顶级实验室《Science》开启基因组测序新纪元

　　

生物通报道，来自Lawrence Livermore国家实验室，美国能源基因组联合研究所，密歇根州立大学，Sanger研究院，Broad研究所，等多个权威实验室的科学家联合发布基因测序数据分类的新标准文章Genome Project Standards in a New Era of Sequencing，该文章刊登在10月9日的Science杂志上。

 

文章通讯作者分别是来自Sanger研究院的D. V. Grafham以及Lawrence Livermore国家实验室的P. S. G. Chain。

 

在过去的10年里，基因组测序技术飞速发展，罗氏454，Illumina，SOLiD以及时下最新的Helicos测序仪，测序仪可谓让人眼花缭乱。然而，对测序数据的分析却一直停滞不前，这10年来，科学家们一直坚持使用着2种分析标准。

 

随着测序技术的进步，旧的标准已经不再适应新的技术，旧的标准存在一些固有的误差，所得出的基因图谱也是一个粗略的基因图谱，并且以往的数据库所包含的数据有限，给基因数据的准确性留下了太多的不确定性，并不能涵盖所有的基因数据，因此，需要一个新的分析标准的要求变得越来越迫切。一个序列中的高度不确定性可能会引导研究人员走向一条耗时长达一年甚至数年的错误道路。

 

据科技日报消息，D. V. Grafham以及Lawrence Livermore带领的研究小组共同提议将现有的测序数据分类从两大类充实为6大类。这6个标准涵盖了从代表公众提交最低要求的“标准草图序列”到代表最高标准的“完成序列”，而“完成序列”的验收标准是每10万个碱基对中最多只能包含一个错误。该项研究的目的是为了让所有主要的基因组中心和基因组研究小组都能用上符合其需要的分类基因组测序数据。而为了尽可能保证基因组序列的完整性，一些较小的研究中心也可采用这个分类等级来建立和提交其研究成果，以帮助其他科学家了解既已完成的工作。

（生物通 小茜）

生物通推荐原文检索

Genome Project Standards in a New Era of Sequencing

P. S. G. Chain,1,2,3,*,, D. V. Grafham,4,, R. S. Fulton,5, M. G. FitzGerald,6, J. Hostetler,7, D. Muzny,8, J. Ali,9 B. Birren,6 D. C. Bruce,1,10 C. Buhay,8 J. R. Cole,3 Y. Ding,8 S. Dugan,8 D. Field,11 G. M. Garrity,3 R. Gibbs,8 T. Graves,5 C. S. Han,1,10 S. H. Harrison,3,* S. Highlander,8 P. Hugenholtz,1 H. M. Khouri,12 C. D. Kodira,6,* E. Kolker,13,14 N. C. Kyrpides,1 D. Lang,12 A. Lapidus,1 S. A. Malfatti,12 V. Markowitz,15 T. Metha,6 K. E. Nelson,7 J. Parkhill,4 S. Pitluck,1 X. Qin,8 T. D. Read,16 J. Schmutz,17 S. Sozhamannan,18 P. Sterk,11 R. L. Strausberg,7 G. Sutton,7 N. R. Thomson,4 J. M. Tiedje,3 G. Weinstock,5 A. Wollam,5 Genomic Standards Consortium Human Microbiome Project Jumpstart Consortium, J. C. Detter10,,

For over a decade, genome sequences have adhered to only two standards that are relied on for purposes of sequence analysis by interested third parties (1, 2). However, ongoing developments in revolutionary sequencing technologies have resulted in a redefinition of traditional whole-genome sequencing that requires reevaluation of such standards. With commercially available 454 pyrosequencing (followed by Illumina, SOLiD, and now Helicos), there has been an explosion of genomes sequenced under the moniker "draft"; however, these can be very poor quality genomes (due to inherent errors in the sequencing technologies, and the inability of assembly programs to fully address these errors). Further, one can only infer that such draft genomes may be of poor quality by navigating through the databases to find the number and type of reads deposited in sequence trace repositories (and not all genomes have this available), or to identify the number of contigs or genome fragments deposited to the database. The difficulty in assessing the quality of such deposited genomes has created some havoc for genome analysis pipelines and has contributed to many wasted hours. Exponential leaps in raw sequencing capability and greatly reduced prices have further skewed the time- and cost-ratios of draft data generation versus the painstaking process of improving and finishing a genome. The result is an ever-widening gap between drafted and finished genomes that only promises to continue (see the figure, page 236); hence, there is an urgent need to distinguish good from poor data sets.