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🧬 TRACK 24 • 10 MODULAR CHAPTERS EPIGENOMICS • CHROMATIN DYNAMICS 🧠 HUNTINGTON'S DISEASE CASE STUDY
Chromatin Dynamics • Peak Calling with MACS3 • Differential Binding • Motif Discovery

Epigenomics – ChIP-Seq Data Analysis

From Raw Reads to a Biological Story: Mapping H3K4me3 Promoter Dynamics in Huntington's Disease 🧠🧬

Have ChIP-seq data (or want to work with it) but aren't sure how to get from raw reads to a biological story? Run the complete production pipeline on real human Huntington's Disease datasets, comparing H3K4me3 promoter marks between HD patients and healthy controls to see how chromatin changes relate to transcriptional collapse and striatal neurodegeneration.

📦 Production Epigenomics & ChIP-Seq Environment Bioconda + Bioconductor
# Install Complete Epigenomics Toolchain in One Command
conda create -n chipseq_env -c bioconda -c conda-forge \
  sra-tools fastqc fastp bowtie2 samtools sambamba bedtools \
  macs3 deeptools homer igv r-base bioconductor-diffbind \
  bioconductor-deseq2 bioconductor-chipseeker bioconductor-clusterprofiler -y
conda activate chipseq_env
Epigenomics ChIP-Seq Graphic
Curriculum Specifications Track 24
Format & Duration
Self-Paced / Guided • 10 Modular Chapters
Core Toolchain
Bowtie2 • MACS3 • DiffBind • deepTools • HOMER
Model Biological System
Huntington's Disease (H3K4me3 Promoters)
Epigenomic Modeling
Dynamic Poisson Lambda • DESeq2 Negative Binomial GLMs
Functional Discovery
ChIPseeker • HOMER Motifs • IGV Verification
Academic Access
100% Free & Open Access
View 10 Modules ↓
🧬 View 10 Modules Curriculum ↗
👥

Who Should Attend

🎓
PhD and Master's Researchers Specializing in Epigenetics, Neuroscience, Chromatin Biology, or Translational Genomics.
🧠
Neuroscience & Disease Biologists Investigating transcriptional dysregulation in neurodegenerative disorders (Huntington's, Parkinson's, ALS).
🔬
Wet-Lab Scientists & Core Facility Staff Transitioning from bench ChIP protocols to independent, script-driven peak calling and quality audits.
💻
Computational Biologists Seeking production-grade standards for MACS3, DiffBind affinity modeling, and ENCODE blacklist filtering.
🎯

Core Competencies & Outcomes

  • Retrieve public ChIP-Seq FASTQs from NCBI GEO/SRA with fasterq-dump and compile MultiQC diagnostics.
  • Execute unspliced Bowtie2 alignment, SAMtools MAPQ ≥ 30 filtering, and Sambamba duplicate marking.
  • Mask ENCODE Unified Blacklists and evaluate library quality with cross-correlation metrics (NSC ≥ 1.05, RSC ≥ 0.8).
  • Model dynamic local Poisson lambda from input controls with MACS3 (narrow & broad peak calls).
  • Quantify differential binding in R via DiffBind and execute negative binomial GLMs using DESeq2.
  • Annotate genomic distributions with ChIPseeker and identify enriched GO/KEGG pathways via clusterProfiler.
  • Discover transcription factor binding motifs with HOMER and visualize coverage profiles in deepTools & IGV.
🔬 4-PHASE CURRICULUM ARCHITECTURE

End-to-End ChIP-Seq Roadmap & Practical Benchmarks

Self-paced, hands-on masterclass curriculum structured into 4 progressive phases spanning 10 hands-on modules with real human Huntington's Disease datasets (GEO GSE68952).

