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Research in the Salomonis lab focuses on the following areas of interest:
The advent of new innovative technologies for single-cell genomics provides nearly limitless opportunities for exploring tissue cellular variation at single-molecule resolution. We develop new computational approaches for single-cell RNA profiling to discover hidden heterogeneity within presumed homogenous populations, novel intermediates and developmental trajectories. Such analyses extend to the identification and removal of technical artifacts (multiplets) and alignment of cell-populations across experiments to obtain insights into disease mechanisms (cellHarmony).
Image: cell transitions during stem cell specification to diverse lineages.
Alternative splicing is a central driver of molecular diversity in complex organisms. Through the production of distinct mRNAs and proteins, the cell is able to increase the ways in which biological processes can be regulated. However, in many common and rare diseases, typical gene splicing is disrupted, resulting in aberrant gene products that can negatively impact cell structure, function, viability or communication. We study the interplay between gene splicing, transcription, and epigenetics to understand pathological states in which global gene splicing is altered, impacted cellular products that contribute to disease and possible avenues for therapeutic intervention.
Image: proteomic impact of alternative splicing in the gene CASP9.
The analysis of large genomics datasets is becoming ever more complex, resulting in a disconnect between experimental biologists and their own data. Our software developers aim to empower non-computational biologists to analyze and interpret their own data by creating intuitive analytical toolkits. To do this, we develop bioinformatics software that has multiple interfaces, including user-friendly graphical interfaces for non-computational users and well-documented command-line interfaces for computational analysts. Our group develops a broad range of applications for this purpose, including those for pathway and gene-set enrichment and visualization (GO-Elite), biological network analysis (NetPerspective), raw RNA-sequencing analysis, data visualization (AltAnalyze) and more.
Image: design schema for ongoing development of AltAnalyze.
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