Gene Prediction: Bioinformatics Protocols for Finding Genes

🧬 Course Title: Gene Prediction: Bioinformatics Protocols for Finding Genes

Instructor: Ahmed Karam, M.Sc.

Course Headline: Master the Art of Annotating Newly Generated and Existing Genomes with Expert Bioinformatics Techniques!

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🔬 Course Description:

Unlock the secrets within genomes with our comprehensive online course, "Gene Prediction: Bioinformatics Protocols for Finding Genes," led by experienced instructor Ahmed Karam, M.Sc. This course is tailored for researchers and enthusiasts aiming to identify gene regions in genomes, be it in laboratories or within databases.

Why Annotate Genomes?

• Essential Step: Gene prediction is crucial for understanding genetic function and diversity.
• Data Overload: Numerous assembled genomes in databases await annotations that reveal their true potential.
• Update Needed: A majority of these genomes lack updated annotations, making them underutilized resources in genetic research.
• Re-Annotation Imperative: For research involving database genomes, re-annotating them is often necessary to gain new insights or validate existing data.

What You'll Learn:

This course provides practical, step-by-step guidance through screen-recorded content, ensuring you can apply gene prediction software effectively. You'll learn a set of well-tested protocols that are accurate and reliable across various organisms:

• Bacteria to Complex Eukaryotes: Protocols applicable for prokaryotic, eukaryotic, protein-coding, and non-coding genes.
• Evaluating Predictions: Techniques to assess the quality of gene predictions made by the software.

Software Tools Covered:

1. tRNAscan-SE 🔍

   • Perfect for predicting tRNA genes without high-end computing resources.

2. Infernal 🎶

   • Ideal for non-coding gene prediction, especially rRNA and other non-coding RNAs. It's effective but requires patience as it can be time-consuming.

3. BRAKER 🚀

   • A powerful tool for predicting protein-coding genes in eukaryotic genomes using ab initio methods, with the option to incorporate RNA-seq and proteomic evidence for improved accuracy. (Requires a high-powered computer)

4. GeMoMa 🌿

   • A program for predicting protein-coding genes by homology, also utilizing RNA-seq data for enhanced precision. (Requires a high-powered computer)

5. Prokka 🛠️

   • A user-friendly tool for predicting protein-coding and non-coding genes in prokaryotic genomes using ab initio methods, with proteomic support. It's efficient and doesn't demand high computational power.

6. BUSCO 🌟

   • A software tool to evaluate the completeness of gene sets, including protein-coding genes in both prokaryotic and eukaryotic organisms. It's also light on computational requirements.

Implementation Environment:

To apply these protocols effectively, it is recommended that you install the Ubuntu distribution on your device or set up a virtual Ubuntu environment using a virtual box. This will ensure compatibility and ease of use while following along with the course materials.

Real-World Application:

By the end of this course, you'll have protocols in text format that you can apply to genomes from databases, which can be instrumental for your graduation project, research papers, or poster presentations.

Join us and become proficient in gene prediction and annotation, a skill set that will open doors in the ever-evolving field of bioinformatics! 🚀🧬

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Enroll Now and Embark on Your Bioinformatics Journey with "Gene Prediction: Bioinformatics Protocols for Finding Genes"!