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Liquid Biopsy & Early Cancer Detection

Detect cancer signals in blood: circulating tumor DNA, methylation patterns, fragmentomics and protein markers for screening, MRD monitoring and treatment response.

ctDNAMRDmethylationMCED
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Default learning track

4 phases · ~16–22 weeks

A baseline path through this sector with milestones, prerequisites, and concrete projects. Click Personalize this roadmap above to have the AI tailor pace, depth, and resources to your background and goals.

1

Phase 1 — Foundations

3–4 weeks 30–40 hours

Build the conceptual and quantitative base needed to read papers and follow tutorials in the sector.

Prerequisites

  • Comfortable with basic biology / health-science vocabulary
  • Ability to install software and use a terminal

Skills you'll build

Python or R basicsLinux shellGit/GitHubReading scientific literature

Hands-on projects

Reproducible analysis notebook

Walk through a published tutorial in fgbio + UMI-aware variant callers + methylKit and reproduce its results on the provided sample data.

Deliverable: GitHub repo with a Jupyter/Quarto notebook, environment.yml, and README

Milestones

  • Set up reproducible Conda/Mamba environment and a public GitHub repo
  • Read and summarize 3 review papers covering the sector landscape
  • Complete an intro statistics or scripting course end-to-end
Citation · documentation
ACMG/AMP variant interpretation standards
ACMG
Why: Category match (Clinical Bioinformatics & Diagnostics)
Dataset · dataset
ClinVar
NCBI
Why: Category match (Clinical Bioinformatics & Diagnostics)
2

Phase 2 — Core tools & datasets

4–6 weeks 60–80 hours

Learn the standard analytical stack of the sector and the canonical public datasets used by professionals.

Prerequisites

  • Completed Phase 1 reproducible notebook
  • Working Python/R environment

Skills you'll build

fgbio + UMI-aware variant callers + methylKitData wranglingQC & exploratory analysisVersion-controlled pipelines

Hands-on projects

Dataset deep-dive on TCGA + public ctDNA cohorts (PCAWG, cfDNA studies)

Pick one study from TCGA + public ctDNA cohorts (PCAWG, cfDNA studies), reproduce the headline result, and write a short technical note on what you found.

Deliverable: GitHub repo + 3-page PDF write-up

Milestones

  • Run the official fgbio + UMI-aware variant callers + methylKit tutorial end-to-end on real data
  • Download and explore one full dataset from TCGA + public ctDNA cohorts (PCAWG, cfDNA studies)
  • Document a clean QC + analysis pipeline that another person could rerun
Citation · documentation
FHIR Genomics implementation guide
HL7
Why: Category match (Clinical Bioinformatics & Diagnostics)
Dataset · dataset
OMIM
Johns Hopkins
Why: Category match (Clinical Bioinformatics & Diagnostics)
3

Phase 3 — Applied projects

6–8 weeks 80–120 hours

Move from tutorials to original analyses on real questions. Start showing your work publicly.

Prerequisites

  • Phase 2 dataset deep-dive complete
  • Comfortable with the sector's primary tooling

Skills you'll build

End-to-end pipeline designStatistical interpretationScientific writingReproducible reports

Hands-on projects

ctDNA MRD pipeline prototype

Build a tumor-informed MRD pipeline on public data: panel design rationale, UMI processing, error-suppressed calling and an LoD analysis.

Deliverable: Pipeline repo + validation report + LoD plots

Milestones

  • Ship one original mini-analysis on a public dataset with clearly stated hypothesis and limitations
  • Engage with the AACR Liquid Biopsy Working Group (post a question, answer one, or share a notebook)
  • Get peer feedback on at least one project and iterate
Citation · course
Python for Everybody
Coursera · Univ. of Michigan
Why: Category match (Clinical Bioinformatics & Diagnostics)
Dataset · tool
VarSome clinical knowledge base
Saphetor
Why: Category match (Clinical Bioinformatics & Diagnostics)
4

Phase 4 — Portfolio & career launch

3–5 weeks 30–50 hours

Package your work, target real roles, and prepare to interview in the sector.

Prerequisites

  • Phase 3 capstone project shipped
  • 2+ public repos under version control

Skills you'll build

Technical CVPortfolio siteInterview prep (case studies + technical questions)Networking

Hands-on projects

Job-ready portfolio package

Curate 2–3 of your strongest sector projects into a portfolio site with clear case-study writeups, plus a 1-page CV tailored to the target role.

Deliverable: Live portfolio URL + PDF CV + cover letter template

Milestones

  • Publish a portfolio site or pinned GitHub README linking to 2–3 projects
  • Tailor CV to 3 real job ads in the sector and submit applications
  • Practice 5 mock technical interviews with sector-specific case studies
Citation · book
R for Data Science (2e)
Hadley Wickham et al.
Why: Category match (Clinical Bioinformatics & Diagnostics)
Dataset · dataset
ClinVar
NCBI
Why: Category match (Clinical Bioinformatics & Diagnostics)

Curated learning resources

Verified, canonical resources from the official providers in this sector. The AI roadmap builder draws from this same library when it personalizes your roadmap.

Courses

Python for Everybody
Coursera · Univ. of Michigan
LinkedIn Learning paths
LinkedIn

Trainings

Software Carpentry lessons
The Carpentries
Kaggle Learn
Kaggle

Documentations

ACMG/AMP variant interpretation standards
ACMG
FHIR Genomics implementation guide
HL7
Git & GitHub fundamentals
GitHub Docs

Tools

VarSome clinical knowledge base
Saphetor

Datasets

ClinVar
NCBI
OMIM
Johns Hopkins

Books

R for Data Science (2e)
Hadley Wickham et al.

Certifications

GA4GH standards
GA4GH

Typical career paths

Liquid Biopsy Bioinformatician

Builds ctDNA/methylation pipelines and MRD calling algorithms.

Clinical Assay Developer (LBx)

Designs and validates liquid-biopsy assays for clinical use.

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