Lucy Skrabanek, Ph.D.

Assistant Professor of Computational Biomedicine in Physiology and Biophysics

  • Assistant Professor of Computational Biomedicine in the Institute for Computational Biomedicine


1300 York Avenue, Room LC-501 E
New York, NY 10065

Research Areas

Research Summary:

Rigor and reproducibility

Rigor and reproducibility is an important component of the scientific process. Scientific rigor is the strict application of the scientific method to ensure robust and unbiased experimental design, methodology, analysis, interpretation and reporting of results. Reproducibility is the ability to recompute data analysis results, given an observed dataset and knowledge of the data analysis pipeline, and includes full transparency in reporting experimental details so that others may reproduce and extend the findings. Reproducibility is evermore critical as high-throughput methods that involve larger and noisier datasets, and where statistical inferences are essential and methods involve many analysis steps in complex computational pipelines, pervade more and more diverse fields of study. I am interested in expanding data analysis education and the routine use of software tools that support data lifecycle management (collection, storage, analysis, dissemination, archiving, and mining) activities that emphasize rigor and reproducibility in experimental design, data collection, analysis and interpretation.

Ethics and privacy in the genomic era

The era of genomic data poses unique ethical and privacy challenges. Genomic data can be used to develop more accurate diagnoses, more rational disease prevention strategies, better treatment selection, and the development of novel therapies. But, as genetic sequencing becomes more commonplace, we need to appreciate the consequences of collecting and storing massive amounts of genetic data, understand how routine genetic screening can impact privacy, and be mindful of the ethics of genetic engineering. What are the benefits, what are the possible risks or harms, and what are the consequences? Our history is replete with examples of unanticipated and undesired consequences. We have an opportunity to think about how genomic information can be used, for ill as well as for good, and to craft principles before it’s too late.

Role of eIF4E in controlling cell growth and proliferation

The eukaryotic translation initiation factor eIF4E is a critical modulator of cellular growth, with functions in the nucleus and cytoplasm. In collaboration with the lab of Dr Kathy Borden in Montreal, we have demonstrated that, in the nucleus, eIF4E associates with and promotes the export of a subset of cell cycle-associated mRNAs via a secondary structure element in the 3’UTR, which is necessary and sufficient for localization of capped mRNAs to eIF4E nuclear bodies, formation of eIF4E-specific ribonucleoproteins in the nucleus, and eIF4E-dependent mRNA export. These findings demonstrate a novel level of regulation of cellular proliferation and provide novel perspectives into the proliferative and oncogenic properties of eIF4E. We are further examining the biochemical and cellular underpinnings of the mRNA export activity of eIF4E and characterizing the relevant regulatory mechanisms keeping this eIF4E activity in check, by identifying those RNAs that undergo efficient eIF4E dependent capping and by characterizing the sequence and/or structural elements in the UTR that endow transcripts with eIF4E dependent capping sensitivity.


Workshops: In 2004, I founded the Tri-Institutional Biomedical Computing Workshop series which offers intensive computational hands-on workshops to faculty, staff and students at Weill Cornell Medical College, Memorial Sloan Kettering Cancer Center and the Rockefeller University ( I developed and continue to teach many of the workshops offered, including Sequence Analysis and Unix.
I have also created a series of computational workshops by the Applied Bioinformatics Core which are designed to foster rigor and reproducibility in scientific computing, offered to the WCM community, and which cover topics such as use of the R language, and RNA-seq data analysis.

Graduate courses: I have directed and taught several courses focusing on bioinformatics and computational methods in biology, including Essentials of Bioinformatics, Scientific Computing in Biomedicine, and Quantitative Understanding in Biology course (qBio I), and participate in several other graduate courses (e.g., cPBSB and Next Gen Methods).

Other teaching activities: I contribute lectures or modules to the CTSC Education Program, Health IT Masters in Informatics and the MD-PhD Pre-Frontiers sessions.

