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Highlights in BioScience                                                          
 

 

 

Highlights in BioScience                      DOI:10.36462/H.BioSci.20184                    May 2018 | Volume1 
 

Page 1 of 7 

 

 

 

 

 

 

 

 

 

 

 

 

 
Citation: Girish K. and  Dubey S. 

(2018). Higher organism’s molecular 

databases: an overvie. Highlights in 

BioScience, Volume 1.Article ID 

20184, dio:10.36462/ H.BioSci.20184 

 

 

 

 

Received: March  5, 2018 

 

Accepted: April  28, 2018 

 

Published: May 10, 2018 

 

 

Copyright:© 2018 Girish and Dubey. 

This is an open access article 

distributed under the terms of the 

Creative Commons Attribution 

License, which permits unrestricted 

use, distribution, and reproduction in 

any medium, provided the original 

author and source are credited. 

 

 

Data Availability Statement: All 

relevant data are within the paper 

 

 

Funding: The authors have no support 

or funding to report. 

 

 

 

 

 

 

Eukaryotic Molecular Biology Databases: 

An Overview 

Kumar Girish
1
 and Singh Dubey

2 

 
1
Department of Biotechnology, Indian Agricultural Research Institute, India. 

2
Department of Biology, Agricultural University, India 

 

 

Abstract 

 

A biological database is a big, structured body of continuous 

information, generally connected with computerized software 

intended to update, query, and recover information elements 

deposited within the framework. A straightforward database could 

be a single folder comprising several data, each carrying the same 

number of data. Such famous databases are GenBank from the 

National Center for Biotechnology Information, SwissProt from the 

Swiss Institute of Bioinformatics and PIR from the Protein 

Information Resource. Biological databases are bibliotheques of life 

science data, gathered from science studies, published literature, 

high-performance experimental technology, and computational 

analysis. Here we brefly described some recently published 

molecular databases.   

 

 

Keywords: biological database, computational analysis, bioinformatics, 

software.  

 

 

 

Introduction 

Bioinformatics is a steadily increasing area of studies fueled by the need to 

handle and evaluate the vast amounts of information produced by omics 

techniques. Decades of studies have produced several known multi-omics tools, 

including genome, proteome and transcriptome (1–3) with the sole objective of 

understanding every part of biology. Here we brefly described recent published 

database such as PhenomicDB, AAgMarker, CSCD, CR2Cancer, 

ActiveDriverDB, AmtDB, HEDD, DiseaseEnhancer, dreamBase, DASHR, 

MSDD, CancerSysDB, mirDIP, MeDReaders, LinkedOmics, Pancan-meQTL, 

PITDB, qPrimerDB, SEECancer, UniLectin3D, TranslatomeDB, Tiss-GDB, and 

TC3A Table (1). 

Review Article                                                                             Open Access   



 
 

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 Table 1. List of some recently published database. 

Database name Link Reference Database name Link Reference 

PhenomicDB 
http://www.phenomicDB.de  Kahraman et al. (4) mirDIP 

http://ophid.utoronto.ca 

/mirDIP/  

Tokar et al. (16) 

AAgMarker http://bioinfo.wilmer.jhu.edu/ 

AAgMarker/  

Pan et al. (5) MeDReaders http://medreader.org/  Wang et al. (17) 

CSCD http://gb.whu.edu.cn/CSCD  Xia et al. (6) LinkedOmics http://www.linkedomics.org  Vasaikar et al. (18) 

CR2Cancer 
http://cis.hku.hk/CR2Cancer  Ru et al. (7) Pancan-meQTL 

http://bioinfo.life.hust.edu.cn/ 

Pancan-meQTL/  

Gong et al. (19) 

ActiveDriverDB https://www.ActiveDriverDB.org  Krassowski et al. (8) PITDB http://pitdb.org  Saha et al. (20) 

AmtDB 
https://amtdb.org  Ehler et al. (9) qPrimerDB 

http://biodb.swu.edu.cn/ 

qprimerdb  

Lu et al. (21) 

HEDD http://zdzlab.einstein.yu.edu/1/ 

hedd.php  

Wang et al. (10) SEECancer 
http://biocc.hrbmu.edu.cn/ 

SEECancer 

Zhang et al. (22) 

