Ivo Große

The paper benchmarks methods for inferring transcription factor binding specificities using a large community challenge and finds deep learning models outperform traditional methods, though positional weight matrices are competitive.

EPIC challenges teams to predict transcription initiation from DNA sequence in non-model metazoans, aiming to assess generalizability beyond human and model species.

This study identifies DNA binding motifs for 98 human transcription factors using meSMiLE-seq, revealing methylation-sensitive binding preferences for some TFs.

The paper uses GHT-SELEX to show many human TFs have high intrinsic sequence specificity and complex DNA binding, often matching in vivo ChIP-seq results.

SNPstar links SNPs in Arabidopsis thaliana to protein structure and function using AlphaFold3 predictions, enabling variant characterization and geographic mapping.

This study links fungal community composition to plant phenotype under moderate warming by showing fungal variation better predicts phenotypic responses than host-microbiome origin matching.

The paper introduces a fast and accurate barcode calling method using k-mer filtering, achieving higher accuracy and speed than existing methods.

This study shows that gene expression patterns in flowering plants evolve rapidly, with divergence rates exceeding those in mammals, and highlights differences in conservation across organs.

The paper evaluates cross-platform DNA motif discovery tools using 4,237 experiments to identify reliable transcription factor binding motifs and highlights challenges in motif detection.

The paper systematically determines the sequence specificity of 332 poorly characterized human transcription factors, identifying over 4,000 experiments and revealing tens of thousands of new TF binding sites.

The paper re-annotates seven plant genomes using RNA-Seq data to identify new lncRNAs and circRNAs, providing a reference for studying plant organ evolution and transcript patterns.

kASA is a k-mer-based tool for metagenomic taxonomic analysis that efficiently uses memory and provides high sensitivity and precision on standard notebooks.

epiGBS2 improves upon epiGBS by offering a more flexible, cost-effective, and user-friendly protocol and bioinformatics pipeline for methylation and genetic analysis.