Duran Chris, Appleby Nikki, Vardy Megan, Imelfort Michael, Edwards David, Batley Jacqueline
Australian Centre for Plant Functional Genomics, School of Land, Crop and Food Sciences, Institute for Molecular Bioscience, University of Queensland, Brisbane, Qld 4072, Australia.
Plant Biotechnol J. 2009 May;7(4):326-33. doi: 10.1111/j.1467-7652.2009.00407.x.
Molecular markers are used to provide the link between genotype and phenotype, for the production of molecular genetic maps and to assess genetic diversity within and between related species. Single nucleotide polymorphisms (SNPs) are the most abundant molecular genetic marker. SNPs can be identified in silico, but care must be taken to ensure that the identified SNPs reflect true genetic variation and are not a result of errors associated with DNA sequencing. The SNP detection method autoSNP has been developed to identify SNPs from sequence data for any species. Confidence in the predicted SNPs is based on sequence redundancy, and haplotype co-segregation scores are calculated for a further independent measure of confidence. We have extended the autoSNP method to produce autoSNPdb, which integrates SNP and gene annotation information with a graphical viewer. We have applied this software to public barley expressed sequences, and the resulting database is available over the Internet. SNPs can be viewed and searched by sequence, functional annotation or predicted synteny with a reference genome, in this case rice. The correlation between SNPs and barley cultivar, expressed tissue type and development stage has been collated for ease of exploration. An average of one SNP per 240 bp was identified, with SNPs more prevalent in the 5' regions and simple sequence repeat (SSR) flanking sequences. Overall, autoSNPdb can provide a wealth of genetic polymorphism information for any species for which sequence data are available.
分子标记用于建立基因型与表型之间的联系,绘制分子遗传图谱,并评估相关物种内部和之间的遗传多样性。单核苷酸多态性(SNP)是最为丰富的分子遗传标记。SNP可通过计算机分析鉴定,但必须注意确保所鉴定的SNP反映真实的遗传变异,而非与DNA测序相关的错误导致的结果。已开发出SNP检测方法autoSNP,用于从任何物种的序列数据中鉴定SNP。对预测SNP的可信度基于序列冗余度,并且计算单倍型共分离分数作为进一步独立的可信度度量。我们扩展了autoSNP方法以生成autoSNPdb,它将SNP和基因注释信息与图形查看器整合在一起。我们已将此软件应用于公开的大麦表达序列,所得数据库可通过互联网获取。SNP可通过序列、功能注释或与参考基因组(在此为水稻)的预测同线性进行查看和搜索。已整理了SNP与大麦品种、表达的组织类型和发育阶段之间的相关性,以便于探究。平均每240 bp鉴定出一个SNP,SNP在5'区域和简单序列重复(SSR)侧翼序列中更为普遍。总体而言,autoSNPdb可为任何有可用序列数据的物种提供丰富的遗传多态性信息。