Daniel E. Schäffer

BatchRefiner: fast, significant improvement in batch integration of single-cell embeddings with ensemble refinement

BatchRefiner improves batch integration of scRNA-seq and scATAC-seq embeddings by ensemble refinement, showing significant performance gains across datasets and metrics.

Fine-mapping candidate neuropsychiatric regulatory variants using cell type-aware comparative genomics

The paper introduces CTACIT to fine-map neuropsychiatric regulatory variants by integrating conservation scores with cell type-specific open chromatin data across species.

Vocal learning-associated convergent evolution in mammalian proteins and regulatory elements

The paper identifies proteins and regulatory elements associated with vocal learning by analyzing bat neural activity and epigenomic data.

Integrating gene annotation with orthology inference at scale

TOGA integrates gene annotation and orthology inference across hundreds of genomes, improving ortholog detection and creating large comparative gene resources.

Relating enhancer genetic variation across mammals to complex phenotypes using machine learning

A machine learning approach identifies enhancer-phenotype associations across mammals, revealing links to brain size and behavior evolution.

Insights into mammalian TE diversity via the curation of 248 mammalian genome assemblies

The paper curates TE content in 248 mammalian genomes, finding trends in recent TE accumulation and associations with dietary habits.

Evolutionary constraint and innovation across hundreds of placental mammals

A 240-species placental mammal alignment resolves constraint to single bases, estimating at least 10.7% of the human genome is constrained, with 48.5% of constrained bases unannotated.

Chiropterans are a hotspot for horizontal transfer of DNA transposons in Mammalia

The paper identifies bats as a hotspot for horizontal transfer of DNA transposons, finding over 200 putative elements and shared transposons across mammals and reptiles.

Characterizing the landscape of viral expression in cancer by deep learning

The paper develops viRNAtrap, a deep learning-based pipeline to identify and characterize viral expression in cancer, revealing new viruses and endogenous viruses linked to poor survival.

Inferring mammalian tissue-specific regulatory conservation by predicting tissue-specific differences in open chromatin

The paper introduces a machine learning method to predict tissue-specific regulatory conservation by analyzing open chromatin patterns across mammals, showing improved performance over nucleotide alignment-based approaches.

Functional innovation in the evolution of the calcium-dependent system of the eukaryotic endoplasmic reticulum

The paper investigates the evolution of the calcium-dependent system in eukaryotes, identifying its origins and key innovations across species.