Learn the methodsfrom the people who run them.
Hands-on courses, workshops and internships in proteomics, metabolomics, NGS data analysis and molecular simulation, taught by the scientists who run these methods daily.
Proteomics
Understand proteomics
From how an Orbitrap measures a peptide to a finished figure, in modules that can be taken alone or as a series.
Understanding proteomics: from sample to spectrum
Two days. How bottom-up proteomics works, what DDA and DIA measure, and how to read a results table critically. No prior MS experience needed.
Background and references
A foundation course on how bottom-up proteomics works: digestion, chromatography, ionisation, Orbitrap mass analysis, DDA and DIA, and how identifications and quantities are derived. Built around the overview of Aebersold and Mann (2016) and our own instrument data.
DIA data analysis with DIA-NN and Spectronaut
Two days, hands-on. Raw data to a quantified protein-group matrix, with QC and export for statistics.
Background and references
Hands-on processing of DIA raw data: library-free and library-based searches in DIA-NN (Demichev et al., 2020), the SWATH/DIA concepts behind them (Ludwig et al., 2018), parameter choices, QC of the output and export for statistics.
- Demichev V et al. (2020). DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat Methods 17:41-44. PubMed · DOI · Free full text
- Ludwig et al. (2018). Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial. Mol Syst Biol. PubMed · DOI · Free full text
Statistics and figures in R for proteomics
Three days, hands-on. Normalisation, imputation, limma, enrichment and publication figures in R.
Background and references
From a protein-group matrix to a publication figure in R: normalisation, missing values, limma-based testing (Ritchie et al., 2015; Kammers et al., 2015), enrichment analysis and volcano, PCA and heatmap figures with ggplot2 and ComplexHeatmap.
- Ritchie ME et al. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47. PubMed · DOI · Free full text
- Kammers K et al. (2015). Detecting Significant Changes in Protein Abundance. EuPA Open Proteom 7:11-19. PubMed · DOI · Free full text
Phosphoproteomics and kinase activity analysis
One day. Enrichment, localisation, site-level statistics and kinase inference on a worked dataset.
Background and references
Enrichment chemistry and acquisition choices (Humphrey et al., 2018), site localisation, and downstream kinase-substrate enrichment (Casado et al., 2013) worked through on a real dataset.
- Humphrey et al. (2018). High-throughput and high-sensitivity phosphoproteomics with the EasyPhos platform. Nat Protoc. PubMed · DOI
- Casado P et al. (2013). Kinase-substrate enrichment analysis provides insights into the heterogeneity of signaling pathway activation in leukemia cells. Sci Signal 6:rs6. PubMed · DOI
Targeted proteomics and PRM assay design
One day. Peptide selection, method building, internal standards and assay validation in Skyline.
Background and references
Choosing peptides, building PRM methods (Peterson et al., 2012), using stable-isotope standards and validating the assay to the appropriate tier (Carr et al., 2014), with Skyline as the working tool.
- Peterson AC et al. (2012). Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics. Mol Cell Proteomics 11:1475-88. PubMed · DOI · Free full text
- Carr SA et al. (2014). Targeted peptide measurements in biology and medicine: best practices for mass spectrometry-based assay development using a fit-for-purpose approach. Mol Cell Proteomics 13:907-17. PubMed · DOI · Free full text
Metabolomics
Metabolomics and lipidomics
Design, processing and annotation taught with the same rigour we apply to client projects.
Understanding metabolomics
One day. Untargeted versus targeted, study design, QC samples and what an annotation level really means.
Background and references
How untargeted and targeted metabolomics differ, how a robust LC-MS study is designed (Dunn et al., 2011), and what the identification confidence levels of the Metabolomics Standards Initiative mean (Sumner et al., 2007).
- Dunn WB et al. (2011). Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc 6:1060-83. PubMed · DOI
- Sumner LW et al. (2007). Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 3:211-221. PubMed · DOI · Free full text
Untargeted data processing with MZmine and MetaboAnalyst
Two days, hands-on. Feature detection, alignment, annotation, statistics and pathway analysis.
