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TCGA data

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14 papers 0 to 25 followers
By Zhang Ying Daying
Paul G Cantalupo, Joshua P Katz, James M Pipas
We have developed a virus detection and discovery computational pipeline, Pickaxe, and applied it to NGS databases provided by The Cancer Genome Atlas (TCGA). We analyzed a collection of whole genome (WGS), exome (WXS), and RNA (RNA-Seq) sequencing libraries from 3052 participants across 22 different cancers. NGS data from nearly all tumor and normal tissues examined contained contaminating viral sequences. Intensive computational and manual efforts are required to remove these artifacts. We found that several different types of cancers harbored Herpesviruses including EBV, CMV, HHV1, HHV2, HHV6 and HHV7...
January 1, 2018: Virology
Darshan S Chandrashekar, Bhuwan Bashel, Sai Akshaya Hodigere Balasubramanya, Chad J Creighton, Israel Ponce-Rodriguez, Balabhadrapatruni V S K Chakravarthi, Sooryanarayana Varambally
Genomics data from The Cancer Genome Atlas (TCGA) project has led to the comprehensive molecular characterization of multiple cancer types. The large sample numbers in TCGA offer an excellent opportunity to address questions associated with tumo heterogeneity. Exploration of the data by cancer researchers and clinicians is imperative to unearth novel therapeutic/diagnostic biomarkers. Various computational tools have been developed to aid researchers in carrying out specific TCGA data analyses; however there is need for resources to facilitate the study of gene expression variations and survival associations across tumors...
August 2017: Neoplasia: An International Journal for Oncology Research
Li Chen, Fenghao Sun, Xiaodong Yang, Yulin Jin, Mengkun Shi, Lin Wang, Yu Shi, Cheng Zhan, Qun Wang
RNA sequencing (RNA-Seq) and microarray are two of the most commonly used high-throughput technologies for transcriptome profiling; however, they both have their own inherent strengths and limitations. This research aims to analyze the correlation between microarrays and RNA-Seq detection of transcripts in the same tissue sample to explore the reproducibility between the techniques. Using data of RNA-Seq v2 and three different microarrays provided by The Cancer Genome Atlas, 11,120 genes of 111 lung squamous cell carcinoma samples were simultaneously detected by the four methods...
September 10, 2017: Gene
Yue Zhao, Tham H Hoang, Pujan Joshi, Seung-Hyun Hong, Charles Giardina, Dong-Guk Shin
We propose a new way of analyzing biological pathways in which the analysis combines both transcriptome data and mutation information and uses the outcome to identify "routes" of aberrant pathways potentially responsible for the etiology of disease. Each pathway route is encoded as a Bayesian Network which is initialized with a sequence of conditional probabilities which are designed to encode directionality of regulatory relationships encoded in the pathways, i.e. activation and inhibition relationships...
July 15, 2017: Methods: a Companion to Methods in Enzymology
Michael I Klein, David F Stern, Hongyu Zhao
BACKGROUND: Personalizing treatment regimes based on gene expression profiles of individual tumors will facilitate management of cancer. Although many methods have been developed to identify pathways perturbed in tumors, the results are often not generalizable across independent datasets due to the presence of platform/batch effects. There is a need to develop methods that are robust to platform/batch effects and able to identify perturbed pathways in individual samples. RESULTS: We present Gene-Ranking Analysis of Pathway Expression (GRAPE) as a novel method to identify abnormal pathways in individual samples that is robust to platform/batch effects in gene expression profiles generated by multiple platforms...
June 26, 2017: BMC Bioinformatics
Ting-Qing Gan, Wen-Jie Chen, Hui Qin, Su-Ning Huang, Li-Hua Yang, Ye-Ying Fang, Lin-Jiang Pan, Zu-Yun Li, Gang Chen
BACKGROUND Lung adenocarcinoma (LUAD) is the most frequent lung cancer. MicroRNAs (miRNAs) are believed to have fundamental roles in tumorigenesis of LUAD. Although miRNAs are broadly recognized in LUAD, the role of microRNA-375 in LUAD is still not fully elucidated. MATERIAL AND METHODS We evaluated the significance of miR-375 expression in LUAD by using analysis of a public dataset from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and a literature review. Furthermore, we investigated the biological function of miR-375 by gene ontology enrichment and target prediction analysis...
May 23, 2017: Medical Science Monitor: International Medical Journal of Experimental and Clinical Research
Zhao Min Deng, Lin Liu, Wen Hai Qiu, Yong Qun Zhang, Hong Yan Zhong, Ping Liao, Yun Hong Wu
BACKGROUND: Molecularly targeted therapies improved survival status of some patients with lung adenocarcinoma, which accounts for 40% of all lung cancers, and in-depth study of gene alterations is important for the personalized treatment. METHODS: The legacy archive data of clinical information and genomic variations under the project TCGA Lung Adenocarcinoma were downloaded from the GDC Data Portal using R package TCGAbiolinks. The significantly aberrant copy number variants segments were figured out using GAIA...
