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ERSAtool: Una interfaz integral de análisis transcriptómico en R/Shiny fácil de usar y adecuada para la educación

Sujith Taridalu1, Ayyappa Kumar Sista Kameshwar1, Masako Suzuki1

  • 1Department of Nutrition, Texas A&M University, Texas, USA.

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Resumen
Este resumen es generado por máquina.

Este estudio presenta ERSAtool, una aplicación R/Shiny que simplifica el análisis de datos de secuenciación de ARN (RNA-seq) para investigadores y estudiantes. Esta herramienta mejora la accesibilidad y reproducibilidad del análisis transcriptómico en la investigación biomédica.

Palabras clave:
análisis RNA-seqherramienta educativaenriquecimiento funcionalvisualización interactivaaplicación Shinyexpresión génica diferencial transcriptómica

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Área de la Ciencia:

  • Bioinformática
  • Biología Computacional
  • Genómica

Sus antecedentes:

  • La secuenciación de ARN (RNA-seq) es crucial para el análisis de la expresión génica en la investigación biomédica.
  • La experiencia compleja en bioinformática es una barrera para muchos investigadores que utilizan RNA-seq.
  • Necesidad de herramientas accesibles para analizar datos transcriptómicos.

Objetivo del estudio:

  • Desarrollar una interfaz gráfica intuitiva para el análisis de RNA-seq.
  • Reducir la barrera técnica para análisis transcriptómicos sofisticados.
  • Apoyar el uso educativo en cursos de bioinformática.

Principales métodos:

  • Desarrolló ERSAtool, una aplicación R/Shiny.
  • Utilizó paquetes establecidos de Bioconductor para el análisis.
  • Se integró con Gene Expression Omnibus (GEO) para metadatos.
  • Implementó análisis de normalización, visualización, expresión diferencial y enriquecimiento funcional.

Principales resultados:

  • ERSAtool proporciona una interfaz gráfica fácil de usar para el flujo de trabajo completo de RNA-seq.
  • Admite varios formatos de entrada y se integra con GEO para metadatos.
  • Facilita el análisis de expresión diferencial y la interpretación funcional (GO, GSEA).
  • Genera informes completos y descargables para la reproducibilidad.

Conclusiones:

  • ERSAtool reduce significativamente la experiencia necesaria para el análisis avanzado de RNA-seq.
  • Mejora la accesibilidad del análisis transcriptómico para la educación y la investigación.
  • Tiene como objetivo acelerar los descubrimientos biológicos al democratizar el análisis de datos de RNA-seq.