PHASE 1

Foundations & Retrieval

Modules 01–02 • Biology, Controls & Read QC

  • ChIP-seq biophysics & antibody validation
  • Input DNA vs IgG control design
  • Downloading raw SRA data with fasterq-dump
  • FastQC metrics & fastp adapter trimming
  • Interactive MultiQC quality aggregation
PHASE 2

Alignment & Filtering

Modules 03–04 • Bowtie2 & Cross-Correlation

  • Unspliced Bowtie2 alignment against GRCh38
  • SAMtools sorting, indexing & MAPQ ≥ 30 filtering
  • Sambamba PCR duplicate marking
  • ENCODE v2/v3 Unified Blacklist filtering
  • Cross-correlation analysis (NSC/RSC metrics)
PHASE 3

Peak Calling & DiffBind

Modules 05–06 • MACS3 & DESeq2 Modeling

  • MACS3 dynamic local Poisson background
  • Narrow vs broad peak calling modes
  • Fraction of Reads in Peaks (FRiP) auditing
  • DiffBind consensus binding matrix construction
  • DESeq2 negative binomial GLMs & FDR control
PHASE 4

Annotation, Motifs & Story

Modules 07–10 • ChIPseeker, deepTools & IGV

  • ChIPseeker genomic feature & TSS annotation
  • clusterProfiler GO/KEGG pathway enrichment
  • HOMER known & de novo motif discovery
  • deepTools 1x normalized BigWigs & heatmaps
  • IGV Desktop multi-track inspection at BDNF loci
COURSE CURRICULUM

10 Comprehensive Modular Chapters

All modules include executable Bash / R scripts and real patient datasets
MODULE 01 Phase 1 • Foundations & Study Design

ChIP-Seq Principles, Histone Modifications & Huntington's Disease Study Design

Biophysical principles of chromatin immunoprecipitation, antibody validation, cross-linking dynamics, active vs repressive histone marks (H3K4me3, H3K27ac, H3K27me3), input/mock controls, and designing a rigorous case study in Huntington's Disease striatal neurodegeneration.

🎯 Key Deliverables:
  • Biological & experimental design matrix with input DNA controls
  • Formal chromatin shearing and antibody validation standard operating protocol
Experimental Design ENCODE Standards GEO SRA
Explore Chapter 01 →
MODULE 02 Phase 1 • Data Retrieval & QC

Downloading Public ChIP-Seq Data from GEO/SRA & Diagnostic Read QC

Fetching raw ChIP-seq sequencing data from NCBI GEO and SRA using fasterq-dump, evaluating base quality scores with FastQC, trimming adapters and poly-G tails with fastp, and compiling interactive MultiQC dashboards to identify low-complexity or degraded libraries.

🎯 Key Deliverables:
  • Automated multi-threaded fasterq-dump retrieval script
  • MultiQC aggregated quality assessment dashboard across HD and control cohorts
sra-tools fasterq-dump FastQC fastp MultiQC
Explore Chapter 02 →
MODULE 03 Phase 2 • Spliced vs Non-Spliced Mapping

High-Throughput Epigenomic Alignment with Bowtie2 & SAMtools MAPQ Filtering

Configuring Bowtie2 for un-spliced epigenomic alignment against GRCh38, understanding end-to-end vs local alignment modes, multi-threading, SAM to BAM conversion, sorting, indexing, and filtering low-quality or multi-mapping reads with SAMtools (MAPQ >= 30).

🎯 Key Deliverables:
  • Optimized Bowtie2 unspliced alignment script against human GRCh38
  • High-confidence MAPQ >= 30 coordinate-sorted, indexed BAM libraries
Bowtie2 Samtools Sambamba GRCh38 Index
Explore Chapter 03 →
MODULE 04 Phase 2 • Duplicates & Blacklists

Duplicate Marking, ENCODE Blacklist Masking & Cross-Correlation Metrics (NSC/RSC)

Differentiating optical from PCR duplicates with Sambamba / Picard, masking high-signal anomalous regions using ENCODE v2/v3 Unified Blacklists with bedtools, and computing Normalized Strand Cross-correlation (NSC) and Relative Strand Cross-correlation (RSC) with phantompeakqualtools.

🎯 Key Deliverables:
  • Sambamba PCR duplicate marking and filtering workflow
  • ENCODE Unified Blacklist artifact removal & strand cross-correlation NSC/RSC metrics
Sambamba Picard bedtools phantompeakqualtools R
Explore Chapter 04 →
MODULE 05 Phase 3 • MACS3 Peak Calling

Calling Peaks with MACS3: Narrow vs Broad Marks & Input Background Modeling

Deep dive into Model-based Analysis of ChIP-Seq (MACS3). Estimating fragment size (d) via sonication shifts, calculating dynamic local Poisson lambda from matched input controls, selecting narrowPeak vs broadPeak modes, setting q-value thresholds, and calculating the Fraction of Reads in Peaks (FRiP).

