Correspondence on NanoVar's performance outlined by Jiang T. et al. in Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation

Article Biochemistry & Molecular Biology

Dysgu: efficient structural variant calling using short or long reads

Kez Cleal et al.

Summary: Structural variation (SV) is important in genome evolution and disease, and Dysgu is a tool that accurately detects SVs using paired-end or long-read sequencing, with high sensitivity and fast running speed.

NUCLEIC ACIDS RESEARCH (2022)

Add to Collection

Article Biochemical Research Methods

Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation

Tao Jiang et al.

Summary: With the rapid development of long-read sequencing technologies, it is now possible to reveal the full spectrum of genetic structural variation (SV). However, the high cost, limited read length, and sequencing errors associated with long-read data have hindered the widespread adoption of SV calling. This study provides important guidelines for selecting long-read sequencing settings for efficient SV calling, with high sequencing coverage being the most influential factor in promoting SV calling.

BMC BIOINFORMATICS (2021)

Add to Collection

Article Multidisciplinary Sciences

Structural variants in the Chinese population and their impact on phenotypes, diseases and population adaptation

Zhikun Wu et al.

Summary: This study conducted long-read sequencing-based structural variant analysis on 405 unrelated Chinese individuals, identifying 132,312 nonredundant structural variants, of which 45.2% are novel. The study also discovered rare deletions associated with anemia and structural variants related to immunity that differentiate the northern and southern Chinese populations.

NATURE COMMUNICATIONS (2021)

Add to Collection

Article Biotechnology & Applied Microbiology

A benchmark of structural variation detection by long reads through a realistic simulated model

Nicolas Dierckxsens et al.

Summary: Accurate simulations of structural variation distributions and sequencing data are essential for new tool development and benchmarking. The Sim-it tool is developed to simulate both structural variation and long-read data, revealing strengths and weaknesses of current available tools. Using these findings, the combiSV method is introduced to combine results from structural variation callers, resulting in a superior call set with increased recall and precision.

GENOME BIOLOGY (2021)

Add to Collection

Article Biochemical Research Methods