Georg Wallmann

FastaLake constructs compact metaproteomic databases from sequence resources to improve peptide identification without requiring matched metagenomic sequencing.

AlphaPeptTools provides a scverse-native Python package for analyzing MS-based proteomics data, enabling scalable analysis and integration with other modalities.

The paper introduces QuantSelect, a deep learning method for fragment ion selection in DIA proteomics, improving quantification accuracy and differential expression sensitivity.

scPortrait integrates single-cell images with proteomics and transcriptomics data to identify cancer-associated macrophage subpopulations and embed images into transcriptome atlases.

AlphaDIA enables end-to-end transfer learning for feature-free proteomics, improving identification and quantification of post-translational modifications.

DO-MS v2.0 optimizes DIA mass spectrometry parameters and quality control for single-cell proteomics, supporting label-free and plexDIA methods with interactive analysis and reporting.

SPARCS enables genome-wide CRISPR screening for spatial cellular phenotypes by combining laser microdissection with deep learning, identifying key genes involved in autophagosome formation.

Multiplexing nonisobaric isotopologous mass tags (mTRAQ) with data-independent acquisition and DIA-NN increases sensitive proteomics throughput 3-fold while cutting missing data below 3% in bulk and single cells.