> For the complete documentation index, see [llms.txt](https://lwm.mit.edu/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://lwm.mit.edu/course-materials/computation-questions/module-1-fastq-to-phenotype.md).

# Module 1: FastQ to Phenotype

Navigating Sequencing Data with AI Workflows

**Date**: Friday, October 2, 2026 (10-11:30 AM)

**Presenters**: Liz Codd (Jones Lab), Alexander Greer (Vander Heiden Lab)

## Overview

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**Central Question**: "How can computational analysis of sequencing modalities be used to reveal cellular mechanisms?"

**Learning Objectives**:

* Understand the data structure and content of various sequencing modalities
* Learn how to manipulate and analyze sequencing data in Python
* Interpret sequencing alignment tracks and peak-caller results
* Connect data layers across modalities into full analytical pipelines
  {% endhint %}

<table data-view="cards"><thead><tr><th></th><th data-hidden data-card-cover data-type="image">Cover image</th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><h4>Presentation Slides</h4></td><td><a href="https://2078575508-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FpK8DMWsoyo5vzRk1L6UL%2Fuploads%2FT5zyjFDmSi6CSdroVzZ1%2Fm1_cover.png?alt=media&amp;token=e91823eb-7750-4352-b98c-70660de29179">m1_cover.png</a></td><td><a href="https://docs.google.com/presentation/d/1-uCuDixgS_jMRB33tYzFA6yN99wyaNBV8oloXtNEUMY/edit?usp=sharing">https://docs.google.com/presentation/d/1-uCuDixgS_jMRB33tYzFA6yN99wyaNBV8oloXtNEUMY/edit?usp=sharing</a></td></tr><tr><td><h4>Code Workbooks</h4></td><td><a href="https://2078575508-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FpK8DMWsoyo5vzRk1L6UL%2Fuploads%2FFTkJDrRVoYQm9FfUne9R%2F1765109208272.png?alt=media&amp;token=27836f71-a47a-49b8-acf1-a5c2e46a8129">1765109208272.png</a></td><td><a href="https://github.com/lizcodd/fastq2pheno_workshop/tree/main">https://github.com/lizcodd/fastq2pheno_workshop/tree/main</a></td></tr><tr><td><h4>Reference Guide</h4></td><td><a href="https://2078575508-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FpK8DMWsoyo5vzRk1L6UL%2Fuploads%2FkqDoJFZYBOWo11m38vMr%2Fchoose-markdown.jpg?alt=media&amp;token=ac805070-e315-4d72-8f54-4bcaa5376197">choose-markdown.jpg</a></td><td><a href="https://drive.google.com/drive/folders/12VPytU0bZ87kSwKvzIfmn9VbsNZYa4HB?usp=sharing">https://drive.google.com/drive/folders/12VPytU0bZ87kSwKvzIfmn9VbsNZYa4HB?usp=sharing</a></td></tr></tbody></table>

## Motivating Problem

The advent of accessible -omics technologies makes it easy to generate sequencing data at scale. But to turn this wealth of data into usable information informing our scientific inquiry, we require sufficiently powerful tools of computational analysis.&#x20;

Several analytical bottlenecks lend themselves to computational analysis methods, including peak-calling susceptibility to noise, sequence biased alignment, and the high dimensionality of multiomics correlations.&#x20;

## Hands-on Exercise

Today's session will involve exploring a real-world ATAC-seq dataset from a [2019 *Nature* study](https://doi.org/10.1038/s41586-019-1763-5), combining Python-based analysis, automated processing pipelines, and discussion of benefits, drawbacks, and follow-ups for sequence-based computational analysis.

The codebase for the hands-on portion of the session can be found at the card linked above.

## Discussion

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1. Where do the three claim modes (alignment track, peak call, model's read) agree and disagree, and which should you trust for what?
   {% endhint %}

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2\. The DM and HSR amplicons have identical sequence, but are in different contexts in the cell (episomal vs. integrated). The accessibility analysis reads differently for each. What could be some explanations for this, and what could we begin to predict about downstream cellular effects?
{% endhint %}

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3\. How might your analysis of **clinical** sequencing data (i.e. with unique individual variants and observable phenotypes) differ from that of well-characterized cell lines? What additional information could be relevant? *(Add'l material: GATA2 +9.5 continued learning notebook.)*
{% endhint %}

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4\. What biases/instrumentation/experimental errors (→ Tn5 sequence bias, batch effects, seq2PRINT/scPrinter stat corrections) are present? Lead for EZ-Share-seq intro
{% endhint %}

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5\. **Experimental Follow-up!** What are some straightforward experiments you could to falsify the last computational result you generated as a follow-up?
{% endhint %}

Ask questions and share your own applications of these techniques in the Collaboration Park (see the **Computation Questions** channel).

{% content-ref url="/pages/nXkZZhFpJGTQPmWCg9j4" %}
[Community](/community.md)
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