Master Gene Expression Analysis with Galaxy: Impactful Bioinformatics without Code
This webinar explores gene expression analysis using the Galaxy bioinformatics platform, enabling impactful RNA-Seq analysis without any coding. Learn how no-code workflows simplify transcriptomics and support data-driven biological insights in modern bioinformatics research.
Course Description
This webinar is designed to introduce gene expression analysis using the Galaxy bioinformatics platform, a widely used no-code solution for RNA-Seq analysis. Participants will learn how gene expression data is processed, analyzed, and interpreted using intuitive Galaxy workflows. The session explains RNA-Seq concepts in a simplified manner, making it accessible to beginners and life science learners. Emphasis is placed on understanding biological insights rather than programming complexity. Real research-based examples demonstrate how Galaxy is used in genomics and transcriptomics studies. This webinar helps learners understand practical bioinformatics workflows used in academic and research settings. It is ideal for those exploring bioinformatics, RNA-Seq analysis, and genomics careers without prior coding experience.
What You'll Learn
What gene expression analysis is and why it matters
Basics of RNA-Seq data analysis
How Galaxy enables bioinformatics analysis without coding
Key steps in RNA-Seq workflows and result interpretation
Curriculum
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Module 1: Introduction to Transcriptomics & the Galaxy Platform
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Core concepts of gene expression, the central dogma, and introduction to modern RNA-Seq technology.
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Navigating the Galaxy ecosystem: history management, importing datasets via URLs or NCBI SRA accession numbers.
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Understanding data formats in transcriptomics: FASTQ, FASTA, GTF/GFF3, and SAM/BAM file structures.
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Module 2: Raw Sequence Preprocessing & Reference Mapping
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Quality assessment: running FastQC to detect adapter contamination, per-base quality drops, and sequence duplication.
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Data trimming: executing Trimmomatic or Cutadapt to clean low-quality reads and prepare files for alignment.
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Splice-aware alignment: using HISAT2 or STAR to map processed transcripts against a reference genome.
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Module 3: Quantitation & Differential Gene Expression Analysis (DGE)
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Counting reads: utilizing FeatureCounts or HTSeq-count to quantify gene and transcript expression matrices.
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Statistical significance modeling: deploying DESeq2 or EdgeR within Galaxy to normalize count data and isolate DEGs.
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Data Visualization: building downstream plots including Volcano plots, sample-to-sample distance matrices, and PCA plots.
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Sharing and reproducibility: extracting reusable workflow blueprints and exporting histories for peer-reviewed publication.
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