Abstract
Currently, different sequencing platforms are used to generate plant genomes and no workflow has been properly developed to optimize time, cost, and assembly quality. We present LeafGo, a complete de novo plant genome workflow, that starts from tissue and produces genomes with modest laboratory and bioinformatic resources in approximately 7 days and using one long-read sequencing technology. LeafGo is optimized with ten different plant species, three of which are used to generate high-quality chromosome-level assemblies without any scaffolding technologies. Finally, we report the diploid genomes of Eucalyptus rudis and E. camaldulensis and the allotetraploid genome of Arachis hypogaea.
Original language | English (US) |
---|---|
Journal | Genome biology |
Volume | 22 |
Issue number | 1 |
DOIs | |
State | Published - Sep 4 2021 |
Bibliographical note
KAUST Repository Item: Exported on 2021-09-07Acknowledgements: We thank Dr. Boubacar Kountche and Prof. Salim AlBabili for insightful discussions on the wet-lab work and their constructive support for BCL, and the KAUST Supercomputing Core Lab for providing support for the computing resources. We thank Dr. Dean Nicolle for the phenotypic identification of the two Eucalyptus species and the three anonymous reviewers for their constructive criticism. We thank the KAUST Core Labs management for supporting this study. In fond memory of our talented colleague Dr. Kamel Jabbari.
ASJC Scopus subject areas
- Genetics
Fingerprint
Dive into the research topics of 'LeafGo: Leaf to Genome, a quick workflow to produce high-quality de novo plant genomes using long-read sequencing technology.'. Together they form a unique fingerprint.Datasets
-
LeafGo - Eucalyptus and Peanut genome sequencing
Driguez, P. (Creator), Bougouffa, S. (Creator), Carty, K. (Creator), Putra, A. (Creator), Jabbari, K. (Creator), Reddy, M. P. (Creator), Soppe, R. W. O. (Creator), Cheung, M. S. (Creator), Fukasawa, Y. (Creator), Ermini, L. (Creator), Driguez, P. (Creator), Carty, K. (Creator) & Fukasawa, Y. (Creator), NCBI, Nov 5 2020
http://hdl.handle.net/10754/671179
Dataset