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测序技术推动了基因组组装（genome assembly）领域诸多新型方法的发展。除极少数例外，此类算法均为启发式算法（heuristic algorithm），且需用户手动设置一个或多个参数。参数调优（parameter tuning）的常用方案之一是：使用携带已知高质量参考基因组（reference genome）的生物体的测序数据进行组装，并通过各类评估指标衡量组装准确性。本研究开发了一套系统，可基于多种评分指标（scoring metrics）评估基因组组装质量，并可对比不同基因组组装工具（assembler）、测序数据类型及参数设置下的组装质量表现。若结合高质量参考基因组与同一生物体的测序读段（sequence reads）这类训练数据使用，本程序可辅助人工筛选适用于近缘物种从头测序（de novo sequencing）的最优测序与组装策略。可用性说明：GNU通用公共许可证（GPL）协议开源代码与使用教程详见 http://ngopt.googlecode.com