
## TCGA_PANCAN_2018 - TCGA's pancancer analysis of
## germline variants in cancer
## (Huang et al., Cell, 2019)
## ACMG_SF - Secondary findings list (ACMG, v3.1)
plyr::count(gene_predisposition$records$cpg_source) |>
dplyr::arrange(dplyr::desc(freq)) |>
dplyr::filter(x != "ACMG_SF")
#> x freq
#> 1 PANEL_APP 280
#> 2 CURATED_OTHER 106
#> 3 CGC&PANEL_APP&TCGA_PANCAN_2018 57
#> 4 PANEL_APP&TCGA_PANCAN_2018 45
#> 5 ACMG_SF&CGC&PANEL_APP&TCGA_PANCAN_2018 28
#> 6 CGC 14
#> 7 TCGA_PANCAN_2018 13
#> 8 CGC&PANEL_APP 8
#> 9 CGC&TCGA_PANCAN_2018 5
#> 10 ACMG_SF&PANEL_APP 2
#> 11 ACMG_SF&TCGA_PANCAN_2018 2
#> 12 ACMG_SF&CGC 1
#> 13 ACMG_SF&CGC&TCGA_PANCAN_2018 1
## Cancer predisposition metadata
gene_predisposition$metadata
#> source
#> 1 Genomics England PanelApp
#> 2 Cancer Gene Census
#> 3 NCBI
#> 4 Huang et al., Cell, 2018
#> 5 Maxwell et al., Am J Hum Genet, 2016
#> 6 Cancer predisposition genes - curated/other
#> 7 ACMG - secondary findings
#> source_description
#> 1 Collection of > 40 dedicated gene panels for various inherited cancer conditions and syndromes
#> 2 Collection of cancer-relevant genes (soma/germline), tumor suppressors/oncogene annotations
#> 3 Basic gene identifiers (symbol, entrez ID, HGNC ID, name, synonyms, function summary)
#> 4 Collection of cancer predisposition genes screened in TCGA's pancancer study
#> 5 Mechanisms of inheritance for cancer predisposition genes
#> 6 Candidate cancer predisposition genes - contributed e.g. by CPSR users
#> 7 Genes recommended for reporting of incidental findings in clinical exome sequencing
#> source_url
#> 1 https://panelapp.genomicsengland.co.uk/
#> 2 https://cancer.sanger.ac.uk/census
#> 3 https://www.ncbi.nlm.nih.gov/gene/
#> 4 https://pubmed.ncbi.nlm.nih.gov/29625052/
#> 5 https://pubmed.ncbi.nlm.nih.gov/27153395/
#> 6 <NA>
#> 7 https://pubmed.ncbi.nlm.nih.gov/35802134/
#> source_citation source_version
#> 1 Martin et al., Nat Genet, 2019; 31676867 v1 (API)
#> 2 Sondka et al., Nat Rev Cancer, 2018; 30293088 v99
#> 3 Brown et al., Nucleic Acids Res, 2015; 25355515 2024-03-31
#> 4 Huang et al., Cell, 2018; 29625052 <NA>
#> 5 Maxwell et al., Am J Hum Genet, 2016; 27153395 <NA>
#> 6 <NA> 20221128
#> 7 Miller et al., Genet Med, 2023; 37347242 v3.2
#> source_abbreviation
#> 1 gepa
#> 2 cgc
#> 3 ncbi
#> 4 tcga_pancan_2018
#> 5 maxwell2016
#> 6 cpg_other
#> 7 acmg_sf
#> source_license
#> 1 Commercial use requires separate agreement
#> 2 Free for non-commercial, academic use. Commercial use requires licensing
#> 3 NCBI data usage policies
#> 4 Free/open access
#> 5 Free/open access
#> 6 Free/open access
#> 7 Free/open access
#> source_license_url
#> 1 https://panelapp.genomicsengland.co.uk/media/files/GEL_-_PanelApp_Terms_of_Use_December_2019.pdf
#> 2 https://cancer.sanger.ac.uk/cosmic/license
#> 3 https://www.ncbi.nlm.nih.gov/home/about/policies/#data
#> 4 <NA>
#> 5 <NA>
#> 6 <NA>
#> 7 https://www.ncbi.nlm.nih.gov/clinvar/docs/acmg/Get cancer gene panels
This shows how to retrieve genes from cancer gene panels defined in Genomics England PanelApp.
