Sector roadmap

Palliative Genomics & Cancer Care

MRD, actionable variants at end-of-line, real-world tumor profiling.

AACR-GENIEOncoKBMSK-IMPACTPCAWG

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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.

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 R + GENIE 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)

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

R + GENIEData wranglingQC & exploratory analysisVersion-controlled pipelines

Hands-on projects

Dataset deep-dive on AACR GENIE

Pick one study from AACR GENIE, 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 R + GENIE tutorial end-to-end on real data
Download and explore one full dataset from AACR GENIE
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)

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

GENIE cohort mining

Mine AACR GENIE for actionable variants in one tumor type and summarise.

Deliverable: Notebook + variant table

Milestones

Ship one original mini-analysis on a public dataset with clearly stated hypothesis and limitations
Engage with the ASCO CancerLinq / AACR community (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)

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.

Typical career paths