Biology subjects

Joeres, R.

Publications and source records attributed to Joeres, R..

3 recordsLinked to original sources

bioRxiv2024

Navigating the Maze of Mass Spectra: A Machine-Learning Guide to Identifying Diagnostic Ions in O-Glycan Analysis

Structural details of oligosaccharides, or glycans, often carry biological relevance, which is why they are typically elucidated using tandem mass spectrometry. Common approaches to distinguish isomers rely on diagnostic glycan fragments for annotating topologies or linkages. Diagnostic fragments are often only known informally among practitioners or stem from individual studies, with unclear validity or generalizability, causing annotation heterogeneity and hampering new analysts. Drawing on a curated set of 237,000 O-glycomics spectra, we here present a rule-based machine learning workflow to uncover quantifiably valid and generalizable diagnostic fragments. This results in fragmentation rules to robustly distinguish common O-glycan isomers. We envision this resource to improve glycan annotation accuracy and concomitantly make annotations more transparent and homogeneous across analysts.

bioinformatics↗

bioRxiv2023

DataSAIL: Data Splitting Against Information Leakage

Information Leakage is an increasing problem in machine learning research. It is a common practice to report models with benchmarks, comparing them to the state-of-the-art performance on the test splits of datasets. If two or more dataset splits contain identical or highly similar samples, a model risks simply memorizing them, and hence, the true performance is overestimated, which is one form of Information Leakage. Depending on the application of the model, the challenge is to find splits that minimize the similarity between data points in any two splits. Frequently, after reducing the similarity between training and test sets, one sees a considerable drop in performance, which is a signal of removed Information Leakage. Recent work has shown that Information Leakage is an emerging problem in model performance assessment. This work presents DataSAIL, a tool for splitting biological datasets while minimizing Information Leakage in different settings. This is done by splitting the dataset such that the total similarity of any two samples in different splits is minimized. To this end, we formulate data splitting as a Binary Linear Program (BLP) following the rules of Disciplined Quasi-Convex Programming (DQCP) and optimize a solution. DataSAIL can split one-dimensional data, e.g., for property prediction, and two-dimensional data, e.g., data organized as a matrix of binding affinities between two sets of molecules, accounting for similarities along each dimension and missing values. We compute splits of the MoleculeNet benchmarks using DeepChem, the LoHi splitter, GraphPart, and DataSAIL to compare their computational speed and quality. We show that DataSAIL can impose more complex learning tasks on machine learning models and allows for a better assessment of how well the model generalizes beyond the data presented during training.

bioinformatics↗

bioRxiv2022

GlyLES: Grammar-based Parsing of Glycans from IUPAC-condensed to SMILES

Glycans are important polysaccharides on cellular surfaces that are bound to glycoproteins and glycolipids. These are one of the most common post-translational modifications of proteins in eukaryotic cells. They play important roles in protein folding, cell-cell interactions, and other extracellular processes. Changes in glycan structures may influence the course of different diseases, such as infections or cancer. Glycans are commonly represented using the IUPAC-condensed notation. IUPAC-condensed is a textual representation of the Symbol Nomenclature for Glycans (SNFG) that assigns a colored, geometrical shape to the main monosaccharides. These symbols are then connected in tree-like structures, visualizing the glycan structure on a polymeric level. Yet for a representation on the atomic level, notations such as SMILES should be used. To our knowledge, there is no easy-to-use, general, open-source, and offline tool to convert the IUPAC-condensed notation to SMILES. Here, we present the open-access Python package GlyLES for the generalizable generation of SMILES representations out of IUPAC-condensed representations. GlyLES uses a grammar to read in the monomer tree from the IUPAC-condensed notation. From this tree, the tool can compute atomic structures of each monomer based on their IUPAC-condensed descriptions. In the last step, it merges all monomers into the atomic structure of a glycan in the SMILES notation. GlyLES is the first package that allows conversion between IUPAC-condensed notations of glycans and SMILES strings. This may have multiple applications, including straightforward visualization, substructure search, molecular modelling and docking, and a new featurization strategy for machine-learning algorithms. GlyLES is available at https://github.com/kalininalab/GlyLES.

bioinformatics↗