## load the data
gene_panels <- get_panels(cache_dir = download_dir)
#> INFO [2024-04-24 14:22:30] Downloading remote dataset from Google Drive to cache_dir
#> INFO [2024-04-24 14:22:31] Reading from cache_dir = '/tmp/Rtmp89Ovrp', argument force_download = FALSE
#> INFO [2024-04-24 14:22:31] Object 'gene_panels' sucessfully loaded
#> INFO [2024-04-24 14:22:31] md5 checksum is valid: fa9c4edbc0cb140897b071890392d9f9
#> INFO [2024-04-24 14:22:31] Retrieved 2608 records
## panel data for genome build grch38
panel_data <- gene_panels$records |>
dplyr::filter(genome_build == "grch38")
## show number of genes in each panel
gene_freq <- as.data.frame(panel_data |>
dplyr::group_by(gepa_panel_name) |>
dplyr::summarise(n = dplyr::n()) |>
dplyr::arrange(desc(n)) |>
dplyr::rename(panel_name = gepa_panel_name))
gene_freq
#> panel_name n
#> 1 DNA Repair Genes pertinent cancer susceptibility 178
#> 2 Childhood solid tumours 121
#> 3 Haematological malignancies cancer susceptibility 107
#> 4 Adult solid tumours cancer susceptibility 105
#> 5 Haematological malignancies for rare disease 89
#> 6 Childhood solid tumours cancer susceptibility 85
#> 7 Adult solid tumours for rare disease 58
#> 8 Multiple monogenic benign skin tumours 46
#> 9 Sarcoma susceptibility 44
#> 10 Sarcoma cancer susceptibility 33
#> 11 GI tract tumours 31
#> 12 Neurofibromatosis Type 1 30
#> 13 Inherited non-medullary thyroid cancer 29
#> 14 Familial breast cancer 27
#> 15 Inherited ovarian cancer (without breast cancer) 26
#> 16 Familial Tumours Syndromes of the central & peripheral Nervous system 21
#> 17 Inherited phaeochromocytoma and paraganglioma 20
#> 18 Inherited polyposis and early onset colorectal cancer - germline testing 19
#> 19 Familial rhabdomyosarcoma 18
#> 20 Inherited renal cancer 18
#> 21 Inherited predisposition to acute myeloid leukaemia (AML) 16
#> 22 Multiple endocrine tumours 16
#> 23 Familial prostate cancer 14
#> 24 Colorectal cancer pertinent cancer susceptibility 13
#> 25 Genodermatoses with malignancies 13
#> 26 Head and neck cancer pertinent cancer susceptibility 12
#> 27 Inherited pancreatic cancer 12
#> 28 Neuroendocrine cancer pertinent cancer susceptibility 12
#> 29 Renal cancer pertinent cancer susceptibility 10
#> 30 Ovarian cancer pertinent cancer susceptibility 9
#> 31 Familial melanoma 8
#> 32 Brain cancer pertinent cancer susceptibility 7
#> 33 Breast cancer pertinent cancer susceptibility 7
#> 34 Inherited predisposition to GIST 7
#> 35 Parathyroid Cancer 7
#> 36 Endometrial cancer pertinent cancer susceptibility 6
#> 37 Inherited MMR deficiency (Lynch syndrome) 5
#> 38 Prostate cancer pertinent cancer susceptibility 5
#> 39 Thyroid cancer pertinent cancer susceptibility 5
#> 40 Bladder cancer pertinent cancer susceptibility 4
#> 41 Upper gastrointestinal cancer pertinent cancer susceptibility 4
#> 42 Melanoma pertinent cancer susceptibility 3
#> 43 Inherited susceptibility to acute lymphoblastoid leukaemia (ALL) 2
#> 44 Rhabdoid tumour predisposition 2Get gene aliases
This shows how to retrieve ambiguous and unambiguous gene aliases (i.e. with respect to primary gene symbols).
