Data collection

The following tables summarize the data sources collected by this database and their brief descriptions, including external databases, PubMed literature, prediction algorithms, etc.

•   Cancer type abbreviation

  Abbreviation	Cancer type	Tissue type	Abbreviation	Cancer type	Tissue type
  ALL	Acute Leukemia	Blood and bone marrow	AML	Acute Myeloid Leukemia	Blood and bone marrow
  LAML	Acute Myeloid Leukemia	Blood and bone marrow	ACC	Adrenocortical Carcinoma	Adrenal cortex
  B-ALL	B Cell Acute Leukemia	Blood and bone marrow	BPLL	B-cell Prolymphocytic Leukemia	Blood and bone marrow
  LIAD	Benign Liver Tumour	Liver	BTCA	Biliary Tract Carcinomas	Biliary tract
  BLCA	Bladder Cancer	Bladder	BOCA	Bone Cancer	Bones
  LGG	Brain Lower Grade Glioma	Brain	BRCA	Breast Invasive Carcinoma	Breast
  CESC	Cervical Squamous Cell Carcinoma	Cervix	CHOL	Cholangiocarcinoma	Bile ducts
  CLLE	Chronic Lymphocytic Leukemia	Blood and bone marrow	CLL	Chronic Lymphocytic Leukemia	Blood and bone marrow
  CMDI	Chronic Myeloid Disorders	Blood and bone marrow	CML	Chronic Myeloid Leukemia	Blood and bone marrow
  COAD	Colon Adenocarcinoma	Colon	COADREAD	Colon and Rectal Cancer	Colon and rectum
  COCA	Colorectal Cancer	Colon and rectum	EOPC	Early Onset Prostate Cancer	Prostate
  ETMR	Embryonal Carcinoma	Germ cells	ESAD	Esophageal Adenocarcinoma	Esophagus
  ESCA	Esophageal Carcinoma	Esophagus	GACA	Gastric Cancer	Stomach
  GBM	Glioblastoma Multiforme	Brain	HNSC	Head and Neck Squamous Cell Carcinoma	Head and neck
  HGG	High Grade Glioma	Brain	KICH	Kidney Chromophobe	Kidney
  KIRC	Kidney Renal Clear Cell Carcinoma	Kidney	KIRP	Kidney Renal Papillary Cell Carcinoma	Kidney
  LICA	Liver Cancer	Liver	LINC	Liver Cancer - NCC	Liver
  LIRI	Liver Cancer - RIKEN	Liver	LIHC	Liver Hepatocellular Carcinoma	Liver
  LIHM	Liver Hepatocellular Macronodules	Liver	LUAD	Lung Adenocarcinoma	Lung
  LUSC	Lung Squamous Cell Carcinoma	Lung	DLBC	Lymphoid Neoplasm Diffuse Large B-cell Lymphoma	Lymph nodes
  MALY	Malignant Lymphoma	Lymph nodes	MB	Medulloblastoma	Brain
  MELA	Melanoma	Skin	MESO	Mesothelioma	Mesothelial tissue (lining of organs)
  NACA	Nasopharyngeal Cancer	Nasopharynx	NKTL	Natural Killer/T-cell Lymphoma	Lymph nodes
  NBL	Neuroblastoma	Nerve tissue	NSCLC	Non Small Cell Lung Cancer	Lung
  ORCA	Oral Cancer	Oral cavity	OV	Ovarian Serous Cystadenocarcinoma	Ovary
  PANCAN	PanCancer	Various tissues	PAAD	Pancreatic Adenocarcinoma	Pancreas
  PACA	Pancreatic Cancer	Pancreas	PAEN	Pancreatic Cancer Endocrine neoplasms	Pancreas
  PBCA	Pediatric Brain Cancer	Brain	PEME	Pediatric Medulloblastoma	Brain
  PCPG	Pheochromocytoma and Paraganglioma	Adrenal gland	PA	Pilocytic Astrocytoma	Brain
  PRAD	Prostate Adenocarcinoma	Prostate	READ	Rectum Adenocarcinoma	Rectum
  RECA	Renal Cancer	Kidney	RT	Rhabdoid Tumors	Various tissues (primarily affects children)
  SARC	Sarcoma	Connective tissues (e.g., bone, muscle, cartilage)	SKCA	Skin Adenocarcinoma	Skin
