Christian Beisel

This study identifies lineage-specific transcriptional network rewiring in Mtb by analyzing proteomic and transcriptomic data from L1 and L2 strains, revealing key regulators like Rv1985c that drive phenotypic differences.

This study shows that ivermectin and moxidectin exposure leads to genomic and transcriptomic changes in bacteria, including ribosome modulation and resistance gene adjustments.

The paper uses hybrid sequencing to analyze the off-target effects of antiparasitic drugs on the human gut microbiome, finding that ivermectin-albendazole significantly alters microbial composition and function.

The paper characterizes repeat-associated mutations in Mycobacterium tuberculosis genomes using long-read sequencing, revealing their significant contribution to genetic variation in an outbreak.

DemoTape is a computational method for demultiplexing targeted single-cell DNA sequencing data, showing improved performance over existing methods for RNA sequencing data.

The paper investigates the role of the protooncogene Ski in regulating the neuron-glia switch during mammalian cerebral cortex development and identifies its interaction with the BMP signaling pathway.

The paper maps transcriptional dynamics in murine cortical development, identifying lineage shifts and gene regulatory networks in neural stem cells, basal progenitors, and newborn neurons.

scROSHI identifies cell types without training data by using pre-existing gene sets and hierarchical relationships, outperforming methods in low-data and high-diversity scenarios.

gExcite is a start-to-end framework for analyzing single-cell gene expression and CITE-seq data, including hashing deconvolution, and is scalable for multiple samples.

The paper analyzes genomes of Mycobacterium africanum strains to identify a new lineage and explore their evolutionary history and phylogeography.

The paper analyzes whole genome sequences of Mycobacterium tuberculosis lineages L1 and L3 to show South Asia's role in their dispersion and identify local adaptation at the esxH locus.

The paper investigates how genomic alterations affect molecular networks in prostate cancer by integrating genomic, transcriptomic, and proteomic data from 105 samples, identifying a sub-network predictive of tumor aggressiveness and recurrence.

The paper examines SARS-CoV-2 genetic diversity within individual patients, finding that a small number of patients carry most of the diversity and that older patients tend to have more diverse viral variants.

SCIM matches single-cell measurements across technologies by learning a technology-invariant latent space and applying bipartite matching, achieving high accuracy in real-world datasets.

The paper introduces DNA-based phenotypic recording to generate large-scale sequence-function datasets and uses deep learning to predict gene regulatory element function with high accuracy.

The paper investigates the fitness cost of rifampicin resistance in Mycobacterium tuberculosis using transcriptomic and proteomic profiling, finding that aberrant gene expression causes the cost and that this burden can be suppressed.

The paper compares the genomes of Mycobacterium africanum Lineage 5 and 6 from Ghana, finding genomic diversity and selection pressure differences suggesting distinct ecological niches.

Treemmer reduces large phylogenetic datasets by removing redundant leaves, preserving diversity while simplifying the dataset.

The paper uses single-cell sequencing and mathematical modeling to estimate miRNA target parameters and uncover their in vivo hierarchy.