Data from: Mauve assembly metrics

High throughput DNA sequencing technologies have spurred the development of numerous novel methods for genome assembly. With few exceptions, these algorithms are heuristic and require one or more parameters to be manually set by the user. One approach to parameter tuning involves assembling data from an organism with an available high quality reference genome, and measuring assembly accuracy using some metrics. We developed a system to measure assembly quality under several scoring metrics, and to compare assembly quality across a variety of assemblers, sequence data types, and parameter choices. When used in conjunction with training data such as a high quality reference genome and sequence reads from the same organism, our program can be used to manually identify an optimal sequencing and assembly strategy for de novo sequencing of related organisms. Availability:
GPL source code and a usage tutorial is at http://ngopt.googlecode.com

高通量DNA测序技术（High throughput DNA sequencing technologies）推动了众多基因组组装（genome assembly）新方法的发展。除极少数例外，此类算法均为启发式（heuristic）算法，且需要用户手动设置一个或多个参数。参数调优（parameter tuning）的一种常见思路，是利用带有高质量参考基因组（reference genome）的生物体的测序数据进行组装，并通过各类指标衡量组装准确性（assembly accuracy）。本研究开发了一套系统，可基于多种评分指标（scoring metrics）评估组装质量，并可对比不同组装工具（assemblers）、序列数据类型（sequence data types）及参数设置下的组装质量。当结合高质量参考基因组、同一生物体的序列读数（sequence reads）等训练数据使用时，本程序可用于手动筛选出适用于近缘生物体从头测序（de novo sequencing）的最优测序与组装策略。获取方式：GPL开源代码及使用教程可访问http://ngopt.googlecode.com