Bioinformatics for High Throughput Proteomics (Short Course)

人类基因组的测序理所当然地被誉为一项重大的科学成就，但很明显，基因组本身所能告诉我们的关于身体如何工作的信息是有限的。也许最重要的是，人类基因组在给定的个体内基本上是不变的，而细胞的蛋白质组成因细胞类型、环境因素和时间而不同。为了获得更好的认识，有必要研究蛋白质组--基因组产生的蛋白质的集合。近年来，在能够识别生物样本(如人体组织和血液)中的蛋白质的实验室方法方面进行了大量投资。这种被称为蛋白质组学的鉴定蛋白质的过程过去很耗时，在给定的实验中只鉴定了少量蛋白质。然而，持续的投资催生了高通量蛋白质组学，这使得样品处理的速度更快，并在给定的时间内识别出更多的蛋白质。与以前的生物分析科学领域(如DNA测序)一样，向高通量的转变导致了蛋白质组学实验产生的数据量的爆炸性增长。为了充分利用这些数据，必须使用计算生物学解决方案(生物信息学)。这个快速发展的领域很难跟上，所以我们建议提供一个短期课程，旨在向蛋白质组学从业者介绍可用的方法，并培训他们如何最好地使用这些方法来促进他们的研究。

The sequencing of the human genome was rightly hailed as a momentous scientific achievement, but it is clear that there are limits to what the genome itself can tell us about how the body works. Perhaps most importantly, the human genome is essentially invariant within a given individual, whereas the protein complement of a cell varies according to the cell type, environmental factors and time. To gain a better insight, it is necessary to study the proteome - the collection of proteins produced by the genome. In recent years, significant investment has been made in laboratory methods capable of identifying proteins in biological samples, such as human tissue and blood. This process of identifying proteins, known as proteomics, used to be time consuming, with only a small number of proteins being identified in a given experiment. However, continued investment has given rise to high throughput proteomics, which allows samples to be processed much faster, and for many more proteins to be identified in a given time. As in previous area of bioanalytical science (e.g. DNA sequencing), the move to high throughput has led to an explosion in the amount of data being produced in proteomics experiments. To fully make use of this data, computational biology solutions (bioinformatics) must be used. This fast moving field is difficult to keep abreast of, so we propose to offer a short course designed to brief proteomics practitioners on the approaches available, and train them in how best to use these approaches to facilitate their research.