Development of integrated web interfaces for Bioconductor genomic data analysis annotation and visualization tools

开发用于 Bioconductor 基因组数据分析注释和可视化工具的集成 Web 界面

• 批准号: BB/E001653/1
• 负责人: Alvis Brazma
• 金额: $ 11.55万
• 依托单位: European Bioinformatics Institute
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2006
• 资助国家: 英国
• 起止时间: 2006 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FE001653%2F1
• 关键词: Development; integrated; web; interfaces; Bioconductor

Genomic data, particularly microarray expression profiling studies, comes in the shape of huge matrices of numbers, anywhere from 10,000 to 6,000,000 rows by hundreds to thousands of columns. These data need to be transformed, standardized, visualized, and annotated. The rows of these matrices report activity (expression levels) of genes under various conditions. The huge data volume, as well as the complexity involved with describing such experimental data, resulted in the creation of a few major public repositories for array-based high throughput genomics data: GEO (NCBI, USA) and ArrayExpress (EBI, Cambridge, UK). Our group at the EBI also has developed Expression Profiler (EP), a web-based platform for exploratory data analysis, which can provide some basic insights into the public data in ArrayExpress. The major thrust of the scientific community's work in creating tools for dealing with such large-scale data has concentrated within the set of open source command-line driven tools collectively called Bioconductor. These tools, or 'packages', are developed by leaders in specialized areas of application: normalization (mathematical methods of making data coming from different laboratories comparable), signalling pathway analysis, clustering analysis, meta-analysis, etc., and are therefore the de facto standard for cutting-edge functional genomics analysis technologies. At the same time, by and large the only users of Bioconductor remain the sophisticated bioinformaticians, while wet-lab biologists (experimentalists who produce the actual data) find the learning curve of the R environment too steep to learn, the R language too complex to master, and the command-line flexibility details too daunting. Moreover, even within Bioconductor, different packages offer different, often incompatible, paradigms of dealing with the data input, output and interchange. There is a definite, clear need to provide easy access to the power of Bioconductor for biologists involved in functional genomics and proteomics experimental research. This project proposes to utilise the EP analytical framework to develop a set of standard, unified look-and-feel web-based interfaces to core Bioconductor modules, which will also make use of the ArrayExpress database. The proposed system will enable biologists to upload securely their experimental data, analyse them with the best available Bioconductor algorithms and to compare or analyse them together with related public high-throughput data in the repository. The data analysis routines will take advantage of the high-power computing infrastructure available at the EBI, and the results will be stored within the system, accessible form anywhere in the world via a web-browser. A further unique advantage is provided by the integration of Bioconductor packages within a set of web interfaces: the interfaces can also be accessed as Web Services, i.e. can be incorporated in automatic data analysis workflows. In other words, even sophisticated bioinformaticians are likely to find this system useful (see attached letters of support).

