Apollo: Collaborative, Interactive Analytics and Annotation of Genome Variants

我们的目标是通过提供交互式探索性的基因组分析， 可视化工具，使研究人员能够完善，纠正和增强初始 自动化结果。基因组测序的成本已经急剧上升， 在过去的十年里减少了几个数量级， 结果是越来越多的研究人员对越来越多的 新的基因组，无论是在种群内还是在每个物种之间。因为 计算基因组分析仍然是一个不完美的艺术，每个新的外显子组或 测序的基因组需要可视化。通过有效的可视化， 基因组解释可以利用人类的感知能力， 整合信息。Web Apollo将提供易于使用、基于Web的 提供多个分布式用户的高性能可视化环境 基因组数据的交互式探索，以及生产所需的工具 高度准确的注释，为下游提供最大的信息 分析.这个项目将使每一个成员的新兴基因组 社区充分动员分子研究数据，无论是通过 个人研究人员或从公共聚合网站提供，以受益 研究遗传学和人类疾病。 该项目将使研究人员能够（i）将变异体视觉投射到蛋白质上 显示器，以查看与蛋白质特征（如活性位点）的交集， 二级结构，以评估生物影响;（二）提高外显子内含子的准确性 通过直接比较蛋白质编码转录本中的相关蛋白质， 集成的多序列比对（MSA）视图;（iii）探索基因组 使用关键网站书签进行快速导航; ㈣分析数据 通过折叠基因组，使基因具有共同的属性， 作为旁系或共享的表型和它们中的变体， 视野;（vi）与选定的团队成员进行安全的实时协作 通过互联网，使用混合云提供私有数据和免费托管的服务器 公共数据。

Our aim is to advance genome analysis by providing interactive exploratory visualization tools enabling researchers to refine, correct and augment initial automated results. The cost of sequencing a genome has been dramatically reduced by several orders of magnitude in the last decade, and the natural consequence is that more and more researchers are sequencing more and more new genomes, both within populations and across every species. Because computational genome analysis remains an imperfect art each new exome or genome that is sequenced requires visualization. With effective visualization, genome interpretation can take advantage of human perceptual capabilities to integrate information. Web Apollo will provide an easy to use, web-based environment offering multiple, distributed users high-performance visualization for interactive exploration of genome data, and the tools needed for producing highly accurate annotations that are maximally informative for downstream analysis. This project will enable every member of the burgeoning genomic community to fully mobilize molecular research data, whether collected by individual researchers or made available from public aggregation sites to benefit investigations into genetics and human diseases. This project will enable researchers to (i) Visually project variants onto protein displays to see intersection with protein features such as active sites and secondary structure to assess biological impact; (ii) Improve exonintron accuracy for protein coding transcripts via direct comparison to related proteins in an integrated multiple sequence alignment (MSA) view; (iii) Explore the genome using bookmarking of key sites for rapid navigation; (iv) Analyze the data interactively by folding of the genome to bring genes with shared attributes such as paralogy or shared phenotypes and the variants within them in the same visual field ; (vi) Collaborate securely in real-time with selected team members over the Internet using hybrid clouds for private data and freely-hosted servers for public data.