RNA Informatics
Listed below are the research projects that our researchers are involved in.
Alternative Splicing, Isoform, Algorithm
We are interested in computational analyses and methods concerning all aspects of alternative splicing such as transcriptome assembly and abundance quantification, the inference of isoform functions, subcellular localization and interactions, the usages of splice sites, the impact of mutations on splicing, etc.
Genome Biology and Chemical Genomics
The Girke lab focuses on fundamental research questions at the intersection of genome biology and chemical genomics. These include: Which factors in genomes, transcriptomes, proteomes and metabolomes are functionally relevant and perturbable by small molecules? What properties of small molecules and their targets are the main drivers for compound-target interactions? How can these insights be used to develop precision perturbation strategies for biological processes with translational applications in both agriculture and human health? To address these questions, the group develops computational methods for analyzing both large-scale omics and small molecule bioactivity data. This includes discovery-oriented projects, as well as algorithm and software development projects for data types from a variety of Big Data technologies, such as NGS, genome-wide profiling approaches and chemical genomics. As part of the multidisciplinary nature of my field, the group frequently collaborates with experimental scientists on data analysis projects of complex biological problems.
VISIT DR. THOMAS GIRKE'S PROFILE
Transcription Control, Epigenetics, Chromatin
The Lonardi lab is primarily interested in the analysis of sequencing data for a variety of molecular biology applications, e.g., de novo genome assembly, epigenetics, gene expression, 3D genome structure, genome editing, among others. In the RNA domain, the group has studied small RNAs in plants (doi:10.1186/1471-2199-9-6 and doi:10.1093/mp/sst051), gene expression in metastatic melanoma (doi:10.1073/pnas.0905139106), cancer type classification (doi:10.1007/978-3-030-00834-5_7), transcription control in the human malaria parasite (doi:10.1101/gr.101063.109 and doi:10.1186/1471-2164-15-347), discovery of transcription factor binding sites (doi:10.1186/1471-2164-12-601), chromatin structure from Hi-C data (doi:10.1093/bioinformatics/btz362, doi:10.1186/s13059-020-02167-0), detection of essential genes (doi:10.1186/s12859-020-03688-y), and genome editing (doi:10.1038/s41467-022-28540-0, doi:10.1016/j.ymben.2019.06.007)
VISIT DR. STEFANO LONARDI'S PROFILE
Statistical Genomics
The Vivian Li Lab is interested in statistical/computational modeling and algorithm development, as well as their applications to bulk-tissue and single-cell high-throughput sequencing data. Major research topics of our lab include (1) developing statistical and machine learning models to denoise, deconvolve, and compare genomics data, and (2) using statistical and computational models to advance knowledge in the regulation of gene expression and RNA processing.
Mathematical and Computational Biology
The Heyrim Cho lab is interested in developing mathematical models that describe the cell state dynamics of transcriptomics and computational methods that allow to efficiently simulate the models. The group has developed phenotype structured cell state models of hematopoiesis and acute myeloid leukemia progression using single-cell RNA sequencing data, and compared/contrasted continuum cell state models in distinct cell state geometries including graph and multi-dimensional space derived from single-cell sequencing data.
VISIT DR. HEYRIM CHO'S PROFILE
Exploring Biophysics Through Computations
The Palermo lab masters molecular dynamics and multiscale modeling, quantum mechanical methods and cryo-electron microscopy (cryo-EM) processing techniques. Through these methods, we perform large-scale simulations of proteins and nucleic acids, with interest in bimolecular allostery, conformational change, and catalysis.