2017: PeerJ
Maria Vila-Casadesús, Meritxell Gironella, Juan José Lozano
MicroRNAs (miRNAs) are small RNAs that regulate the expression of target mRNAs by specific binding on the mRNA 3'UTR and promoting mRNA degradation in the majority of cases. It is often of interest to know the specific targets of a miRNA in order to study them in a particular disease context. In that sense, some databases have been designed to predict potential miRNA-mRNA interactions based on hybridization sequences. However, one of the main limitations is that these databases have too many false positives and do not take into account disease-specific interactions...
2016: PloS One
George S Krasnov, Alexey A Dmitriev, Nataliya V Melnikova, Andrew R Zaretsky, Tatiana V Nasedkina, Alexander S Zasedatelev, Vera N Senchenko, Anna V Kudryavtseva
The contribution of different mechanisms to the regulation of gene expression varies for different tissues and tumors. Complementation of predicted mRNA-miRNA and gene-transcription factor (TF) relationships with the results of expression correlation analyses derived for specific tumor types outlines the interactions with functional impact in the current biomaterial. We developed CrossHub software, which enables two-way identification of most possible TF-gene interactions: on the basis of ENCODE ChIP-Seq binding evidence or Jaspar prediction and co-expression according to the data of The Cancer Genome Atlas (TCGA) project, the largest cancer omics resource...
April 20, 2016: Nucleic Acids Research
Antonio Colaprico, Tiago C Silva, Catharina Olsen, Luciano Garofano, Claudia Cava, Davide Garolini, Thais S Sabedot, Tathiane M Malta, Stefano M Pagnotta, Isabella Castiglioni, Michele Ceccarelli, Gianluca Bontempi, Houtan Noushmehr
The Cancer Genome Atlas (TCGA) research network has made public a large collection of clinical and molecular phenotypes of more than 10 000 tumor patients across 33 different tumor types. Using this cohort, TCGA has published over 20 marker papers detailing the genomic and epigenomic alterations associated with these tumor types. Although many important discoveries have been made by TCGA's research network, opportunities still exist to implement novel methods, thereby elucidating new biological pathways and diagnostic markers...
May 5, 2016: Nucleic Acids Research
Daniel Wai-Hung Ho, Alan Ka-Lun Kai, Irene Oi-Lin Ng
This study systematically evaluates the TCGA whole-transcriptome sequencing data of hepatocellular carcinoma (HCC) by comparing the global gene expression profiles between tumors and their corresponding nontumorous liver tissue. Based on the differential gene expression analysis, we identified a number of novel dysregulated genes, in addition to those previously reported. Top-listing upregulated (CENPF and FOXM1) and downregulated (CLEC4G, CRHBP, and CLEC1B) genes were successfully validated using qPCR on our cohort of 65 pairs of human HCCs...
September 2015: Frontiers of Medicine
Yitan Zhu, Yanxun Xu, Donald L Helseth, Kamalakar Gulukota, Shengjie Yang, Lorenzo L Pesce, Riten Mitra, Peter Müller, Subhajit Sengupta, Wentian Guo, Jonathan C Silverstein, Ian Foster, Nigel Parsad, Kevin P White, Yuan Ji
BACKGROUND: Genetic interactions play a critical role in cancer development. Existing knowledge about cancer genetic interactions is incomplete, especially lacking evidences derived from large-scale cancer genomics data. The Cancer Genome Atlas (TCGA) produces multimodal measurements across genomics and features of thousands of tumors, which provide an unprecedented opportunity to investigate the interplays of genes in cancer. METHODS: We introduce Zodiac, a computational tool and resource to integrate existing knowledge about cancer genetic interactions with new information contained in TCGA data...
August 2015: Journal of the National Cancer Institute
Joseph D Khoury, Nizar M Tannir, Michelle D Williams, Yunxin Chen, Hui Yao, Jianping Zhang, Erika J Thompson, Funda Meric-Bernstam, L Jeffrey Medeiros, John N Weinstein, Xiaoping Su
Elucidation of tumor-DNA virus associations in many cancer types has enhanced our knowledge of fundamental oncogenesis mechanisms and provided a basis for cancer prevention initiatives. RNA-Seq is a novel tool to comprehensively assess such associations. We interrogated RNA-Seq data from 3,775 malignant neoplasms in The Cancer Genome Atlas database for the presence of viral sequences. Viral integration sites were also detected in expressed transcripts using a novel approach. The detection capacity of RNA-Seq was compared to available clinical laboratory data...
August 2013: Journal of Virology
Yong-Wan Kim, Dimpy Koul, Se Hoon Kim, Agda Karina Lucio-Eterovic, Pablo R Freire, Jun Yao, Jing Wang, Jonas S Almeida, Ken Aldape, W K Alfred Yung
BACKGROUND: The Cancer Genome Atlas (TCGA) project is a large-scale effort with the goal of identifying novel molecular aberrations in glioblastoma (GBM). METHODS: Here, we describe an in-depth analysis of gene expression data and copy number aberration (CNA) data to classify GBMs into prognostic groups to determine correlates of subtypes that may be biologically significant. RESULTS: To identify predictive survival models, we searched TCGA in 173 patients and identified 42 probe sets (P = ...
July 2013: Neuro-oncology
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