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Video Experimental Relacionado

Updated: Feb 6, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
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Protocol simplificado para secuenciación de ARN masiva: desde la extracción de datos hasta el análisis de expresión

Abdullah Al Mohit1, Niher Ranjan Das2, Arushi Jain1

  • 1Plant Molecular Biology Laboratory, Faculty of Life Sciences and Biotechnology, South Asian University, New Delhi, India.

Current protocols
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Resumen
Este resumen es generado por máquina.

Este protocolo simplifica el análisis de datos de secuenciación de ARN (RNA-seq) utilizando herramientas gratuitas y computación en la nube. Hace que los estudios de expresión génica sean accesibles para investigadores con hardware y experiencia técnica limitados.

Palabras clave:
Secuenciación de ARN (RNA-seq)Bioinformática basada en la nubeExpresión diferencialAnálisis de expresión génica

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Área de la Ciencia:

  • Genómica
  • Bioinformática
  • Biología Computacional

Sus antecedentes:

  • La secuenciación de ARN de próxima generación (RNA-seq) es una herramienta poderosa para el análisis de la expresión génica en todo el genoma.
  • El análisis tradicional de RNA-seq requiere importantes recursos computacionales y habilidades avanzadas de bioinformática.
  • El acceso limitado al hardware y la experiencia dificulta la adopción generalizada de RNA-seq.

Objetivo del estudio:

  • Presentar un protocolo simplificado de principio a fin para el análisis de datos de RNA-seq.
  • Permitir a los investigadores con recursos limitados realizar estudios integrales de expresión génica.
  • Hacer que el análisis de RNA-seq sea reproducible y accesible utilizando herramientas gratuitas y plataformas en la nube.

Principales métodos:

  • Utiliza herramientas bioinformáticas gratuitas (SRA Toolkit, FastQC, Trimmomatic, BWA/HISAT2, Samtools, Subread) y plataformas basadas en la nube (Google Colab).
  • Cubre todo el flujo de trabajo: descarga de datos, control de calidad, recorte de lecturas, alineación, recuento de lecturas, normalización (TPM) y visualización.
  • Integra Python y R para el análisis de expresión génica diferencial (pyDESeq2) y el enriquecimiento funcional (g:Profiler).

Principales resultados:

  • Se establece un protocolo reproducible y fácil de usar para el análisis de datos de RNA-seq.
  • El flujo de trabajo procesa con éxito los datos brutos de secuenciación en valores de expresión normalizados y visualizaciones.
  • Se realizan análisis de expresión génica diferencial y enriquecimiento funcional de manera eficiente.

Conclusiones:

  • Este protocolo reduce significativamente la barrera de entrada para el análisis de datos de RNA-seq.
  • Empodera a los investigadores con recursos computacionales limitados para llevar a cabo estudios avanzados de expresión génica.
  • El método mejora la accesibilidad y asequibilidad del análisis de RNA-seq en entornos de investigación.