DiseaseEnhancer http://biocc.hrbmu.edu.cn/ 

DiseaseEnhancer/  

Zhang et al. (11) UniLectin3D 
https://www.unilectin.eu/ 

unilectin3D  

Mariethoz et al. (23) 

dreamBase http://rna.sysu.edu.cn/dreamBase  Zheng et al. (12) TranslatomeDB http://www.translatomedb .net/  Liu et al. (24) 

DASHR 
http://lisanwanglab.org/DASHRv2  Kuksa et al. (13) Tiss-GDB 

http://zhaobioinfo.org/ 

TissGDB  

Kim et al. (25) 

MSDD http://tdb.ccmb.res.in/msdb  Yue et al. (14) TC3A http://tc3a.org  Feng et al. (26) 

CancerSysDB https://cancersys.uni-koeln.de Krempel et al. (15) 
   

 

PhenomicDB 

By combining government genotype/phenotype 

information from a broad spectrum of model organisms and 

modern humans, Kahraman et al. (4) built a multi-species 

genotype / phenotype repository. They collected these riches 

of information into a single embedded database through 

coarse-grained semiconductor mapping of phenotypic 

information areas, including prevalent gene codes (NCBI 

Gene) and the use of related orthology. PhenomicDB enables 

researchers to concurrently compare and browse recognized 

phenotypes for a specified gene or a collection of genes from 

distinct species with its use-case-oriented interface. It can be 

found at http://www.phenomicDB.de.  

 

AAgMarker 

Pan et al. (5) has collected many released raw 

information sets on proteome microarrays acquired from 

serum profiling assays and offers a toolbox for mining these 

information. AAgMarker's present edition includes 854 

serum samples with 136 092 proteins. For 12 diseases, such 

as Alzheimer's disease, Bechet's disease, and Parkinson's 

disease, a total of 7803 (4470 non-redundant) candidate auto 

virus biomarkers were recognized and gathered. In order to 

quantitatively evaluate these biomarkers, seven statistical 

parameters were implemented. Users can use fundamental 

search, sophisticated search and browse to collect, analyze 

and compare the datasets. These biomarkers can also be 

downloaded in terms of disease. The AAgMarker can be 

found at http://bioinfo.wilmer.jhu.edu/AAgMarker/. 

 

 

 

CSCD 

Xia et al. (6) gathered 228 complete samples of RNA or 

polyA(-) RNA-seq from both cancer and ordinary cell lines 

and found 272,152 circR-NAs specific to cancer. In ordinary 

samples only a total of 950 962 circRNAs were recognized, 

and in both tumor and ordinary samples 170 909 circRNAs 

were detected, that could be even farther used as non-tumor 

context. A cancer-specific circRNA databases  http://gb.whu. 

edu.cn/CSCD were built. They anticipated the microRNA 

response element locations and RNA binding protein 

positions for each circRNA in order to comprehend the 

functional impacts of circRNAs. In addition, prospective 

open reading frames were anticipated to show translatable 

circRNAs. They also identified the splicing occurrences in 

linear transcripts of each circRNA to comprehend the 

correlation between linear splicing and back splicing. As the 

first extensive circRNA database specific to cancer. 

 

CR2Cancer 

Ru et al. (7) introduced CR2Cancer, an extensive 

database for CRs in human cancer annotation and 

visualization built through high-throughput data analysis and 

mining literature. For more than 400 CRs across various 

kinds of cancer, they gathered and incorporated genomic, 

transcriptomic, proteomic, clinical and functional data. They 

also constructed various kinds of CR-related relationships, 

including dependent and autonomous cancer type 

relationships. In addition, about 6000 items of aberrant 

molecular alterations and CR interactions were manually 

curated from 5007 publications in the development of cancer. 

