Summary of Gerstein Lab Research in 2018

During 2018 the Gerstein lab was involved in numerous research projects in biomedical data science, in particular involving human genomics, next-generation sequencing, genomic privacy, and text mining.

Core Publications – PsychENCODE

A large part of the lab’s research is focused on disease genomics and more specifically in differential expression and function of genes in brain diseases. The lab is a member of a large consortium (PsychENCODE) which resulted in leading a major analysis project. Our analysis uncovered genomic elements in the brain concerning psychiatric disorders using a deep-learning model (Wang et al., 2018). The analysis also developed lists of brain-specific enhancers, eQTLs, and regulatory networks.

Other Core Publications

Additionally, we developed PrivaSig, a tool that identifies information leakage from functional genomics signal profiles (Harmanci & Gerstein, 2018). Genomic privacy, leakage detection and anonymization of RNA-seq profiles are very important in the era of human genomics and next-generation sequencing. Finally, also in the field of human genomics, we have developed a catalog of predicted functional upstream open reading frames in humans (uORFs) (McGillivray et al., 2018). Latent uORFs in mRNA transcripts can modify the translation of coding sequences by altering ribosome activity. By building a simple Bayesian classifier using 89 attributes of uORFs we were able to extrapolate to a comprehensive catalog of likely functional uORFs.

Book reviews, opinions, and commentary

In 2018, the Gerstein lab participated in the scientific public discourse through book reviews, opinion articles, and commentaries. Published in Science, Dov Greenbaum and Mark Gerstein reviewed “21 Lessons for the 21st Century” by Yuval Noah Harari (Greenbaum & Gerstein, 2018). In Cell, Dov Greenbaum and Mark Gerstein reviewed “Who We Are and How We Got Here: Ancient DNA and the New Science of the Human Past” by David Reich (Greenbaum & Gerstein, 2018). We also published the relationship between text mining and systems biology. In one example, by examining the frequencies of terms in systems biology publications, we can analyze the trends in research focus (Kong & Gerstein, 2018). Finally, we did a data-science oriented newspaper Op-Ed.