A Practical Guide to The Cancer Genome Atlas (TCGA)

Wang, Zhining; Jensen, Mark A.; Zenklusen, Jean C.

doi:10.1007/978-1-4939-3578-9_6

Cited by 499 publications

(404 citation statements)

References 2 publications

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“…The next‐generation sequencing (NGS) revolution has opened up a new age in cancer genomics. More than 20,000 cancer genomes/exomes have been sequenced by three large consortia: The Cancer Genome Atlas (TCGA, http://cancergenome.nih.gov/), a project of the National Cancer Institute and the National Human Genome Research Institute, the UK Cancer Genome Project from the Sanger Institute (http://www.sanger.ac.uk/), and the International Cancer Genome Consortium (ICGC, http://icgc.org/) [McDermott et al., ; Joly et al., ; Wang et al., ]. As a result of the decreased costs of new sequencers, data on multiple cancer genomes are also released by individual teams, but, in most cases, only the sequencing of a specific subset of cancer genes panel is performed.…”

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confidence: 99%

Synonymous Somatic Variants in Human Cancer Are Not Infamous: A Plea for Full Disclosure in Databases and Publications

2017

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Single-nucleotide variants (SNVs) are the most frequent genetic changes found in human cancer. Most driver alterations are missense and nonsense variants localized in the coding region of cancer genes. Unbiased cancer genome sequencing shows that synonymous SNVs (sSNVs) can be found clustered in the coding regions of several cancer oncogenes or tumor suppressor genes suggesting purifying selection. sSNVs are currently underestimated, as they are usually discarded during analysis. Furthermore, several public databases do not display sSNVs, which can lead to analytical bias and the false assumption that this mutational event is uncommon. Recent progress in our understanding of the deleterious consequences of these sSNVs for RNA stability and protein translation shows that they can act as strong drivers of cancer, as demonstrated for several cancer genes such as TP53 or BCL2L12. It is therefore essential that sSNVs be properly reported and analyzed in order to provide an accurate picture of the genetic landscape of the cancer genome.

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confidence: 99%

Synonymous Somatic Variants in Human Cancer Are Not Infamous: A Plea for Full Disclosure in Databases and Publications

2017

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“…The genome-wide mutation datasets across 17 types of cancer were downloaded from TCGA as maf files [35, 36]. Only the Hi-Seq platform datasets were considered in our analysis.…”

Section: Methodsmentioning

confidence: 99%

Comparative analysis of protein interactome networks prioritizes candidate genes with cancer signatures

Sahni

2016

Oncotarget

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Comprehensive understanding of human cancer mechanisms requires the identification of a thorough list of cancer-associated genes, which could serve as biomarkers for diagnoses and therapies in various types of cancer. Although substantial progress has been made in functional studies to uncover genes involved in cancer, these efforts are often time-consuming and costly. Therefore, it remains challenging to comprehensively identify cancer candidate genes. Network-based methods have accelerated this process through the analysis of complex molecular interactions in the cell. However, the extent to which various interactome networks can contribute to prediction of candidate genes responsible for cancer is still enigmatic. In this study, we evaluated different human protein-protein interactome networks and compared their application to cancer gene prioritization. Our results indicate that network analyses can increase the power to identify novel cancer genes. In particular, such predictive power can be enhanced with the use of unbiased systematic protein interaction maps for cancer gene prioritization. Functional analysis reveals that the top ranked genes from network predictions co-occur often with cancer-related terms in literature, and further, these candidate genes are indeed frequently mutated across cancers. Finally, our study suggests that integrating interactome networks with other omics datasets could provide novel insights into cancer-associated genes and underlying molecular mechanisms.

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“…Large‐scale sequencing projects, such as the 1,000 Genomes Project, the Cancer Genome Atlas, and the Personal Genome Project, generated high‐coverage human genomes providing the reference sequences that can be used to detect human genome contaminations in genomic and metagenomic data sets. In order to detect human contaminations, metagenomes need to be compared with the human genome.…”

Section: Human‐originated Contaminationsmentioning

confidence: 99%

Contaminations in (meta)genome data: An open issue for the scientific community

Pasquadibisceglie

Proietto

et al. 2019

IUBMB Life

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In recent years, the high throughput and the low cost of next‐generation sequencing (NGS) technologies have led to an increase of the amount of (meta)genomic data, revolutionizing genomic research studies. However, the quality of sequencing data could be affected by experimental errors derived from defective methods and protocols. This represents a serious problem for the scientific community with a negative impact on the correctness of studies that involve genomic sequence analysis. As a countermeasure, several alignment and taxonomic classification tools have been developed to uncover and correct errors. In this critical review some of these integrated software tools and pipelines used to detect contaminations in reference genome databases and sequenced samples are reported. In particular, case studies of bacterial contaminations, contaminations of human origin, mitochondrial contaminations of ancient DNA, and cross contaminations are examined.

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A Practical Guide to The Cancer Genome Atlas (TCGA)

Cited by 499 publications

References 2 publications

Synonymous Somatic Variants in Human Cancer Are Not Infamous: A Plea for Full Disclosure in Databases and Publications

Synonymous Somatic Variants in Human Cancer Are Not Infamous: A Plea for Full Disclosure in Databases and Publications

Comparative analysis of protein interactome networks prioritizes candidate genes with cancer signatures

Contaminations in (meta)genome data: An open issue for the scientific community

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