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Methodology - Coggle Diagram
Methodology
sample collection
Input: Blood or tissue samples from patients with autoimmune diseases (e.g., rheumatoid arthritis, lupus) and cancer.
Output:Biological materials ready for omics analysis.
1
RNA-Seq & scRNA-Seq
Process: High-throughput sequencing of RNA and single-cell RNA for gene expression profiling.
Tools: Illumina HiSeq, 10X Genomics, Seurat, CellRanger.
2
Proteomics & Phosphoproteomics
Process: Mass spectrometry-based protein and phosphoprotein analysis
Tools: MaxQuant, Proteome Discoverer
3
Whole-Exome Sequencing (WES)
Process: Sequencing the exons of patient DNA to identify genetic variants
Tools: BWA, GATK, ANNOVAR
4
Bioinformatics: Preprocessing & Alignment
Process: Aligning reads to the human genome, removing duplicates, and quantifying expression
Tools: STAR, HISAT2, featureCounts
5
Differential Expression Analysis
Process: Identify differentially expressed genes/proteins between disease and control samples
Tools: DESeq2, edgeR
6
Pathway Enrichment
Process: Identify overrepresented pathways in disease samples
Tools: GSEA, KEGG, Reactome
7
Interaction Networks
Process: Build protein-protein interaction (PPI) networks to identify key regulatory nodes
Tools: STRING, Cytoscape
8
Machine Learning (Biomarker Discovery)
Process: Use multi-omics data to train models for biomarker discovery
Tools: Scikit-learn, TensorFlow
9
Experimental Validation (CRISPR, Cytokine Modulation)
Process: Validate key genes using gene editing (CRISPR) and functional assays
Tools: CRISPR-Cas9, qRT-PCR, flow cytometry
10
In Vivo Testing (Mouse Models)
Process: Use mouse models to test therapeutic interventions
Tools: Collagen-induced arthritis (RA), tumor xenograft models
11