Online materials: With the Applied Bioinformatics Core, we have developed publicly available online self-paced learning materials for R and GitLab (;


Recent Publications:

  1. Oren, DA, Wei, Y, Skrabanek, L, Chow, BK, Mommsen, T, Mojsov, S et al.. Structural Mapping and Functional Characterization of Zebrafish Class B G-Protein Coupled Receptor (GPCR) with Dual Ligand Selectivity towards GLP-1 and Glucagon. PLoS One. 2016;11 (12):e0167718. doi: 10.1371/journal.pone.0167718. PubMed PMID:27930690 PubMed Central PMC5145181.
  2. Ban, Y, Tozaki, T, Taniyama, M, Skrabanek, L, Nakano, Y, Ban, Y et al.. Multiple SNPs in intron 41 of thyroglobulin gene are associated with autoimmune thyroid disease in the Japanese population. PLoS One. 2012;7 (5):e37501. doi: 10.1371/journal.pone.0037501. PubMed PMID:22662162 PubMed Central PMC3360768.
  3. Stefan, M, Jacobson, EM, Huber, AK, Greenberg, DA, Li, CW, Skrabanek, L et al.. Novel variant of thyroglobulin promoter triggers thyroid autoimmunity through an epigenetic interferon alpha-modulated mechanism. J Biol Chem. 2011;286 (36):31168-79. doi: 10.1074/jbc.M111.247510. PubMed PMID:21757724 PubMed Central PMC3173071.
  4. Jacobson, EM, Yang, H, Menconi, F, Wang, R, Osman, R, Skrabanek, L et al.. Employing a recombinant HLA-DR3 expression system to dissect major histocompatibility complex II-thyroglobulin peptide dynamism: a genetic, biochemical, and reverse immunological perspective. J Biol Chem. 2009;284 (49):34231-43. doi: 10.1074/jbc.M109.041574. PubMed PMID:19776016 PubMed Central PMC2797193.
Search PubMed

Selected Publications:

  1. Blood transcriptomic markers of Mycobacterium tuberculosis load in sputum. Dupnik KM, Bean JM, Lee MH, Jean Juste MA, Skrabanek L, Rivera V, Vorkas CK, Pape JW, Fitzgerald DW, Glickman M. Int J Tuberc Lung Dis. 2018  22(8):950-958. PMID: 29991407
  2. Features of Circulating Parainfluenza Virus Required for Growth in Human Airway. Palermo LM, Uppal M, Skrabanek L, Zumbo P, Germer S, Toussaint NC, Rima BK, Huey D, Niewiesk S, Porotto M, Moscona A. MBio. 2016 7(2):e00235. PMID: 26980833
  3. Multiple SNPs in intron 41 of thyroglobulin gene are associated with autoimmune thyroid disease in the Japanese population. Ban Y, Tozaki T, Taniyama M, Skrabanek L, Nakano Y, Ban Y, Hirano T. PLoS One. 2012;7(5):e37501. PMID: 22662162
  4. Stability of eukaryotic translation initiation factor 4E mRNA is regulated by HuR, and this activity is dysregulated in cancer. Topisirovic I, Siddiqui N, Orolicki S, Skrabanek LA, Tremblay M, Hoang T, Borden KL. Mol Cell Biol. 2009 29(5):1152-62. PMID: 19114552
  5. Scan2S: increasing the precision of PROSITE pattern motifs using secondary structure constraints. Skrabanek L, Niv MY. Proteins. 2008 72(4):1138-47. PMID: 18320586
  6. Computational prediction of protein-protein interactions. Skrabanek L, Saini HK, Bader GD, Enright AJ. Mol Biotechnol. 2008 38(1):1-17. PMID: 18095187
  7. eIF4E is a central node of an RNA regulon that governs cellular proliferation. Culjkovic B, Topisirovic I, Skrabanek L, Ruiz-Gutierrez M, Borden KL. J Cell Biol. 2006 175(3):415-26. PMID: 17074885