You are here

Home

The Cancer Genome Atlas (TCGA)

The Cancer Genome Atlas (TCGA) is a collaboration between the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI) that has generated comprehensive, multi-dimensional maps of the key genomic changes in 33 types of cancer. The TCGA dataset, comprising more than two petabytes of genomic data, has been made publically available, and this genomic information helps the cancer research community to improve the prevention, diagnosis, and treatment of cancer. [Source: TCGA]

ParSel

Submitted by ChenLiang on Sun, 09/10/2017 - 20:04

It is known that tumor micro-RNAs (miRNA) can define patient survival and treatment response. We present a framework to identify miRNAs which are predictive of cancer survival. The framework attempts to rank the miRNAs by exploring their collaborative role in gene regulation. Our approach tests a significantly large number of combinatorial cases leveraging parallel computation. We carefully avoided parametric assumptions involved in evaluations of miRNA expressions but used rigorous statistical computation to assign an importance score to a miRNA.

Rating: 
Average: 5 (1 vote)

MethHC

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

We present MethHC (http://MethHC.mbc.nctu.edu.tw), a database comprising a systematic integration of a large collection of DNA methylation data and mRNA/microRNA expression profiles in human cancer. DNA methylation is an important epigenetic regulator of gene transcription, and genes with high levels of DNA methylation in their promoter regions are transcriptionally silent. Increasing numbers of DNA methylation and mRNA/microRNA expression profiles are being published in different public repositories.

Rating: 
Average: 5 (1 vote)

BiCliques Merging

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

MicroRNAs (miRNAs) are post-transcriptional regulators that repress the expression of their targets. They are known to work cooperatively with genes and play important roles in numerous cellular processes. Identification of miRNA regulatory modules (MRMs) would aid deciphering the combinatorial effects derived from the many-to-many regulatory relationships in complex cellular systems. Here, we develop an effective method called BiCliques Merging (BCM) to predict MRMs based on bicliques merging.

Rating: 
4
Average: 4 (2 votes)

iBFE

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

High-throughput biotechnologies have been widely used to characterize clinical samples from various perspectives e.g., epigenomics, genomics and transcriptomics. However, because of the heterogeneity of these technologies and their outputs, individual analysis of the various types of data is hard to create a comprehensive view of disease subtypes. Integrative methods are of pressing need.

Rating: 
5
Average: 5 (1 vote)

miRTarVis+

Submitted by ChenLiang on Sun, 09/10/2017 - 20:31

In this paper, we present miRTarVis+, a Web-based interactive visual analytics tool for miRNA target predictions and integrative analyses of multiple prediction results. Various microRNA (miRNA) target prediction algorithms have been developed to improve sequence-based miRNA target prediction by exploiting miRNA-mRNA expression profile data. There are also a few analytics tools to help researchers predict targets of miRNAs.

Rating: 
Average: 5 (1 vote)

OncomiR

Submitted by ChenLiang on Tue, 01/09/2018 - 17:26

Dysregulation of microRNAs (miRNAs) is extensively associated with cancer development and progression. miRNAs have been shown to be biomarkers for predicting tumor formation and outcome. However, identification of the relationships between miRNA expression and tumor characteristics can be difficult and time-consuming without appropriate bioinformatics expertise. To address this issue, we present OncomiR, an online resource for exploring miRNA dysregulation in cancer.

Rating: 
Average: 5 (1 vote)

Radiogenomics

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

Magnetic Resonance Imaging (MRI) has been routinely used for the diagnosis and treatment of breast cancer. However, the relationship between the MRI tumor phenotypes and the underlying genetic mechanisms remains under-explored. We integrated multi-omics molecular data from The Cancer Genome Atlas (TCGA) with MRI data from The Cancer Imaging Archive (TCIA) for 91 breast invasive carcinomas.

Rating: 
Average: 5 (1 vote)

CCmiR

Submitted by ChenLiang on Tue, 01/09/2018 - 17:39

The identification of microRNA (miRNA) target sites is important. In the past decade, dozens of computational methods have been developed to predict miRNA target sites. Despite their existence, rarely does a method consider the well-known competition and cooperation among miRNAs when attempts to discover target sites. To fill this gap, we developed a new approach called CCmiR, which takes the cooperation and competition of multiple miRNAs into account in a statistical model to predict their target sites.

Rating: 
Average: 5 (1 vote)

CR2Cancer

Submitted by ChenLiang on Tue, 01/09/2018 - 17:43

Chromatin regulators (CRs) can dynamically modulate chromatin architecture to epigenetically regulate gene expression in response to intrinsic and extrinsic signalling cues. Somatic alterations or misexpression of CRs might reprogram the epigenomic landscape of chromatin, which in turn lead to a wide range of common diseases, notably cancer. Here, we present CR2Cancer, a comprehensive annotation and visualization database for CRs in human cancer constructed by high throughput data analysis and literature mining.

Rating: 
Average: 5 (1 vote)

MIRAGAA

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

Cancer evolves through microevolution where random lesions that provide the biggest advantage to cancer stand out in their frequent occurrence in multiple samples. At the same time, a gene function can be changed by aberration of the corresponding gene or modification of microRNA (miRNA) expression, which attenuates the gene. In a large number of cancer samples, these two mechanisms might be distributed in a coordinated and almost mutually exclusive manner.

Rating: 
Average: 5 (1 vote)

Pages

Subscribe to The Cancer Genome Atlas (TCGA)