The Miner Suite: State of the Art Bioinformatic Tools and Data Resources

Miner Suite：最先进的生物信息工具和数据资源

• 批准号: 7592989
• 负责人: John N Weinstein
• 金额: $ 105.17万
• 依托单位: DIVISION OF BASIC SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7592989
• 关键词: Adopted; Adoption; Algorithms; Architecture; Arts; Atlases; Award; Binding; Bioinformatics; Biological; Biology; CCR; Cancer Center; Cancer cell line; Categories; Chemicals; Clinical Trials; Code; Communities; Complex; Computer software; Computers; Contracts; DNA Methylation; Data; Databases; Deposition; Development; Diagnosis; Disease; Division of Cancer Epidemiology and Genetics; Electronics; Gene Expression; Genes; Genetic Markers; Genome; Genomics; Goals; Grant; Heating; Image; Imagery; International; Internet; Java; Lead; Link; Literature; Machine Learning; Malignant Neoplasms; Maps; Metadata; Methods; Microarray Analysis; Molecular; Molecular Profiling; Molecular Target; Monoclonal Antibodies; Morphologic artifacts; Names; Online Systems; Ontology; Pattern; Personal Satisfaction; Predisposition; PubMed; Publications; Publishing; Purpose; Quality Control; RNA Splicing; Research; Research Infrastructure; Research Personnel; Resources; Science; Site; Software Engineering; Spottings; Staff Development; System; Testing; Therapeutic; Time; Transcript; Translating; United States National Institutes of Health; Variant; Visual; Work; anticancer research; base; cancer Biomedical Informatics Grid; cancer genetics; cancer genome; catalyst; cell type; comparative genomic hybridization; data integration; design; follow-up; forest; genome wide association study; mecarzole; novel; programs; software development; statistics; success; tool

The Genomics and Bioinformatics Group (GBG) represents the Center for Cancer Researchs most substantial enterprise in bioinformatics. That bioinformatic activity is well-integrated with the experimental activities of the group. In fact, all of the algorithmic and software developments have been motivated by needs of the groups experimental science. Over the last several years weve developed the Miner Suite as a state-of-the-art, web-based, portable, principally-Java set of tools and databases focused on needs that we found were not being served by other bioinformatic developments. Our web site, http://discover.nci.nih.gov, has been featured in Science and, locally, in the NIH Catalyst. The following is a list of the Miner Suite modules, ending with four that went online in 2007: CellMiner, SpliceMiner, AffyProbeMiner, and LeFEminer: MedMiner accelerates searching and organization of PubMed literature 5-10 fold (L. Tanabe, et al.). MatchMiner translates among gene identifiers for lists of genes from microarrays and other high-throughput omic platforms (K. Bussey, et al.). GoMiner leverages the Gene Ontology for lists of genes (e.g., from microarrays) (B. Zeeberg, et al.). CIMminer produces Clustered Image Maps (CIMs) (i.e., clustered heat maps), the ever-present icon of Postgenomic biology. We introduced CIMs in the early 1990s. LeadScope/LeadMiner links molecular targets (e.g., in the NCI-60 cancer cell lines) to 27,000 chemical substructures in a fluently browsable format (with