# Spring 2024 Committee Meeting

**Student:** Meghan Sleeper

**Project:** Identifying commonly differentially methylated regions (DMRs) in colorectal cancer tissue samples

**Committee members:** Gordon Wolfe, David Keller, Carter Tilquist

## Updates

:::success
Since our [last committee meeting](https://hackmd.io/@msleeper/r15e_Q8L6), I have converted my pipeline into an automated workflow using Snakemake, added quality control steps to my pipeline, and organized sample metadata into a uable format accessed by the pipeline.
:::

### General accomplishments

:tada: Accepted a summer bioinformatics internship with [Bio-Rad](https://www.bio-rad.com/)!! :tada:

:chart_with_upwards_trend: Volunteered as a coach at DataFest 2024

### Project specific accomplishments

:white_check_mark: Built out an automated [analysis workflow](https://github.com/MSleeper1/dmr_workflow/tree/main) with Snakemake

:white_check_mark: Added important Quality Control steps to my workflow

:white_check_mark: Reorganized sample [metadata](https://csuchico-my.sharepoint.com/:x:/r/personal/msleeper1_csuchico_edu/Documents/MS_sample_data_info.xlsx?d=w69524862b4d24ca088cdd0d993e46d8c&csf=1&web=1&e=MFCePJ) for easy use with automated workflow. Also added a few samples to analysis list.

:white_check_mark: Further developed my methods and plan for analysis

### Challenges / Roadblocks

- Disk space on farm was highly impacted this semster raising challenges in data analysis. I have addressed this by implementing methods that use temporary memory for intermediate files.
- This semester I experienced general difficulty making forward progress with producing any results for my project. I have been focussed on how to reset and pivot as needed.

## Background review

:::info
My project seeks to aggregate genome wide methylation data for colorectal cancer patients and healthy patients and identify common differentially methylated regions across all samples.
:::

### Methylation patterns observed in cancers:

Epigenome-wide association studies (EWAS) investigate relationships between epigenetic modifications across the entire genome and a particular condition.

Differentially methylated regions (DMRs) associated with a condition are identified by comparing methylation in two groups.


Methylation arrays, despite covering ~2% of potential methylated regions, are commonly used due to cost considerations.

Whole genome methylation sequencing, is limited in usage due to high costs, often restricting analysis to one cancer tissue sample.

There is value in aggregating WGBS data from various studies and analysing as a collective.

## UPDATED Questions and Hypotheses

1. What DMRs are shared across various colorectal cancer tissue samples?

- I expect to find DMRs that cover regions previously identified as CRC drivers, such as hypermethylated oncogenes and hypomethylated promoter regions of tumor suppressor genes.

- I also expect to find DMRs within intergenic regions that may not be previously described in the literature.

2. Are the most statistically significant DMRs within regions that would be covered by array-based methods or are there DMRs identified in the less studied intergenic regions?

- I expect to find some highly significant DMRs within intergenic regions not covered by array-based methods.

3. How do relative cellular contributions of various cell types in healthy colorectal samples vary from CRC tumor samples?

- I expect healthy colorectal tissue will contain methylome contributions from colorectal epithelial cells, colon fibroblasts, and blood cells as determined by previous studies.

- Additionally, I expect that CRC tumor samples will have a larger cellular contribution from epithelial cells relative to healthy colorectal tissue since CRCs are primarily carcinomas.