## load the data
gene_alias <- get_alias(cache_dir = download_dir)
#> INFO [2024-04-24 14:22:31] Downloading remote dataset from Google Drive to cache_dir
#> INFO [2024-04-24 14:22:37] Reading from cache_dir = '/tmp/Rtmp89Ovrp', argument force_download = FALSE
#> INFO [2024-04-24 14:22:37] Object 'gene_alias' sucessfully loaded
#> INFO [2024-04-24 14:22:37] md5 checksum is valid: ed63777a9aae1d0792364ad37d938149
#> INFO [2024-04-24 14:22:37] Retrieved 135493 records
## number of gene synonyms that are ambiguous
nrow(dplyr::filter(gene_alias$records, ambiguous == TRUE))
#> [1] 7067
## show structure of alias records
head(gene_alias$records)
#> alias symbol entrezgene n_primary_map ambiguous source
#> 1 'C-K-RAS KRAS 3845 1 FALSE NCBI
#> 2 (FM-3) NMUR1 10316 1 FALSE NCBI
#> 3 (IV)-44 IGHVIV-44-1 28337 1 FALSE NCBI
#> 4 (ppGpp)ase HDDC3 374659 1 FALSE NCBI
#> 5 A-116A10.1 NAE1 8883 1 FALSE NCBI
#> 6 A-152E5.1 CCL22 6367 1 FALSE NCBI
#> is_primary_symbol
#> 1 FALSE
#> 2 FALSE
#> 3 FALSE
#> 4 FALSE
#> 5 FALSE
#> 6 FALSEGet GENCODE transcripts
This shows how to retrieve GENCODE transcripts for
grch37 and grch38.
## load the data
gene_gencode <- get_gencode(cache_dir = download_dir)
#> INFO [2024-04-24 14:22:37] Downloading remote dataset from Google Drive to cache_dir
#> INFO [2024-04-24 14:22:44] Reading from cache_dir = '/tmp/Rtmp89Ovrp', argument force_download = FALSE
#> INFO [2024-04-24 14:22:44] Object 'gene_gencode_45_111' sucessfully loaded
#> INFO [2024-04-24 14:22:44] md5 checksum is valid: 6f4444b05d5388b55c2a0c4e295c3d42
#> INFO [2024-04-24 14:22:44] Retrieved records
## number of transcript records - grch37
nrow(gene_gencode$records$grch37)
#> [1] 196520
## number of transcript records - grch38
nrow(gene_gencode$records$grch38)
#> [1] 252930
## show colnames for transcript records
colnames(gene_gencode$records$grch38)
#> [1] "chrom" "start"
#> [3] "end" "transcript_start"
#> [5] "transcript_end" "strand"
#> [7] "ensembl_gene_id" "ensembl_transcript_id"
#> [9] "ensembl_transcript_id_full" "ensembl_protein_id"
#> [11] "symbol" "hgnc_id"
#> [13] "entrezgene" "name"
#> [15] "gene_biotype" "transcript_biotype"
#> [17] "tag" "refseq_protein_id"
#> [19] "refseq_transcript_id" "mane_select"
#> [21] "mane_plus_clinical" "principal_isoform_flag"
#> [23] "uniprot_acc" "uniprot_id"
#> [25] "ensembl_version" "gencode_version"
#> [27] "uniprot_version"
## show metadata for underlying resources
gene_gencode$metadata
#> source
#> 1 GENCODE
#> 2 Ensembl Biomart
#> 3 UniprotKB
#> 4 APPRIS
#> source_description
#> 1 Human gene transcripts
#> 2 API for retrieval of gene and transcript cross-references (MANE, RefSeq)
#> 3 UniProt identifiers and accessions with cross-references to Ensembl
#> 4 Prinicipal transcript isoform annotation
#> source_url
#> 1 https://www.gencodegenes.org/
#> 2 https://www.ensembl.org/biomart/martview
#> 3 https://www.uniprot.org
#> 4 https://apprisws.bioinfo.cnio.es/landing_page/
#> source_citation source_version
#> 1 Frankish et al., Nucleic Acids Res, 2021; 33270111 45
#> 2 Cunningham et al., Nucleic Acids Res, 2022; 34791404 111
#> 3 UniProt Consortium, Nucleic Acids Res, 2021; 33237286 2024_02
#> 4 Rodriguez et al, Nucleic Acids Res, 2022; 34755885 2024-03-31
#> source_abbreviation source_license
#> 1 gencode Free/open access
#> 2 ensembl EMBL-EBI terms of use
#> 3 uniprot CC BY 4.0
#> 4 appris Free/open access
#> source_license_url
#> 1 <NA>
#> 2 https://www.ebi.ac.uk/about/terms-of-use
