TRIAGE toolkit version 2
TRIAGE: A Toolkit for Streamlined Discovery of Regulatory Genes and Elements
Overview
The TRIAGE methods1-3 provide a powerful framework for analysing regulatory elements in both bulk and single-cell RNA sequencing (RNA-seq) datasets. Originally developed as the TRIAGE R package4, it now includes both an R package and complementary Python workflows, together forming the expanded TRIAGE toolkit. By leveraging consortium-level H3K27me3 data5, TRIAGE enables researchers to uncover the regulatory basis of cell identity and state. It integrates seamlessly into standard RNA-seq analysis workflows, offering efficient and adaptable pipelines for transcriptomic data exploration and visualisation. In the current release, TRIAGE expands its utility beyond the transcriptome-focused analysis of version 1. Through the newly added TRIAGEccs3 module, the TRIAGE toolkit now enables users to input genomic regions of interest, such as genomic coordinates of novel lncRNAs or loci identified from genome-wide association studies (GWAS), and rank them based on their potential regulatory roles.
Key Features of TRIAGE toolkit (version 2)
• Regulatory Gene Prioritisation: TRIAGEgene1 introduces a ranking system to identify key regulatory genes based on functional significance.
• Cell Clustering Enhancement: TRIAGEcluster2 refines cell clustering by identifying regulatory genes that define cell identity in single-cell RNA-seq data.
• Functional Gene Clustering: TRIAGEparser2 categorises gene lists into clusters with distinct biological functions, pinpointing key regulatory components within gene networks.
• Regulatory Element Prioritisation: TRIAGEccs3 allows users to input genomic coordinates of novel long non-coding RNAs (lncRNAs) or other genomic regions of interest, ranking them by regulatory potential.
• Seamless Integration: Designed to work efficiently within existing data analysis workflows, the toolkit provides a versatile solution for regulatory gene and element analysis.
Applications
TRIAGE methods1-3 and the TRIAGE R package4 have been successfully applied to a wide range of datasets (e.g. refs6-12), demonstrating its ability to uncover regulatory mechanisms in diverse biological contexts. The toolkit has provided valuable insights into gene regulation, development, and disease mechanisms. Its seamless integration into standard analysis workflows positions it as a useful resource for exploring regulatory elements and mechanisms across diverse biological systems, with potential for novel discoveries in both biological and medical research.
Access
The documentation for TRIAGE toolkit version 2 can be found here: https://tinyurl.com/triage2doc
For use of the TRIAGE toolkit only for academic research or teaching, please request the Academic Research and Teaching Licence.
For use of the TRIAGE toolkit for commercial research by or on behalf of an entity, please request the General Use Licence.
Once orders have been approved and the applicable fee paid, a link will be provided to download the TRIAGE toolkit. In addition to the main package, two bedgraph files will be accessible which Licensee will also need to download in order to use TRIAGEccs.
Acknowledgement
Aspects of the TRIAGE toolkit were developed using processed H3K27me3 data (BigWig files) from the EpiMap Repository (https://compbio.mit.edu/epimap). The EpiMap consortium aggregated and re-processed ENCODE, Roadmap, GGR and imputed datasets.
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expand_more library_books References (12)
- Shim, W. J. et al. (2020), Conserved Epigenetic Regulatory Logic Infers Genes Governing Cell Identity., Cell Systems, 11, 625-639 e613
- Sun, Y. et al. (2023), Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity, Nucleic Acids Research, 51, e62
- Sinniah, E. et al (2024), Epigenetic constraint of cellular genomes evolutionarily links genetic variation to function, bioRxiv
- Zhao, Q. et al. (2025), TRIAGE: an R package for regulatory gene analysis., Briefings in Bioinformatics, 26
- Boix, C. A., James, B. T., Park, Y. P., Meuleman, W. & Kellis, M. (2021), Regulatory genomic circuitry of human disease loci by integrative epigenomics., Nature, 590, 300-307
- Wu, Z. et al. (2024), Wnt dose escalation during the exit from pluripotency identifies tranilast as a regulator of cardiac mesoderm., Developmental Cell, 59, 705-722 e708
- Plaisance, I. et al. (2023), A transposable element into the human long noncoding RNA CARMEN is a switch for cardiac precursor cell specification., Cardiovascular Research, 119, 1361-1376
- Friedman, C. E. et al. (2023), HOPX-associated molecular programs control cardiomyocyte cell states underpinning cardiac structure and function., Developmental Cell
- Afonso, J. et al (2023), Repressive epigenetic mechanisms, such as the H3K27me3 histone modification, were predicted to affect muscle gene expression and its mineral content in Nelore cattle., Biochemistry and Biophysics Reports, 33, 101420
- Wehrens, M. et al. (2022), Single-cell transcriptomics provides insights into hypertrophic cardiomyopathy, Cell Reports, 39, 110809
- Qiu, C. et al (2022), Systematic reconstruction of cellular trajectories across mouse embryogenesis, Nature Genetics, 54, 328-341
- Kojic, M. et al. (2021), Elp2 mutations perturb the epitranscriptome and lead to a complex neurodevelopmental phenotype, Nature Communications, 12, 2678
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expand_more cloud_download Supporting documents (2)Product brochureTRIAGE toolkit version 2.pdfreadme.txt (661 B)Additional files may be available once you've completed the transaction for this product. If you've already done so, please log into your account and visit My account / Downloads section to view them.