Abstract
Long-read sequencing technologies, such as PacBio and Oxford Nanopore, have revolutionized genomics by providing more accurate and comprehensive insights into complex genomes, including structural variants, repetitive regions, and gene isoforms. However, the analysis of long-read sequencing data presents unique challenges, including high error rates, large data volumes, and the need for specialized bioinformatics tools. This article discusses the integrative analysis of long-read sequencing data, focusing on methods for improving read accuracy, aligning long reads to reference genomes, detecting structural variants, and analyzing transcriptomes. We also explore the advantages and limitations of long-read sequencing technologies, as well as the future directions for integrating long-read data with short-read sequencing and other omics data..
All articles published in the American Journal of Bioinformatics (AJB) are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Under this license, authors retain full copyright of their work and grant permission to anyone to:
-
Share — copy and redistribute the material in any medium or format
-
Adapt — remix, transform, and build upon the material for any purpose, even commercially
Conditions:
-
Attribution: Appropriate credit must be given to the original author(s) and the source, a link to the license must be provided, and any changes made must be indicated.
-
No additional restrictions: You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
This open-access license ensures that scholarly work published in AJB is freely accessible and usable, promoting knowledge dissemination and academic collaboration worldw