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.

Our work is currently being supported by NIH grants R01GM124531 and R01GM129085.

We have openings for doctoral and postdoctoral positions.

Recent News

  • 8/2021 - Our paper on Alubaster and Alu exonization events in frontal cortex is accepted in Frontiers Molecular Biosciences (with Kathy Burns, Lindsay Payer, Corina Antonescu and Guangyu Yang)
  • 5/2021 - Jutils paper on alternative splicing visualization tools is accepted in Bioinformatics (with Guangyu Yang, Leslie Cope and Zitong He)
  • 3/2021 - Zoom talk at University of Texas Southwestern, Department of Bioinformatics, Dallas, TX
  • 12/2020 - Zoom talk at the California State University - Fullerton, Department of Mathematics, Fullerton, CA 
  • 12/2020 - Zoom talk and interview at OxfordGlobal’s NextGen Sequencing & Omics USA Congress on simultaneous multi-sample approaches
  • 7/2020 - Zoom talk at the SPLICING2020 conference on our most recent differential splicing detection tool MntJULiP (with Guangyu Yang)
  • 5/2020 - Posters on detection of Alu RNA insertions and differential splicing from RNA-seq data, at the CSHL's (Virtual) Biology of Genomes Meeting (with Guangyu YangLindsay Payer and Kathy Burns)
  • 11/2019 - Paper describing our simultaneous multi-sample transcript assembler PsiCLASS was published in Nature Communications (with Li Song, Sarven Sabunciyan and Guangyu Yang)
  • 9/2019 - We received an NIH R01 grant to design tools for analyzing massive collections of RNA-seq data

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