P. Blower, et al.). MIMminer provides searchable electronic forms (eMIMs) of the elegant, scholarly Molecular Interaction Maps developed by K. Kohn, M. Aladjem, and Y. Pommier. High-throughput GoMiner extends GoMiners statistics and visualizations to encompass large sets of microarrays (e.g., from time course studies or clinical trials) (B. Zeeberg, et al.). AbMiner provides a browsable relational database of available monoclonal antibodies that we have characterized. It includes our quality control results and multiple link-outs (S. Major, et al.). SmudgeMiner diagnoses spatial artifacts on Affymetrix and spotted arrays (M. Reimers, et al.). MethMiner provides pattern visualization and statistics for DNA methylation. Program not yet public. (S. Kim, et al.). Miner Suite programs that went online publicly in 2007 were as follows: CellMiner provides the NCI-60 and other molecular profile databases in a SQL-searchable relational format, with metadata on the experimental platforms and cell types (U. Shankavaram, S. Varma, et al. -- see 2007 publication in Molecular Cancer Therapeutics). SpliceMiner provides a robust infrastructure for dealing with transcript splice variation (A. Kahn, M. Ryan, et al. -- see 2007 publication in BMC Bioinformatics). AffyProbeMiner provides what we believe to be the best tool for re-mapping Affymetrix probes to achieve sharper results from the commercially available Affymetrix arrays. It also integrates the re-mapped array data with GoMiner and High-Throughput GoMiner (H.-F. Liu, B. Zeeberg, et al. -- see 2007 publication in Bioinformatics). LeFEminer is a web-based implementation of the LeFE (Learner of Functional Enrichment) algorithm, which uses the random forest machine-learning paradigm in a novel way to predict functional relationships from microarray and other high-throughput data types. (G. Eichler, et al. -- see 2007 publication in Genome Biology). Those programs, largely designed and implemented under a competed contract with SRA International, (contract #263-01-D-0050, CIO-SP2 Delivery Order 2313028) are freely available and used by thousands of investigators worldwide. The success of the programs is attributable in part to our adoption of the Agile software development paradigm, which promotes close, iterative interaction between software engineers, biologists, and bioinformaticists. That success is also partially attributable to adoption of Unit and System Testing methods; whenever code is re-deposited in our version control system, its automatically subjected to >1700 tests to minimize the chance that changes made have broken something elsewhere in the overall code base. The development team won an SRA Project Excellence Award for the Miner Suite (1 of 4 awarded out of >700 competing projects). Were integrating the Miner Suite applications with a variety of public bioinformatic software projects, including caBIG, The Cancer Genome Atlas, and the CGEMS genome-wide association project. caBIG: We were awarded two caBIG pilot grants (one to caBIG-enable GoMiner, the other to caBIG-enable our NCI-60 databases). The group, particularly lead software engineer David Kane, has made strong contributions to caBIGs Integrative