Quantifying the impact of public omics data

Perez‐Riverol, Yasset; Zorin, Andrey; Dass, Gaurhari; Vu, Minh Tuan; Xu, Pan; Glonț, Mihai; Vizcaíno, Juan Antonio; Jarnuczak, Andrew F.; Petryszak, Robert; Ping, Peipei; Hermjakob, Henning

doi:10.1038/s41467-019-11461-w

Cited by 93 publications

(66 citation statements)

References 37 publications

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“…The method /dataset/getSimilar retrieve all the datasets that are similar to a specific dataset by metadata (see original manuscript of OmicsDI for the similarity algorithm explanation (1)). Another important method /dataset/getMergeCandidates enables to retrieve candidate datasets that can be considered as replicates (5).…”

Section: Design and Implementationmentioning

confidence: 99%

“…An important feature of this method is to be able to provide the URI of the files that are closer to the user query (Figure 1). OmicsDI stores for each dataset a primary source of the resource and all the replicates of it to avoid duplications when the dataset is replicated in multiple providers, for example, GEO and ArrayExpress, or PRIDE and MassIVE (5).…”

Section: Data Files Geolocationmentioning

confidence: 99%

“…As discussed above, one of the interesting features of OmicsDI is the possibility to retrieve the information about replicates of the same dataset (5). For example, the dataset https://www.omicsdi.org/dataset/pride/PXD003213 is stored in two different places: PRIDE (9) and MassIVE (10).…”

Section: Example Use Casesmentioning

confidence: 99%

“…In addition, public data deposition is growing in all omics disciplines, because it is considered to be a good scientific practice (e.g. to enable reproducibility) and/or it is mandated by funding agencies and scientific journals (5). These new developments haves triggered new challenges and opportunities to make data Findable, Accessible, Interoperable and Re-usable (FAIRhttps://www.force11.org/group/fairgroup/fairprinciples) (6).…”

Section: Introductionmentioning

confidence: 99%

“…In 2016, we released the first version of the Omics Discovery Index (OmicsDIhttps://www.omicsdi.org) as a light-weight system to aggregate datasets across multiple public omics data resources (1,5). OmicsDI collects datasets from multiple repositories and databases representing genomics, transcriptomics, proteomics, metabolomics and multiomics datasets, as well as computational models of biological processes.…”

Section: Introductionmentioning

confidence: 99%

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The Omics Discovery REST interface

Dass

et al. 2020

Preprint

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AbstractThe Omics Discovery Index is an open source platform that can be used to access, discover and disseminate omics datasets. OmicsDI integrates proteomics, genomics, metabolomics, models and transcriptomics datasets. Using an efficient indexing system, OmicsDI integrates different biological entities including genes, transcripts, proteins, metabolites and the corresponding publications from PubMed. In addition, it implements a group of pipelines to estimate the impact of each dataset by tracing the number of citations, reanalysis and biological entities reported by each dataset. Here, we present the OmicsDI REST interface to enable programmatic access to any dataset in OmicsDI or all the datasets for a specific provider (database). Clients can perform queries on the API using different metadata information such as sample details (species, tissues, etc), instrumentation (mass spectrometer, sequencer), keywords and other provided annotations. In addition, we present two different libraries in R and Python to facilitate the development of tools that can programmatically interact with the OmicsDI REST interface.

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Section: Design and Implementationmentioning

confidence: 99%

Section: Data Files Geolocationmentioning

confidence: 99%

Section: Example Use Casesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Omics Discovery REST interface

Dass

et al. 2020

Preprint

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Challenges in the Multivariate Analysis of Mass Cytometry Data: The Effect of Randomization

et al. 2019

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Cytometry by time-of-flight (CyTOF) has emerged as a high-throughput single cell technology able to provide large samples of protein readouts. Already, there exists a large pool of advanced high-dimensional analysis algorithms that explore the observed heterogeneous distributions making intriguing biological inferences. A fact largely overlooked by these methods, however, is the effect of the established data preprocessing pipeline to the distributions of the measured quantities. In this article, we focus on randomization, a transformation used for improving data visualization, which can negatively affect multivariate data analysis methods such as dimensionality reduction, clustering, and network reconstruction algorithms. Our results indicate that randomization should be used only for visualization purposes, but not in conjunction with high-dimensional analytical tools.Single cell cytometry allows the detection of cell components in a high-throughput fashion. One of its latest versions is Cytometry by Time Of Flight (CyTOF) (1). The advantage of CyTOF compared to traditional flow cytometry is that high atomic weight metal reporters typically not found in a biological sample are employed for cell tagging, allowing the quantification of more than 40 cell parameters simultaneously. Such large number of parameters enables this technology to provide multivariate data sets with emerging properties that are well suited to advanced computational analysis (2,3). For example, unsupervised learning techniques like clustering and dimensionality reduction are typically used for cell phenotyping (4,5). In combination with statistical tests or supervised learning approaches, these methods are also employed for associating phenotypes or clinical outcomes to relevant cell subsets or protein markers (6,7). Clustering and dimensionality reduction are also commonly employed to visualize patterns in the data, marker relationships in the high-dimensional space or the phenotypic progression trajectories of cell subsets (8). Very recently, network-based methods have also been applied on CyTOF data, for automatic cell population identification (9) and the prediction of protein signaling networks using automated causal discovery algorithms (10).Despite the accelerated development of CyTOF-dedicated analysis methods, there is still no well-established data preprocessing consensus. This is mainly because the preceding standardization of experimental procedures is still in its infancy (11,12). In general, there are at least three distinct sources of technical variation in CyTOF. The first is the drop in the instrument sensitivity and the change in oxidation rate over long sample running times that causes signal fluctuations (13). Second,

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Decoding communication patterns of the innate immune system by quantitative proteomics

Sukumaran

Coish

Yeung

et al. 2019

Journal of Leukocyte Biology

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The innate immune system is a collective network of cell types involved in cell recruitment and activation using a robust and refined communication system. Engagement of receptor-mediated intracellular signaling initiates communication cascades by conveying information about the host cell status to surrounding cells for surveillance and protection. Comprehensive profiling of innate immune cells is challenging due to low cell numbers, high dynamic range of the cellular proteome, low abundance of secreted proteins, and the release of degradative enzymes (e.g., proteases).However, recent advances in mass spectrometry-based proteomics provides the capability to overcome these limitations through profiling the dynamics of cellular processes, signaling cascades, post-translational modifications, and interaction networks. Moreover, integration of technologies and molecular datasets provide a holistic view of a complex and intricate network of communications underscoring host defense and tissue homeostasis mechanisms. In this Review, we explore the diverse applications of mass spectrometry-based proteomics in innate immunity to define communication patterns of the innate immune cells during health and disease. We also provide a technical overview of mass spectrometry-based proteomic workflows, with a focus on bottom-up approaches, and we present the emerging role of proteomics in immune-based drug discovery while providing a perspective on new applications in the future.

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Quantifying the impact of public omics data

Cited by 93 publications

References 37 publications

The Omics Discovery REST interface

The Omics Discovery REST interface

Challenges in the Multivariate Analysis of Mass Cytometry Data: The Effect of Randomization

Decoding communication patterns of the innate immune system by quantitative proteomics

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