Biomedical subjects

Johannes Rainer

Publications and source records attributed to Johannes Rainer.

6 recordsLinked to original sources

PubMed2026

Community-driven advances in computational mass spectrometry: The perspective of EuBIC-MS members.

Advances in data acquisition, artificial intelligence, and integrative bioinformatics are driving the rapid evolution of computational mass spectrometry, and in turn, transforming modern proteomics, metabolomics, and lipidomics. These developments have greatly increased the scale and complexity of mass spectrometry data, underscoring the importance of evolving accurate, transparent, efficient and reproducible data processing workflows. Addressing these challenges requires collaborative innovation that brings together expertise in software engineering, statistics, and biology. The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS), an initiative of the European Proteomics Association (EuPA), fosters a culture of open, community-driven development through its biennial Developers Meetings and Winter Schools. This commentary summarizes the scientific background and outcomes of the EuBIC-MS Developers Meeting 2025, which took place in Novacella, Italy. Three keynote presentations highlighted major frontiers in the field: deep proteome and phosphoproteome profiling, text mining for protein-protein interaction extraction, and scalable proteomics for AI-driven drug discovery. Seven community-selected hackathons addressed emerging challenges such as single-cell proteomics data analysis, FAIR metadata extraction, deep learning frameworks, R-Python interoperability, and DIA validation. Together, these efforts demonstrate the potential for scientific and technical innovation to arise from open collaboration, and highlight how community-driven initiatives can accelerate progress in computational mass spectrometry. SIGNIFICANCE: Modern proteomics increasingly depends on computational advances to translate complex, high-dimensional data into biological knowledge. The EuBIC-MS Developers Meeting 2025 exemplifies how community-driven collaboration can directly accelerate this process by bringing together experts from bioinformatics, statistics, and experimental proteomics to co-develop open, interoperable, and reproducible analytical tools. By fostering shared software frameworks, transparent benchmarking, and collaborative problem solving, the EuBIC-MS community helps ensure that technological innovation translates into reliable biological insights. This collaborative model strengthens the foundation for quantitative, system-level understanding of proteomes and establishes a sustainable path for integrating artificial intelligence and next-generation data acquisition into routine biological discovery. This commentary shows some current highlights in the field of computational mass spectrometry and community-based approaches undertaken during the most recent Developers Meeting to solve these challenges. The approaches discussed and initiated during the meeting - ranging from deep proteome profiling and phosphosite mapping to text mining, single-cell data analysis, and FAIR metadata extraction - address key bottlenecks that currently limit the biological interpretability and comparability of proteomics data.

Mass Spectrometry↗

PubMed2006

CARMAweb: comprehensive R- and bioconductor-based web service for microarray data analysis.

CARMAweb (Comprehensive R-based Microarray Analysis web service) is a web application designed for the analysis of microarray data. CARMAweb performs data preprocessing (background correction, quality control and normalization), detection of differentially expressed genes, cluster analysis, dimension reduction and visualization, classification, and Gene Ontology-term analysis. This web application accepts raw data from a variety of imaging software tools for the most widely used microarray platforms: Affymetrix GeneChips, spotted two-color microarrays and Applied Biosystems (ABI) microarrays. R and packages from the Bioconductor project are used as an analytical engine in combination with the R function Sweave, which allows automatic generation of analysis reports. These report files contain all R commands used to perform the analysis and guarantee therefore a maximum transparency and reproducibility for each analysis. The web application is implemented in Java based on the latest J2EE (Java 2 Enterprise Edition) software technology. CARMAweb is freely available at https://carmaweb.genome.tugraz.at.

Cluster Analysis↗

PubMed2005

Identification of glucocorticoid-response genes in children with acute lymphoblastic leukemia.

