基因君官网
2015年12月17日讯/生物谷BIOON/近日，来自伊坎西奈山医学院的科学家们公开发布了一个新的数据分析软件，它可以帮助基因组学研究人员确定带有更高的效率和准确性的遗传疾病的因素。这些发现昨日发表在《PLoS Computational Biology》和《Scientific Reports》杂志上。
MEGENA(多尺度嵌入基因共表达网络分析)项目的基因表达数据到一个三维球体上，这帮助学家们研究了复杂网络分层结构模式特征的疾病，如癌症、肥胖，阿尔茨海默氏症。测试数据来自癌症基因组图谱(TCGA)，MEGENA在乳腺癌和肺癌中鉴定了新的监管目标，优于其它共表达分析方法。
第二个工具SuperExactTest建立了第一个理论框架来评估多组交叉点的统计学意义，并促使使用者比较非常大的数据集，如来自全基因组关联研究(GWAS)和微分表达式分析的基因集。科学家应用SuperExactTest测试 TCGA和GWAS数据，鉴定一组核心的癌症基因和检测复杂疾病之间的相关模式。这两个工具都来自于多尺度网络建模实验室，该实验室由遗传学和基因组科学副教授zhangbin带领研究。
“这些工具填补重要的和未满足需求的基因组学。” Zhang博士说。“MEGENA将帮助科学家在复杂疾病方面理解新颖的途径和关键目标，而SuperExactTest通过比较大量的基因签名将提供一个更清晰的基因组图。”
“我们的研究组致力于制作高性能的分析工具，并共享广泛的基因组学资源用来帮助我们所有人产出最好的结果。”Eric Schadt博士说。“这些新工具展示了深思熟虑和创造性的解决方案，用来帮助全世界的科学家所面对的挑战。”（基因宝jiyinbao.com）
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doi: 10.1371/journal.pcbi.1004574
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Multiscale Embedded Gene Co-expression Network Analysis
Won-Min Song, Bin Zhang
Abstract Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.