🎯 Key Deliverables:
  • Production MACS3 narrowPeak and broadPeak callsets with matched input controls
  • Fraction of Reads in Peaks (FRiP) quality audit verifying ENCODE benchmarks (>1%)
MACS3 bedtools FRiP Analysis Python
Explore Chapter 05 →
MODULE 06 Phase 3 • Differential Binding

Differential Binding Analysis with DiffBind & DESeq2 in R

Configuring DiffBind sample sheets, building a consensus peak matrix across biological replicates, affinity score normalization (TMM / RLE), contrast design (Huntington's Disease vs Healthy Controls), and executing negative binomial GLMs via DESeq2 to detect hyper- and hypo-methylated promoter regions.

🎯 Key Deliverables:
  • DiffBind consensus binding matrix and correlation heatmaps across replicates
  • FDR-adjusted DESeq2 differential binding peak catalog with fold change shrinkage
DiffBind DESeq2 edgeR ggplot2 EnhancedVolcano
Explore Chapter 06 →
MODULE 07 Phase 4 • Functional Annotation

Functional Genomic Annotation & Pathway Enrichment (ChIPseeker & clusterProfiler)

Mapping differential peak coordinates to genomic features (promoter, 5' UTR, 3' UTR, exons, introns, distal intergenic) using ChIPseeker with TxDb.Hsapiens.UCSC.hg38.knownGene, plotting TSS distance distributions, and performing Gene Ontology and KEGG pathway over-representation analysis with clusterProfiler.

🎯 Key Deliverables:
  • ChIPseeker genomic feature distributions and TSS distance plots
  • clusterProfiler GO Biological Process and KEGG pathway enrichment dotplots
ChIPseeker clusterProfiler org.Hs.eg.db ReactomePA
Explore Chapter 07 →
MODULE 08 Phase 4 • Motif Discovery

Transcription Factor Motif Enrichment & Regulatory Factor Discovery (HOMER)

Uncovering transcription factor binding motifs within differential H3K4me3 peak summits. Running HOMER findMotifsGenome.pl with GC-content matched background, identifying known and de novo motifs (SP1, CREB, REST/NRSF), and mapping transcriptional repressor occupancy in Huntington's Disease.

🎯 Key Deliverables:
  • HOMER known and de novo enriched motif consensus matrices and sequence logos
  • Quantified transcription factor co-factor enrichment for SP1, CREB, and REST/NRSF
HOMER findMotifsGenome.pl MEME-ChIP Tomtom
Explore Chapter 08 →
MODULE 09 Phase 4 • Signal Visualization

deepTools: BigWig Signal Normalization, TSS Matrix Computation & Heatmaps

Generating genome-wide coverage tracks in BigWig format with deepTools bamCoverage using 1x genome normalization (RPKM/BPM), calculating coverage matrices around Transcription Start Sites (TSS +/- 3kb), and generating publication-ready composite profile plots and clustered heatmaps with plotHeatmap.

🎯 Key Deliverables:
  • 1x genome normalized (RPKM/BPM) BigWig tracks for patients and controls
  • Composite TSS signal profiles and k-means clustered heatmaps via deepTools
deepTools bamCoverage computeMatrix plotHeatmap plotProfile
Explore Chapter 09 →
MODULE 10 Phase 4 • IGV & Biological Story

Integrative Genomics Viewer (IGV) Exploration & Biological Synthesis of HD Dysregulation

Loading multi-track BigWig files, narrowPeak calls, and gene models into IGV for visual verification; exploring critical Huntington's Disease loci including BDNF, HTT, and DARPP-32/PPP1R1B; synthesizing the entire multi-day analytical journey into a compelling, publication-ready biological narrative.

🎯 Key Deliverables:
  • IGV multi-track session files verifying chromatin collapse at BDNF and DARPP-32
  • End-to-end biological synthesis report mapping chromatin dynamics to HD neurodegeneration
IGV Desktop BigWig narrowPeak GENCODE hg38
Explore Chapter 10 →
The Biological Mechanism

How Epigenetic Lesions Drive Huntington's Neurodegeneration

Huntington's Disease provides one of the clearest demonstrations of chromatin pathology in human medicine. Mutant huntingtin protein contains an expanded polyglutamine tract that binds directly to transcriptional co-activators and histone acetyltransferases ($CBP/p300$), while disrupting histone methyltransferase complexes that deposit the active promoter mark H3K4me3.