基因组数据，特别是微阵列表达谱研究，以巨大的数字矩阵的形式出现，从 10,000 到 6,000,000 行乘以数百到数千列。这些数据需要进行转换、标准化、可视化和注释。这些矩阵的行报告了各种条件下基因的活性（表达水平）。庞大的数据量以及描述此类实验数据的复杂性，导致创建了一些用于基于阵列的高通量基因组数据的主要公共存储库：GEO（NCBI，美国）和 ArrayExpress（EBI，英国剑桥）。我们 EBI 的团队还开发了 Expression Profiler (EP)，这是一个基于 Web 的探索性数据分析平台，可以提供对 ArrayExpress 中公共数据的一些基本见解。科学界在创建处理此类大规模数据的工具方面的主要工作集中在一组统称为 Bioconductor 的开源命令行驱动工具中。这些工具或“包”由专业应用领域的领导者开发：标准化（使来自不同实验室的数据具有可比性的数学方法）、信号通路分析、聚类分析、荟萃分析等，因此是尖端功能基因组学分析技术的事实上的标准。与此同时，总的来说，Bioconductor 的唯一用户仍然是经验丰富的生物信息学家，而湿实验室生物学家（产生实际数据的实验家）发现 R 环境的学习曲线太陡峭而难以学习，R 语言太复杂而难以掌握，命令行灵活性细节太令人畏惧。此外，即使在 Bioconductor 中，不同的包也提供不同的、通常不兼容的处理数据输入、输出和交换的范例。明确、明确地需要为参与功能基因组学和蛋白质组学实验研究的生物学家提供轻松使用 Bioconductor 的功能。该项目建议利用 EP 分析框架为核心 Bioconductor 模块开发一套标准的、统一的外观和感觉的基于 Web 的界面，该模块也将利用 ArrayExpress 数据库。拟议的系统将使生物学家能够安全地上传他们的实验数据，使用现有的最佳 Bioconductor 算法对其进行分析，并将它们与存储库中的相关公共高通量数据进行比较或分析。数据分析例程将利用 EBI 提供的高功率计算基础设施，结果将存储在系统内，可通过网络浏览器在世界任何地方访问。 Bioconductor 软件包在一组 Web 界面中的集成提供了另一个独特的优势：这些界面也可以作为 Web 服务进行访问，即可以合并到自动数据分析工作流程中。换句话说，即使是经验丰富的生物信息学家也可能会发现该系统有用（请参阅随附的支持信）。

项目成果

Gene Expression Atlas update--a value-added database of microarray and sequencing-based functional genomics experiments.

• DOI: 10.1093/nar/gkr913
• 发表时间: 2012-01
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Kapushesky M;Adamusiak T;Burdett T;Culhane A;Farne A;Filippov A;Holloway E;Klebanov A;Kryvych N;Kurbatova N;Kurnosov P;Malone J;Melnichuk O;Petryszak R;Pultsin N;Rustici G;Tikhonov A;Travillian RS;Williams E;Zorin A;Parkinson H;Brazma A
• 通讯作者: Brazma A

A pipeline for RNA-seq data processing and quality assessment.

• DOI: 10.1093/bioinformatics/btr012
• 发表时间: 2011-03-15
• 期刊: Bioinformatics (Oxford, England)
• 作者: Goncalves A;Tikhonov A;Brazma A;Kapushesky M
• 通讯作者: Kapushesky M

ArrayExpress update--trends in database growth and links to data analysis tools.

• DOI: 10.1093/nar/gks1174
• 发表时间: 2013-01
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Rustici G;Kolesnikov N;Brandizi M;Burdett T;Dylag M;Emam I;Farne A;Hastings E;Ison J;Keays M;Kurbatova N;Malone J;Mani R;Mupo A;Pedro Pereira R;Pilicheva E;Rung J;Sharma A;Tang YA;Ternent T;Tikhonov A;Welter D;Williams E;Brazma A;Parkinson H;Sarkans U
• 通讯作者: Sarkans U

REMBI: Recommended Metadata for Biological Images-enabling reuse of microscopy data in biology.

• DOI: 10.1038/s41592-021-01166-8
• 发表时间: 2021-12
• 期刊: Nature methods
• 影响因子: 48
• 作者: Sarkans U;Chiu W;Collinson L;Darrow MC;Ellenberg J;Grunwald D;Hériché JK;Iudin A;Martins GG;Meehan T;Narayan K;Patwardhan A;Russell MRG;Saibil HR;Strambio-De-Castillia C;Swedlow JR;Tischer C;Uhlmann V;Verkade P;Barlow M;Bayraktar O;Birney E;Catavitello C;Cawthorne C;Wagner-Conrad S;Duke E;Paul-Gilloteaux P;Gustin E;Harkiolaki M;Kankaanpää P;Lemberger T;McEntyre J;Moore J;Nicholls AW;Onami S;Parkinson H;Parsons M;Romanchikova M;Sofroniew N;Swoger J;Utz N;Voortman LM;Wong F;Zhang P;Kleywegt GJ;Brazma A
• 通讯作者: Brazma A