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Lipidomics

Lipidomics is the large-scale study of cellular lipids — their structures, quantities, interactions, and dynamics. Workflows rely on LC-MS/MS (DDA/DIA), shotgun lipidomics, and ion-mobility separation; analysis pipelines focus on lipid identification against LIPID MAPS, quantification with internal standards, batch correction, and pathway/network interpretation linked to membrane biology, signaling and metabolic disease.

LipidSearchLIPID MAPSMS-DIALLipidyzer
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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 MS-DIAL + LIPID MAPS tools 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
LIPID MAPS — structures, tools & nomenclature
LIPID MAPS Consortium
Why: Exact sector match (Lipidomics)
Dataset · tool
LIPID MAPS Lipidomics Tools (LipidFinder, Bulk Structure Search)
LIPID MAPS
Why: Exact sector match (Lipidomics)
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

MS-DIAL + LIPID MAPS toolsData wranglingQC & exploratory analysisVersion-controlled pipelines

Hands-on projects

Dataset deep-dive on MetaboLights / Metabolomics Workbench lipidomics studies

Pick one study from MetaboLights / Metabolomics Workbench lipidomics 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 MS-DIAL + LIPID MAPS tools tutorial end-to-end on real data
  • Download and explore one full dataset from MetaboLights / Metabolomics Workbench lipidomics studies
  • Document a clean QC + analysis pipeline that another person could rerun
Citation · documentation
LipidSearch / Lipid Annotator (vendor pipelines reference)
Thermo Fisher / Agilent
Why: Exact sector match (Lipidomics)
Dataset · tool
MS-DIAL — untargeted MS data processing (incl. lipidomics)
RIKEN / Tsugawa Lab
Why: Exact sector match (Lipidomics)
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

Plasma lipidomics re-analysis

Re-process a public LC-MS lipidomics dataset end-to-end (peak picking, lipid ID against LIPID MAPS, normalization) and reproduce its headline differential-lipid findings.

Deliverable: Reproducible repo + annotated lipid table + 4-page methods/results brief

Milestones

  • Ship one original mini-analysis on a public dataset with clearly stated hypothesis and limitations
  • Engage with the LIPID MAPS and International Lipidomics Society community (post a question, answer one, or share a notebook)
  • Get peer feedback on at least one project and iterate
Citation · course
Metabolomics in Life Sciences (covers lipidomics fundamentals)
FutureLearn · Univ. of Birmingham
Why: Exact sector match (Lipidomics)
Dataset · tool
Skyline for lipidomics (targeted MS)
MacCoss Lab
Why: Exact sector match (Lipidomics)
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 · training
EMBL-EBI Training — metabolomics & lipidomics courses
EMBL-EBI
Why: Exact sector match (Lipidomics)
Dataset · dataset
LipidBlast in-silico spectra
Fiehn Lab · UC Davis
Why: Exact sector match (Lipidomics)

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

Metabolomics in Life Sciences (covers lipidomics fundamentals)
FutureLearn · Univ. of Birmingham
Python for Everybody
Coursera · Univ. of Michigan
LinkedIn Learning paths
LinkedIn

Trainings

EMBL-EBI Training — metabolomics & lipidomics courses
EMBL-EBI
Software Carpentry lessons
The Carpentries
Kaggle Learn
Kaggle

Documentations

LIPID MAPS — structures, tools & nomenclature
LIPID MAPS Consortium
LipidSearch / Lipid Annotator (vendor pipelines reference)
Thermo Fisher / Agilent
Git & GitHub fundamentals
GitHub Docs

Tools

LIPID MAPS Lipidomics Tools (LipidFinder, Bulk Structure Search)
LIPID MAPS
MS-DIAL — untargeted MS data processing (incl. lipidomics)
RIKEN / Tsugawa Lab
Skyline for lipidomics (targeted MS)
MacCoss Lab

Datasets

LipidBlast in-silico spectra
Fiehn Lab · UC Davis
MetaboLights repository (lipidomics studies)
EMBL-EBI
Metabolomics Workbench (lipidomics studies)
NIH Common Fund
SwissLipids database
SIB

Books

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

Communitys

International Lipidomics Society — resources & guidelines
ILS

Typical career paths

Lipidomics Scientist

Designs and runs LC-MS/MS lipidomics experiments, performs lipid ID, QC and statistics for biomedical or nutrition studies.

Lipidomics Bioinformatician

Builds reproducible pipelines (MS-DIAL, LipidSearch, Skyline) and integrates lipid features with clinical or multi-omics data.

Ready to make this your own?

Answer a short profile and the AI builder will tailor every phase — pace, hours, tools, and resources — to your background and goals in this sector.

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