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Course Requirements

  • Motivation to Learn: A strong desire to engage with and understand the material.
  • Basic Knowledge: Familiarity with biology and molecular biology concepts.
  • Interest in Technology: Eagerness to learn about the latest technologies in Next-Generation Sequencing (NGS).
  • Software: Free software will be utilized, so no additional software purchases are necessary.
  • Hardware: A laptop with a minimum of 4GB RAM and 100GB of hard disk space.

Course Description

  • Introduction to RNA-Seq technology and its application in transcriptomics research.
  • Explore the complete RNA-Seq workflow, from raw data to differential gene expression analysis.
  • Gain hands-on experience with quality control tools such as FASTQC and Trimmomatic.
  • Learn alignment techniques using software like STAR or HISAT2 to map reads to reference genomes.
  • Perform gene expression quantification using tools like HTSeq or featureCounts.
  • Analyze differential gene expression with DESeq2 or edgeR and interpret biological significance


Course Outcomes

  • Understand RNA-Seq technology and its relevance in studying gene expression.
  • Perform quality assessment and pre-processing of raw RNA-Seq data.
  • Align RNA-Seq reads to reference genomes and evaluate alignment quality.
  • Quantify gene expression levels and generate expression matrices.
  • Identify differentially expressed genes and interpret their functional implications.
  • Apply RNA-Seq analysis techniques to research projects in genomics and transcriptomics.

Rules & Regulations

  • Attendance: Students must watch at least 75% of the recorded sessions to meet the minimum participation criteria.
  • Feedback and Communication: Maintain open communication with instructors for clarifications and actively provide constructive feedback to enhance the course experience.
  • Certification: A certificate of completion will be awarded to students who fulfill the attendance requirement and complete all necessary course components.

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Super admin

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RNA Seq Data Analysis for differential gene expression

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