A Golden Age for Working with Public Proteomics Data

Martens, Lennart; Vizcaíno, Juan Antonio

doi:10.1016/j.tibs.2017.01.001

Cited by 97 publications

(91 citation statements)

References 67 publications

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“…In metabolomics, Reznik et al 29 used MS data from eleven studies to measure the extent of metabolic variation across tumours. A similar trend is starting to be observed in proteomics, where reuse of public datasets is becoming increasingly popular, with multiple applications 30,31 . Some examples where joint reanalysis of large public datasets has been performed involved the creation of comprehensive maps of the human proteome 32 and of human protein complexes 33 , or the characterisation of the functional human phosphoproteome 34 .…”

Section: Introductionmentioning

confidence: 59%

An integrated landscape of protein expression in human cancer

Jarnuczak

Najgebauer

Barzine

et al. 2019

Preprint

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Using public proteomics datasets, mostly available through the PRIDE database, we assembled a proteomics resource for 191 cancer cell lines and 246 clinical tumour samples, across 13 cancer lineages. We found that baseline protein abundance in cell lines was generally representative of tumours. However, when considering differences in protein expression between tumour subtypes, as exemplified in the breast lineage, many of these changes were no longer recapitulated in the cell line models. Integration of proteomics and transcriptomics data suggested that the level of transcriptional control in cell lines changed significantly depending on their lineage. Additionally, in agreement with previous studies, variation in mRNA levels was often a poor predictor of changes in protein abundance. To our knowledge, this work constitutes the first meta-analysis study including cancer-related proteomics datasets. We anticipate this aggregated dataset will be of significant aid to future studies requiring a reference to baseline protein expression in cancer.

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Section: Introductionmentioning

confidence: 59%

An integrated landscape of protein expression in human cancer

Jarnuczak

Najgebauer

Barzine

et al. 2019

Preprint

Self Cite

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“…Third, we will review an important clinical application of proteogenomics -- cancer biology -- to demonstrate how this field in particular is poised to move towards truly integrative analysis of the massive data. We will focus on aspects that expand on other excellent reviews published lately that have discussed computational methods and human diseases in more detail [18][19][20][21][22][23][24][25]. …”

Section: Integrating Protein Knowledge Into Omics Analysesmentioning

confidence: 99%

Integration of large-scale multi-omic datasets: A protein-centric view

Rendleman

Choi

Vogel

2018

Current Opinion in Systems Biology

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Innovative mass spectrometry-based proteomics has enabled routine measurements of protein abundance, localization, interactions, and modifications, covering unique aspects of gene expression regulation and function. It is now time to move from isolated analyses of these datasets toward true integration of proteomics with other data types to gain insights from the interactions and interdependencies of biomolecules. When combined with genomic or transcriptomic data, proteomics expands genome annotation to identify variant or missing genes. Dynamic proteomic measurements can move analysis from predominantly concentration-based framework to that of synthesis and degradation of proteins. Proteomic data from thousands of cancer patients can foster identification of novel pathogenic mutations via detection of protein sequence changes that lead to dysregulated pathways in various tumors. Such comprehensive efforts can exploit the synergy arising from large and complex datasets to advance virtually every field of biology.

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“…The HUPO proteomics standard initiatives (http://www.psidev.info/) aims at standardizing data annotation in metabolomics and proteomics [5][6][7]. The open source ISA framework aims at standardizing scientific resources, processes and projects [8][9][10] in several areas of life science.…”

Section: Introductionmentioning

confidence: 99%

BioTracs: A transversal framework for computational workflow standardization and traceability

Abi-Ghanem

Ouattara

2020

Preprint

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BackgroundThe need of digital tools for integrative analysis is today important in most scientific areas. It leads to several community-driven initiatives to standardize the sharing of data and computational workflows. However, there exists no open agnostic framework to model and implement computation workflows, in particular in bioinformatics. It is therefore difficult for data scientists to share transparently and integrate heterogeneous analysis processes coming from different scientific domains, programing languages, projects or teams. Results2 We present here BioTracs, a transversal framework for computational workflow standardization and traceability. It is based on PRISM architecture (Process Resource Interfacing SysteM), an agnostic open architecture we introduce here to standardize the way processes and resources can be modelled and interfaced in computational workflows to ensure traceability, reproducibility and facilitate sharing. BioTracs is today implemented in MATLAB and available under open source license on GitHub. Several BioTracs-derived applications are also available online. They were successfully applied to large-scale metabolomics and clinical studies and demonstrated flexibility and robustness. ConclusionsAs an implementation of the PRISM architecture, BioTracs paved the way to an open framework in which bioinformatics could specify ad model workflows. PRISM architecture is designed to provide scalability and transparency from the code to the project level we less efforts. It could also be implemented using open objectoriented languages such as Python, C++ or java.

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A Golden Age for Working with Public Proteomics Data

Cited by 97 publications

References 67 publications

An integrated landscape of protein expression in human cancer

An integrated landscape of protein expression in human cancer

Integration of large-scale multi-omic datasets: A protein-centric view

BioTracs: A transversal framework for computational workflow standardization and traceability

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