组学数据的高性能分析:卡坦萨罗麦格纳希腊大学的经验

Giuseppe Agapito, P. Guzzi, M. Cannataro
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引用次数: 0

摘要

一些组学学科,如基因组学、蛋白质组学和相互作用组学,由于高通量实验平台的可用性(例如,下一代测序、微阵列、质谱,举几个例子),正在产生大量的实验组学数据,科学界对这些学科的兴趣越来越大。然而,组学数据的有效分析需要大量的数据存储以及用于数据预处理、分析和集成的新颖算法和数据结构。因此,用于分析组学数据的并行生物信息学工具(通常在云上提供)开始可用。本文综述了用于组学数据预处理和分析的并行和分布式生物信息学工具。该描述包括在卡坦扎罗的Magna Graecia大学生物信息学实验室开发的一些工具,并使用大学医院提供的真实数据进行验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
High Performance Analysis of Omics Data: Experiences at University Magna Graecia of Catanzaro
Several omics disciplines, such as genomics, proteomics, and interactomics , are gaining an increasing interest in the scientific community due to the availability of high throughput experimental platforms (e.g. next generation sequencing, microarray, mass spectrometry, to cite a few), that are producing an overwhelming amount of experimental omics data. However, efficient analysis of omics data requires large data stores as well as novel algorithms and data structures for data preprocessing, analysis, and integration. As a result, parallel bioinformatics tools for the analysis of omics data, often made available on the Cloud, start to be available. This paper surveys some parallel and distributed bioinformatics tools for the preprocessing and analysis of omics data. The description includes some tools developed at the Bioinformatics Laboratory of the University Magna Graecia of Catanzaro and validated using real data made available by the University Hospital.
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