Category: bulk RNA-seq
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How to Analyze Circular RNA-seq Data for Absolute Beginners Part 13-2: Advanced CircRNA Detection and Differential Expression with CIRI3
Introduction: Advancing Beyond CIRCexplorer2 with CIRI3 In Part 13 of my RNA-seq tutorial series, we explored circular RNA (circRNA) analysis using CIRCexplorer2, learning how these fascinating non-linear RNA molecules form through back-splicing and play important roles in gene regulation, disease mechanisms, and potential therapeutic applications. While CIRCexplorer2 provides an excellent introduction to circRNA analysis, the
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How to Build Gene Regulatory Networks from RNA-seq Data Using GENIE3 – Complete Step-by-Step Guide For Absolute Beginners
Introduction: Understanding Gene Regulatory Network Inference In the complex choreography of cellular function, transcription factors (TFs) act as master conductors, orchestrating when and where genes are expressed. Understanding which transcription factors regulate which target genes is fundamental to deciphering how cells respond to stimuli, how developmental programs unfold, and how diseases emerge from regulatory dysfunction.
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How to Build Gene Co-expression Networks from RNA-seq Data Using WGCNA – Complete Step-by-Step Guide For Absolute Beginners
Introduction: Understanding Gene Co-expression Networks In the intricate machinery of living cells, genes rarely act in isolation. Instead, they work together in coordinated networks, with groups of genes being co-expressed to carry out specific biological functions. Understanding these relationships is fundamental to deciphering how cells respond to stimuli, how diseases develop, and how we might
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How to Cluster RNA-seq Data to Uncover Gene Expression Patterns: Hierarchical and K-means Methods for Absolute Beginners
Introduction: Understanding Clustering in RNA-seq Analysis In the vast landscape of gene expression data, patterns often hide in plain sight. Among thousands of genes measured simultaneously, groups of genes may share similar expression patterns across samples, suggesting coordinated biological functions or responses. Clustering analysis serves as a powerful computational microscope that brings these hidden patterns
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How to Perform Master Regulator Analysis on RNA-seq Data Using RegEnrich and RTN – A Complete Beginner’s Guide
Discover the transcription factors controlling gene expression changes in your RNA-seq experiments Introduction: Understanding Master Regulator Analysis In the intricate symphony of gene regulation, not all transcription factors play equal roles. Some act as “master regulators” – key transcription factors that orchestrate broad changes in gene expression programs, controlling entire networks of downstream genes. Identifying
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How to Analyze RNA-seq Data for Absolute Beginners Part 16-2: Fusion Gene Detection with FusionCatcher
Introduction: Advanced Fusion Detection for Cancer Research Building on our previous exploration of fusion gene detection with STAR-Fusion (Part 16), we now delve into FusionCatcher, a specialized tool that has become the gold standard for detecting somatic fusion genes in cancer samples. While both tools excel at fusion detection, FusionCatcher offers unique capabilities that make
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How To Analyze CRISPR Screen Data For Complete Beginners – From FASTQ Files To Biological Insights
A comprehensive step-by-step guide to uncover gene function using CRISPR screening and MAGeCK analysis Introduction: Understanding CRISPR Screening Technology In the rapidly evolving landscape of functional genomics, CRISPR screening has emerged as one of the most powerful tools for systematically investigating gene function. This revolutionary technique allows researchers to interrogate thousands of genes simultaneously, revealing
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How to Analyze RNAseq Data for Absolute Beginners 21: A Comprehensive Guide to Batch Effects & Covariates Adjustment
Introduction to Batch Effects in RNA-seq Analysis In high-throughput sequencing experiments, batch effects represent one of the most challenging technical hurdles researchers face. These systematic variations arise not from biological differences between samples but from technical factors in the experimental process. Understanding and properly adjusting for batch effects is essential for generating reliable and reproducible
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How to Analyze RNAseq Data for Absolute Beginners Part 20: Comparing limma, DESeq2, and edgeR in Differential Expression Analysis
Introduction Differential expression (DE) analysis represents a fundamental step in understanding how genes respond to different biological conditions. When we perform RNA sequencing, we’re essentially taking a snapshot of all the genes that are active (or expressed) in our samples at a given moment. However, the real biological insights come from understanding how these expression
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How to Analyze RNAseq Data for Absolute Beginners Part 19: Understanding RNA-Seq Gene Expression Normalization
Introduction: Why Normalization Matters in RNA-Seq Analysis RNA sequencing (RNA-seq) has revolutionized our ability to measure gene expression, but the raw data needs careful processing to yield meaningful biological insights. In this comprehensive guide, we’ll explore why normalization is crucial and how to convert between different expression metrics using R. Building on our previous tutorials
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Recent Posts
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- How to Analyze Single-Cell RNA-seq Data from Patient-Derived Xenograft (PDX) Models — Complete Beginner’s Guide Part 8: Processing Human-Mouse Mixed Samples
- How to Analyze Single-Cell RNA-seq Data – Complete Beginner’s Guide Part 7: Trajectory and Pseudotime Analysis Using Monocle 3
- How to Convert BAM Files Back to FASTQ Files: A Practical Guide for NGS Analysis
Tags
Alternative Splicing Analysis ATAC-seq BAM cancer genomics ChIP-seq chromatin accessibility CNV DESeq2 Differential Expression edgeR FASTQ GATK Mutect2 gene expression heatmap HOMER HPC Isoform limma MACS2 MAF miRNA miRNA-seq MSigDB Normalization peak calling RNA-seq somatic mutations Transcript VCF whole genome sequencing