#> 3 https://creativecommons.org/licenses/by/4.0/
#> 4 <NA>Get DNA repair genes
## load the data
gene_dna_repair <- gene_basic$records |>
dplyr::filter(!is.na(woods_dnarepair_class)) |>
dplyr::select(symbol, woods_dnarepair_class,
woods_dnarepair_activity)
## count number of genes in each class
dna_repair_class_freq <- as.data.frame(
gene_dna_repair |>
dplyr::group_by(woods_dnarepair_class) |>
dplyr::summarise(n = dplyr::n()) |>
dplyr::arrange(desc(n)) |>
dplyr::rename(dna_repair_class = woods_dnarepair_class))
dna_repair_class_freq
#> dna_repair_class
#> 1 Nucleotide excision repair (NER)
#> 2 Homologous recombination
#> 3 Fanconi anemia
#> 4 DNA polymerases (catalytic subunits)
#> 5 Other conserved DNA damage response genes
#> 6 Base excision repair (BER)
#> 7 Ubiquitination and modification
#> 8 Mismatch excision repair (MMR)
#> 9 Other identified genes with known or suspected DNA repair function
#> 10 Editing and processing nucleases
#> 11 Non-homologous end-joining
#> 12 Other BER and strand break joining factors
#> 13 Genes defective in diseases associated with sensitivity to DNA damaging agents
#> 14 Chromatin Structure and Modification
#> 15 Direct reversal of damage
#> 16 Modulation of nucleotide pools
#> 17 Poly(ADP-ribose) polymerase (PARP) enzymes that bind to DNA
#> 18 Repair of DNA-topoisomerase crosslinks
#> 19 Unknown
#> n
#> 1 28
#> 2 21
#> 3 17
#> 4 15
#> 5 15
#> 6 11
#> 7 11
#> 8 10
#> 9 9
#> 10 8
#> 11 7
#> 12 6
#> 13 5
#> 14 3
#> 15 3
#> 16 3
#> 17 3
#> 18 2
#> 19 1Get TSO500 genes
## load the data
gene_tso500 <- gene_basic$records |>
dplyr::filter(!is.na(illumina_tso500))
## number of genes covered by TSO500 panel
nrow(gene_tso500)
#> [1] 523
## types of variant types covered
illumina_tso500_variant_freq <- as.data.frame(
gene_tso500 |>
dplyr::group_by(illumina_tso500) |>
dplyr::summarise(n = dplyr::n()) |>
dplyr::arrange(desc(n)))
illumina_tso500_variant_freq
#> illumina_tso500 n
#> 1 SNV_INDEL 433
#> 2 CNA_GAIN,SNV_INDEL 33
#> 3 RNA_FUSION,SNV_INDEL 31
#> 4 CNA_GAIN,RNA_FUSION,SNV_INDEL 22
#> 5 CNA_LOSS,RNA_FUSION,SNV_INDEL 2
#> 6 CNA_LOSS,SNV_INDEL 2Session Info
# set eval = FALSE if you don't want this info (useful for reproducibility)
# to appear
sessionInfo()
#> R version 4.3.3 (2024-02-29)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
#>
#> locale:
#> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
#> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
#> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] geneOncoX_0.8.6
#>
#> loaded via a namespace (and not attached):
#> [1] jsonlite_1.8.8 dplyr_1.1.4 compiler_4.3.3 crayon_1.5.2
#> [5] Rcpp_1.0.12 tidyselect_1.2.1 stringr_1.5.1 jquerylib_0.1.4
#> [9] systemfonts_1.0.6 textshaping_0.3.7 yaml_2.3.8 fastmap_1.1.1
#> [13] plyr_1.8.9 R6_2.5.1 generics_0.1.3 curl_5.2.1
#> [17] knitr_1.46 htmlwidgets_1.6.4 tibble_3.2.1 desc_1.4.3
#> [21] bslib_0.7.0 pillar_1.9.0 rlang_1.1.3 DT_0.33
#> [25] utf8_1.2.4 stringi_1.8.3 cachem_1.0.8 lgr_0.4.4
#> [29] xfun_0.43 fs_1.6.3 sass_0.4.9 memoise_2.0.1
#> [33] cli_3.6.2 withr_3.0.0 pkgdown_2.0.9 magrittr_2.0.3
#> [37] crosstalk_1.2.1 digest_0.6.35 lifecycle_1.0.4 vctrs_0.6.5
#> [41] evaluate_0.23 gargle_1.5.2 glue_1.7.0 ragg_1.3.0
#> [45] googledrive_2.1.1 fansi_1.0.6 httr_1.4.7 rmarkdown_2.26
#> [49] purrr_1.0.2 tools_4.3.3 pkgconfig_2.0.3 htmltools_0.5.8.1References
Bailey, Matthew H, Collin Tokheim, Eduard Porta-Pardo, Sohini Sengupta,
Denis Bertrand, Amila Weerasinghe, Antonio Colaprico, et al. 2018.