Background and references
Peak picking, alignment and annotation in MZmine 3 (Schmid et al., 2023), then statistics and pathway analysis in MetaboAnalyst 6.0 (Pang et al., 2024), with the pitfalls of each step pointed out.
- Schmid R et al. (2023). Integrative analysis of multimodal mass spectrometry data in MZmine 3. Nat Biotechnol 41:447-449. PubMed · DOI · Free full text
- Pang Z et al. (2024). MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation. Nucleic Acids Res 52:W398-W406. PubMed · DOI · Free full text
Lipidomics fundamentals and annotation
One day. Lipid classes, extraction, internal standards and annotation at the right level.
Background and references
Lipid classes, extraction, internal standards and shorthand annotation at the level the data support, following the primer of Züllig et al. (2020) and the LIPID MAPS nomenclature (Liebisch et al., 2020).
- Züllig T, Trötzmüller M, Köfeler HC (2020). Lipidomics from sample preparation to data analysis: a primer. Anal Bioanal Chem 412:2191-2209. PubMed · DOI · Free full text
- Liebisch G et al. (2020). Update on LIPID MAPS classification, nomenclature, and shorthand notation for MS-derived lipid structures. J Lipid Res 61:1539-1555. PubMed · DOI · Free full text
NGS
NGS data analysis
Command line first, then the analysis: each module builds on the previous one.
Linux, BASH and R for biologists
Two days, hands-on. The prerequisite for every NGS module; no programming background required.
Background and references
The command line, file handling, scripting, R basics and reproducible project organisation (Sandve et al., 2013), taught as the prerequisite for every NGS module.
- Sandve GK et al. (2013). Ten simple rules for reproducible computational research. PLoS Comput Biol 9:e1003285. PubMed · DOI · Free full text
Bulk RNA-seq analysis end to end
Three days, hands-on. FASTQ to differential expression, enrichment and figures.
Background and references
QC, alignment or pseudo-alignment, quantification, DESeq2 differential expression (Love et al., 2014) and enrichment, following the best practices of Conesa et al. (2016), on a public dataset or your own.
- Conesa A et al. (2016). A survey of best practices for RNA-seq data analysis. Genome Biol 17:13. PubMed · DOI · Free full text
- Love MI, Huber W, Anders S (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550. PubMed · DOI · Free full text
Single-cell RNA-seq analysis
Two days, hands-on. QC, clustering, annotation and trajectories in Seurat or scanpy.
Background and references
QC, normalisation, integration, clustering, annotation and trajectory analysis in Seurat or scanpy, following current best practices (Luecken and Theis, 2019; Heumos et al., 2023).
- Luecken MD, Theis FJ (2019). Current best practices in single-cell RNA-seq analysis: a tutorial. Mol Syst Biol 15:e8746. PubMed · DOI · Free full text
- Heumos L et al. (2023). Best practices for single-cell analysis across modalities. Nat Rev Genet 24:550-572. PubMed · DOI · Free full text
Variant calling and annotation
Two days, hands-on. WGS, WES and panel data to a prioritised variant list.
Background and references
From FASTQ to an annotated, prioritised variant list: alignment, calling with DeepVariant or GATK (Poplin et al., 2018), VEP annotation and ACMG classification principles (Richards et al., 2015).
- Poplin R et al. (2018). A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol 36:983-987. PubMed · DOI
- Richards S et al. (2015). Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med 17:405-24. PubMed · DOI · Free full text
Microbiome analysis: 16S and shotgun
Two days, hands-on. QIIME 2, diversity statistics and an introduction to shotgun metagenomics.
Background and references
Amplicon processing in QIIME 2 (Bolyen et al., 2019), diversity statistics and differential abundance, with an introduction to shotgun metagenomics design and analysis (Quince et al., 2017).