  SKCM	Skin Cutaneous Melanoma	Skin	LMS	Soft Tissue Cancer - Leiomyosarcoma	Soft tissues (e.g., smooth muscle)
  STAD	Stomach Adenocarcinoma	Stomach	T-ALL	T Cell Acute Leukemia	Blood and bone marrow
  TGCT	Testicular Germ Cell Tumor	Testicles	THYM	Thymoma	Thymus
  THCA	Thyroid Carcinoma	Thyroid gland	UTCA	Uterine Cancer - Carcinosarcoma	Uterus
  UCS	Uterine Carcinosarcoma	Uterus	UCEC	Uterine Corpus Endometrial Carcinoma	Uterus (endometrium)
  UVM	Uveal Melanoma	Eye (uveal tract)	WT	Wilms Tumor	Kidney
  	Adenomatoid Odontogenic Tumor	Jaw (odontogenic tissue)		Ameloblastoma	Jaw (odontogenic tissue)
  	Aneurysm	Blood vessels		Angiosarcoma	Blood vessels or lymphatic vessels
  	Basal Cell Carcinoma	Skin		Brain Cancer	Brain
  	Cardiac Cancer	Heart		Chondrosarcoma	Cartilage
  	Chordoma	Spine or skull base		Choriocarcinoma	Placenta
  	Cutaneous Squamous Carcinoma	Skin		Ductal Carcinomas	Ducts
  	Epithelial Cancer	Epithelial tissues		Ewing Sarcoma	Bone or soft tissues
  	External Auditory Canal Squamous Cell Carcinoma	Ear canal		Fibroma	Fibrous tissues
  	Gallbladder Cancer	Gallbladder		Gastrointestinal Cancer	Digestive system
  	Germ Cell Tumor	Reproductive organs		Glioma	Brain or spinal cord
  	Hemangioma	Blood vessels		Hematologic Cancer	Blood or bone marrow
  	Invasive Mucinous Adenocarcinoma	Various tissues		Kaposi's Sarcoma	Blood vessels or lymphatic vessels
  	Laryngeal and Hypopharyngeal Carcinoma	Larynx or hypopharynx		Leukemia	Blood and bone marrow
  	Lung Cancer	Lung		Lymphoblastic Leukemia	Blood and bone marrow
  	Lymphoma	Lymph nodes or lymphatic tissues		Malignant Mesothelioma	Mesothelial tissues (lining of organs)
  	Maxillary Sinus Squamous Cell Carcinoma	Maxillary sinus		Meningioma	Meninges
  	Monocytic Leukemia	Blood and bone marrow		Murine Plasmacytomas	Plasma cells
  	Myeloid Leukemia	Blood and bone marrow		Myeloma	Bone marrow
  	Neural System Tumors Syndrome	Nervous system		Neurofibromatosis	Nervous system
  	Neurological Tumors	Nervous system		Oligoastrocytoma	Brain
  	Oligodendroglioma	Brain		Osteosarcoma	Bone
  	Parathyroid Cancer	Parathyroid glands		Parotid Cancer	Parotid glands
  	Penile Cancer	Penile		Phaeochromocytoma	Adrenal glands
  	Pituitary Tumor	Pituitary gland		Prolactinoma	Pituitary gland (prolactin-secreting cells)
  	Promyelocytic Leukemia	Blood and bone marrow		Retinoblastoma	Retina
  	Retroperitoneal Liposarcoma	Retroperitoneal tissues		Salivary Gland Cancer	Salivary glands
  	Seminoma	Testicles		Sinonasal Squamous Cell Carcinoma	Sinonasal cavity
  	Small Cell Lung Cancer	Lung		Spinal Cancer	Spinal cord
  	Squamous Carcinoma	Squamous epithelial cells		Synovial Sarcoma	Soft tissues around joints (synovial tissues)
  	Teratocarcinoma	Germ cells (ovaries or testicles)		Testicular Cancer	Testicles
  	Tongue Cancer	Tongue		Transitional Cell Carcinoma	Urinary tract
  	Trophoblastic Tumor	Placenta		Urothelial Cancer	Urinary tract
  	Uterus Cancer	Uterus		Vulvar Squamous Cell Carcinoma	Vulva
  	Wilms' tumor	Kidney

