ArticleMethods in molecular biology (Clifton, N.J.)2025
RNA-Seq and Gene Set Enrichment Analysis (GSEA) in Peripheral Blood Mononuclear Cells (PBMCs).
Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Article
- Single-cell RNA-seq and machine learning identify HMGN2 as a lactylation-associated hub gene in heart failure.PloS one · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA sequencing (RNA-Seq) and analysis methods like gene set enrichment analysis (GSEA) stand at the forefront of modern molecular biology, offering unparalleled insights into gene expression dynamics and transcriptomic landscapes. In this chapter, we focus on the application of RNA-Seq technology and the GSEA method. First, we provide a real-world workflow for RNA-Seq to unravel the intricate interplay between host factors and HIV-1 infection within peripheral blood mononuclear cells (PBMCs). We elucidate the core principles of RNA-Seq and its pivotal role in identifying HIV-1 restriction factors and understanding their regulatory mechanisms in PBMCs. We provide the essential steps of the RNA-Seq workflow tailored to PBMCs, encompassing sample preparation, library construction, sequencing, and advanced bioinformatics. We underscore how RNA-Seq enables precise quantification of gene expression levels, detection of alternative splicing, and exploration of novel transcripts pertinent to HIV-1 restriction. Moreover, we delve into the intricacies of HIV-1 restriction factor discovery using RNA-Seq data, shedding light on the diverse repertoire of PBMC host defense mechanisms. We discuss the challenges of RNA-Seq data analysis, including normalization, differential expression analysis, and the integration of multi-omics datasets, and propose strategies to mitigate these barriers. Next, we provide an overview of GSEA, a powerful computational method that extends beyond traditional differential gene expression analysis. This tool can provide deeper insights into the functional significance of gene expression changes. By leveraging curated gene sets representing biological pathways, molecular functions, or cellular processes, GSEA enables the identification of coordinated changes in gene expression within predefined gene sets. In our example, we applied GSEA in transcriptomic studies, highlighting its ability to uncover subtle yet coordinated changes in gene expression that may not be evident by individual gene analysis. We demonstrate how GSEA enhances our understanding of complex biological phenomena, informs hypothesis generation, and identifies potential therapeutic targets. Overall, RNA-Seq and GSEA are indispensable tools for elucidating the complex interactions in host cells altered by physiologic processes, disease states or infectious agents like HIV-1, thereby advancing our ability to identify potential targets for therapeutic interventions.
Indexed as
Identifiers
39900759What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.