Enabling proteomic studies with RNA-Seq: The proteome of tomato pollen as a test case
Enabling proteomic studies with RNA-Seq: The proteome of tomato pollen as a test case
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DOI：
10.1002/pmic.201100164
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发表时间：
2012-03-01
期刊：
影响因子：
3.4
通讯作者：
Rose, Jocelyn K. C.
中科院分区：
文献类型：
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作者：
Lopez-Casado, Gloria;Covey, Paul A.;Rose, Jocelyn K. C.
有效的蛋白质组分析通常被认为在很大程度上取决于高质量的DNA参考数据库的可用性。因此，蛋白质组学长期以来受到分类学的限制，对非模式生物的蛋白质组的研究进展有限。然而，下一代测序（NGS），特别是RNA-Seq，现在允许以低成本对表达的基因进行深度覆盖检测，这反过来又潜在地促进肽质谱与同源基因序列的匹配。为了测试这一点，我们进行了定量分析的花粉蛋白质组从驯化番茄（番茄）和两个野生亲戚表现出不同的交配系统和种间生殖障碍。使用通过454焦磷酸测序创建的定制番茄RNA-Seq数据库，鉴定了超过1200种蛋白质，其中子集显示基因型之间或相应转录本积累中的表达差异。重要的是，当质谱用于询问通过传统测序技术生成的番茄序列的高度策划的社区数据库或RNA-Seq数据库时，在表征的蛋白质组中没有观察到重大的定性或定量差异。我们的结论是，RNA-Seq提供了一个具有成本效益和强大的蛋白质鉴定平台，并将越来越有价值的蛋白质组学领域。
Effective proteome profiling is generally considered to depend heavily on the availability of a high-quality DNA reference database. As such, proteomics has long been taxonomically restricted, with limited inroads being made into the proteomes of non-model organisms. However, next generation sequencing (NGS), and particularly RNA-Seq, now allows deep coverage detection of expressed genes at low cost, which in turn potentially facilitates the matching of peptide mass spectra with cognate gene sequence. To test this, we performed a quantitative analysis of the proteomes of pollen from domesticated tomato (Solanum lycopersicum) and two wild relatives that exhibit differences in mating systems and in interspecific reproductive barriers. Using a custom tomato RNA-Seq database created through 454 pyrosequencing, more than 1200 proteins were identified, with subsets showing expression differences between genotypes or in the accumulation of the corresponding transcripts. Importantly, no major qualitative or quantitative differences were observed in the characterized proteomes when mass spectra were used to interrogate either a highly curated community database of tomato sequences generated through traditional sequencing technologies, or the RNA-Seq database. We conclude that RNA-Seq provides a cost-effective and robust platform for protein identification and will be increasingly valuable to the field of proteomics.