Summary Visualizations of Gene Ontology Terms With GO-Figure!

Reijnders, Maarten J.M.F.; Waterhouse, Robert M.

doi:10.3389/fbinf.2021.638255

Cited by 71 publications

(62 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GO term enrichment results were clustered with GO-Figure! 54 and visualized using the R package treemap. Canonical correlations between all technical, clinical, and demographic variables were calculated using the canCorPairs function and visualized using the plotCorrMatrix function from the Bioconductor package variancePartition 29,55 .…”

Section: Methodsmentioning

confidence: 99%

Acute COVID-19 gene-expression profiles show multiple etiologies of long-term sequelae

Thompson

Simons

Wilkins

et al. 2021

Preprint

View full text Add to dashboard Cite

Two years into the SARS-CoV-2 pandemic, the post-acute sequelae of infection are compounding the global health crisis. Often debilitating, these sequelae are clinically heterogeneous and of unknown molecular etiology. Here, a transcriptome-wide investigation of this new condition was performed in a large cohort of acutely infected patients followed clinically into the post-acute period. Gene expression signatures of post-acute sequelae were already present in whole blood during the acute phase of infection, with both innate and adaptive immune cells involved. Plasma cells stood out as driving at least two distinct clusters of sequelae, one largely dependent on circulating antibodies against the SARS-CoV-2 spike protein and the other antibody-independent. Altogether, multiple etiologies of post-acute sequelae were found concomitant with SARS-CoV-2 infection, directly linking the emergence of these sequelae with the host response to the virus.

show abstract

Section: Methodsmentioning

confidence: 99%

Acute COVID-19 gene-expression profiles show multiple etiologies of long-term sequelae

Thompson

Simons

Wilkins

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Specifically, PyLiger incorporates GOATOOLS (Klopfenstein et al ., 2018) for gene ontology enrichment testing and GO-Figure! (Reijnders and Waterhouse, 2021) for visualizing enriched GO terms. For example, given a list of differentially expressed genes, users can easily run a PyLiger function to identify a list of significantly enriched GO terms.…”

Section: Resultsmentioning

confidence: 99%

PyLiger: Scalable single-cell multi-omic data integration in Python

Welch

2021

Preprint

View full text Add to dashboard Cite

Motivation: LIGER is a widely-used R package for single-cell multi-omic data integration. However, many users prefer to analyze their single-cell datasets in Python, which offers an attractive syntax and highly-optimized scientific computing libraries for increased efficiency. Results: We developed PyLiger, a Python package for integrating single-cell multi-omic datasets. PyLiger offers faster performance than the previous R implementation (2-5× speedup), interoperability with AnnData format, flexible on-disk or in-memory analysis capability, and new functionality for gene ontology enrichment analysis. The on-disk capability enables analysis of arbitrarily large single-cell datasets using fixed memory.

show abstract

“…Then, we used TopGO (Alexa et al, 2006;Alexa, 2021) to discover enriched Gene Ontology terms, and we performed cluster analysis (using GoFigure! (Reijnders and Waterhouse, 2021)).…”

Section: Methodsmentioning

confidence: 99%

A Cartography of Differential Gene Methylation in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Different Network Roles in the Protein-Protein Interactions Network Play Different, Biologically Relevant, Roles

Wilberforce

Riva

2021

Preprint

View full text Add to dashboard Cite

Myalgic Encephalomyelitis, also known as Chronic Fatigue Syndrome (ME/CFS), is a debilitating illness characterised by severe fatigue and associated with immune dysfunction. Previous studies of DNA methylation (epigenetic changes that can affect the gene transcription) have found evidence of changes in immune cells for ME/CFS. However these studies have been limited by their small sample size, precluding the ability to detect changes to methylation of smaller magnitude. Therefore, to achieve a larger sample size and detect small changes to DNA methylation, we aggregate three comparable datasets and analyse them in unison. We find 10,824 differentially methylated genes, with a very small average change. We then turn our attention to the network structure of the Protein-Protein interaction, which we built from the currently known interactions of relevant proteins, and localising the network cartography framework, we identify 184 hub genes. A distinct structuring emerges, with different hub types playing differing, meaningful, biological roles. Supporting previous theories about ME/CFS, Gene ontology enrichment analysis of these hubs reveal that they are involved in immune system processes, including response to TGF-β and LPS, as well as mitochondrial functioning. We also show that dopaminergic signalling may potentially contribute to immune pathology in ME/CFS. Our results demonstrate the potentiality of network cartographic approaches in shedding light on the epigenetic contribution to the immune dysregulation of ME/CFS.

show abstract

Summary Visualizations of Gene Ontology Terms With GO-Figure!

Cited by 71 publications

References 18 publications

Acute COVID-19 gene-expression profiles show multiple etiologies of long-term sequelae

Acute COVID-19 gene-expression profiles show multiple etiologies of long-term sequelae

PyLiger: Scalable single-cell multi-omic data integration in Python

A Cartography of Differential Gene Methylation in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Different Network Roles in the Protein-Protein Interactions Network Play Different, Biologically Relevant, Roles

Contact Info

Product

Resources

About