Twelve quick steps for genome assembly and annotation in the classroom

Abstract

Eukaryotic genome sequencing and de novo assembly, once the exclusive domain of well-funded international consortia, have become increasingly affordable, thus fitting the budgets of individual research groups. Third-generation long-read DNA sequencing technologies are increasingly used, providing extensive genomic toolkits that were once reserved for a few select model organisms. Generating high-quality genome assemblies and annotations for many aquatic species still presents significant challenges due to their large genome sizes, complexity, and high chromosome numbers. Indeed, selecting the most appropriate sequencing and software platforms and annotation pipelines for a new genome project can be daunting because tools often only work in limited contexts. In genomics, generating a high-quality genome assembly/annotation has become an indispensable tool for better understanding the biology of any species. Herein, we state 12 steps to help researchers get started in genome projects by presenting guidelines that are broadly applicable (to any species), sustainable over time, and cover all aspects of genome assembly and annotation projects from start to finish. We review some commonly used approaches, including practical methods to extract high-quality DNA and choices for the best sequencing platforms and library preparations. In addition, we discuss the range of potential bioinformatics pipelines, including structural and functional annotations (e.g., transposable elements and repetitive sequences). This paper also includes information on how to build a wide community for a genome project, the importance of data management, and how to make the data and results Findable, Accessible, Interoperable, and Reusable (FAIR) by submitting them to a public repository and sharing them with the research community.

Whole-Genome Sequencing Analysis to Identify Infection with Multiple Species of Nontuberculous Mycobacteria

“…Only one of our clinical sample was identified as mixed NTM species based on the rpoB sequence types found. Our failure to detect the hsp65 gene from metagenomic assemblies might be due to the low quality of, or errors in the draft assemblies [ 45 ]. In some metagenomic assemblies, we could not detect the 16S rRNA gene.…”

easyfm: An easy software suite for file manipulation of Next Generation Sequencing data on desktops

“…With the broad implementation of NGS technologies in the life sciences, genomics and transcriptomics sequencing data are generated at an unprecedented rate [1–3]. Rapid progress in NGS technologies has brought massively high-throughput sequencing data to support research questions across many research fields, enabling a new era of genomic research [2,3]. Simultaneously, this advancement has brought enormous challenges in data analysis, of which efficient, standardized and consistent analysis are fundamental steps for maintaining reproducibility, especially for biologists [1,3].…”

“…Rapid progress in NGS technologies has brought massively high-throughput sequencing data to support research questions across many research fields, enabling a new era of genomic research [2,3]. Simultaneously, this advancement has brought enormous challenges in data analysis, of which efficient, standardized and consistent analysis are fundamental steps for maintaining reproducibility, especially for biologists [1,3]. However, many of the available tools for NGS data analysis require higher-order computational experience (e.g.…”

“…With the broad implementation of NGS technologies in the life sciences, genomics and transcriptomics sequencing data are generated at an unprecedented rate [1][2][3]. Rapid progress in NGS technologies has brought massively high-throughput sequencing data to support research questions across many research fields, enabling a new era of genomic research [2,3].…”

“…Simultaneously, this advancement has brought enormous challenges in data analysis, of which efficient, standardized and consistent analysis are fundamental steps for maintaining reproducibility, especially for biologists [1,3]. However, many of the available tools for NGS data analysis require higher-order computational experience (e.g.…”