•   Mutation annotation

  Data Type	Resource	Description
  Cancer genomics	TCGA(The Cancer Genome Atlas) MC3
  	ICGC(International Cancer Genome Consortium) Release28
  	METABRIC(Molecular Taxonomy of Breast Cancer International Consortium)
  Driver mutation resource	dbNSFP v4.3a	dbNSFP is a database developed for functional prediction and annotation of all potential non-synonymous single-nucleotide variants (nsSNVs) in the human genome.
  	CanDriS	CanDriS is a software which uses an empirical Bayesian procedure to calculate the posterior probability of a site to be cancer-driving for all sites in a gene.
  	FASMIC	FASMIC is a comprehensive database containing experimental evidence on the functional impacts of somatic mutations detected in human cancer.
  	COSMIC	COSMIC, the Catalogue Of Somatic Mutations In Cancer, is the world's largest and most comprehensive resource for exploring the impact of somatic mutations in human cancer.
  Clinical implication info	CGI	Cancer Genome Interpreter is a database that helps interpret the results of cancer genomic testing and provides information on the clinical relevance of genetic variants in cancer
  	PMKB	Precision Medicine Knowledge Base (PMKB) is an interactive online application for collaborative editing, maintenance, and sharing of structured clinical-grade cancer mutation interpretations.
  	OncoKB	OncoKB™ annotates the biologic and oncogenic effects and prognostic and predictive significance of somatic molecular alterations. Potential treatment implications are stratified by the level of evidence that a specific molecular alteration is predictive of drug response on the basis of US Food and Drug Administration labeling, National Comprehensive Cancer Network guidelines, disease-focused expert group recommendations, and scientific literature.
  	CLinVar	ClinVar is a freely accessible, public archive of reports of the relationships among human variations and phenotypes, with supporting evidence.
  	CIViC	CIViC provides an educational resource to support better understanding of the current state of precision medicine. It may also provide useful summaries and links to relevant published evidence for the clinical relevance of specific variants.