- Bolyen E et al. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37:852-857. PubMed · DOI · Free full text
- Quince C et al. (2017). Shotgun metagenomics, from sampling to analysis. Nat Biotechnol 35:833-844. PubMed · DOI
Structure
Molecular docking and MD simulation
Taught on our own compute, so every participant runs a real docking and a real simulation.
Molecular docking workshop
One day, hands-on. Preparation, docking, pose analysis and ADMET filtering.
Background and references
Receptor and ligand preparation, grid definition, docking with AutoDock Vina (Trott and Olson, 2010), pose analysis and the honest limits of docking scores, with ADMET filtering in SwissADME (Daina et al., 2017).
- Trott O, Olson AJ (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455-461. PubMed · DOI · Free full text
- Daina A, Michielin O, Zoete V (2017). SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7:42717. PubMed · DOI · Free full text
MD simulation with GROMACS
Two days, hands-on. Build, equilibrate and run a protein and a protein-ligand system.
Background and references
System building, solvation, minimisation, equilibration and production runs in GROMACS (Van Der Spoel et al., 2005), and how to read a trajectory without over-interpreting it (Hollingsworth and Dror, 2018).
- Van Der Spoel D et al. (2005). GROMACS: fast, flexible, and free. J Comput Chem 26:1701-1718. PubMed · DOI
- Hollingsworth SA, Dror RO (2018). Molecular Dynamics Simulation for All. Neuron 99:1129-1143. PubMed · DOI · Free full text
Trajectory analysis and binding free energy
One day, hands-on. Stability metrics, clustering and MM/PBSA with gmx_MMPBSA.
Background and references
RMSD, RMSF, hydrogen bonds, clustering and MM/PBSA with gmx_MMPBSA (Valdés-Tresanco et al., 2021), including what the method can and cannot claim (Genheden and Ryde, 2015).
- Valdés-Tresanco MS et al. (2021). gmx_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS. J Chem Theory Comput 17:6281-6291. PubMed · DOI
- Genheden S, Ryde U (2015). The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opin Drug Discov 10:449-461. PubMed · DOI · Free full text
AlphaFold and homology modelling in practice
Half day. Obtain, judge and prepare a predicted structure for docking or MD.
Background and references
Running and reading AlphaFold predictions (Jumper et al., 2021), including complexes with AlphaFold 3 (Abramson et al., 2024), judging pLDDT and PAE, and preparing a model for docking or MD.
- Jumper J et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature 596:583-589. PubMed · DOI · Free full text
- Abramson J et al. (2024). Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630:493-500. PubMed · DOI · Free full text
Formats
How training is delivered
Pick the format that fits the group; the content and the instructors are the same.
Live online workshops
Short sessions over several days, screen-shared and recorded.
Background and references
Short, hands-on sessions of two to three hours over several days, with screen-shared walkthroughs and exercises on shared datasets; the format recommended for life scientists by Via et al. (2013). Recordings and materials stay with the participants.
- Via A et al. (2013). Best practices in bioinformatics training for life scientists. Brief Bioinform 14:528-537. PubMed · DOI · Free full text
On-site training at your institute
One to five days at your department, on your data where possible.
Background and references
One- to five-day courses delivered at your department or facility, on your instruments and data where possible. Group sizes are kept small so that every participant runs the analysis themselves.
- Via A et al. (2013). Best practices in bioinformatics training for life scientists. Brief Bioinform 14:528-537. PubMed · DOI · Free full text
Bioinformatics internships
Four to twelve weeks, remote, with a supervised project and report.
Background and references
Structured remote internships of four to twelve weeks covering Linux, R, RNA-seq pipelines, BLAST and BASH scripting, with a supervised project and a written report at the end.
- Sandve GK et al. (2013). Ten simple rules for reproducible computational research. PLoS Comput Biol 9:e1003285. PubMed · DOI · Free full text
Custom curriculum for a department or facility
Modules combined or written around your group's questions.
