Evaluating nanopore sequencing data processing pipelines for structural variation identification.

ABSTRACT:

Background

Structural variations (SVs) account for about 1% of the differences among human genomes and play a significant role in phenotypic variation and disease susceptibility. The emerging nanopore sequencing technology can generate long sequence reads and can potentially provide accurate SV identification. However, the tools for aligning long-read data and detecting SVs have not been thoroughly evaluated.

Results

Using four nanopore datasets, including both empirical and simulated reads, we evaluate four alignment tools and three SV detection tools. We also evaluate the impact of sequencing depth on SV detection. Finally, we develop a machine learning approach to integrate call sets from multiple pipelines. Overall SV callers' performance varies depending on the SV types

SUBMITTER: Zhou A 

PROVIDER: S-EPMC6857234 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature