期刊
NATURE PROTOCOLS
卷 13, 期 6, 页码 1213-1231出版社
NATURE PORTFOLIO
DOI: 10.1038/nprot.2018.025
关键词
-
资金
- ATIP-Avenir (CNRS/INSERM)
- Fondation ARC pour la Recherche sur le Cancer [PJA20151203273, PDF20150602803]
- Marie Curie Career Integration [322035]
- Agence Nationale de la Recherche [ANR-16-CE12-0019, ANR-13-BSV6-0006-01, ANR-11-LABX-0028-01]
- Canceropole PACA
- DuPont Young Professor Award
- Agence Nationale de la Recherche (ANR) [ANR-13-BSV6-0006, ANR-16-CE12-0019] Funding Source: Agence Nationale de la Recherche (ANR)
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 similar to 41 h to generate a complete and well-annotated genome from similar to 100x 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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据