CR2Cancer offers an easy-to-use online interface to navigate, 

http://www.phenomicdb.de/
http://bioinfo.wilmer.jhu.edu/%20AAgMarker/
http://bioinfo.wilmer.jhu.edu/%20AAgMarker/
http://medreader.org/
http://gb.whu.edu.cn/CSCD
http://www.linkedomics.org/
http://cis.hku.hk/CR2Cancer
http://bioinfo.life.hust.edu.cn/%20Pancan-meQTL/
http://bioinfo.life.hust.edu.cn/%20Pancan-meQTL/
https://www.activedriverdb.org/
http://pitdb.org/
https://amtdb.org/
http://biodb.swu.edu.cn/%20qprimerdb
http://biodb.swu.edu.cn/%20qprimerdb
http://zdzlab.einstein.yu.edu/1/%20hedd.php
http://zdzlab.einstein.yu.edu/1/%20hedd.php
http://biocc.hrbmu.edu.cn/%20SEECancer
http://biocc.hrbmu.edu.cn/%20SEECancer
http://biocc.hrbmu.edu.cn/%20DiseaseEnhancer/
http://biocc.hrbmu.edu.cn/%20DiseaseEnhancer/
https://www.unilectin.eu/%20unilectin3D
https://www.unilectin.eu/%20unilectin3D
http://rna.sysu.edu.cn/dreamBase
http://lisanwanglab.org/DASHRv2
http://zhaobioinfo.org/%20TissGDB
http://zhaobioinfo.org/%20TissGDB
http://tdb.ccmb.res.in/msdb
http://tc3a.org/
https://cancersys.uni-koeln.de/
http://www.phenomicdb.de/
http://bioinfo.wilmer.jhu.edu/AAgMarker/


 
 

                                                                                                                                     Eukaryotic Molecular Biology Databases 

 

 

 

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search and download interesting information conveniently. 

http://cis.hku.hk/CR2Cancer, is readily accessible. 

 

ActiveDriverDB 

ActiveDriverDB is a comprehensive database of human 

proteogenomics which annotate disorder mutations and 

variants of the population via the PTM lens. More than 

385,000 released PTM locations with 3.6 million 

replacements from the Cancer Genome Atlas (TCGA), the 

ClinVar disease gene database, and human genome mapping 

projects were incorporated (8). The database involves site-

specific protein interaction networks, upstream enzymes like 

kinases, and those enzyme-targeted drugs. Also, by 

evaluating advantages and losses of kinase-bound gene 

motifs, anticipated network-rewiring effect of mutations. For 

researching PTM-associated mutations, ActiveDriverDB 

offers comprehensive visualization, processing, navigating, 

and search opportunities. Users can interactively submit 

mutation datasets and use the coding tool in pipelines. 

Integrative assessment of mutations and PTMs, as illustrated 

by case studies of TP53, BRCA2 and VHL, can assist 

decipher molecular processes of disease and phenotypes. It 

can be found at https://www.ActiveDriverDB.org. 

 

AmtDB 

AmtDB, the first database of ancient human 

mitochondrial genomes, is presented by Ehler et al. (9). 

Release edition includes 1107 hand-cured ancient samples, 

freely available for download, along with personal 

descriptors including geographic place, radiocarbon dating, 

and association to archeological culture. An interactive map 

for sample position visualization is also available in the 

database. On https://amtdb.org, AmtDB is a main platform 

for ancestral population genetic research. 

 

HEDD 

Wang et al. (10) founded the Human Enhancer Disease 

Database (HEDD) to promote enhancers research and their 

prospective functions in complicated human disorders. For 

some 2,8 million human enhancers recognized by EN-CODE, 

FANTOM5 and RoadMap, HEDD presently offers extensive 

genomic data with disease connection results based on 

enhancer-gene and gene-disease links. It also offers web-

based analytical tools to conceptualize enhancer networks 

and rank enhancers in a particular gene network given a 

collection of targeted genes. At http://zdzlab.einstein.yu.edu/ 

1/hedd.php, HEDD is readily available. 

DiseaseEnhancer 

Zhang et al. (11) developed DiseaseEnhancer for 

disease-associated enhancers, a manually curated database. 

As of July 2017, in 143 human illnesses, DiseaseEnhancer 

involves 847 disease-associated enhancers. Database 

attributes include fundamental data about enhancers; varieties 

of diseases; related variations of enhancers and their 

phenotypes mediated. They also include a website function 

for exporting any request outcomes to a folder and 

downloading the complete database. DiseaseEnhancer offers 

scientists with a successful opportunity for knowing enhancer 

deregulation in pathogenesis of disease and identifying new 

biomarkers for diagnosis and treatment of disorder. You can 

easily access DiseaseEnhancer at http://biocc.hrbmu.edu.cn/ 

DiseaseEnhancer/. 

 

DreamBase 

Zheng et al. (12) established dreamBase to enable 

research into DNA alteration, RNA regulation and protein 

interacting from multidimensional high-throughput 

sequencing information of probable expressed pseudogenes. 

