{
  "id": 11390014,
  "title": "SCITRAM: a Single-cell Integrated transcription regulation modeler for predicting master transcription factors",
  "url": "https://urgent.news/2026/10/01/scitram-a-single-cell-integrated-transcription-regulation-modeler-for",
  "topic": "science",
  "section": "Science",
  "published": "2026-10-01T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.30.755779v1?rss=1"
  },
  "original_language": "en",
  "account": "Single-cell omics technologies have transformed our comprehension of complex biological systems by revealing the intricate molecular heterogeneity within individual cells. While the generation of this data has become more accessible, processing it remains a challenge, often requiring command-line driven tools or commercial solutions. Currently, single-cell transcriptomics analysis primarily focuses on categorizing cells based on gene expression signatures, without delving into the deconvolution of gene regulatory programs and their corresponding master transcription factors that drive cell diversity.\n\nIntroducing SCITRAM (Single-Cell Integrated TRAnscription regulation Modeler), a user-friendly, standalone computational tool designed to predict master transcription factors from single-cell transcriptomics data. SCITRAM begins by reconstructing a primary gene regulatory network, which then serves as a template to model the transcriptional cascades initiated by the computational activation of transcription factors discovered within the system.\n\nThe effectiveness of SCITRAM was evaluated across various cell fate transition events, including T cell exhaustion during treatment of solid tumors, lineage stratification during pancreatic endocrinogenesis, and the formation of nervous tissue in cerebral organoid models. The results demonstrate that SCITRAM offers a convenient and comprehensive platform for handling extensive single-cell transcriptomics data, facilitating the exploration of transcriptional regulatory programs and the identification of their master transcription factors, all without the need for command-line interfaces.",
  "summary": "Single-cell omics technologies have provided unprecedented access to molecular cell heterogeneity, revolutionizing our understanding of complex living systems. Although generating such type of data has become a streamlined process, data processing still relies on command-line driven tools, or commercial solutions. Furthermore, single-cell transcriptomics analysis is usually reduced to stratifying…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}