ArticleGenome research2018
Maftools: efficient and comprehensive analysis of somatic variants in cancer.
Article in Genome research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06680791 (Molecular Classification in Relation to Prevention of Endometrial Cancer Recurrence and Lifestyle Factors), which is not on this map. Cited by 3,429 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Molecular Classification in Relation to Prevention of Endometrial Cancer Recurrence and Lifestyle Factors
Who cites it
3,429 citing papers in PubMed, 1 synthesis or guideline pooled it.
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3,369 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Numerous large-scale genomic studies of matched tumor-normal samples have established the somatic landscapes of most cancer types. However, the downstream analysis of data from somatic mutations entails a number of computational and statistical approaches, requiring usage of independent software and numerous tools. Here, we describe an R Bioconductor package, Maftools, which offers a multitude of analysis and visualization modules that are commonly used in cancer genomic studies, including driver gene identification, pathway, signature, enrichment, and association analyses. Maftools only requires somatic variants in Mutation Annotation Format (MAF) and is independent of larger alignment files. With the implementation of well-established statistical and computational methods, Maftools facilitates data-driven research and comparative analysis to discover novel results from publicly available data sets. In the present study, using three of the well-annotated cohorts from The Cancer Genome Atlas (TCGA), we describe the application of Maftools to reproduce known results. More importantly, we show that Maftools can also be used to uncover novel findings through integrative analysis.
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