Silvana Konermann

The paper uses a genomic language model to cluster and annotate noncoding sequences in prokaryotic genomes, identifying structured RNAs and their potential functions.

This study shows that extracellular cGAMP worsens neuroinflammation in aging and MS by activating STING in microglia, astrocytes, and inhibitory neurons, with ENPP1 acting as a protective brake.

State, a set-based transformer trained on over 100 million perturbed cells, predicts perturbation responses in new cellular contexts better than mean, linear and deep learning baselines.

The paper introduces COMBINE, a platform that screens epigenetic effector domain pairs to identify synergistic and antagonistic interactions, revealing new mechanisms for transcriptional regulation.

ProPer-seq is a cost-effective, barcode-free single-cell perturbation profiling method that enables scalable analysis of synthetic biology perturbations and reveals transcriptional control principles.

This study engineers recombinases to improve DNA insertion efficiency and specificity in human cells, achieving 53% integration and 97% genome-wide specificity.

This paper describes the engineering of IS622 bridge recombinases to enable efficient and specific DNA rearrangements in the human genome, including insertion, inversion, and excision of up to 0.93 megabases.

scBaseCount is a single-cell RNA sequencing database that automates metadata extraction and data processing using AI, offering a large, standardized collection of gene expression data across multiple organisms and tissues.

This paper introduces RESPLICE, a CRISPR-based trans-splicing system that efficiently edits endogenous human transcripts, achieving up to 90% trans-splicing efficiency in sorted cells.

The paper demonstrates that IS110 insertion sequences use a structured RNA to direct sequence-specific DNA recombination, enabling modular genome editing.

The paper uses CRISPR screens to identify mucins as key host factors that restrict SARS-CoV-2 infection in human lung cells.

Portal integrates large-scale single-cell datasets efficiently and accurately by preserving biological variation across samples, platforms, and species.

CloneRetriever identifies and retrieves rare clones from heterogeneous populations to study clonal fitness and selection mechanisms.