Comprehensive outline of whole exome sequencing data analysis tools available in clinical oncology

Bartha, Áron and Győrffy, Balázs (2019) Comprehensive outline of whole exome sequencing data analysis tools available in clinical oncology. CANCERS, 11 (11). ISSN 2072-6694


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Whole exome sequencing (WES) enables the analysis of all protein coding sequences in the human genome. This technology enables the investigation of cancer-related genetic aberrations that are predominantly located in the exonic regions. WES delivers high-throughput results at a reasonable price. Here, we review analysis tools enabling utilization of WES data in clinical and research settings. Technically, WES initially allows the detection of single nucleotide variants (SNVs) and copy number variations (CNVs), and data obtained through these methods can be combined and further utilized. Variant calling algorithms for SNVs range from standalone tools to machine learning-based combined pipelines. Tools for CNV detection compare the number of reads aligned to a dedicated segment. Both SNVs and CNVs help to identify mutations resulting in pharmacologically druggable alterations. The identification of homologous recombination deficiency enables the use of PARP inhibitors. Determining microsatellite instability and tumor mutation burden helps to select patients eligible for immunotherapy. To pave the way for clinical applications, we have to recognize some limitations of WES, including its restricted ability to detect CNVs, low coverage compared to targeted sequencing, and the missing consensus regarding references and minimal application requirements. Recently, Galaxy became the leading platform in non-command line-based WES data processing. The maturation of next-generation sequencing is reinforced by Food and Drug Administration (FDA)-approved methods for cancer screening, detection, and follow-up. WES is on the verge of becoming an affordable and sufficiently evolved technology for everyday clinical use. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

Item Type: Article
Additional Information: Export Date: 19 November 2019 Correspondence Address: Győrffy, B.; Semmelweis University, Department of Bioinformatics, 2ndDepartment of PediatricsHungary; email: Funding text 1: Acknowledgments: The study was supported by the NVKP_16-1-2016-0037, 2018-1.3.1-VKE-2018-00032 and KH-129581 grants of the National Research, Development and Innovation Office of Hungary. Testing and evaluation of tools was performed using infrastructure and support provided by ELIXIR.
Uncontrolled Keywords: CANCER; single nucleotide polymorphism; review; human; polymerase chain reaction; gene mutation; quantitative analysis; machine learning; Oncology; immunomodulating agent; cancer diagnosis; follow up; cancer immunotherapy; Melanoma; Copy number variation; fluorescence in situ hybridization; comparative genomic hybridization; cell heterogeneity; nicotinamide adenine dinucleotide adenosine diphosphate ribosyltransferase inhibitor; bioinformatics; bioinformatics; Microsatellite Instability; Renal cell carcinoma; cancer genetics; Homologous recombination; web browser; Next generation sequencing; Whole exome sequencing; Whole exome sequencing; non small cell lung cancer; checkpoint kinase inhibitor;
Subjects: R Medicine / orvostudomány > RC Internal medicine / belgyógyászat > RC0254 Neoplasms. Tumors. Oncology (including Cancer) / daganatok, tumorok, onkológia
R Medicine / orvostudomány > RJ Pediatrics / gyermekgyógyászat
Depositing User: MTMT SWORD
Date Deposited: 29 Nov 2019 15:56
Last Modified: 29 Nov 2019 15:56

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