The ability of glucocorticoids (GCs) to kill lymphoid cells led to their inclusion in essentially all chemotherapy protocols for lymphoid malignancies, particularly childhood acute lymphoblastic leukemia (ALL). GCs mediate apoptosis via their cognate receptor and subsequent alterations in gene expression. Previous investigations, including expression profiling studies with subgenome microarrays in model systems, have led to a number of attractive, but conflicting, hypotheses that have never been tested in a clinical setting. Here, we present a comparative whole-genome expression profiling approach using lymphoblasts (purified at 3 time points) from 13 GC-sensitive children undergoing therapy for ALL. For comparisons, expression profiles were generated from an adult patient with ALL, peripheral blood lymphocytes from GC-exposed healthy donors, GC-sensitive and -resistant ALL cell lines, and mouse thymocytes treated with GCs in vivo and in vitro. This generated an essentially complete list of GC-regulated candidate genes in clinical settings and experimental systems, allowing immediate analysis of any gene for its potential significance to GC-induced apoptosis. Our analysis argued against most of the model-based hypotheses and instead identified a small number of novel candidate genes, including PFKFB2, a key regulator of glucose metabolism; ZBTB16, a putative transcription factor; and SNF1LK, a protein kinase implicated in cell-cycle regulation.

Adolescent↗

PubMed2005

Molecular taxonomy and ecology of Pseudallescheria, Petriella and Scedosporium prolificans (Microascaceae) containing opportunistic agents on humans.

The main purpose of the present paper is to establish the connection between phylogenetic and morphological data and ecological features of strains of Pseudallescheria, Petriella, and Scedosporium. For the phylogenetic analysis sequences of the ITS region and the large subunit (partial sequences) of the rDNA were used. Cultural characteristics were observed on MEA 2 % and Weitzman-Silva Hutner Agar. Results showed, that three major groups could be differentiated, corresponding to Pseudallescheria, Petriella and S. prolificans. Among Petriella species only Pe. setifera is reasonably delimited. Pe. musispora was found to be synonymous with Pe. setifera. S. prolificans proved to be a homogenous species on the basis of ITS-sequences. Morphologically, Pseudallescheria and Petriella are distinguished by ostiolate vs non-ostiolate ascomata, a bipartition reflected also in ITS sequence data. We hypothesise a secondary loss of the ostiole of Pseudallescheria due to its ecological preferences. Infraspecific grouping within the highly variable species P. boydii is consistent for at least one clade in the ITS tree. The evolution of lineages with increased virulence within P. boydii is discussed.

Base Sequence↗

PubMed2005

Glucocorticoid-induced apoptosis and glucocorticoid resistance in acute lymphoblastic leukemia.

Glucocorticoids (GC) induce cell cycle arrest and apoptosis in lymphoid cells, and therefore constitute a central component in the treatment of lymphoid malignancies, particularly childhood acute lymphoblastic leukemia (ALL). In spite of its clinical significance and considerable efforts in many laboratories, however, the molecular basis of GC-induced apoptosis and the clinically important resistance phenomenon remains poorly defined. The anti-leukemic GC effects are critically dependent upon sufficient expression of the GC receptor (GR) throughout the response. In ALL cell lines, this is associated with, and may depend upon, GR autoinduction. In corresponding in vitro models, GC resistance frequently results from mutations in the GR gene and/or deficient regulation of its expression. The downstream components of the pathway, i.e., the GC-regulated genes responsible for cell death induction, have been studied by microarray-based comparative expression profiling, resulting in identification of a considerable number of GC-regulated candidate genes. Their possible function in the death response is, however, still undefined. One model predicts direct regulation of the apoptotic machinery, e.g., components of the "Bcl-2 rheostat", while a complementary hypothesis suggests deleterious GC effects on essential cellular functions, such as metabolism, production of and/or response to oxygen radicals, general transcription/translation, pH and volume control, etc. These regulatory effects may entail cell death, particularly if maintained for sufficient time through GR autoinduction. The latter form of cell death may occur even in the absence of functional apoptotic machinery (e.g., when caspases are blocked), but in this case appears to entail a more necrotic morphology. Taken together, GC may induce different types of cell death through distinct molecular pathways, depending on the cellular context. GC resistance might frequently result from defective GR expression, perhaps the most efficient means to target multiple antileukemic pathways.

Animals↗

PubMed2003

Client-server environment for high-performance gene expression data analysis.

SUMMARY: We have developed a platform independent, flexible and scalable Java environment for high-performance large-scale gene expression data analysis, which integrates various computational intensive hierarchical and non-hierarchical clustering algorithms. The environment includes a powerful client for data preparation and results visualization, an application server for computation and an additional administration tool. The package is available free of charge for academic and non-profit institutions.

Computing Methodologies↗