Differential Gene Expression in Cancer: RNA-seq Data Analysis
Unlock the power of genomic data with our intensive crash course on Differential Gene Expression (DGE) in Cancer. This course bridges the gap between raw sequencing data and biological discovery, teaching you how to identify the molecular drivers of malignancy using industry-standard RNA-seq workflows.
Course Description
In the era of precision medicine, RNA-seq is the gold standard for understanding how gene activity shifts in cancer cells. Whether it’s identifying biomarkers for early detection or discovering therapeutic targets, the ability to analyze transcriptomic data is one of the most sought-after skills in bioinformatics today.
What You'll Learn
By the end of this course, you will be able to:
Process raw RNA-seq datasets from the Gene Expression Omnibus (GEO).
Identify statistically significant gene expression changes between tumor and normal samples.
Visualize complex genomic data to communicate findings effectively.
Hypothesize which biological pathways are hijacked in specific cancer types.
Curriculum
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"1=Introduction to RNAseq and it’s basic terminologies
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2=Tools installation in Linux for Gene Expression analysis
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3=Quality control and Trimming of reads
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4=Indexing of Genome and Alignment of Reads
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5=Normalization of Data (Cufflinks)
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6=Merging of Data and Differential expression of genes
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7=Understanding of DEG results
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8=Annotation of DEG
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9=Functional and Pathway Enrichment Analysis
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10=Network Analysis"
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