MassIVE.quant: a community resource of quantitative mass spectrometry–based proteomics datasets

Choi, Meena; Carver, Jeremy; Chiva, Cristina; Tzouros, Manuel; Huang, Ting; Tsai, Tsung-Heng; Pullman, Benjamin; Bernhardt, Oliver M.; Hüttenhain, Ruth; Teo, Guo Ci; Perez‐Riverol, Yasset; Muntel, Jan; Müller, Marion; Goetze, Sandra; Pavlou, Maria; Verschueren, Erik; Wollscheid, Bernd; Nesvizhskii, Alexey I.; Reiter, Lukas; Dunkley, Tom; Sabidó, Eduard; Bandeira, Nuno; Vitek, Olga

doi:10.1038/s41592-020-0955-0

Cited by 74 publications

(62 citation statements)

References 18 publications

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“…ProteomicsDB (Schmidt et al, 2018), Human Protein Atlas (HPA) (Thul and Lindskog, 2018), PRIDE (Perez-Riverol et al, 2019), PeptideAtlas (Desiere et al, 2006), MassIVE.quant (Choi et al, 2020), jPOSTrepo (Okuda et al, 2017), iProX (Ma et al, 2019), and Panorama Public (Sharma et al, 2018) Proteomic annotations (structural features)…”

Section: Methodsology Functionalitiesmentioning

confidence: 99%

Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions

et al. 2021

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The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on nutrition and homeostasis. Many chronic diseases such as diabetes, cancer, inflammatory bowel diseases are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites have recently been identified, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high-throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to limitations in parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a system and community level. In the past decade, computational biology and machine learning methodologies have been developed with the aim of filling the existing gaps. Due to the agnostic nature of the tools, they have been applied in diverse disease contexts to analyze and infer the interactions between the microbiome and host molecular components. Some of these approaches allow the identification and analysis of affected downstream host processes. Most of the tools statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic interactions between individual microbes/microbiome and the host and the opportunities for basic and clinical research. These could include but are not limited to the development of activity- and mechanism-based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.

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Section: Methodsology Functionalitiesmentioning

confidence: 99%

Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions

et al. 2021

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“…Recently, multiple resources have started to systematically reanalyze proteomics public quantitative datasets, including MassIVE.Quant, ExpressionAtlas and ProteomicsDB 4,11,19 .…”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

“…However, thus far been, it has been mainly limited to benchmarking studies and/or applications related to peptide and protein identification, with resources such as PeptideAtlas 3 and GPMDB 9 systematically reanalyzing data from ProteomeXchange 10 . Recently, new efforts like ProteomicsDB 11 , MassiVE.Quant 4 and Expression Atlas 12 have started to include reanalysed quantitative public datasets to present baseline and differential protein expression.…”

Section: Introductionmentioning

confidence: 99%

“…The amount of proteomics data in public repositories is growing at an unprecedented rate 1, 2 . ProteomeXchange (PX) is a consortium of multiple proteomics resources including the PRIDE database 2 , PASSEL and PeptideAtlas 3 , MassIVE 4 , jPOST 5,6 , iProX 7 , and Panorama Public 8 . As of May 2021, over 25,000 datasets have been submitted to ProteomeXchange data repositories.…”

Section: Introductionmentioning

confidence: 99%

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A proteomics sample metadata representation for multiomics integration, and big data analysis

Dai

Füllgrabe

Pfeuffer

et al. 2021

Preprint

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The amount of public proteomics data is increasing at an extraordinary rate. Hundreds of datasets are submitted each month to ProteomeXchange repositories, representing many types of proteomics studies, focusing on different aspects such as quantitative experiments, post-translational modifications, protein-protein interactions, or subcellular localization, among many others. For every proteomics dataset, two levels of data are captured: the dataset description, and the data files (encoded in different file formats). Whereas the dataset description and data file formats are supported by all ProteomeXchange partner repositories, there is no standardized format to properly describe the sample metadata and their relationship with the dataset files in a way that fully allows their understanding or re-analysis. It is left to the users choice whether to provide or not an ad hoc document containing this information. Therefore, in many cases, understanding the study design and data requires going back to the associated publication. This can be tedious and may be restricted in the case of non-open access publications. In many cases, this problem limits the generalization and reuse of public proteomics data. Here we present a standard representation for sample metadata tailored to proteomics datasets produced by the HUPO Proteomics Standards Initiative and supported by ProteomeXchange resources. We repurposed the existing data format MAGE-TAB used routinely in the transcriptomics field to represent and annotate proteomics datasets. MAGE-TAB-Proteomics defines a set of annotation rules that the datasets submitted to ProteomeXchange should follow, ranging from sample properties to data analysis protocols. We also introduce a crowdsourcing project that enabled the manual curation of over 200 public datasets using MAGE-TAB-Proteomics. In addition, we describe an ecosystem of tools and libraries that were developed to validate and submit sample metadata-related information to ProteomeXchange. We expect that these tools will improve the reproducibility of published results and facilitate the reanalysis and integration of public proteomics datasets.

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“… 14 We hence find it worthwhile to plug into this user base and provide an alternative means to interpret the output from the MaxLFQ pipeline. We specifically note the potential of the increasingly more common practice of depositing results of processing analyses to repositories, such as MassIVE 3 and PRIDE. 9 …”

Section: Introductionmentioning

confidence: 99%

Triqler for MaxQuant: Enhancing Results from MaxQuant by Bayesian Error Propagation and Integration

The

Käll

2021

J. Proteome Res.

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Error estimation for differential protein quantification by label-free shotgun proteomics is challenging due to the multitude of error sources, each contributing uncertainty to the final results. We have previously designed a Bayesian model, Triqler, to combine such error terms into one combined quantification error. Here we present an interface for Triqler that takes MaxQuant results as input, allowing quick reanalysis of already processed data. We demonstrate that Triqler outperforms the original processing for a large set of both engineered and clinical/biological relevant data sets. Triqler and its interface to MaxQuant are available as a Python module under an Apache 2.0 license from .

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MassIVE.quant: a community resource of quantitative mass spectrometry–based proteomics datasets

Cited by 74 publications

References 18 publications

Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions

Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions

A proteomics sample metadata representation for multiomics integration, and big data analysis

Triqler for MaxQuant: Enhancing Results from MaxQuant by Bayesian Error Propagation and Integration

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