Based on a series of data sets ChIP-seq and DNase-seq, 

genome-wide binding patterns of different transcription-

associated variables were recognized around pseudo-gene 

loci. By incorporating some 18 000 RNA-seq information, 

they evaluated pseudogene expression profiles and examined 

their modelsof co-expressionin 32 cancers and 31 ordinary 

tissues with their parent genes.They revealed complicated 

post-transcription regulation networks comprising 275 

microRNAs and 1201 pseudo-genes by connecting binding 

sites with microRNA. They researched transcriptome-wide 

relationships based on 458 CLIP-seq datasets between RNA 

binding enzymes (RBPs) and pseudo-genes. They also linked 

1039 RNA alteration locations to 635 pseudo-genes in 

combination with epi-transcriptome sequencing information. 

This database would provide perspectives into pseudo-genes ' 

transcription regulating, expression, features and mechanisms 

as well as their functions in biological procedures and 

illnesses. At http://rna.sysu.edu.cn/dreamBase, DreamBase is 

readily available. 

 

DASHR 

Kuksa et al. (13) created a comprehensive catalog of 

annotation, expression, handling, conservation, tissue 

specificity and other biological characteristics for all human 

sncRNA genes and mature products obtained from all main 

RNA groups. The DASHR (Small Human Non-Coding 

RNAs Database) database is the first to incorporate human 

sncRNA gene as well as mature product profiles from 

numerous RNA-seq protocols. For both GRCh38/hg38 and 

GRCh37/hg19 assemblies, 185 tissue / cell forms and 

sncRNA annotations and > 800 curated tests from ENCODE 

and GEO / SRA are incorporated into DASHR. In addition, 

DASHR is the first to comprise both recognized and novel 

http://cis.hku.hk/CR2Cancer
https://www.activedriverdb.org/
https://amtdb.org/
http://zdzlab.einstein.yu.edu/%201/hedd.php
http://zdzlab.einstein.yu.edu/%201/hedd.php
http://biocc.hrbmu.edu.cn/%20DiseaseEnhancer/
http://biocc.hrbmu.edu.cn/%20DiseaseEnhancer/
http://rna.sysu.edu.cn/dreamBase


 
 

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sncRNA loci detected by unsupervised segmentation, earlier 

unnoted. DASHR also contains > 3,200,000 annotations for 

non-small genes of RNA and other genomic characteristics. 

In addition, an improved user interface, integrated 

experiment-by-locus table display, sncRNA locus sorting and 

biological feature processing is introduced.  On http:// 

lisanwanglab.org/DASHRv2, DASHR is readily accessible. 

 

MSDD 

Yue et al. (14) have been described as MSDD, 

presently documenting 525 associations among 182 human 

miRNAs, 197 SNPs, 153 genes, and 164 human illnesses 

through an analysis of over 2000 articles released. Each 

organization includes data on miRNAs, SNPs, miRNA target 

genes and disorder designations, SNP places and allies, 

miRNA dysfunctional model, experimental methods, a short 

functional description, initial reference and extra annotation. 

MSDD offers a user-friendly interface for browsing, 

retrieving, downloading and submitting new information 

conveniently. MSDD There is free access to http://tdb.ccmb. 

res.in/msdb. 

 

CancerSysDB               

The Cancer Systems Biology Database (CancerSysDB) 

was created by Krempel et al. (15), a tool for extremely 

flexible cancer queries and data analysis across various data 

types and various research. A CancerSysDB primary openly 

accessible instance can be used to achieve extremely versatile 

queries across various kinds of information as shown in 

extremely appropriate instances of use. They also show how 

the CancerSysDB can be used in the study network The 

Cancer Genome Atlas (TCGA) to classify predictive cancer 

based on all-exome information from 9091 individuals. 

CancerSysDB At https://cancersys.uni-koeln.de, it is publicly 

accessible. 

 

mirDIP 

Tokar et al. (16) provided mirDIP, providing practically 

152 million predictions of human microRNA targets 

collected across 30 various resources. They also incorporated 

an interdisciplinary score, statistically implied from the 

predictions obtained and appointed to each unique 

microRNA-target activity to provide a unified measure of 

trust. They show that integration of predictions throughout 

various assets does not accumulate predictive bias towards 

biological procedures or pathways. mirDIP is accessible free 

of charge at http://ophid.utoronto.ca/mirDIP/. 

