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Bioinformatics: Guide to RNA-seq with No Coding Required!

Bioinformatics: Guide to RNA-seq with No Coding Required!

Bioinformatics: Guide to RNA-seq with No Coding Required! Learn to process & analyse RNA-seq data without code: Transcriptomics, Differential expression, STAR, Pathway analysis

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Bioinformatics is the field of science that combines biology, computer science, and information technology to analyze and interpret biological data. RNA-seq (short for "RNA sequencing") is a technique used in bioinformatics to analyze the expression levels of genes in a sample of tissue. It involves sequencing the RNA molecules present in the tissue and using computational tools to analyze the data.

RNA-seq can be used to study a wide range of biological processes, such as gene expression, alternative splicing, and post-transcriptional modifications. It is a powerful tool for understanding the underlying mechanisms of diseases and for identifying potential therapeutic targets.

One of the benefits of RNA-seq is that it allows researchers to study gene expression at the single-cell level, providing a more detailed and accurate picture of the underlying biology. It is also a highly scalable and efficient method, allowing researchers to study large numbers of samples in a relatively short amount of time.

While RNA-seq requires some knowledge of programming and bioinformatics, there are many resources available that can help researchers get started with this technique without having to learn how to code. These resources include online tutorials, software programs, and commercial services that provide everything needed to perform RNA-seq analysis.

In summary, RNA-seq is a powerful and versatile technique that is widely used in bioinformatics to study gene expression and other biological processes. While some programming and bioinformatics knowledge may be helpful, it is not strictly required, as there are many resources available to help researchers get started with this technique.

What you'll learn

  • The basics of Next Generation Sequencing and how it can be used for Differential gene expression analysis via RNA sequencing.
  • Preprocessing RNA sequencing data.
  • Aligning the reads to a genome.
  • Transcript quantification.
  • Differential Expression.
  • Gene ontology and Pathway analysis
  • Ultimately understand how technologies like RNA sequencing could be used to identify specific genes that can cause certain conditions.

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