•   Gene annotation

  Data Type	Resource	Description
  Sequences and Annotations of Human Genes	Ensembl GRCh37 Release75	Ensembl GRCh75 is a database that provides a comprehensive and integrated source of annotation of mainly vertebrate genome sequences.
  Driver gene resource	Two-Component CN/CS	Two-component CN/CS contains two CN/CS models (CN/CS-Pos, CN/CS-H) which can be used to comprehensively analyze the selection pressures of different sites in the cancer genome. According to the relationship between the selection pressure of genes and the functions they played in tumor progression, Two-component CN/CS can be applied to identify driver genes and mini-driver genes.
  	MODIG	MODIG, a graph attention network (GAT)-based framework to identify cancer driver genes by combining multi-omics pan-cancer data (mutations, copy number variants, gene expression and methylation levels) with multi-dimensional gene networks.
  	COSMIC CGC	The Cancer Gene Census (CGC) is an ongoing effort to catalogue those genes which contain mutations that have been causally implicated in cancer and explain how dysfunction of these genes drives cancer.
  	SEECancer	SEECancer database aims to present the comprehensive cancer evolutionary stage-specific somatic events (including early-specific, late-specific, relapse-specific, metastasis-specific, drug-resistant and drug-induced genomic events) and their temporal orders.
  	OncoKB	OncoKB™ annotates the biologic and oncogenic effects and prognostic and predictive significance of somatic molecular alterations. Potential treatment implications are stratified by the level of evidence that a specific molecular alteration is predictive of drug response on the basis of US Food and Drug Administration labeling, National Comprehensive Cancer Network guidelines, disease-focused expert group recommendations, and scientific literature.
  	CCGD	The Candidate Cancer Gene Database (CCGD) is developed to disseminate the results of transposon-based forward genetic screens in mice that identify candidate cancer genes.
  	PanSoftware	PanSoftware applied to PanCancer data identified 299 cancer driver genes with implications regarding their anatomical sites and cancer/cell types.
  	NCG v7.1	NCG collects 3,347 cancer driver genes from Census of Cancer Genes (CGC), Vogelstein, Science 2013, Saito, Nature 2020 and screenings of cancer tissues, as well as 95 healthy drivers from screenings of non-cancer tissues.
  	CIViC	CIViC provides an educational resource to support better understanding of the current state of precision medicine. It may also provide useful summaries and links to relevant published evidence for the clinical relevance of specific variants.
  	CancerMine	CancerMine is a literature-mined database of drivers, oncogenes and tumor suppressors in cancer. It is a valuable resource for cancer researchers and clinicians to understand the genetic underpinnings of different cancer types.
  	TSGene	TSGene 2.0 aims to support cancer research by maintaining a high quality tumor suppressor gene list for pan-cancer analysis. This database serves a comprehensive, fully classified, richly and accurately annotated tumor suppressor gene knowledgebase, with extensive cross-references and querying interfaces freely accessible to the scientific community.
  	ONGene	ONGene database aims to support oncogene research by maintaining a high quality oncogene database that serves as a comprehensive, fully classified, richly and accurately annotated oncogene resource, with extensive cross-references and querying interfaces freely accessible to the scientific community.
  	CIGene	CIGene serves as a valuable resource to efficiently define cancer initiation events, including somatic mutations, gene regulation, and gene interactions.
  	GCGene	GCGene is a comprehensive gene resource for gastric cancer. It includes: Curated gastric cancer genes from thousands of literatures, comprehensive annotations including regulatory information, gene expression profiles from normal and cancer tissues, somatic mutations from multiple cancer types.
  	ECGene	ECGene is committed to establishing a comprehensive gene resource for endometrial cancer. It includes: Curated endometrial cancer genes from thousands of literatures, gene expression summary for type 1 and type 2 endometrial cancers from 12 studies, comprehensive annotations such as color marked KEGG pathways, precomputed lncRNA co-expression networks using TCGA matched samples, somatic mutations from thousands cancer patients
  	MSGene	MSGene, a database that aims to provide an unbiased, centralized, publicly available and regularly updated collection of genetic association studies performed on MS phenotypes.
  	BCGene	Brain cancer-related genes and their associated literature, as well as quickly determine related brain functions by using pre-computed analyses of cancer genomics and the Allen Brain Atlas.
  Driver gene annotation	dbNSFP v4.3a	dbNSFP is a database developed for functional prediction and annotation of all potential non-synonymous single-nucleotide variants (nsSNVs) in the human genome.
  	OGEE	OGEE is an Online GEne Essentiality database to enhance our understanding of the essentiality of genes.
  	CSGene	CSGene is committed to establishing a comprehnsive gene resource for cell senescence. It includes: Literature data, biological pathways, gene expression profiles, homologs.
  	CellAge	CellAge Database of Cell Senescence Genes. Cell senescence can be defined as the irreversible cessation of cell division of normally proliferating cells. Human cells become senescent from progressive shortening of telomeres as cells divide, stress or oncogenes.
  	REGene	REGene is committed to establishing a comprehnsive gene resource for Regeneration. It includes: Literature data, biological pathways,gene expression profiles, orghologs
  	TissGDB	TissGDB is the tissue-specific gene annotation database in cancer, aiming to provide a resource or reference for cancer and the related disease studies in the context of tissue specificity.