MeDReaders 

A total of 731 TFs that could attach to methylated DNA 

sequences in human and mouse research mentioned in the 

literature were individually selected by Wang et al. (17). Six 

human cell lines and one mouse cell line derived from the 

ENCODE and GEO database were used in silico approaches 

to anticipate methylated and un-methylated patterns of 292 

TFs by incorporating whole genome bisulfite sequencing 

(WGBS) and ChIP-Seq data. The database of MeDReaders 

provides an extensive resource for further research and 

experiment plans to guide. The portal can be found at 

http://medreader.org/. 

 

LinkedOmics 

The database of LinkedOmics (18) includes information 

on multi-omics and clinical information for 32 kinds of 

cancer and a total of 11,158 clients from the TCGA initiative. 

It is also the first multi-omics repository to incorporate 

worldwide proteomics information from chosen TCGA tumor 

samples based on mass spectrometry (MS) produced by the 

Clinical Proteomic Tumor Analysis Consortium (CPTAC). 

Linked Omics has over a billion information points in total., 

To enable a extensive assessment of these data, the web 

application Linked Omics developed three analytical 

modules. The Link Finder module enables for flexible 

discovery of connections between a molecular or clinical trait 

of concern and all other characteristics, offering a chance to 

evaluate and visualize connections for each cancer cohort 

between billions of attribute pairs. They show that Linked 

Omics offers biologists and clinicians with a distinctive 

platform for accessing, analyzing and comparing multi-omics 

cancer information within and across tumor kinds. On 

http://www.linkedomics.org, Linked Omics is readily 

accessible. 

 

Pancan-meQTL 

 Gong et al. (19) provided Pancan-meQTL, a 

repository that integrates genome-wide genotype and DNA 

methylation information to provide meQTLs across 23 

cancer kinds from the Cancer Genome Atlas. In maximum, 

8,028,964 cis-meQTLs and 965,050 trans-meQTLs were 

recognized. Overall patient survival rates are correlated with 

23,432 meQTLs. In addition, 2,214 458 meQTLs were 

recognized that overlap with identified loci recognized by 

genome-wide correlation studies. Pancan-meQTL offers an 

easy-to-use web interface http://bioinfo.life.hust.edu.cn/ 

Pancan-meQTL/ for browsing, scanning and accessing of 

interesting information. 

 

PITDB 

PITDB (20) is a free repository of translated genomic 

elements (TGEs) found in PIT (transcriptomics-informed 

proteomics) studies. In PIT, both RNA-seq transcriptomics as 

well as proteomic mass spectrometry are used to evaluate a 

https://cancersys.uni-koeln.de/
http://ophid.utoronto.ca/mirDIP/
http://medreader.org/
http://www.linkedomics.org/
http://bioinfo.life.hust.edu.cn/%20Pancan-meQTL/
http://bioinfo.life.hust.edu.cn/%20Pancan-meQTL/


 
 

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sample. Transcripts constructed from RNA-seq reads are 

being used to develop a library of sample-specific amino acid 

sequences that are searched for the obtained mass spectra, 

allowing the detection of any TGE, not just those in 

canonical proteome databases. PITDB includes more than 74 

000 separate TGEs from four species at the moment of 

writing, backed by more than 600 000 matches in the peptide 

spectrum. The database, available at http://pitdb.org, offers 

guiding evidence for each TGE, often from multiple studies 

and an indicator of trust in the assessment and form of the 

TGE, varying from recognized protein to multiple protein 

component kinds including different splice isoforms, to a 

putative novel molecule. 

 

qPrimerDB 

Lu et al. (21) established the qPrimerDB repository 

based on the design of an automatic gene-specific qPCR 

primer and the verification workflow depending on 

thermodynamics. The qPrimerDB database is the most 

comprehensive qPCR primary database currently available, 

with a web front-end that provides gene-specific and pre-

computed primary pairs across 147 major organisms 

including human, mouse, zebra fish, yeast, thale cress, rice, 

and corn. This database provides 3331426 of the best 

priming pairs for each gene, based on priming pair coverage, 

and 47760359 alternative gene-specific priming pairs, that 

can be easily downloaded in batch format. For qPCR priming 

pairs for 66 randomly chosen genes, the specificity and 

effectiveness was validated in six distinct species through 

qPCR assays and gel electrophoresis and it is open to the 

public via http://biodb.swu.edu.cn/qprimerdb. 