Background and references
We combine modules from this page, or write new ones, around the questions your group actually works on, from a proteomics core that wants DIA competence to a clinical department starting with metabolomics.
- Via A et al. (2013). Best practices in bioinformatics training for life scientists. Brief Bioinform 14:528-537. PubMed · DOI · Free full text
Scientific figure and manuscript clinic
A working session on your own figures and methods text.
Background and references
A working session on your own figures and methods text: data presentation that shows the distribution rather than a bar (Weissgerber et al., 2015), reproducible reporting and the methods detail reviewers ask for.
- Weissgerber TL et al. (2015). Beyond bar and line graphs: time for a new data presentation paradigm. PLoS Biol 13:e1002128. PubMed · DOI · Free full text
- Sandve GK et al. (2013). Ten simple rules for reproducible computational research. PLoS Comput Biol 9:e1003285. PubMed · DOI · Free full text
Choosing
Which format fits?
| Live online workshop | On-site course | Internship | |
|---|---|---|---|
| Duration | 2 to 3 hours per day over 2 to 5 days | 1 to 5 full days | 4 to 12 weeks, part- or full-time |
| Group size | Up to 20 | Up to 12 for hands-on modules | Individual |
| Hands-on data | Shared example datasets; your own where time allows | Your instruments and data where possible | A supervised project on real data |
| Best for | Distributed groups, refreshers, single modules | Departments and core facilities | Students and early-career scientists |
| Materials | Slides, scripts, recordings | Slides, scripts, datasets | Project code and a written report |
| Certificate | Participation | Participation | Completion, with supervisor letter |
How a project runs
From samples to answers.
- 01
Scope
Tell us who is attending, what they can do today and what they should be able to do afterwards.
- 02
Curriculum
We propose modules, duration, format and a quotation; you adjust.
- 03
Set-up
Software checklist and example data sent a week ahead so day one starts with analysis, not installation.
- 04
Deliver
Sessions on real data with exercises; questions answered as they come.
- 05
Follow-up
Materials, recordings and 30 days of email support for the exercises.
What to bring
Course requirements.
| Item | Requirement | Notes |
|---|---|---|
| Laptop | 8 GB RAM or more; rights to install software | Windows, macOS or Linux; cloud workspaces available for R and Linux modules |
| Prior knowledge | None for the foundation courses | Basic R or command line for the analysis modules; the Linux and R module covers it |
| Your own data | Optional | Analysed in the hands-on sessions where time allows; send a sample file in advance |
| Group | 1 to 20 participants | Hands-on modules are capped at 12 on site |
We send a software checklist and example data a week before the course.
What you get
Deliverables.
- Slides and annotated scripts for every module
- Example datasets and the expected results, so exercises can be repeated later
- Recordings of live online sessions
- Certificate of participation or completion
- 30 days of email support for the course exercises
- For internships: a supervised project, code review and a written report
Questions we are asked most.
The scientists who run the proteomics, metabolomics, sequencing and simulation work in our laboratory. The examples come from real projects, anonymised where needed.
Not for the foundation courses. The analysis modules assume basic R or command-line skills, which the Linux, BASH and R module teaches in two days.
Yes. On-site courses of one to five days are delivered across India; online workshops reach everywhere else.
Yes, where time allows. Send a representative file in advance and we will build an exercise around it.
A certificate of participation for workshops and courses, and a certificate of completion with a supervisor letter for internships.
Use the contact form, describe your background and what you want to learn, and attach a CV. We reply within a week with a proposed project and start date.
One laboratory, six ways in.
Most projects combine two of these. The same scientists scope, run and analyse all of them, so a proteomics study can pick up metabolomics, sequencing or a structural question without changing provider.
Train your team on your own data.
Tell us who is attending and what they need to be able to do afterwards. We reply with a curriculum, a schedule and a quotation.