Research
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.
Current Projects:
- Quantitative Information in Biological Systems
- Quantitative Understanding in Biology I (https://physiology.weill.cornell.edu/people/banfelder/qbio/)
- Analysis of Next-Generation Sequencing Data
- UNIX workshop (https://chagall.weill.cornell.edu/UNIXcourse/)
- R workshop (https://chagall.weill.cornell.edu/Rcourse/)
- RNA-seq workshop (https://chagall.weill.cornell.edu/RNASEQcourse/)
- Bioinformatics walk-in clinics (Tuesdays 1-2pm, LC-504)
Bio
Luce read Genetics at Trinity College Dublin, before getting her PhD from Trinity in 2000, when she studied whole genome duplication with Ken Wolfe in the Genetics Department. She joined the Institute of Computational Biology at Mount Sinai in 2001, and came to Weill Cornell in 2003.
Selected Publications:
The eukaryotic translation initiation factor eIF4E reprograms alternative splicing. Ghram M et al. EMBO J. 2023 42(7):e110496.
The eukaryotic translation initiation factor eIF4E elevates steady-state m7G capping of coding and noncoding transcripts. Culjkovic-Kraljacic B et al. Proc Natl Acad Sci U S A. 2020 117(43):26773-26783.
Structural Mapping and Functional Characterization of Zebrafish Class B G-Protein Coupled Receptor (GPCR) with Dual Ligand Selectivity towards GLP-1 and Glucagon. Oren DA et al. PLoS One. 2016 11(12):e0167718.
Features of Circulating Parainfluenza Virus Required for Growth in Human Airway. Palermo LM et al. mBio. 2016 7(2):e00235.
Multiple SNPs in intron 41 of thyroglobulin gene are associated with autoimmune thyroid disease in the Japanese population. Ban Y et al. PLoS One. 2012;7(5):e37501.