Tribe: The collaborative platform for reproducible web-based analysis of gene sets

Zelaya, René A.; Wong, Aaron K.; Frase, Alex T.; Ritchie, Marylyn D.; Greene, Casey S.

doi:10.1101/055913

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…It is important to note that these modules are then somewhat specific to whole blood. We obtained the gene sets from http://www.biir.net/public_wikis/module_annotation/V2_Trial_8_Modules and converted to Entrez ID using Tribe (Zelaya et al, 2016). (These converted gene sets are available in our analysis GitHub repository.)…”

Section: Declaration Of Interestsmentioning

confidence: 99%

MultiPLIER: A Transfer Learning Framework for Transcriptomics Reveals Systemic Features of Rare Disease

et al. 2019

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Section: Declaration Of Interestsmentioning

confidence: 99%

MultiPLIER: A Transfer Learning Framework for Transcriptomics Reveals Systemic Features of Rare Disease

et al. 2019

Self Cite

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“…To get a detailed view of a signature or a group of signatures, users can retrieve their constituent genes using annotate_genes_in_signatures(). Users can also download existing GO terms and KEGG pathways [16] from the TRIBE web server [26] using fetch_geneset() and associate signatures to known GO and KEGG pathways using annotate_signatures_with_genesets(). The TRIBE web server also allows users to build and share their own custom gene sets, so the connection to this resource enables custom gene set analysis as well.…”

Section: R Packagementioning

confidence: 99%

ADAGE signature analysis: differential expression analysis with data-defined gene sets

Tan

Huyck

et al. 2017

Preprint

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Background: Gene set enrichment analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data. Results: Here we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses signatures extracted from public expression data. We first extract expression signatures from public data using ADAGE, a neural network-based feature extraction approach. We next identify signatures that are differentially active under a given treatment. Our results demonstrate that these signatures represent biological processes that are perturbed by the experiment. Because these signatures are directly learned from data without supervision, they can identify uncurated or novel biological processes. We implemented ADAGE signature analysis for the bacterial pathogen Pseudomonas aeruginosa. For the convenience of different user groups, we implemented both an R package (ADAGEpath) and a web server (http://adage.greenelab.com) to run these analyses. Both are open-source to allow easy expansion to other organisms or signature generation methods. We applied ADAGE signature analysis to an example dataset in which wild-type and Δanr mutant cells were grown as biofilms on the Cystic Fibrosis genotype bronchial epithelial cells. We mapped active signatures in the dataset to KEGG pathways and compared with pathways identified using GSEA. The two approaches generally return consistent results; however, ADAGE signature analysis also identified a signature that revealed the molecularly supported link between the MexT regulon and Anr. Conclusions: We designed ADAGE signature analysis to perform gene set analysis using data-defined functional gene signatures. This approach addresses an important gap for biologists studying non-traditional model organisms and those without extensive curated resources available. We built both an R package and web server to provide ADAGE signature analysis to the community.

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“…However, the functional effects of a gene are often context-dependent, and context can be difficult to capture, let alone make Findable, Accessible, Interoperable, and Reusable (FAIR) ( 14 ). A previous server, Tribe, allowed the capturing and sharing of collections of genes (gene sets) ( 15 ). However, it supported a subset of identifiers and did not plug into a widely used ecosystem of APIs.…”

Section: Introductionmentioning

confidence: 99%

MyGeneset.info: an interactive and programmatic platform for community-curated and user-created collections of genes

Ávila

Rubinetti

Zhou

et al. 2023

Nucleic Acids Research

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Gene definitions and identifiers can be painful to manage–more so when trying to include gene function annotations as this can be highly context-dependent. Creating groups of genes or gene sets can help provide such context, but it compounds the issue as each gene within the gene set can map to multiple identifiers and have annotations derived from multiple sources. We developed MyGeneset.info to provide an API for integrated annotations for gene sets suitable for use in analytical pipelines or web servers. Leveraging our previous work with MyGene.info (a server that provides gene-centric annotations and identifiers), MyGeneset.info addresses the challenge of managing gene sets from multiple resources. With our API, users readily have read-only access to gene sets imported from commonly-used resources such as Wikipathways, CTD, Reactome, SMPDB, MSigDB, GO, and DO. In addition to supporting the access and reuse of approximately 180k gene sets from humans, common model organisms (mice, yeast, etc.), and less-common ones (e.g. black cottonwood tree), MyGeneset.info supports user-created gene sets, providing an important means for making gene sets more FAIR. User-created gene sets can serve as a way to store and manage collections for analysis or easy dissemination through a consistent API.

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Tribe: The collaborative platform for reproducible web-based analysis of gene sets

Abstract: Background:

Cited by 4 publications

References 27 publications

MultiPLIER: A Transfer Learning Framework for Transcriptomics Reveals Systemic Features of Rare Disease

MultiPLIER: A Transfer Learning Framework for Transcriptomics Reveals Systemic Features of Rare Disease

ADAGE signature analysis: differential expression analysis with data-defined gene sets

MyGeneset.info: an interactive and programmatic platform for community-curated and user-created collections of genes

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