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Giraffe: A tool for comprehensive processing and visualization of multiple long-read sequencing data.
- Source :
-
Computational and structural biotechnology journal [Comput Struct Biotechnol J] 2024 Aug 09; Vol. 23, pp. 3241-3246. Date of Electronic Publication: 2024 Aug 09 (Print Publication: 2024). - Publication Year :
- 2024
-
Abstract
- Third-generation sequencing techniques have become increasingly popular due to their capacity to produce long, high-quality reads. Effective comparative analysis across various samples and sequencing platforms is essential for understanding biological mechanisms and establishing benchmark baselines. However, existing tools for long-read sequencing predominantly focus on quality control (QC) and processing for individual samples, complicating the comparison of multiple datasets. The lack of comprehensive tools for data comparison and visualization presents challenges for researchers with limited bioinformatics experience. To address this gap, we present Giraffe (https://github.com/lrslab/Giraffe&#95;View), a Python3-based command-line tool designed for comparative analysis and visualization across diverse samples and platforms. Giraffe facilitates the assessment of read quality, sequencing bias, and genomic regional methylation proportions for both DNA and direct RNA sequencing reads. Its effectiveness has been demonstrated in various scenarios, including comparisons of sequencing methods (whole genome amplification vs. shotgun), sequencing platforms (Oxford Nanopore Technology, ONT vs. Pacific Biosciences, PacBio), tissues (kidney marrow with and without blood), and biological replicates (kidney marrows).<br />Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (© 2024 The Authors.)
Details
- Language :
- English
- ISSN :
- 2001-0370
- Volume :
- 23
- Database :
- MEDLINE
- Journal :
- Computational and structural biotechnology journal
- Publication Type :
- Academic Journal
- Accession number :
- 39279873
- Full Text :
- https://doi.org/10.1016/j.csbj.2024.08.003