Key Molecular Loci Discovered in this Training:

  • BDNF (Brain-Derived Neurotrophic Factor): Massive collapse of promoter IV H3K4me3 ($p < 10^{-6}$), shutting down cortical trophic support for striatal neurons.
  • PPP1R1B / DARPP-32: Downregulation of the primary signal transduction hub of dopamine signaling in medium spiny neurons.
  • REST/NRSF Target Promoters: Aberrant transcriptional repression driven by failure to sequester REST in the cytoplasm.
Launch Free Epigenomics Curriculum →
🧠🧬

Real Patient Datasets

Post-mortem striatum & prefrontal cortex from Huntington's patients vs. age-matched neurotypical controls (NCBI GEO GSE68952).

Verified ENCODE Quality Standards
Peer-Reviewed Foundations

📚 Foundational Literature & Methodology Benchmarks

Primary methodology papers and benchmark citations for Track 24

🧠 Epigenetics, Chromatin Biology & Neurodegeneration

  • Landt SG, Marinov GK, Kundaje A, et al. (2012). ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Research 22(9):1813–1831. doi:10.1101/gr.136184.111
  • Amemiya HM, Kundaje A, Boyle AP. (2019). The ENCODE Blacklist: Identification of Problematic Regions of the Genome. Scientific Reports 9:9354. doi:10.1038/s41598-019-45839-z
  • Hodges A, Strand AD, Aragaki AK, et al. (2006). Regional and cellular gene expression changes in human Huntington's disease brain. Human Molecular Genetics 15(6):965–977. doi:10.1093/hmg/ddl013
  • Zuccato C, Ciammola A, Rigamonti D, et al. (2001). Loss of huntingtin-mediated BDNF gene transcription in Huntington's disease. Science 293(5529):493–498. doi:10.1126/science.1059581

⚡ Read Preprocessing & Epigenomic Alignment

  • Langmead B, Salzberg SL. (2012). Fast gapped-read alignment with Bowtie 2. Nature Methods 9(4):357–359. doi:10.1038/nmeth.1923
  • Tarasov A, Vilella AJ, Cuppen E, et al. (2015). Sambamba: fast processing of NGS alignment formats. Bioinformatics 31(12):2032–2034. doi:10.1093/bioinformatics/btv098
  • Chen S, Zhou Y, Chen Y, Gu J. (2018). fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34(17):i884–i890. doi:10.1093/bioinformatics/bty560
  • Danecek P, Bonfield JK, Liddle J, et al. (2021). Twelve years of SAMtools and BCFtools. GigaScience 10(2):giab008. doi:10.1093/gigascience/giab008

📊 Peak Calling & Differential Affinity

  • Zhang Y, Liu T, Meyer CA, et al. (2008). Model-based analysis of ChIP-Seq (MACS). Genome Biology 9(9):R137. doi:10.1186/gb-2008-9-9-r137
  • Ross-Innes CS, Stark R, Teschendorff AE, et al. (2012). Differential oestrogen receptor binding is associated with clinical outcome in breast cancer (DiffBind). Nature 481:389–393. doi:10.1038/nature10730
  • Love MI, Huber W, Anders S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15:550. doi:10.1186/s13059-014-0550-8
  • Robinson MD, McCarthy DJ, Smyth GK. (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26(1):139–140. doi:10.1093/bioinformatics/btp616

🔬 Functional Annotation, Motifs & Signal Visualization

  • Yu G, Wang LG, He QY. (2015). ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 31(14):2382–2383. doi:10.1093/bioinformatics/btv145
  • Heinz S, Benner C, Spann N, et al. (2010). Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities (HOMER). Molecular Cell 38(4):576–589. doi:10.1016/j.molcel.2010.05.004
  • Ramírez F, Ryan DP, Grüning B, et al. (2016). deepTools2: a next generation web tool for deep-sequencing data analysis. Nucleic Acids Research 44(W1):W160–W165. doi:10.1093/nar/gkw257
  • Robinson JT, Thorvaldsdóttir H, Winckler W, et al. (2011). Integrative genomics viewer. Nature Biotechnology 29(1):24–26. doi:10.1038/nbt.1754
  • Wu T, Hu E, Xu S, et al. (2021). clusterProfiler 4.0: A versatile toolkit for translating omics data to biological knowledge. The Innovation 2(3):100141. doi:10.1016/j.xinn.2021.100141

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