Computational approaches for cancer genomics
Identifying somatic intergenic mutations of relevance in cancer
(Sprache: Englisch)
Cancer is a complex genomic disease characterized by accumulation of somatic mutations over the lifetime of a patient. Identification of somatic driver mutations that contribute to tumorigenesis is a major goal of cancer genomics. With the recent advances...
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Cancer is a complex genomic disease characterized by accumulation of somatic mutations over the lifetime of a patient. Identification of somatic driver mutations that contribute to tumorigenesis is a major goal of cancer genomics. With the recent advances in the sequencing technologies it became possible to study somatic mutations on the whole-genome scale in multiple cancers. While most of the cancer genomics studies were previously focused on identification of driver mutations affecting exons, several examples of driver events within the non-protein-coding regions of the genome were identified, including the recurrent TERT promoter mutations. Such findings have spurred searches for similar examples of recurrent non-coding mutations using computational cancer genomics. In this book I present several computational approaches aimed to identify somatic driver mutations of relevance in cancer, with a specific focus on intergenic regions of the genome.
Autoren-Porträt von Vasilisa A. Rudneva
Rudneva, Vasilisa A.Dr. Rudneva was born in Moscow, Russia. She studied bioengineering and bioinformatics at Lomonosov Moscow State University and received her PhD from Ruprecht-Karls-Universität Heidelberg in 2016. Dr. Rudneva works in the field of pediatric neuro-oncology, her research was published in such prestigeous journals as Nature and Lancet Oncology.
Bibliographische Angaben
- Autor: Vasilisa A. Rudneva
- 2018, 100 Seiten, Maße: 22 cm, Kartoniert (TB), Englisch
- Verlag: AV Akademikerverlag
- ISBN-10: 3330516445
- ISBN-13: 9783330516441
Sprache:
Englisch
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