•   Module annotation

  Data Type	Resource	Description
  Driver module	HotNet2	HotNet2 (diffusion-oriented subnetworks) is a general algorithm for identifying high weight subnetworks in a vertex-weighted network. HotNet2 was developed for identifying significantly mutated groups of interacting genes from large cancer sequencing studies.
  	Hierarchical-hotnet	Hierarchical HotNet, an algorithm that finds a hierarchy of altered subnetworks. Hierarchical HotNet assesses the statistical significance of the resulting subnetworks over a range of biological scales and explicitly controls for ascertainment bias in the network.
  	ActivePathways	ActivePathways, a tool for integrating multiple omics datasets and identifying gene sets that are over-represented in a list or matrix of genes.
  Interactome	STRING	STRING is a database of known and predicted protein-protein interactions. The interactions include direct (physical) and indirect (functional) associations; they stem from computational prediction, from knowledge transfer between organisms, and from interactions aggregated from other (primary) databases.
  	MODIG	MODIG, a graph attention network (GAT)-based framework to identify cancer driver genes by combining multi-omics pan-cancer data (mutations, copy number variants, gene expression and methylation levels) with multi-dimensional gene networks.

•   Non-coding annotation

  Data Type	Resource	Description
  Database	CNCDatabase	CNCDatabase provides the list of non-coding cancer drivers in gene promoters, enhancers, ncRNAs and CTCF-cohesin insulators published in over 25 studies.
  	Lnc2Cancer v3.0	Lnc2Cancer is a manually curated database that provides comprehensive experimentally supported associations between lncRNA or circRNA and human cancer.
  	EVLncRNAs v2.0	EVlncRNAs is a comprehensive, manually curated, high-quality and freely accessible resource of integrated sequence, structure, functional, and phenotypic information of experimentally validated long noncoding RNAs from all species.
  	CTRR-ncRNA	CTRR-ncRNA is the first public database that curates experimentally supported data related to non-coding RNAs related to cancer resistance and cancer recurrence from published literature, aiming to provide a comprehensive and precise resource for research on treatment resistance and recurrence prevention.
  	ncRI	Non-coding RNAs in Inflammation (ncRI) provides a manually curated database for experimentally validated non-coding RNAs in inflammatary disease.
  Literature	Noncoding Variants Connect Enhancer Dysregulation with Nuclear Receptor Signaling in Hematopoietic Malignanc	Combining targeted resequencing of hematopoietic lineage-associated CREs and mutation discovery, this study uncovered 1,836 recurrently mutated CREs containing leukemia-associated noncoding variants. By enhanced CRISPR/dCas9-based CRE perturbation screening and functional analyses, this study identified 218 variant-associated oncogenic or tumor-suppressive CREs in human leukemia.
  	Cis-regulatory mutations with driver hallmarks in major cancers	By integrating whole-genome sequencing, genetic data, and allele-specific gene expression from TCGA, this study identified 320 somatic non-coding mutations that affect gene expression in cis (FDR<0.25).
  	Pan-cancer analysis of non-coding recurrent mutations and their possible involvement in cancer pathogenesis	This study identified 21,574 recurrent mutations in non-coding regions that were shared by at least two different samples from both COSMIC and TCGA databases. Among them, 580 candidate cancer-related non-coding recurrent mutations were identified based on epigenomic and chromatin structure datasets.

Mutation page

This page delineates the detailed information for driver mutations at the pan-cancer and tumor-type levels. For each driver mutation, we provide essential details on basic information, functional predictions, mutation prevalence in cancer, and potential clinical applications.

Search option

In the search page, users can input the interested mutation name to retrieve the driver mutation. Click on the "Reset" button to reset the search. Click on "Example 1", "Example 2" or "Example 3" to retrieve sample cases. The search results are linked to the detailed pages of related driver mutations. It's recommended to search for mutations in a specified format (e.g., 'PIK3CA_H1047R'). Alternatively, you can enter the gene (e.g., 'PIK3CA') to get a full list.

Full List

•   Full list of possible mutations associated with queried gene

  The full list of possible mutations associated with the queried gene, and allow clicking on a row in the table to get detailed information about mutation.

