A survey and evaluation of Web-based tools/databases for variant analysis of TCGA data

Zhang, Zhuo; Li, Hao; Jiang, Shuai; Li, Ruijiang; Li, Wanying; Chen, Hebing; Bo, Xiaochen

doi:10.1093/bib/bby023

Cited by 62 publications

(48 citation statements)

References 72 publications

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“…Therefore, we carried out this discovery to indicate the potential application of DNA methylation in STSs epigenomes classification. The TCGA database is a publicly available resource that contains more than 30 large cohorts of human tumors with a comprehensive multidimensional analysis [37], these large sample sizes are absolutely the basis for us to provide an in-depth understanding of the etiology of STSs. In this study, the whole genome DNA methylation sites corresponding to 269 STSs samples were also obtained from TCGA database and methylation sites in the gene promoter regions were first applied to select the prognosis associated CpG sites.…”

Section: Discussionmentioning

confidence: 99%

DNA methylation patterns–based subtype distinction and identification of soft tissue sarcoma prognosis

Fan

et al. 2020

Preprint

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Background: Soft tissue sarcomas (STSs) are heterogeneous at the clinical with a variable tendency of aggressive behavior. Methods: In this study, we constructed a specific DNA methylation-based classification to identify the distinct prognosis-subtypes of STSs based on the DNA methylation spectrum from the TCGA database.Results: Eventually, samples were clustered into four subgroups, and their survival curves were distinct from each other. Meanwhile, the samples in each subgroup reflected differentially in several clinical features. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was also conducted on the genes of the corresponding promoter regions of the above‐described specific methylation sites, revealing that these genes were mainly concentrated in certain cancer‑associated biological functions and pathways. In addition, we calculated the differences among clustered methylation sites and performed the specific methylation sites with LASSO algorithm. The selection operator algorithm was employed to derive a risk signature model, and a prognostic signature based on these methylation sites performed well for risk stratification in STSs patients. At last, a nomogram consisted of clinical features and risk score was developed for the survival prediction. Conclusion: In conclusion, this study declares that DNA methylation-based STSs subtype classification is highly relevant for future development of personalized therapy as it identifies the prediction value of patient prognosis.

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Section: Discussionmentioning

confidence: 99%

DNA methylation patterns–based subtype distinction and identification of soft tissue sarcoma prognosis

Fan

et al. 2020

Preprint

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“…The application of modern data,such as the use of databases and arti cial intelligence,can quickly process information and integrate accuracy.Now many online resource websites have become an indispensable tool for our scienti c research and speed up our work needs. Databases like TCGA [24] and oncomine [25] are the two major databases that we commonly use. But in the end it still depends on the repeated demonstration of experimental data.…”

Section: Discussionmentioning

confidence: 99%

Analysis of Paeoniflorin Treatment of Cancer Based on Bioinformatic Network and Potential Target Prediction

yongan¹,

Yu²,

Li³

et al. 2020

Preprint

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In the synthesis of chemical drugs used to treat various cancers and tumors,more and more scientists are now focusing on the development of natural extracts.Paeoniflorin,as an important compounds of the total glucosides of peony,,had therapeutic effects on a variety of cancers in vivo and in vitro experiments.The purpose of this study was to use biological information analysis to reveal the basic mechanism and potential molecular mechanism of paeoniflorin treatment.We evaluated the efficacy of paeoniflorin through Traditional Chinese Medicine Systems Pharmacology Database(TCMSP).The potential targets of paeoniflorin were identified and summarized by Comparative Toxicogenomics Database (CTD),and GeneMANIA protein interaction analysis was performed online.Also.We used WebGestalt for Gene Ontology(GO) analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG) analysis and functional annotation.Finally,cytoscape was used to construct the paeoniflorin-target-pathway network to predict the results.

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“…We report on multiple layers of batch effects associated with the sequencing centers contributing to TCGA, which are evident upon examination of called germline variants from thousands of samples. These systematic biases raise an urgent need to identify their exact source, be it experimental [21], technical [17, 18] or computational [22]. Understanding the sources of biases is essential for the ongoing effort to mitigate and adjust for such biases from high-throughput collection and data compilation [23, 24].…”

Section: Discussionmentioning

confidence: 99%

Substantial Batch Effects in TCGA Exome Sequences Undermine Pan-Cancer Analysis of Germline Variants

Rasnic

Brandes

Zuk³

et al. 2018

Preprint

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BackgroundIn recent years, research on cancer predisposition germline variants has emerged as a prominent field. The identity of somatic mutations is based on a reliable mapping of the patient germline variants. In addition, the statistics of germline variants frequencies in healthy individuals and cancer patients is the basis for seeking candidates for cancer predisposition genes. The Cancer Genome Atlas (TCGA) is one of the main sources of such data, providing a diverse collection of molecular data including deep sequencing for more than 30 types of cancer from >10,000 patients.MethodsOur hypothesis in this study is that whole exome sequences from healthy blood samples of cancer patients are not expected to show systematic differences among cancer types. To test this hypothesis, we analyzed common and rare germline variants across six cancer types, covering 2,241 samples from TCGA. In our analysis we accounted for inherent variables in the data including the different variant calling protocols, sequencing platforms, and ethnicity.ResultsWe report on substantial batch effects in germline variants associated with cancer types. We attribute the effect to the specific sequencing centers that produced the data. Specifically, we measured 30% variability in the number of reported germline variants per sample across sequencing centers. The batch effect is further expressed in nucleotide composition and variant frequencies. Importantly, the batch effect causes substantial differences in germline variant distribution patterns across numerous genes, including prominent cancer predisposition genes such as BRCA1, RET, MAX, and KRAS. For most of known cancer predisposition genes, we found a distinct batch-dependent difference in germline variants.ConclusionTCGA germline data is exposed to strong batch effects with substantial variabilities among TCGA sequencing centers. We claim that those batch effects are consequential for numerous TCGA pan-cancer studies. In particular, these effects may compromise the reliability and the potency to detect new cancer predisposition genes. Furthermore, interpretation of pan-cancer analyses should be revisited in view of the source of the genomic data after accounting for the reported batch effects.

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A survey and evaluation of Web-based tools/databases for variant analysis of TCGA data

Cited by 62 publications

References 72 publications

DNA methylation patterns–based subtype distinction and identification of soft tissue sarcoma prognosis

DNA methylation patterns–based subtype distinction and identification of soft tissue sarcoma prognosis

Analysis of Paeoniflorin Treatment of Cancer Based on Bioinformatic Network and Potential Target Prediction

Substantial Batch Effects in TCGA Exome Sequences Undermine Pan-Cancer Analysis of Germline Variants

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