Giraffe: A tool for comprehensive processing and visualization of multiple long-read sequencing data.

DNA methylation Direct RNA sequencing Long read sequencing Read quality comparison Sequencing bias

Journal

Computational and structural biotechnology journal
ISSN: 2001-0370
Titre abrégé: Comput Struct Biotechnol J
Pays: Netherlands
ID NLM: 101585369

Informations de publication

Date de publication:
Dec 2024
Historique:
received: 26 05 2024
revised: 06 08 2024
accepted: 06 08 2024
medline: 16 9 2024
pubmed: 16 9 2024
entrez: 16 9 2024
Statut: epublish

Résumé

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_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).

Identifiants

pubmed: 39279873
doi: 10.1016/j.csbj.2024.08.003
pii: S2001-0370(24)00262-9
pmc: PMC11393587
doi:

Types de publication

Journal Article

Langues

eng

Pagination

3241-3246

Informations de copyright

© 2024 The Authors.

Déclaration de conflit d'intérêts

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.

Auteurs

Xudong Liu (X)

Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, China.

Yanwen Shao (Y)

Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, China.

Zhihao Guo (Z)

Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, China.

Ying Ni (Y)

Department of Biomedical Sciences, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, China.

Xuan Sun (X)

Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong Special Administrative Region.
ZeBlast Technology Limited, Hong Kong Science Park, Hong Kong Special Administrative Region.

Anskar Yu Hung Leung (AYH)

Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong Special Administrative Region.
ZeBlast Technology Limited, Hong Kong Science Park, Hong Kong Special Administrative Region.
Centre for Oncology and Immunology, Hong Kong Science Park, Hong Kong Special Administrative Region.

Runsheng Li (R)

Department of Infectious Diseases and Public Health, Jockey Club College of Veterinary Medicine and Life Sciences, City University of Hong Kong, Hong Kong, China.
Department of Precision Diagnostic and Therapeutic Technology, City University of Hong Kong Shenzhen Futian Research Institute, Shenzhen, China.
Tung Biomedical Sciences Centre, City University of Hong Kong, Hong Kong, China.

Classifications MeSH