  

Detail

•   Basic information of queried mutation

  The basic information contains the 'Level' defined by OncoTriMD, 'Funcional annotation', and related links for the searched mutation. Users can search the external annotation information for the mutation by clicking on the corresponding hyperlink.

  

  

•   Functional prediction result

  

•   Prevalence in cancer

  

•   Clinical implication

  

Gene page

This page delineates the detailed information for driver genes at the pan-cancer and tumor-type levels. This page is structured to provide users with a comprehensive understanding of the annotated driver genes, such as the basic information, driving role in cancer, mutation prevalence, tumor differential expression, and potential clinical applications.

Search option

On the search page, users can input the interested gene name to retrieve the driver gene. Also, users can click the interested gene name in the gene-cloud graph for detailed information. Click on the "Reset" button to reset the search. Click on "Example" to retrieve sample cases.

Detail

•   Basic information of queried gene

  The basic information contains the 'Level' defined by OncoTriMD, 'Functional annotation', and related links for the searched gene. Users can search the external annotation information for the gene by clicking on the corresponding hyperlink.

  

  

•   Functional prediction result

  

•   Prevalence in cancer

  

•   Gene expression

  

•   Clinical implication

  

Network page

This page delineates the detailed information for driver modules predicted by HotNet2 or Hierarchical-hotnet, including the specific genes contained in the driver module, the GO function annotation, and the interaction network of genes in the module.

Search option

On the search page, users can select the interested method, program, and project from the drop-down box, and enter the gene name of interest to retrieve the driver module. Click on the "Reset" button to reset the search. Click on "Example 1" or "Example 2" to retrieve sample cases.

Full List

•   All relevant driver modules table

  Column head	Description
  Driver module	Predicted driver module results by HotNet2, Hierarchical-hotnet, ActivatePathways, or pathway where the queried genes appear in the KEGG cancer pathway.
  #Genes	The number of genes in the driver module.
  Cancer-type enrichments	The module is predicted to be a driver module in different cancer types.
  Project	The corresponding cancer types.
  Program	The corresponding cancer cohort.
  Source	HotNet2, Hierarchical-hotnet, ActivatePathways, or KEGG cancer pathway.

  

Detail

•   Annotation table

  

•   Interaction networks

  

  

  

Non-coding page

This page delineates the detailed information for non-coding drivers collected from several reputable databases and literature. For each non-coding driver, we collected its basic information, regulatory mechanisms, biological functions, and potential clinical applications from multiple sources.

Search option

On the search page, users can input the interested gene name, element type, cancer type, and/or evidence type to retrieve the data. OncoTriMD supports the fuzzy search and will return all matching records. The search results are linked to the detailed pages of related non-coding drivers.

Detail

•   Basic information of queried gene or non-coding RNA

  The basic information and related links for the queried gene or non-coding RNA. Users can search the external annotation information for the gene by clicking on the corresponding hyperlink.

  

•   Annotation table

  Column head	Description
  	Click the '+' on the left to display a detailed annotation of the entry.
  Source	Data source for the entry.
  PubMed ID	PubMed ID for the entry. Click to check the literature.
  Gene symbol	Gene symbol for the entry.
  Element	The element type of each entry encompasses various categories, including non-coding RNA (such as lncRNA, circRNA, miRNA), regulatory regions (such as enhancer, promoter, 3'UTR, 5'UTR) and pseudogenes.
  Cancer Type	The related cancer type for the entry.
  Mechanism/Function/Clinical	Indicates whether the entry contains information on mechanism, function and clinical. Click the '+' on the left to display a detailed annotation of the entry.
  Evidence Type	The evidence type of the enrty. 1 represents experimentally validated evidence support, 2 represents differential gene expression association, and 3 represents results of computational prediction. Move the cursor over the evidence type and the method used to obtain the evidence will be hovered.

