Mouzo, D. | Search bioRxiv

bioRxiv2024

GeneSetCluster 2.0: a comprehensive toolset for summarizing and integrating gene-sets analysis

BackgroundGene-Set Analysis (GSA) is commonly used to analyze high-throughput experiments. However, GSA cannot readily disentangle clusters or pathways due to redundancies in upstream knowledge bases, which hinders comprehensive exploration and interpretation of biological findings. To address this challenge, we developed GeneSetCluster, an R package designed to summarize and integrate GSA results. Over time, we and users as well identified limitations in the original version, such as difficulties in managing redundancies across multiple gene-sets, large computational times, and its lack of accessibility for users without programming expertise. ResultsWe present GeneSetCluster 2.0, a comprehensive upgrade that delivers methodological, computational, interpretative, and user-experience enhancements. Methodologically, GeneSetCluster 2.0 introduces a novel approach to address duplicated gene-sets and implements a seriation-based clustering algorithm that reorders results, aiding pattern identification. Computationally, the package is optimized for parallel processing, significantly reducing execution time. GeneSetCluster 2.0 enhances cluster annotations by associating clusters with relevant tissues and biological processes to improve biological interpretation, particularly for human and mouse data. To broaden accessibility, we have developed a user-friendly web application enabling non-programmers to use it. This version also ensures seamless integration between the R package, catering to users with programming expertise, and the web application for broader audiences. We evaluated the updates in a single-cell RNA public dataset. ConclusionGeneSetCluster 2.0 offers substantial improvements over its predecessor. Furthermore, by bridging the gap between bioinformaticians and clinicians in multidisciplinary teams, GeneSetCluster 2.0 facilitates collaborative research. The R package and web application, along with detailed installation and usage guides, are available on GitHub (https://github.com/TranslationalBioinformaticsUnit/GeneSetCluster2.0), and the web application can be accessed at https://translationalbio.shinyapps.io/genesetcluster/.

bioinformatics↗

bioRxiv2023

Uncovering the cis-regulatory program of early human B-cell commitment and its implications in the pathogenesis of B-cell acute lymphoblastic leukemia

Dysregulation of the early stages of B-cell lymphopoiesis, orchestrating the development of cellular immunity, may induce malignant transformations. Therefore, it is essential to characterize the gene regulatory network (GRN) driving B-cell lymphopoiesis in healthy individuals to uncover malignancy mechanisms. To this end, we generated a dataset that included paired human data for chromatin accessibility and gene expression in eight B-cell precursor stages, providing the first deep characterization of early B-cell lymphopoiesis, including the identification of regulatory elements and the reconstruction of the GRN. Using this data, we recapitulated well-known regulatory elements and revealed new regulons, such as ELK3, enriched in pro-B cells with a putative role in cell cycle progression. Moreover, a single-cell multi-omics analysis validated and enhanced the resolution of the regulatory landscape recovered by bulk data, revealing MYBL2 and ZNF367 as specific regulons of cycling cell states, and CEBPA associated with lymphoid multipotent progenitors (LMPPs). Importantly, this dataset enabled us to uncover B-cell acute lymphoblastic leukemia (B-ALL) triggers. We identified different cellular origins of malignant transformation depending on the B-ALL subtype, including the association of the ETV6-RUNX1 with pro-B cells and the increased expression of ELK3 in this ALL subtype. Overall, our dataset provides the most comprehensive atlas to date of early human B-cell regulation (B-rex; https://translationalbio.shinyapps.io/brex/), facilitating further understanding of B-cell differentiation in health and disease.

immunology↗