Field notes – Methylation & Colitis

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Short, evidence-aware notes connecting methods from the wider cfDNA field to this project’s design. These posts distinguish published feasibility from methods that are ready to use in a small UC cohort.

What Ranking Does to a Correlation

Every pair of samples in a real ulcerative colitis methylation study correlates above 0.95. Ranking the data does not fix that — but it does something more specific, and…

What is a Biological Signature?

machine learning

A fingerprint identifies a person from a patch of skin. A biological signature does it with molecules — and pulling one out of data takes four steps, of which the last is…

Could a blood draw ever replace part of a colonoscopy?

clinical translation

ulcerative colitis

colorectal cancer

The clinical problems an integrated epigenetic and mucosal-immune signal could address—and the ones it could not

From EM-seq reads to an integrated study matrix

The alignment, methylation, fragmentomics, DMR, and sIgA processing flow for a genomics audience

CD86: the context-setting signal for T-cell activation

adaptive immunity

How antigen-presenting cells tune CD28 and CTLA-4 signaling

CDH1: E-cadherin and the architecture of an epithelium

epithelial barrier

Adherens junctions, β-catenin, tissue cohesion, and the consequences of reduced expression

CDKN2A: two tumour-suppressor programs in one locus

tumour suppressor

How p16INK4a and p14ARF protect cell-cycle control and what promoter silencing can remove

CLMP: an adhesion molecule with intestinal consequences

epithelial barrier

Cell contact, intestinal development, and what reduced expression could mean

FAM217B: an honest gene profile at the edge of knowledge

ulcerative colitis

What is known, what UC promoter hypermethylation showed, and why mechanism should not be invented

FOXE1: a developmental transcription factor used as a methylation marker

transcription factor

Strong developmental biology, limited colon mechanism, and a potentially useful neoplasia signal

GDNF: trophic signaling across nerves, stroma, and epithelium

intestinal biology

RET/GFRα signaling, enteric biology, and why loss has context-dependent effects

IL10: the intestinal immune brake

ulcerative colitis

How IL-10 restrains inflammation and why methylation does not automatically report its activity

KIAA1614: a candidate link to polarity, still largely uncharacterized

ulcerative colitis

UC-associated promoter methylation, predicted cellular roles, and the limits of mechanistic inference

NLRC4: detecting bacterial invasion machinery

mucosal immunity

NAIP–NLRC4 inflammasomes, epithelial defense, and the cost of losing the response

NLRP3: a danger sensor with a two-sided intestinal role

ulcerative colitis

Inflammasome assembly, IL-1β/IL-18 maturation, pyroptosis, and context dependence

RIBC2: a ciliary protein candidate in a non-ciliated methylation story

What model-organism experiments show and what cannot yet be transferred to colonic UC tissue

SIGLEC5: reading sialic-acid identity with an inhibitory receptor

innate immunity

Innate immune restraint, paired-Siglec complexity, and the meaning of UC hypomethylation

SYNE1: mechanically linking the nucleus to the cell

nuclear envelope

Nesprin-1, nuclear architecture, giant-gene complexity, and methylation as a candidate marker

More mitochondria, less efficient respiration

colorectal cancer

oxidative stress

How inflammation-driven mitochondrial turnover can amplify mtDNA damage, sustain colitis, and reshape cancer risk.

Protocol 2: blood processing into plasma and serum aliquots

blood processing

Why two blood fractions feed two different assays and which pre-analytical fields must travel with them

Protocol 1: 18 consented participants and three clinical groups

How consent, group assignment, participant IDs, and paired specimens become the study manifest

Protocol 3: colonic biopsy preparation

How an anatomically anchored biopsy becomes tissue DNA—and which context must not be lost

Protocol 6: NEBNext EM-seq library preparation

library preparation

From fragmented DNA to indexed Illumina libraries that encode modified-cytosine state

Protocol 7: paired-end sequencing on the Illumina NovaSeq 6000

paired-end sequencing

How pooled EM-seq libraries become demultiplexed FASTQs and sequencing QC

Protocol 4A: PANAMAX 48 extraction of plasma cfDNA

cfDNA extraction

What the automated extractor does, what comes out, and why the exact kit configuration matters

Protocol 4B: PANAMAX 48 extraction of tissue genomic DNA

Automated magnetic-bead purification from prepared colonic tissue to high-molecular-weight DNA

Protocol 8: serum sIgA by ELISA

From triplicate wells and a calibration curve to one reviewed participant concentration

Protocol 5: UltraShear fragmentation of tissue genomic DNA

DNA fragmentation

Why UltraShear is an enzyme reagent—not a machine—and what its fragment distribution contributes

From gene list to pathway neighborhoods

ulcerative colitis

colorectal cancer

How the project’s 14 candidate genes connect—and where Reactome correctly leaves gaps.

This methylation study, explained to a machine-learning scientist

machine learning

Inputs, targets, feature spaces, valid ML tasks, and why 18 participants demand restraint

This methylation study, explained to a statistician

Experimental units, estimands, contrasts, pairing, count models, technical replication, and multiplicity

Three data sources, one patient-level picture

What plasma cfDNA, colonic tissue DNA, and serum sIgA each contribute—and what their processed data may look like

What are histones—and how does a cell pack two metres of DNA?

gene expression

From cell nucleus to nucleosome, and how CpG methylation can help open or close access to genes

Where do these genomics words come from?

A short field guide to cfDNA, methylation, fragmentomics, EM-seq, and Reactome

From ulcerative colitis to colorectal cancer

ulcerative colitis

colorectal cancer

What progression means, which symptoms and biomarkers matter, how risk changes over time, and why the global burden is shifting

How can colitis reshape DNA methylation?

ulcerative colitis

DNA methylation

gene regulation

From inflammatory signaling and epithelial repair to CpG islands, gene regulation, and candidate cancer-related field effects

How leading labs turn cfDNA into a disease signal

machine learning

A practical comparison of feature engineering, statistics, and machine learning across fragmentomics and methylation programs

From limma to fragmentomics: how the software stack evolved

A practical history of well-tested packages for methylation sequencing, regional inference, annotation, and disease fingerprints

limma versus DSS for methylation: a statistical comparison

Linear-model empirical Bayes versus beta-binomial count modeling—and why raw EM-seq CpG evidence favors DSS in this study

Packages for a methylation–fragmentomics disease fingerprint

reproducibility

A pragmatic open-source stack for EM-seq, fragment features, pathway analysis, integration, and future progression models

Which methylated genes matter in colitis-to-cancer progression?

ulcerative colitis

colorectal cancer

The candidate silenced and immune genes highlighted in the project presentation—and what the evidence does and does not show

How are methylation and fragmentomics data analyzed statistically?

From beta-binomial CpG models and DMRs to paired tissue–plasma contrasts, multiple testing, and honest validation

What is enzymatic methyl sequencing?

How EM-seq reads DNA methylation while preserving precious low-input cfDNA—and why that matters for this study

What is fragmentomics?

How the size, ends, and genomic positions of cfDNA fragments provide clues about chromatin and tissue of origin

What is KEGG?

A short guide to KEGG pathways, enrichment analysis, and responsible interpretation of methylation-associated gene lists

What is Reactome?

How Reactome’s curated reaction network supports pathway enrichment and mechanistic interpretation in methylation studies

What is secretory IgA—and why track it?

An ELI18 guide to sIgA, what a serum ELISA can reveal, and how this immune signal complements DNA methylation

Tissue versus plasma methylation sequencing: why compare both?

tissue of origin

Tissue is the close-up; plasma is the blood-borne mixture. Pairing them reveals which colon methylation signals might become accessible biomarkers.