We build open-source tools for curious minds. Check out our code, explore our projects, and maybe find something useful.
Turn deaminase-based measurements into interpretable chromatin signals.
A toolkit for processing, quality control, and downstream analysis of deaminase-based chromatin accessibility data. It quantifies editing events in aligned reads and produces genomic signal tracks for visualization and regulatory analysis.
Connect DNA sequence, chromatin accessibility, and gene expression in single cells.
A Python framework for modeling gene regulation from paired single-cell RNA-seq and ATAC-seq data. It combines transcription factor expression, peak accessibility, and sequence features to predict gene expression and investigate regulatory relationships.
Compare motif-associated chromatin accessibility across single cells.
A Python implementation of chromVAR for analyzing transcription factor motif-associated variability in single-cell ATAC-seq data. Built on AnnData and MuData, it connects regulatory activity analysis with Scanpy and Muon workflows.
Find candidate transcription factor binding motifs in DNA sequences.
A Python implementation of MOODS motif matching, accelerated with Numba. It scans DNA using position weight matrices and supports higher-order models and sequence variants, providing motif annotations for regulatory genomics workflows.
Estimate chromatin accessibility from sparse single-cell ATAC-seq data.
scOpen uses matrix factorization to recover accessibility estimates and compact representations of cells. These outputs support cell-state analysis and the study of relationships between regulatory regions.
Published 2021
Li Z., Kuppe C., et al. Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen. Nature Communications 12, 6386 (2021). DOI: 10.1038/s41467-021-26530-2.
Connect transcription factors, enhancers, and genes in single cells.
scMEGA integrates single-cell gene expression and chromatin accessibility to infer enhancer-based gene regulatory networks. It helps identify regulatory programs associated with differentiation and disease-related changes in cell state.
Published 2023
Li Z., Nagai J. S., Kuppe C., Kramann R., Costa I. G. scMEGA: single-cell multi-omic enhancer-based gene regulatory network inference. Bioinformatics Advances 3, vbad003 (2023). DOI: 10.1093/bioadv/vbad003.
Integrate genomic regions and signals to investigate gene regulation.
The Regulatory Genomics Toolbox brings together methods for ATAC-seq, DNase-seq, and ChIP-seq analysis. Footprinting, motif analysis, and visualization help connect epigenomic measurements to transcription factor activity and regulatory function.
Published 2023
Li Z., Kuo C.-C., et al. RGT: a toolbox for the integrative analysis of high throughput regulatory genomics data. BMC Bioinformatics 24, 79 (2023). DOI: 10.1186/s12859-023-05184-5.
