RNA-seq has transformed gene expression analysis, enabling profiling of thousands of genes in a single experiment. The process from raw data to actionable insights requires computational tools, statistical approaches, and biological expertise.
R: FastQC and QuasR ensure raw reads meet quality standards and handle trimming.
Python: Libraries like pyfastx and wrappers for FastQC automate preprocessing tasks.
R: Tools such as Rsubread simplify genome alignments.
Python: Use pysam for BAM/SAM file handling or scripts to optimize tools like STAR.
R: Tools like tximport and Kallisto streamline transcript-level data analysis.
Python: Employ pandas for exploring quantification outputs or htseq for read counting.
R: Apply normalization techniques using DESeq2 or edgeR.
Python: Libraries like Scanpy and numpy enable flexible normalization workflows.
R: Perform robust analysis with limma, DESeq2, or edgeR.
Python: Utilize statsmodels or deseq2py for statistical modeling and visualization.
Integrate RNA-seq with proteomics, epigenomics, or metabolomics for multi-omics insights.
Python Insight: Frameworks like scanpy and scvi-tools enable multi-omics integration for advanced analyses.
Multi-omics approaches are unlocking the future of precision medicine.
👀Stay tuned for 👀
Part 2: Scanpy or Seurat? What they offer and what drives the choice?
Part 3: Differential expression analysis and functional enrichment.
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