 

SEECancer 

Zhang et al. (22) has constructed a database on 

SEECancer, which serves to show an extensive 

developmental stage of cancer-specific somatic occurrences 

and their temporal orders. By manually curing more than 10 

000 published papers, 1231 developmental stage-specific 

genomic occurrences and 5772 temporal orders containing 

82 human cancers and 23 tissue origins were gathered and 

recorded in the SEECancer database. Each entry includes the 

somatic occurrence, the developmental phase, the sort of 

cancer, the identification strategy and the appropriate 

evidence. SEECancer offers an easy-to-use tool for browsing, 

finding and downloading developmental stage-specific 

somatic occurrences and temporal associations in multiple 

cancers.Increasing regard to the evolution of cancer genome, 

the required data in SEECancer has facilitated the knowledge 

of cancer etiology and the growth of developmental therapy, 

and has helped clinicians to find biomarkers for tumor 

progression surveillance. It is freely available via 

http://biocc.hrbmu.edu.cn/SEECancer. 

 

UniLectin3D 

Mariethoz et al. (23) has created a UniLectin3D 

database, a curated database that classifies source and fold 

lectins, cross-links to literature, many glycoscience databases 

and functional information such as recognized specificity. 

The database offers thorough data on lectins, their linked 

glycan ligands as well as their relationships using the 

Protein–Ligand Interaction Profiler (PLIP) server. Special 

attention was paid to the depiction of linked glycan ligands 

using easy graphical depiction and statistical format of cross-

linking to other glycol scientific databases.They designed the 

layout of the database structure and navigation instruments to 

account for all species, and the search for oligosaccharide 

epitopes complexed inside selected binding regions. 

UniLectin3D is available link https://www.unilectin.eu/ 

unilectin3D.  

 

TranslatomeDB 

Liu et al. (24) has developed an extensive 

TranslatomeDB database that offers a set and embedded 

assessment of published and client-generated translatom 

sequencing information. The present edition involves 2453 

Ribo-seq, 10 RNC-seq and 1394 associated mRNA-seq 

datasets for 13 species. In relation to dataset sets, the 

database emphasizes the evaluation tasks. All datasets were 

evaluated using a unified, reliable, precise and 

experimentally verifiable pipeline centered on the FANSe3 

mapping algorithm and edgeR for DGE analysis. 

TranslatomeDB also enables clients to upload their own 

datasets and use the same unified pipeline to evaluate their 

information. Accessible at http://www.translatomedb.net/.  

 

TissGDB 

Kim et al. (25) constructed Tiss-GDB (Tissue Specific 

Gene DataBase for Cancer) by collecting and manually 

curated 2461 tissue-specific genes (TissGenes) across 22 

tissue classes corresponding to 28 cancers of the Cancer 

Genome Atlas (TCGA) from three different tissue-specific 

gene cell assets: the Human Protein Atlas (HPA), the Tissue-

specific Gene Expression and Regulation (TiGER) and the 

Genoty map. These 2461 TissGenes also conducted gene 

expression, somatic mutation, and prognostic marker-based 

analyzes of 28 kinds of cancer using TCGA information. 

TissGDB sets out seven types of annotations: TissGene-

Exp,TissGene Summary,  TissGeneMut, and TissGene-

miRNA. It is freely via http://zhaobioinfo.org/TissGDB. 

 

http://pitdb.org/
http://biodb.swu.edu.cn/qprimerdb
http://biocc.hrbmu.edu.cn/SEECancer
https://www.unilectin.eu/%20unilectin3D
https://www.unilectin.eu/%20unilectin3D
http://www.translatomedb.net/
http://zhaobioinfo.org/TissGDB


 
 

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TC3A 

Feng et al. (26) proved that CFIm25-mediated 3 UTR 

reducing through APA encourages in vitro and in vivo 

development of glioblastoma tumors and further underscores 

its importance for tumorgenesis. Cancer 3 UTR Atlas 

(TC3A), an extensive APA use tool for 10,537 tumors across 

32 kinds of cancer, is reported. These APA occurrences are 

possibly novel prognostic biomarkers and may uncover novel 

processes for the regulation of cancer-causing genes. TC3A 

is constructed on top of the current cBioPortal de facto 

standard. A big group of current cBioPortal consumers and 

clinical researchers will therefore discover TC3A acquainted 

and immediately available at http://tc3a.org. 

 

 

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