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Detailed evaluation of cancer sequencing pipelines in different microenvironments and heterogeneity levels
Author(s) -
Batuhan Kısakol,
Şahin Sarihan,
Mehmet Arif Ergün,
Mehmet Baysan
Publication year - 2021
Publication title -
turkish journal of biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.323
H-Index - 38
eISSN - 1303-6092
pISSN - 1300-0152
DOI - 10.3906/biy-2008-8
Subject(s) - pipeline (software) , pipeline transport , benchmarking , computer science , data mining , computational biology , raw data , complement (music) , dna sequencing , biology , genetics , gene , engineering , phenotype , marketing , environmental engineering , complementation , business , programming language
The importance of next generation sequencing (NGS) rises in cancer research as accessing this key technology becomes easier for researchers. The sequence data created by NGS technologies must be processed by various bioinformatics algorithms within a pipeline in order to convert raw data to meaningful information. Mapping and variant calling are the two main steps of these analysis pipelines, and many algorithms are available for these steps. Therefore, detailed benchmarking of these algorithms in different scenarios is crucial for the efficient utilization of sequencing technologies. In this study, we compared the performance of twelve pipelines (three mapping and four variant discovery algorithms) with recommended settings to capture single nucleotide variants. We observed significant discrepancy in variant calls among tested pipelines for different heterogeneity levels in real and simulated samples with overall high specificity and low sensitivity. Additional to the individual evaluation of pipelines, we also constructed and tested the performance of pipeline combinations. In these analyses, we observed that certain pipelines complement each other much better than others and display superior performance than individual pipelines. This suggests that adhering to a single pipeline is not optimal for cancer sequencing analysis and sample heterogeneity should be considered in algorithm optimization.

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