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Long-read sequencing data analysis for yeasts

Nature Protocols volume 13, pages 1213–1231 (2018)Cite this article

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Abstract

Long-read sequencing technologies have become increasingly popular due to their strengths in resolving complex genomic regions. As a leading model organism with small genome size and great biotechnological importance, the budding yeast Saccharomyces cerevisiae has many isolates currently being sequenced with long reads. However, analyzing long-read sequencing data to produce high-quality genome assembly and annotation remains challenging. Here, we present a modular computational framework named long-read sequencing data analysis for yeasts (LRSDAY), the first one-stop solution that streamlines this process. Starting from the raw sequencing reads, LRSDAY can produce chromosome-level genome assembly and comprehensive genome annotation in a highly automated manner with minimal manual intervention, which is not possible using any alternative tool available to date. The annotated genomic features include centromeres, protein-coding genes, tRNAs, transposable elements (TEs), and telomere-associated elements. Although tailored for S. cerevisiae, we designed LRSDAY to be highly modular and customizable, making it adaptable to virtually any eukaryotic organism. When applying LRSDAY to an S. cerevisiae strain, it takes ∼41 h to generate a complete and well-annotated genome from ∼100× Pacific Biosciences (PacBio) running the basic workflow with four threads. Basic experience working within the Linux command-line environment is recommended for carrying out the analysis using LRSDAY.

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Figure 1: Overview of the LRSDAY directory system.
Figure 2: The LRSDAY workflow.
Figure 3: Genome-wide dot plots of the S. cerevisiae SK1 genome assembly generated in the LRSDAY testing example.

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Acknowledgements

We thank the developers of the MAKER software for allowing us to incorporate MAKER into the LRSDAY auto-installation process. We thank G. Fischer, S. O'Donnell, and L. Tattini for testing LRSDAY and providing valuable feedback. This work was supported by ATIP-Avenir (CNRS/INSERM), Fondation ARC pour la Recherche sur le Cancer (PJA20151203273), Marie Curie Career Integration Grants (322035), Agence Nationale de la Recherche (ANR-16-CE12-0019, ANR-13-BSV6-0006-01, and ANR-11-LABX-0028-01), Cancéropôle PACA (AAP émergence 2015), and a DuPont Young Professor Award to G.L. J.-X.Y. was supported by a postdoctoral fellowship from Fondation ARC pour la Recherche sur le Cancer (PDF20150602803).

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Authors and Affiliations

  1. Université Côte d'Azur, CNRS, INSERM, IRCAN, Nice, France

    Jia-Xing Yue & Gianni Liti

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  1. Jia-Xing Yue
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  2. Gianni Liti
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Contributions

J.-X.Y. designed, implemented, and tested the LRSDAY workflow. G.L. coordinated the work. J.-X.Y. and G.L. wrote the manuscript.

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Correspondence to Jia-Xing Yue or Gianni Liti.

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Yue, JX., Liti, G. Long-read sequencing data analysis for yeasts. Nat Protoc 13, 1213–1231 (2018). https://doi.org/10.1038/nprot.2018.025

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