Is DIA proteomics data FAIR? Current data sharing practices, available bioinformatics infrastructure and recommendations for the future.
Is DIA proteomics data FAIR? Current data sharing practices, available bioinformatics infrastructure and recommendations for the future.
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作者：
近年来，由于仪器和数据分析方法等多个技术方面的发展，数据非依赖型采集（DIA）蛋白质组学技术已经取得了巨大的进步。然而，在FAIR（可发现性、可获取性、互操作性和可重用性）数据原则方面，DIA数据仍有许多可能的改进之处。这包括更具针对性的数据共享实践和开放数据标准，因为蛋白质组学的公共数据库和数据标准在设计时大多考虑的是数据依赖型采集（DDA）数据。在此，我们首先描述蛋白质组学（总体而言）以及特别是DIA方法在FAIR数据背景下的当前技术水平。为了改善DIA数据的现状，我们对未来提出以下建议：（i）开发一种光谱库的开放数据标准；（ii）强制要求在蛋白质组交换（ProteomeXchange）资源中提供DIA实验所使用的光谱库；（iii）提高蛋白质组学标准倡议（Proteomics Standards Initiative）所制定的数据标准对DIA数据的支持；（iv）提高蛋白质组交换资源对DIA数据集的支持，包括更具针对性的元数据要求。
Data independent acquisition (DIA) proteomics techniques have matured enormously in recent years, thanks to multiple technical developments in, for example, instrumentation and data analysis approaches. However, there are many improvements that are still possible for DIA data in the area of the FAIR (Findability, Accessibility, Interoperability and Reusability) data principles. These include more tailored data sharing practices and open data standards since public databases and data standards for proteomics were mostly designed with DDA data in mind. Here we first describe the current state of the art in the context of FAIR data for proteomics in general, and for DIA approaches in particular. For improving the current situation for DIA data, we make the following recommendations for the future: (i) development of an open data standard for spectral libraries; (ii) make mandatory the availability of the spectral libraries used in DIA experiments in ProteomeXchange resources; (iii) improve the support for DIA data in the data standards developed by the Proteomics Standards Initiative; and (iv) improve the support for DIA datasets in ProteomeXchange resources, including more tailored metadata requirements.
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DOI：
10.1093/database/bat009
发表时间：
2013
期刊：
Database : the journal of biological databases and curation
影响因子：
--
作者：
Mayer G;Montecchi-Palazzi L;Ovelleiro D;Jones AR;Binz PA;Deutsch EW;Chambers M;Kallhardt M;Levander F;Shofstahl J;Orchard S;Vizcaíno JA;Hermjakob H;Stephan C;Meyer HE;Eisenacher M;HUPO-PSI Group
通讯作者：
HUPO-PSI Group
影响因子：
4.4
作者：
Deutsch EW;Perez-Riverol Y;Chalkley RJ;Wilhelm M;Tate S;Sachsenberg T;Walzer M;Käll L;Delanghe B;Böcker S;Schymanski EL;Wilmes P;Dorfer V;Kuster B;Volders PJ;Jehmlich N;Vissers JPC;Wolan DW;Wang AY;Mendoza L;Shofstahl J;Dowsey AW;Griss J;Salek RM;Neumann S;Binz PA;Lam H;Vizcaíno JA;Bandeira N;Röst H
通讯作者：
Röst H
影响因子：
4.4
作者：
Hulstaert, Niels;Shofstahl, Jim;Perez-Riverol, Yasset
通讯作者：
Perez-Riverol, Yasset
蛋白质组学样本元数据表示多组学集成和大数据分析。
影响因子：
16.6
作者：
Dai C;Füllgrabe A;Pfeuffer J;Solovyeva EM;Deng J;Moreno P;Kamatchinathan S;Kundu DJ;George N;Fexova S;Grüning B;Föll MC;Griss J;Vaudel M;Audain E;Locard-Paulet M;Turewicz M;Eisenacher M;Uszkoreit J;Van Den Bossche T;Schwämmle V;Webel H;Schulze S;Bouyssié D;Jayaram S;Duggineni VK;Samaras P;Wilhelm M;Choi M;Wang M;Kohlbacher O;Brazma A;Papatheodorou I;Bandeira N;Deutsch EW;Vizcaíno JA;Bai M;Sachsenberg T;Levitsky LI;Perez-Riverol Y
通讯作者：
Perez-Riverol Y
影响因子：
48
作者：
Deutsch EW;Perez-Riverol Y;Carver J;Kawano S;Mendoza L;Van Den Bossche T;Gabriels R;Binz PA;Pullman B;Sun Z;Shofstahl J;Bittremieux W;Mak TD;Klein J;Zhu Y;Lam H;Vizcaíno JA;Bandeira N
通讯作者：
Bandeira N