Literature Review
Literature behind the analytical roadmap
This curated set connects the team’s shared papers to decisions in the study. Reviews orient the field; methods papers justify feasibility; neither substitutes for validation in ulcerative colitis. Publisher and DOI links are used instead of redistributing article PDFs.
Start with the reviews
REVIEW · CONCEPTUAL FOUNDATION
Fragmentomics, methylation, and tissue of origin
Lo and colleagues explain why cfDNA fragment size, end motifs, nucleosome organization, topology, and methylation can reveal aspects of tissue of origin (Lo, Han, Jiang, and Chiu, 2021). It is the clearest foundation for treating the physical fragment and its methylation state as complementary measurements.
Use here: define the feature families and the biological interpretation boundary.
REVIEW · TRANSLATIONAL LANDSCAPE
Fragmentomics in cancer
Tsui, Jiang, and Lo review current fragmentomic features, sequencing and computational approaches, clinical studies, and unresolved standardization questions (Tsui, Jiang, and Lo, 2025).
Use here: prioritize reproducible exploratory outputs; do not transfer cancer classifier performance to UC.
Methods and proof-of-concept papers
| Paper | What it contributes | How it changes this project | Main limitation here |
|---|---|---|---|
| EM-seq from low-input DNA (Vaisvila, Ponnaluri, Sun, and others, 2021) | Enzymatic methyl sequencing at single-base resolution, including comparisons using cfDNA | Supports the assay choice and strict insert-size/library QC | Technical performance is not clinical validity |
| FRAGMA (Zhou, Kang, Jiang, and others, 2022) | Links CpG methylation to cfDNA cleavage profiles and end motifs | Motivates exploratory fragment-end and methylation-aware summaries | Developed in other biological and disease settings |
| FinaleMe (Liu, Reed, Lo, Choudhury, and others, 2024) | Infers methylation and tissue-of-origin signals from cfDNA fragmentation patterns | Provides a computational precedent for joint fragmentomic–epigenomic analysis | Its WGS model is not automatically transferable to EM-seq data |
| Plasma cfDNA methylation in SLE (Liu, Wang, Zhang, Pei, and Chen, 2025) | Demonstrates genome-wide cfDNA methylation analysis in an inflammatory autoimmune disease | Reinforces the need to distinguish inflammation-associated signal from neoplasia | SLE biology and the published assay/model differ from UC |
Colombian genomics context
The CÓDIGO consortium illustrates the value of local genomics infrastructure, bioinformatics capacity, and ancestry-aware interpretation in Colombia (Mariño-Ramírez, Sharma, Hamilton, and others, 2025). It does not provide a methylation reference panel for this study, but it strengthens the rationale for transparent provenance and locally relevant validation.
Databases and reproducible resources
- KEGG PATHWAY: manually curated molecular interaction and reaction maps for pathway interpretation.
- Reactome Pathway Browser: curated human pathways and hierarchical pathway context.
- NCI Genomic Data Commons: harmonized cancer genomic data, including TCGA resources for external triangulation.
- NEB EM-seq tools and example data: implementation resource associated with the EM-seq method paper.
Read the KEGG explainer → · Read the Reactome explainer →
Evidence from oncology, SLE, or public cancer datasets can support feasibility, select features, and test biological coherence. It cannot directly validate prediction of UC progression. That requires a sufficiently powered, longitudinal, independent cohort.