Jan Grau

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.

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.

The paper introduces GeMoRNA, a transcript reconstruction method that improves coding sequence prediction in RNA-seq data across multiple species.

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 designs effective dsRNAs from functionally characterized siRNAs to provide highly specific and potent protection against Cucumber Mosaic Virus in plants.

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 uses time-resolved RNA-seq data to study the dynamic expression of TALE target genes during rice infection by Xanthomonas oryzae pathogens, identifying direct and secondary targets and non-coding transcripts.

The paper assembles repetitive TALomes in Xanthomonas using Oxford Nanopore sequencing and presents a correction pipeline to improve accuracy.

The paper improves TALE target prediction by integrating epigenetic features, showing that combining chromatin accessibility and DNA methylation data enhances prediction accuracy.

The paper introduces a TF motif analysis toolbox that integrates DNA methylation and intra-motif dependencies, finding that CpG methylation generally reduces TF binding likelihood.

PrediTALE improves TALE target prediction by incorporating biological insights and quantitative data, identifying novel virulence targets and highlighting limitations in current gene annotations.

The paper presents a method for predicting cell type-specific transcription factor binding sites using chromatin accessibility and binding motifs, achieving state-of-the-art performance through iterative training and ensemble prediction.

The paper extends the GeMoMa gene prediction tool to use RNA-seq data and conservation signals, showing improved performance over existing tools in plants, animals, and fungi.

The paper identifies expression divergence in the TIR1/AFB-AUX/IAA-ARF auxin signaling module between Arabidopsis species, suggesting it contributes to natural variation in auxin responses.