PRIDE Inspector Toolsuite: Moving Toward a Universal Visualization Tool for Proteomics Data Standard Formats and Quality Assessment of ProteomeXchange Datasets

Perez‐Riverol, Yasset; Xu, Qing Wei; Wang, Rui; Uszkoreit, Julian; Griss, Johannes; Sánchez, Aniel; Reisinger, Florian; Csordás, Attila; Ternent, Tobias; del-Toro, Noemi; Dianes, José A.; Eisenacher, Martin; Hermjakob, Henning; Vizcaíno, Juan Antonio

doi:10.1074/mcp.o115.050229

Cited by 163 publications

(125 citation statements)

References 53 publications

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“…Protein annotation was done with the gene ontology (GO) terms from NCBI (downloaded March 21 and 22, 2016) . The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD007066.…”

Section: Methodsmentioning

confidence: 99%

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Proteomics analysis of human breast milk to assess breast cancer risk

et al. 2018

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Detection of breast cancer (BC) in young women is challenging because mammography, the most common tool for detecting BC, is not effective on the dense breast tissue characteristic of young women. In addition to the limited means for detecting their BC, young women face a transient increased risk of pregnancy-associated BC. As a consequence, reproductively active women could benefit significantly from a tool that provides them with accurate risk assessment and early detection of BC. One potential method for detection of BC is biochemical monitoring of proteins and other molecules in bodily fluids such as serum, nipple aspirate, ductal lavage, tear, urine, saliva and breast milk. Of all these fluids, only breast milk provides access to a large volume of breast tissue, in the form of exfoliated epithelial cells, and to the local breast environment, in the form of molecules in the milk. Thus, analysis of breast milk is a non-invasive method with significant potential for assessing BC risk. Here we analyzed human breast milk by mass spectrometry (MS)-based proteomics to build a biomarker signature for early detection of BC. Ten milk samples from eight women provided five paired-groups (cancer versus control) for analysis of dysregulatedproteins: two within woman comparisons (milk from a diseased breast versus a healthy breast of the same woman) and three across women comparisons (milk from a woman with cancer versus a woman without cancer). Despite a wide range in the time between milk donation and cancer diagnosis (cancer diagnosis occurred from 1 month before to 24 months after milk donation), the levels of some proteins differed significantly between cancer and control in several of the five comparison groups. These pilot data are supportive of the idea that molecular analysis of breast milk will identify proteins informative for early detection and accurate assessment of BC risk, and warrant further research. Data are available via ProteomeXchange with identifier PXD007066.

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Section: Methodsmentioning

confidence: 99%

“…A database search was performed on the pkl files to identify proteins using in- [43]. The mass spectrometry proteomics data have been deposited to the Pro-teomeXchange Consortium via the PRIDE [44][45][46][47] partner repository with the dataset identifier PXD007066.…”

Section: Reagents and Proteomics Analysismentioning

confidence: 99%

Proteomics analysis of human breast milk to assess breast cancer risk

et al. 2018

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“…Human-readable formats provide cross-platform compatibility and can live in-perpetuity independently from the source data and original instrument manufacturer. These formats continue to evolve and are supported by a diverse ecosystem of software tools and frameworks for bioinformatic analysis [47][48][49][50][51][52][53][54][55][56][57][58]. These advantages are tempered by the technical difficulties of establishing efficient schemes for random access to high-dimension data incorporated within XML-formatted files.…”

Section: Discussionmentioning

confidence: 99%

multiplierzv2.0: A Python-based ecosystem for shared access and analysis of native mass spectrometry data

et al. 2017

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The continued evolution of modern mass spectrometry instrumentation and associated methods represents a critical component in efforts to decipher the molecular mechanisms which underlie normal physiology and understand how dysregulation of biological pathways contributes to human disease. The increasing scale of these experiments combined with the technological diversity of mass spectrometers presents several challenges for community-wide data access, analysis, and distribution. Here we detail a redesigned version of multiplierz, our Python software library which leverages our common application programming interface (mzAPI) for analysis and distribution of proteomic data. New features include support for a wider range of native mass spectrometry file types, interfaces to additional database search engines, compatibility with new reporting formats, and high-level tools to perform post-search proteomic analyses. A GUI desktop environment, mzDesktop, provides access to multiplierz functionality through a user friendly interface. multiplierz is available for download from: https://github.com/BlaisProteomics/multiplierz; and mzDesktop is available for download from: https://sourceforge.net/projects/multiplierz/.

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“…Inspector Toolsuite for the analysis of public datasets (36). It also can be fully integrated into OpenMS pipelines, when using KNIME as workflow environment.…”

Section: Discussionmentioning

confidence: 99%