Cancer Research and Architecture Working Groups. The Cancer Genome Atlas: Working with TCGA development staff, we used SpliceMiner as the basis for development of bioinformatic infrastructure to support the integration of genotypic/phenotypic data of multiple types. That infrastructure has now been adopted by TCGAs Data Integration Committee for the purpose. CGEMS: Working with bioinformaticists in the NCI Division of Cancer Epidemiology and Genetics (DCEG), we have developed a program package provisionally named ChromMiner for visual integration of SNP data from the genome-wide association studies with phenotypic data on the cancers (including gene expression and comparative genomic hybridization data). That program package is central to a proposal, which I was instrumental in drafting, to combine the epidemiological/genotypic expertise of DCEG with the phenotypic expertise of CCR. The proposal is titled DCEG/CCR Plan for Follow-up of Genomic Regions of Association Identified by CGEMS

基因组学和生物信息学组（GBG）代表了癌症研究中心最重要的生物信息学企业。这种生物信息学活动与该组的实验活性充分融合。实际上，所有算法和软件开发都是出于实验科学的需求激励的。在过去的几年中，我们开发了矿工套件作为最先进的，基于网络的，便携式，主要是java的工具和数据库集，这些工具和数据库的重点是我们发现的其他生物信息学发展并未为我们提供的需求。我们的网站http：//discover.nci.nih.gov已在科学和本地介绍NIH Catalyst。以下是Miner Suite模块的列表，其结尾是2007年在线上的四个：Cellminer，Spliceminer，Affyprobeminer和Lefeminer：MedMiner加速了PubMed文学的搜索和组织5-10倍（L. Tanabe等）。 Matchminer在基因标识符之间转换了来自微阵列和其他高通量imic平台的基因列表（K. Bussey等人）。 Gominer利用基因本体论来获取基因列表（例如，来自微阵列）（B. Zeeberg等）。 CIMMINER产生聚集的图像图（CIMS）（即聚类热图），这是后基因组生物学的永远存在的图标。我们在1990年代初推出了CIM。 LeadScope/LeadMiner将分子靶标（例如，在NCI-60癌细胞系中）与27,000个化学子结构（以流利的隆隆形式）（使用P. Blower等）。 Mimminer提供了由K. Kohn，M。Aladjem和Y. Pommier开发的优雅的学术分子相互作用图的可搜索电子形式（EMIMS）。 高通量Gominer扩展了Gominers的统计和可视化，以涵盖大量的微阵列（例如，来自时间课程研究或临床试验）（B. Zeeberg等）。 Abminer提供了我们已经表征的可用单克隆抗体的可浏览关系数据库。它包括我们的质量控制结果和多个链接（S. Major等）。 Smudgeminer诊断在Affymetrix和斑点阵列上的空间伪影（M. Reimers等）。 Methminer提供了模式的可视化和DNA甲基化的统计数据。程序尚未公开。 （S. Kim等）。矿工套件在2007年公开在线上的计划如下：CellMiner以SQL-Search-Search-Search的关系格式提供NCI-60和其他分子概况数据库，并在实验平台和细胞类型上提供元数据（U. Shankavaram，S。S. Varma等）。 SpliceMiner提供了一种强大的基础架构来处理转录剪接变异（A. Kahn，M。Ryan等人 - 参见BMC BioInformatics中的2007年出版物）。 AffyProbeminer提供了我们认为是重新映射Affymetrix探针的最佳工具，以从市售的Affymetrix阵列中获得更清晰的结果。它还将重新映射的阵列数据与Gominer和高通量Gominer（H.-F。Liu，B。Zeeberg等人等）集成在一起。 LeFeminer是LEFE（功能丰富）算法的基于Web的实现，该算法以新颖的方式使用随机森林机器学习范式来预测从微阵列和其他高通量数据类型的功能关系。 （G。Eichler等人 - 参见2007年基因组生物学出版物）。这些计划在与SRA International（合同＃263-01-D-0050，CIO-SP2交付订单2313028）的主要合同（合同＃263-01-D-0050合同）中得到了广泛设计和实施。该程序的成功归因于我们采用了敏捷软件开发范式，该范围促进了软件工程师，生物学家和生物信息学家之间的紧密迭代互动。这种成功也部分归因于采用单位和系统测试方法。每当在我们的版本控制系统中重新沉积代码时，其自动进行了> 1700个测试，以最大程度地减少更改的机会，从而破坏了整个代码库中其他位置。开发团队为矿工套件获得了SRA项目卓越奖（> 700个竞争项目中的4个奖项中的1个）。正在将Miner Suite应用程序与包括Cabig，Cancer Genome Atlas和CGEMS全基因组协会项目在内的各种公共生物信息学项目相结合。 Cabig：我们获得了两项Cabig Pilot赠款（一个是对Cabig-Enable Enable Gominer，另一个用于Cabig-Enable-enci-60数据库）。该小组，特别是首席软件工程师David Kane，为Cabigs综合癌症研究和建筑工作组做出了巨大贡献。 癌症基因组地图集：与TCGA开发人员合作，我们使用SpliceMiner作为生物信息学基础设施发展的基础，以支持多种类型的基因型/表型数据的整合。 TCGAS数据集成委员会现已通过该目的采用了该基础设施。 CGEMS：与生物信息学家在NCI癌症流行病学和遗传学（DCEG）中合作，我们已经开发了一个程序包，用于临时命名为Chromminer，用于Chromminer，用于与基因组全基因组关联研究的SNP数据的视觉整合，该研究与癌症的表型数据（包括基因表达和比较基因组疗法数据）。该计划包是一项提案的核心，我在制图中发挥了作用，将DCEG的流行病学/基因型专业知识与CCR的表型专业知识相结合。该提案的标题为DCEG/CCR计划，用于随访CGEM确定的关联基因组区域