Clancy Trevor, Hovig Eivind
Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, 0310 Oslo, Norway.
Department of Tumor Biology, Institute for Cancer Research, The Norwegian Radium Hospital, Oslo University Hospital, 0310 Oslo, Norway ; Biomedical Research Group, Department of Informatics, Faculty of Mathematics and Natural Sciences, University of Oslo, 0310 Oslo, Norway ; Institute of Cancer Genetics and Informatics, The Norwegian Radium Hospital, Oslo University Hospital, 0310 Oslo, Norway.
Biomed Res Int. 2014;2014:363408. doi: 10.1155/2014/363408. Epub 2014 Sep 21.
Recently, the Immunological Genome Project (ImmGen) completed the first phase of the goal to understand the molecular circuitry underlying the immune cell lineage in mice. That milestone resulted in the creation of the most comprehensive collection of gene expression profiles in the immune cell lineage in any model organism of human disease. There is now a requisite to examine this resource using bioinformatics integration with other molecular information, with the aim of gaining deeper insights into the underlying processes that characterize this immune cell lineage. We present here a bioinformatics approach to study differential protein interaction mechanisms across the entire immune cell lineage, achieved using affinity propagation applied to a protein interaction network similarity matrix. We demonstrate that the integration of protein interaction networks with the most comprehensive database of gene expression profiles of the immune cells can be used to generate hypotheses into the underlying mechanisms governing the differentiation and the differential functional activity across the immune cell lineage. This approach may not only serve as a hypothesis engine to derive understanding of differentiation and mechanisms across the immune cell lineage, but also help identify possible immune lineage specific and common lineage mechanism in the cells protein networks.
最近,免疫基因组计划(ImmGen)完成了其目标的第一阶段,即了解小鼠免疫细胞谱系背后的分子调控网络。这一里程碑成果产生了人类疾病任何模型生物中免疫细胞谱系最全面的基因表达谱集合。现在有必要利用生物信息学将该资源与其他分子信息整合起来,以便更深入地了解构成这个免疫细胞谱系特征的潜在过程。我们在此展示一种生物信息学方法,用于研究整个免疫细胞谱系中的差异蛋白相互作用机制,该方法通过将亲和传播应用于蛋白质相互作用网络相似性矩阵来实现。我们证明,将蛋白质相互作用网络与免疫细胞最全面的基因表达谱数据库整合起来,可用于生成关于免疫细胞谱系中调控分化和差异功能活性潜在机制的假设。这种方法不仅可以作为一个假设引擎,用于推导对免疫细胞谱系中分化和机制的理解,还有助于在细胞蛋白质网络中识别可能的免疫谱系特异性和共同谱系机制。