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Related Experiment Video

Updated: May 14, 2026

Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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Published on: November 7, 2025

Interpreting personal transcriptomes: personalized mechanism-scale profiling of RNA-seq data.

Alan Perez-Rathke1, Haiquan Li, Yves A Lussier

  • 1Department of Medicine, University of Illinois at Chicago, Chicago, IL 60612, USA. perezrat@uic.edu

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 21, 2013
PubMed
Summary

The Functional Analysis of Individual Microarray Expression (FAIME) method effectively analyzes RNA-seq data for personalized medicine. It identifies disease mechanisms at the single-sample level, improving diagnostic and therapeutic predictions.

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Published on: August 8, 2013

Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Gene-level signatures for complex diseases often lack single-sample applicability and mechanistic insight.
  • Personalized medicine requires methods that analyze individual patient data for targeted therapies.

Purpose of the Study:

  • To extend the Functional Analysis of Individual Microarray Expression (FAIME) method to RNA-seq data.
  • To identify underlying biological mechanism signatures at the single-sample level for complex diseases.
  • To assess FAIME's performance in analyzing gastric cancer RNA-seq data.

Main Methods:

  • Applied the FAIME method to publicly available gastric cancer RNA-seq datasets.
  • Translated individual sample transcriptomes into pathway-scale scores.
  • Validated FAIME's ability to predict deregulated pathways and clinical phenotypes.

Main Results:

  • FAIME successfully translated transcriptomes to pathway scores for gastric cancer.
  • The method accurately predicted deregulated pathways (Precision=75%, Recall=92%) and phenotypes across datasets and platforms.
  • FAIME differentiated cancer from normal samples and performed comparably to cross-sample methods in pathway identification.

Conclusions:

  • The FAIME method is effective for RNA-seq data analysis at the single-sample level.
  • FAIME facilitates mechanism-level biomarker discovery for improved diagnosis and treatment.
  • This approach holds promise for advancing personalized medicine through detailed biological insights.