Gerstein lab research
During 2021 our research highlights included findings in genomic privacy, transcriptional regulation, disease genomics and wearable technology. The lab also wrote a comment on quantum computing for biological sciences. Other core publications focused on cancer genomics and a genome browsing tool. Our works were published in journals including Cell Systems, Nature Methods, and others.
Core Publications Highlights
Last year's most significant accomplishment related to preserving genomic privacy, against the reduction of anonymization that has come with next-generation sequencing (1 & 8, see below). We developed a method for genotype imputation with encrypted inputs and outputs, enabling it to confidentially occur in cloud spaces with suboptimal security (1).
Our work in transcriptional regulation also took an important step by developing a deep-learning method to discover and demarcate regulatory regions of the genome (i.e., enhancers) (2), improving the precision of demarcation from our past work, which applied supervised learning to this task. In addition, we developed a novel method to simulate a single cell assay for studying regulatory regions (scATAC-seq), an important step toward providing a gold standard baseline against which the efficacy of real-world scATAC-seq data analysis approaches can be compared (3). Our newest interest is biosensor signal analysis to improve the precision of phenotyping (4, 5 & 12). Our first contribution is Bayesian modeling of data from multiple sensors worn concurrently to assess changes in lifestyles and public policies (4). This interest immersed us in global concerns about wearable sensor quality assurance, data standardization, and privacy. We convened a panel of academic stakeholders to discuss these concerns (5). Proceedings supported the need for a networking hub connecting researchers and manufacturers to accomplish these goals. The new Sports Tech Research Network has taken our advice on board. Finally, we continued to publish many papers on cancer genomics (9, 10 & 11).
We complemented our accomplishments in genomic privacy research with a perspective article about privacy problems specific to the field of functional genomics, highlighting privacy threats and mitigation techniques at the respective steps of the data generation, sharing, analysis, and summarization (6). A newer interest we have begun exploring is quantum computing for its potential applications to biology. Therefore, we published a comment overviewing quantum computing for biologists and suggesting areas to apply it, including sequence analysis, genetics, functional genomics, and neuroimaging phenotyping (7).
Core Publication List (includes full references of citations above)
1. G. Gürsoy, et al.. Privacy-preserving genotype imputation with fully homomorphic encryption. Cell Syst (2021) https:/doi.org/10.1016/j.cels.2021.10.003
2. Z. Chen, et al. DECODE: a Deep-learning framework for Condensing enhancers and refining boundaries with large-scale functional assays. Bioinformatics (2021) https:/doi.org/10.1093/bioinformatics/btab283
3. Z. Chen, et al. SCAN-ATAC-Sim: a scalable and efficient method for simulating single-cell ATAC-seq data from bulk-tissue experiments. Bioinformatics (2021) https:/doi.org/10.1093/bioinformatics/btaa1039
4. J. Liu, et al. Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions. PLoS Comput Biol (2021) https:/doi.org/10.1371/journal.pcbi.1009303
5. G.I. Ash, et al. Establishing a Global Standard for Wearable Devices in Sport and Exercise Medicine: Perspectives from Academic and Industry Stakeholders. Sports Med (2021) https:/doi.org/10.1007/s40279-021-01543-5
6. G. Gürsoy G, et al. Functional genomics data: privacy risk assessment and technological mitigation. Nat Rev Genet (2021) https:/doi.org/10.1038/s41576-021-00428-7
7. P.S. Emani, et al. Quantum computing at the frontiers of biological sciences. Nat Methods (2021) https:/doi.org/10.1038/s41592-020-01004-3
8. G. Gürsoy, et al. Recovering genotypes and phenotypes using allele-specific genes. Genome Biol (2021) https:/doi.org/10.1186/s13059-021-02477-x
9. A.J. Armstrong, et al. Molecular medicine tumor board: whole-genome sequencing to inform on personalized medicine for a man with advanced prostate cancer. Prostate Cancer Prostatic Dis (2021) https:/doi.org/10.1038/s41391-021-00324-5
10. X. Li, et al. Whole-genome sequencing of phenotypically distinct inflammatory breast cancers reveals similar genomic alterations to non-inflammatory breast cancers. Genome Med (2021) https:/doi.org/10.1186/s13073-021-00879-x
11. H. Mohsen, et al. Network propagation-based prioritization of long tail genes in 17 cancer types. Genome Biol (2021) https:/doi.org/10.1186/s13059-021-02504-x
12. S. Lou, et al. Gene Tracer: A smart, interactive, voice-controlled Alexa skill for gene information retrieval and browsing, mutation annotation, and network visualization. Bioinformatics (2021) https:/doi.org/10.1093/bioinformatics/btab107
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