“Comprehensive Characterization of Cancer Driver Genes and
Mutations.” Cell 173 (2): 371–385.e18. http://dx.doi.org/10.1016/j.cell.2018.02.060.
Lever, Jake, Eric Y Zhao, Jasleen Grewal, Martin R Jones, and Steven J M
Jones. 2019. “CancerMine: A Literature-Mined Resource
for Drivers, Oncogenes and Tumor Suppressors in Cancer.” Nat.
Methods 16 (6): 505–7. http://dx.doi.org/10.1038/s41592-019-0422-y.
Liu, Xiaoming, Chang Li, Chengcheng Mou, Yibo Dong, and Yicheng Tu.
2020. “dbNSFP V4: A Comprehensive
Database of Transcript-Specific Functional Predictions and Annotations
for Human Nonsynonymous and Splice-Site SNVs.”
Genome Med. 12 (1): 103. http://dx.doi.org/10.1186/s13073-020-00803-9.
Martin, Antonio Rueda, Eleanor Williams, Rebecca E Foulger, Sarah Leigh,
Louise C Daugherty, Olivia Niblock, Ivone U S Leong, et al. 2019.
“PanelApp Crowdsources Expert Knowledge to Establish
Consensus Diagnostic Gene Panels.” Nat. Genet. 51
(November): 1560–65. http://dx.doi.org/10.1038/s41588-019-0528-2.
Martı́nez-Jiménez, Francisco, Ferran Muiños, Inés Sentı́s, Jordi Deu-Pons,
Iker Reyes-Salazar, Claudia Arnedo-Pac, Loris Mularoni, et al. 2020.
“A Compendium of Mutational Cancer Driver Genes.” Nat.
Rev. Cancer 20 (10): 555–72. https://www.nature.com/articles/s41568-020-0290-x.
Repana, Dimitra, Joel Nulsen, Lisa Dressler, Michele Bortolomeazzi,
Santhilata Kuppili Venkata, Aikaterini Tourna, Anna Yakovleva, Tommaso
Palmieri, and Francesca D Ciccarelli. 2019. “The Network of Cancer
Genes (NCG): A Comprehensive Catalogue of Known and
Candidate Cancer Genes from Cancer Sequencing Screens.”
Genome Biol. 20 (1): 1. http://dx.doi.org/10.1186/s13059-018-1612-0.
Sondka, Zbyslaw, Sally Bamford, Charlotte G Cole, Sari A Ward, Ian
Dunham, and Simon A Forbes. 2018. “The COSMIC Cancer
Gene Census: Describing Genetic Dysfunction Across All Human
Cancers.” Nat. Rev. Cancer 18 (11): 696–705. http://dx.doi.org/10.1038/s41568-018-0060-1.
Wood, R D, M Mitchell, J Sgouros, and T Lindahl. 2001. “Human
DNA Repair Genes.” Science 291 (5507):
1284–89. http://dx.doi.org/10.1126/science.1056154.