Screening Informatics

筛选信息学核心（SI） SI目标。SI将：1）加强中心内部与检测供应商和MLPCN专业的合作 化学中心; 2）为检测和筛选程序的QC和QA提供支持; 3）跟踪 化合物，跟踪分析，跟踪筛选结果; 4）支持数据采集和分析; 5）命中分析 化学中心; 6）在软件或硬件故障后提供快速数据恢复; 7）开发和 实现多路数据集可视化和分析工具。 SI进度报告。已经建立了一个协作信息学基础设施来支持数据， 中心内部的信息和知识管理，重点是整合和实时协作 在中心核心和外部合作伙伴之间。基础架构的构建具有灵活性和适应性 主要使用开源软件包和主服务器的Web接口（Web服务器- http://nmmlsc.health.unm.edu、Wiki服务器-http：//paprika.health.unm.edu/wiki和RoadRunner化学品 数据库筛选服务器-http://screening.health.unm.edu/rrnmmlsc/。数据文件服务器- http://anaheim.health.unm.edu）。两个额外的Linux集群，分别有32个和96个处理器， 32 GB高内存机器用于大型和超大型数据集的高性能数据挖掘作业 数十万或数百万的化合物。在8 TB DELL上进行夜间增量备份 在DELL ML 6000 Library Series LTO-3设备上备份MD 1000备份服务器和每月完整磁带备份 已在主服务器上实施，并计划将备份服务加入筛选 工作站。已实施试验和HTS程序的数据处理、QC和QA工作流程。 结果保存在PubChem中。在MLSCN试验阶段，SI成员还实施了 先进的数据挖掘和虚拟筛选技术，用于命中识别和优化， MLPCN不需要，因为化学信息学将在综合中心和专业中心进行 化学中心。

SCREENING INFORMATICS CORE (SI) SI Aims. SI will: 1) enhance collaboration within the Center, with assay providers, and MLPCN Specialty Chemistry Centers; 2) provide support for QC and QA for assay and screening procedures; 3) track compounds, track assays, track screening results; 4) support data acquisition and analysis; 5) hit analysis for Chemstry Centers; 6) provide quick recovery of data after software or hardware failures; 7) develop and implement visualization and analysis tools for multiplex data sets. SI Progress Report. A collaborative informatics infrastructure has been implemented to support data, information and knowledge management within the Center, focusing on integration and real-time collaboration among the Center cores and with external partners. The infrastructure was built for flexibility and adaptability using mostly open source software packages and web interfaces to the main server (Web server - http://nmmlsc.health.unm.edu, Wiki server - http://paprika.health.unm.edu/wiki, and the RoadRunner chemical database screening server - http://screening.health.unm.edu/rrnmmlsc/. and Data File server - http://anaheim.health.unm.edu). Two additional Linux clusters, with 32 and 96 processors, respectively, and a 32 GB high memory machine are used for high performance data mining jobs on large and very large data sets of hundreds of thousands or millions of compounds. Nightly incremental backup on an 8 terabyte DELL PowerVault MD1000 backup server and monthly full tape backup on a DELL ML6000 Library Series LTO-3 unit have been implemented for the main servers, with plans to add the backup service to the screening workstations. Data processing, QC and QA workflows for assay and HTS procedures have been implemented. Results were deposited into PubChem. During the MLSCN pilot phase, members of the SI also implemented advanced data mining and virtual screening techniques used for hit identification and optimization which will not be required for MLPCN since cheminformatics is to be carried out in Comprehensive Centers and Specialty Chemistry Centers.