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J Proteome Res. 2016 Oct 7;15(10):3896-3903. Epub 2016 Sep 12.

hEIDI: An Intuitive Application Tool To Organize and Treat Large-Scale Proteomics Data.

Hesse AM1,2,3, Dupierris V1,2,3, Adam C1,2,3, Court M1,2,3, Barthe D1,2,3, Emadali A1,2,3, Masselon C1,2,3, Ferro M1,2,3, Bruley C1,2,3.

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Univ. Grenoble Alpes, BIG-BGE, F-38000 Grenoble, France.
CEA, BIG-BGE, F-38000 Grenoble, France.
Inserm U1038, BGE, F-38000 Grenoble, France.


Advances in high-throughput proteomics have led to a rapid increase in the number, size, and complexity of the associated data sets. Managing and extracting reliable information from such large series of data sets require the use of dedicated software organized in a consistent pipeline to reduce, validate, exploit, and ultimately export data. The compilation of multiple mass-spectrometry-based identification and quantification results obtained in the context of a large-scale project represents a real challenge for developers of bioinformatics solutions. In response to this challenge, we developed a dedicated software suite called hEIDI to manage and combine both identifications and semiquantitative data related to multiple LC-MS/MS analyses. This paper describes how, through a user-friendly interface, hEIDI can be used to compile analyses and retrieve lists of nonredundant protein groups. Moreover, hEIDI allows direct comparison of series of analyses, on the basis of protein groups, while ensuring consistent protein inference and also computing spectral counts. hEIDI ensures that validated results are compliant with MIAPE guidelines as all information related to samples and results is stored in appropriate databases. Thanks to the database structure, validated results generated within hEIDI can be easily exported in the PRIDE XML format for subsequent publication. hEIDI can be downloaded from .


data management; identification results; protein grouping; quantitative proteomics; relational databases

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