Kumar Chanchal, Mann Matthias
Department of Proteomics and Signal Transduction, Max-Planck Institute for Biochemistry, Am Klopferspitz 18, D-82152 Martinsried, Germany.
FEBS Lett. 2009 Jun 5;583(11):1703-12. doi: 10.1016/j.febslet.2009.03.035. Epub 2009 Mar 21.
Proteomics has made tremendous progress, attaining throughput and comprehensiveness so far only seen in genomics technologies. The consequent avalanche of proteome level data poses great analytical challenges for downstream interpretation. We review bioinformatic analysis of qualitative and quantitative proteomic data, focusing on current and emerging paradigms employed for functional analysis, data mining and knowledge discovery from high resolution quantitative mass spectrometric data. Many bioinformatics tools developed for microarrays can be reused in proteomics, however, the uniquely quantitative nature of proteomics data also offers entirely novel analysis possibilities, which directly suggest and illuminate biological mechanisms.
蛋白质组学已经取得了巨大进展,其通量和全面性目前仅在基因组学技术中可见。随之而来的蛋白质组水平数据雪崩给下游解读带来了巨大的分析挑战。我们综述了定性和定量蛋白质组学数据的生物信息学分析,重点关注用于功能分析、数据挖掘以及从高分辨率定量质谱数据中发现知识的当前和新兴范式。许多为微阵列开发的生物信息学工具可在蛋白质组学中重复使用,然而,蛋白质组学数据独特的定量性质也提供了全新的分析可能性,这些可能性直接揭示并阐明了生物学机制。