Unified Feature Association Networks through Integration of Transcriptomic and Proteomic Data
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ABSTRACT:
PROVIDER: PRJNA557355 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Transcriptomic and proteomic data from human cells infected with Dengue virus was used to infer a number of networks to determine which network inference methods were best for linking protiens and transcripts in the same network. GENIE3, a random forest method, was found to be the best and once inferred with this method networks were interrogated to gain knowledge regarding host pathogen interactions surrounding Dengue infection.
2019-08-18 | GSE135079 | GEO
Project description:High-throughput multi-omics studies and corresponding network analyses of multi-omic data have rapidly expanded their impact over the last 10 years. As biological features of different types (e.g. transcripts, proteins, metabolites) interact within cellular systems, the greatest amount of knowledge can be gained from networks that incorporate multiple types of -omic data. However, biological and technical sources of variation diminish the ability to detect cross-type associations, yielding networks dominated by communities comprised of nodes of the same type. We describe here network building methods that can maximize edges between nodes of different data types leading to integrated networks, networks that have a large number of edges that link nodes of different-omic types (transcripts, proteins, lipids etc). We systematically rank several network inference methods and demonstrate that, in many cases, using a random forest method, GENIE3, produces the most integrated networks. This increase in integration does not come at the cost of accuracy as GENIE3 produces networks of approximately the same quality as the other network inference methods tested here. Using GENIE3, we also infer networks representing antibody-mediated Dengue virus cell invasion and receptor-mediated Dengue virus invasion. A number of functional pathways showed centrality differences between the two networks including genes responding to both GM-CSF and IL-4, which had a higher centrality value in an antibody-mediated vs. receptor-mediated Dengue network. Because a biological system involves the interplay of many different types of molecules, incorporating multiple data types into networks will improve their use as models of biological systems. The methods explored here are some of the first to specifically highlight and address the challenges associated with how such multi-omic networks can be assembled and how the greatest number of interactions can be inferred from different data types. The resulting networks can lead to the discovery of new host response patterns and interactions during viral infection, generate new hypotheses of pathogenic mechanisms and confirm mechanisms of disease.
| S-EPMC6748406 | biostudies-literature
Project description:Proteomic data from dengue virus infected U-937 cells. PVD_C samples were infected via antibody-mediated or DC-SIGN receptor mediated entry routes with wild-type DENV-4 or mock inoculum; time points 2, 8, 16, and 24 hours; 5 biological replicates. PVD_L samples were infected via antibody-mediated or DC-SIGN receptor mediated entry routes with wild-type DENV-1 or mock inoculum; time points 2, 6, 10, 18, 24, and 30 hours; 5 biological replicates.
2019-08-22 | PXD014780 | Pride
Project description:Single cell RNA sequencing (scRNA-seq) has advanced the assessment of cellular heterogeneity at the single-cell resolution by identifying transcriptional similarities and differences. Data resources of scRNA-seq have been largely produced and extensively studied for the mouse retina. They serve as a powerful tool to study cellular components, transcriptome relationships, and regulatory mechanisms underlying various retinal diseases and biological processes. The large volume of mouse retinal scRNA-seq data has been released in separate repositories, limiting their widespread use in mouse retina communities. In this work, we are presenting a unified single-cell atlas for adult wild-type mouse retina using our in-house generated single-cell RNA-seq data complementing public datasets. The collected data account for over 323,000 single cells. After data integration, cell clustering, and cell type annotation, we have annotated 11 major classes and over 120 retinal cell types to form a unified single-cell reference for the mouse retina. To facilitate the public use of the reference, we have deposited it on CELLxGENE, UCSC Cell Browser, and Single Cell Portal for visualization and gene expression exploration. The unified atlas is also released to annotate new mouse retinal cells using scArches utilities. This unified reference serves an easy-to-use data resource of mouse retina communities.
2024-02-15 | GSE243413 | GEO
Project description:Resolving single-cell gene expression by pseudo-temporal integration of transcriptomic and proteomic datasets.
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Project description:One of the greatest prospects of multimodal single-cell analysis is the ability to integrate single-cell transcriptomic and proteomic datasets to compare transcription and translation profiles to investigate regulation between phenotypes. However, the integration of these complex datasets remains challenging. In this study, we demonstrate the use of pseudo-temporal cell orders as a mutual axis for integrating of scRNA-Seq and scp-MS data. We collected temporal samples of HEK293F cells undergoing hypoxia to isolate the variance of hypoxia from the tightly coupled variance of cell cycle. Markers for hypoxia were then used to construct pseudo-temporal orders in each dataset which were used to reveal transcription and translation dynamics of attenuated respiration in single cells. This approach allowed us to integrate single-cell transcriptomics and proteomics methods to understand the regulatory mechanisms governing biological phenotypes.
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Project description:Archaeological materials are a finite resource, and efforts should be made to minimize destructive analyses. This can be achieved by using protocols combining extraction of several lines of evidence, which decreases the material needed for analyses while maximizing the information yield. Archaeological dental calculus is a source of several different types of biomolecules, as well as microfossils, and can tell us about the human host, microbiome, diet, and even occupational activities. Here, we present a unified protocol allowing for simultaneous extraction of DNA and proteins from archaeological dental calculus. We evaluate the protocol on dental calculus from a range of ages and estimated preservation states, and compare it against standard DNA-only and protein-only protocols. We find that most aspects of downstream analyses are unaffected by the unified protocol, although minor shifts in the recovered proteome can be detected. Protein recovery depends on both the amount of starting material and choice of extraction protocol, whereas DNA recovery is significantly lowered through the unified protocol. However, DNA recovery from dental calculus is generally very high, and we found no differences in DNA fragment characteristics or taxonomic profile. In conclusion, the unified protocol allows for simultaneous extraction of two complementary lines of evidence from archaeological dental calculus without compromising downstream analyses, thereby minimizing the need for destructive analysis of this finite resource.
2020-04-27 | PXD015817 | Pride
Project description:A unified sequence catalogue of the human gut microbiome
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Project description:A unified synthesis of skeletally-diverse sulfonyl fluorides was developed that relied upon photoredox-catalysed dehydrogenative couplings between hetaryl sulfonyl fluorides and hydrogen donor building blocks. A set of 32 diverse probes were prepared, and then screened against Trypanosoma brucei. Four of the probes were found to have sub-micromolar anti-trypanosomal activity. A chemical proteomic approach, harnessing an alkynylated analogue and broad-spectrum fluorophosphonate tools, provided insights into the observed anti-trypanosomal activity, which likely stems from covalent modification of multiple protein targets.
2024-10-16 | PXD056504 | Pride