Mason Christopher E, Afshinnekoo Ebrahim, Tighe Scott, Wu Shixiu, Levy Shawn
Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York 10065, USA;; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York 10065, USA;; Feil Family Brain & Mind Research Institute, Weill Cornell Medicine, New York, New York 10065, USA.
Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York 10065, USA;; The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, New York 10065, USA;; School of Medicine, New York Medical College, Valhalla, New York 10595, USA.
J Biomol Tech. 2017 Apr;28(1):8-18. doi: 10.7171/jbt.17-2801-006. Epub 2017 Mar 17.
Challenges and biases in preparing, characterizing, and sequencing DNA and RNA can have significant impacts on research in genomics across all kingdoms of life, including experiments in single-cells, RNA profiling, and metagenomics (across multiple genomes). Technical artifacts and contamination can arise at each point of sample manipulation, extraction, sequencing, and analysis. Thus, the measurement and benchmarking of these potential sources of error are of paramount importance as next-generation sequencing (NGS) projects become more global and ubiquitous. Fortunately, a variety of methods, standards, and technologies have recently emerged that improve measurements in genomics and sequencing, from the initial input material to the computational pipelines that process and annotate the data. Here we review current standards and their applications in genomics, including whole genomes, transcriptomes, mixed genomic samples (metagenomes), and the modified bases within each (epigenomes and epitranscriptomes). These standards, tools, and metrics are critical for quantifying the accuracy of NGS methods, which will be essential for robust approaches in clinical genomics and precision medicine.
在DNA和RNA的制备、表征及测序过程中所面临的挑战和偏差，可能会对涵盖所有生命王国的基因组学研究产生重大影响，包括单细胞实验、RNA分析及宏基因组学（跨多个基因组）研究。在样本处理、提取、测序及分析的每个环节，都可能出现技术假象和污染。因此，随着下一代测序（NGS）项目变得更加全球化和普遍化，对这些潜在误差来源的测量和基准测试至关重要。幸运的是，最近出现了多种方法、标准和技术，可改善从初始输入材料到处理和注释数据的计算流程等基因组学和测序中的测量。在此，我们综述当前标准及其在基因组学中的应用，包括全基因组、转录组、混合基因组样本（宏基因组）以及其中每个样本内的修饰碱基（表观基因组和表观转录组）。这些标准、工具和指标对于量化NGS方法的准确性至关重要，而这对于临床基因组学和精准医学中的稳健方法必不可少。
