Reactome: An Open Knowledgebase of Human Pathways

项目摘要 我们寻求更新人类生物学Reactome知识库的核心运营资金， 路径和过程。Reactome是一个精心策划的，开放获取的生物分子途径数据库，可以免费 被生物研究界的所有成员使用和重新分发。它被临床医生、遗传学家、 cists，基因组学研究人员和分子生物学家解释高通量实验的结果， 研究，由生物信息学家寻求开发新的算法，从基因组研究中挖掘知识， 以及系统生物学家建立正常和疾病变异途径的预测模型。 我们的策展人，具有细胞和分子生物学背景的博士级科学家与in密切合作， 社区内的独立调查人员收集机器可读的人类生物学描述， 途径。每一条路径在出版前都经过广泛的检查和同行评审，以确保其主张 是支持的主要文献，和人类分子事件推断从orthopolysones在 动物模型具有可审计的推理链。Curated Reactome途径目前涵盖8930种蛋白质- 编码基因（占基因组翻译部分的44%）和约150个RNA基因。我们还提供网络， 通过保守的机器学习方法预测可靠的“功能交互”（FI），该方法涵盖了 额外的3300个基因，大约占已知基因组的60%。 在接下来的五年里，我们将：（1）策划新的大分子实体，临床上重要的蛋白质 序列变体和同种型，药物样分子，以及这些实体形成的复合物，进入新的反应， （2）用针对重大疾病的替代途径补充正常途径， opmental生物学;（3）扩展和自动化我们的工具，用于策展，管理和社区注释; (4)使用概率图形模型和布尔网络集成途径建模技术， 途径和网络扰动研究;（5）开发额外的引人注目的软件接口， 计算和实验室生物学家用户;和（6）和改善外展生物信息学家，分子双- 医生和临床研究人员。

Project Summary We seek renewal of the core operating funding for the Reactome Knowledgebase of Human Biological Pathways and Processes. Reactome is a curated, open access biomolecular pathway database that can be freely used and redistributed by all members of the biological research community. It is used by clinicians, geneti- cists, genomics researchers, and molecular biologists to interpret the results of high-throughput experimental studies, by bioinformaticians seeking to develop novel algorithms for mining knowledge from genomic studies, and by systems biologists building predictive models of normal and disease variant pathways. Our curators, PhD-level scientists with backgrounds in cell and molecular biology work closely with in- dependent investigators within the community to assemble machine-readable descriptions of human biological pathways. Each pathway is extensively checked and peer-reviewed prior to publication to ensure its assertions are backed up by the primary literature, and that human molecular events inferred from orthologous ones in animal models have an auditable inference chain. Curated Reactome pathways currently cover 8930 protein- coding genes (44% of the translated portion of the genome) and ~150 RNA genes. We also offer a network of reliable ‘functional interactions’ (FIs) predicted by a conservative machine-learning approach, which covers an additional 3300 genes, for a combined coverage of roughly 60% of the known genome. Over the next five years, we will: (1) curate new macromolecular entities, clinically significant protein sequence variants and isoforms, and drug-like molecules, and the complexes these entities form, into new reac- tions; (2) supplement normal pathways with alternative pathways targeted to significant diseases and devel- opmental biology; (3) expand and automate our tools for curation, management and community annotation; (4) integrate pathway modeling technologies using probabilistic graphical models and Boolean networks for pathway and network perturbation studies; (5) develop additional compelling software interfaces directed at both computational and lab biologist users; and (6) and improve outreach to bioinformaticians, molecular bi- ologists and clinical researchers.