Thomas Lengauer

The paper develops a web service called geno2pheno[bNAbs] that predicts HIV-1 resistance to broadly neutralizing antibodies using statistical models and compares its performance to other methods.

The paper introduces MAGAR, a framework to identify methylation quantitative trait loci (methQTLs) and distinguish common from cell-type-specific effects using DNA methylation and genotyping data.

This paper introduces a reference-free deconvolution protocol for DNA methylomes to identify hidden cell populations in complex samples, applied to lung cancer data from TCGA.

The paper compares DNase I-Seq, ATAC-Seq, and NOMe-Seq to identify assay-specific features and their impact on chromatin accessibility and gene expression prediction.

The paper integrates single cell RNA-seq with bulk epigenomic data to identify distinct regulatory states in bidirectional promoters and their associations with structural features and TF binding.

The paper identifies partially methylated domains (PMDs) as widespread epigenomic features across human and mouse cells, showing cell-type specificity and links to gene expression, replication timing, and proliferation.

The paper predicts tissue-specific gene expression across species using epigenome data from reference organisms.

This study compares the epigenomes of neutrophils and monocytes to identify regulatory elements and chromatin states, revealing differences in enhancer activity and DNA methylation.

This paper introduces TEPIC, a method that combines open-chromatin data with TF binding affinities to predict gene expression, achieving results comparable to TF ChIP-seq.