  

•   Detailed annotation of the entry

  This table shows a detailed annotation of the entry. Regulatory mechanisms document the type of regulation to which the element is subjected within the context of cancer, including transcription factor (TF), enhancer, variant, microRNA (miRNA), epigenetic modification, and alteration in gene expression. Biological functions document how the element contributes to cancer development and progression, such as cell growth, apoptosis/autophagy, epithelial-mesenchymal transition (EMT), immunity escape, alterations in coding ability and inflammation. Clinical applications document the potential use of the element as a biomarker for cancer, including its ability to predict clinical events such as metastasis, recurrence, circulation, drug/therapy/stress-resistance, and prognosis.

  

Cancer page

This page systematically organizes driver mutations, driver genes, driver modules, and non-coding drivers in different cancer types. Users can access the cancer-specific driver list exclusively provided by OncoTriMD based on the mutation cohort and cancer type of interest.

Search option

On the search page, users can select interested programs and cancer types from the drop-down box to search for drivers specific to that cancer type. Click on the "Reset" button to reset the search. Click on "Example" to retrieve sample cases.

Detail

•   Driver mutation table

  Column head	Description
  Level	The level of mutations is determined according to our accumulated evidence and customized criteria. For detailed ranking criteria, please consult the framework diagram provided in the database.
  Gene symbol	Gene symbol for the entry.
  Gene ID	The Ensembl gene ID of the gene.
  ENST	The Ensembl transcript ID of the gene.
  Mutation	The specific mutation of the protein by indicating the original amino acid, its position in the protein sequence, and the mutated amino acid.
  Frequency	The number of somatic mutations observed at this cancer type.
  Q(z)	The posterior probability of this amino acid site being a cancer driver.
  M(z)	The posterior mean of recurrent mutations of this amino acid site.
  OncoVar	Whether or not annotated by OncoVar
  OncoKB	Whether or not annotated by OncoKB.

  

•   Driver gene table

  Column head	Description
  Level	The level of genes is determined according to our accumulated evidence and customized criteria. For detailed ranking criteria, please consult the framework diagram provided in the database.
  Gene symbol	Gene symbol for the entry.
  Gene type	Gene type identified by Two-Component CN/CS.
  Gene ID	The Ensembl gene ID of the gene.
  #Nonsynonymous	The number of nonsynonymous mutations counted in the protein-coding region of the gene.
  #Synonymous	The number of synonymous mutations counted in the protein-coding region of the gene.
  CN/CS	The CN/CS ratio of a gene is defined by the ratio of the nonsynonymous mutation rate to the synonymous mutation rate in cancer samples.
  P value	The χ2 test was conducted for the gene to test the statistical significance of the difference between the CN/CS values and 1.
  OncoVar	Whether or not annotated by OncoVar
  OncoKB	Whether or not annotated by OncoKB.

  

•   Driver module table

  Column head	Description
  Driver module	The driver module results.
  #Genes	The number of genes in the driver module.
  Cancer-type enrichments	The module is predicted to be a driver module in different cancer types.
  Project	The corresponding cancer types.
  Program	The corresponding cancer cohort(TCGA/ICGC).

  

•   Non-coding driver table

  Column head	Description
  	Click the '+' on the left to display a detailed annotation of the entry.
  Source	Data source for the entry.
  PubMed ID	PubMed ID for the entry. Click to check the literature.
  Gene symbol	Gene symbol for the entry.
  Element	The element type of each entry encompasses various categories, including non-coding RNA (such as lncRNA, circRNA, miRNA), regulatory regions (such as enhancer, promoter, 3'UTR, 5'UTR) and pseudogenes.
  Cancer Type	The related cancer type for the entry.
  Mechanism/Function/Clinical	Indicates whether the entry contains information on mechanism, function and clinical. Click the '+' on the left to display a detailed annotation of the entry.
  Evidence Type	The evidence type of the enrty. 1 represents experimentally validated evidence support, 2 represents differential gene expression association, and 3 represents results of computational prediction. Move the cursor over the evidence type and the method used to obtain the evidence will be hovered.

  

•   The clinical implications associated with this cancer type

  

Tool page

This page provides web tools of three in-house cancer driver prediction tools to help users freely explore cancer drivers in their mutation data through a standardized workflow.

•   CanDriS

  

•   MODIG

  

•   C_N/C_S Calculator