Liliana Florea's Research Group @ Johns Hopkins University

About us 

Our group is developing computational methods to model and solve problems in biology and medicine, fueled by the wealth of sequence data generated by new and evolving sequencing technologies. We design accurate and efficient algorithms and software tools to tackle current problems in genome assembly, gene and alternative splicing annotation, microRNA genomics and host-pathogen interaction, and apply them to characterize the genetic and genomic landscape of different cellular states and different organisms.

Find out more about our methods at our GitHub Splicebox repository and from our Software pages.

Our work is currently being supported by NIH grant R01GM129085.

Recent News

  • 1/2024 - Manuscript describing our new tool eXAlu, for predicting exonization of Alu elements, is on bioRxiv
  • 1/2024 - Manuscript describing MntJULiP and Jutils, our tools for differential splicing detection in the presence of covariates, is on bioRxiv 
  • 11/2023 - Poster on the MntJULiP and Jutils tools for discovery of differential splicing events in complex data with covariates at ASHG 2023 (with Edward Wui Wang Lui and Guangyu Yang)
  • 10/2023 - Talk on our decades-long work on splice graph-based transcript reconstruction at the SorinFest: Phase Transitions conference honoring Sorin Istrail, at Brown University (YouTube)
  • 7/2023 - Talk on differential splicing analysis of human data with MntJULiP at SPLICING2023, Caparica, Portugal
  • 7/2023 - Collaborative paper on the transcriptomics of the interaction of B. burgdorferi and human dendritic cells (Mark Soloski's lab)
  • 5/2023 - Talk on our Deep Learning model of Alu exonization at WMESA2023, Penn State University
  • 4/2023 - Collaborative paper on a mouse model of Down syndrome (Roger Reeves' lab)
  • 9/2022 - Paper describing our differential splicing detection tool MntJULiP appeared in Genome Biology (with Guangyu Yang and Sarven Sabunciyan)
  • 5/2022 - Poster on Alu exonization prediction using a Deep Learning model, at the CSHL's Biology of Genomes Meeting (with Zitong He)
  • 11/2021 - Poster on our Probabilistic Deep Learning (PDL) model of alternative splicing, at the CSHL's (Virtual) Genome Informatics Meeting (with Guangyu Yang and Zitong He)

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