General literature
For an overview of methods used in proteomics, see our PTMomics review as well as our editorial about reproducibility. We also published a paper about transparent reporting of the different steps in the data analysis workflow: ref. We published tutorials for CrossTalkMapper, VSClust + complex analysis and general PTM analysis, and have further training material on GitHub.

Tool suites for quantitative Omics
Check out our tool suite OmicsQ facilitating data processing and further analysis with PolySTest, VSClust et al. (ref).
We also collaborated with the development of a web application for LC-MS metabolomics data: MetaboLink.

Statistical tests: PolySTest
New statistical test for data with low replicates numbers and many missing values, combined with well-performing statistical tests for high confidence detection of differentially regulated features (ref.) PolySTest.
The old LimmaRP approach where we showed the power of combining limma and rank products (ref.) is still accessible here: LimmaRP

Biopharma LC-MS applications
Recent work demonstrates and compares LC-MS-based host-cell-protein quantitation methods in the USP 1132.1 context, including qualification and benchmarking across workflows (ref).

Data clustering
VSClust considers feature variance leading to more accurate clustering results (ref. and tutorial). VSClust. See also how to estimate appropriate parameter values for fuzzy c-means clustering (ref). (old app: FuzzyClust)

Protein Complexes
ComplexBrowser to investigate the behavior of protein complexes in your proteomics data set (ref). CoExpresso to look for co-regulatory patterns within hundreds of human cell types (ref).

PTM crosstalk
Large-scale estimation of crosstalk between nearby residues (ref. and ref.). Interplay scores estimate the quantitative crosstalk between PTMs on a protein (ref. and ref.). Crosstalk patterns can be visualized by CrosstalkMapper.

Internal fragment ions
Internal fragments ions are considered crucial for the identification of proteoforms in top-down mass spectrometry but are ubiquitous and noisy. We show how they can be used to validate proteoforms (ref).

Middle-down mass spectrometry
We develop and apply a workflow to quantify PTMs on histone tails (refs). ProteoformQuant is under active development for improved quantification of proteoforms in middle-down MS data.

CrosstalkDB
With quantitative data from middle-down and top-down mass spectrometry, the web server collects and analyzes the input files, followed by statistical assessment of the crosstalk between measured PTMs (refs).

Computational models
Taking simple rules for writing, propagating and deleting histone PTMs on chromatin, we were able to reproduce global patterns measured by ChIP-seq experiments. The implementation of crosstalk rules results in a rich spatial and temporal behavior (ref).

Peptide representations for deep learning
In collaboration with the Röttger group, we developed an environment to retrieve millions of mass spectra (MS1 and MS2) from the public repository PRIDE, to categorize them in a database, and to create data representations that can be directly used for machine learning purposes: MS2AI (refs).

Blind application of deep learning methods to MS leads to high bias and inaccurate predictions. We investigated the impact of data variability on the prediction bias in (ref).

See also the AIMe registry to report AI-based biomedical results in a standardized and reproducible manner (ref) and our community paper about machine learning in proteomics (ref).

Standardization and community efforts
See the proposed notation of proteoforms: ProForma (ref). Accurate metadata annotation is crucial for ensuring reproducibility and data repurposing. Together with the EuBIC community, we developed a data standard for proteomics metadata: (refs.). As part of ELIXIR DK, we annotate proteomics tools for bio.tools (refs.) and are part of the proteomics community (see also white paper, ref). We also recently contributed to a community perspective on preventing proteomics data tombs via shared standards, incentives, and stewardship (ref).

WOMBAT-P
We implemented four scalable and portable workflows for label-free data analysis as part of an ELIXIR implementation study. They allow to systematically compare the performance of different data analysis workflows (ref).

Antibodies
Antibodies are notoriously difficult to identify due to their large sequence variety. Taking advantage of the Observed Antibody Space, we developed a workflow to identify antibodies in mass spectrometry data (ref).

ProtProtocols
As part of a project within the EuBIC initiative, we developed a framework for fully reproducible, documented and user-friendly pipelines for specific cases of proteomics data analysis. Within this framework, we created IsoProt, a full data analysis pipeline for iTRAQ/TMT data (ref). Check it out here: IsoProt at GitHub – download it via our docker-launcher

Biological evolution
See my former studies of aging, sympatric speciation and competitive cellular automata (refs).

Simulations
Almost anything can be simulated on the computer including sand dunes, opinion dynamics and linguistics (refs).

Statistical Mechanics
See my work on generalized entropies and Fokker-Planck equations (refs).