Advanced Molecular Analysis (AMA)
"We will profile and integrate personal multi-omics data (such as genomics, transcriptomics, epigenomics, etc.) to study human disease at single cell resolution."
-Feng Yue, PhD, Director, Center for Advanced Molecular Analysis
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Many human diseases are associated with certain genetic signatures, either inherited from parents or acquired after birth due to environment or random events. Our goal is to use modern genomic technologies to identify such genetic variants in patients, including single-point mutations and large-scale structural variants, and study how they contribute to aberrant gene regulation and human diseases. Our state-of-the-art repertoire includes single-cell RNA-Seq, snATAC-Seq, and scHi-C, as well as different long-reads sequencing such as Nanopore-seq and optical mapping.
Our center will develop both novel technologies and computational tools to perform genome-wide analysis of patient genomes. We will generate and integrate personal multi-omics data (such as genomics, transcriptomics and epigenomics) to study human disease at single cell resolution. By combining modern genomic approach and advanced machine learning techniques, we aim to identify disease-specific biomarkers and use them to predict disease risk and clinical outcomes.
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Primary Members:
Benjamin Singer
Eric Neilson
Fei Li Kuang
Feng Yue
Huiping Liu
Mazhar Adli
Richard Green
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Secondary Members:
Alexander Misharin
Barbara Stranger
Elizabeth McNally
Farzaneh Sorond
Fei Li Kuang
Kyubum Lee
Minoli Perera
Paul Reyfman
Raj Tuliani
Sabah Kadri
Sanjay Mehrotra
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We welcome any collaboration with the goal of integrating machine learning with genomics and use the knowledge to better understand human disease. We are particularly interested in translational genomics, where the outcome of the research is directly related with patient diagnosis, stratification, and targeted treatment.
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Using machine learning to study 3D genome organization: Recently, Dr. Yue’s group developed a supervised learning algorithm that can accurately detect chromatin interactions between essential genes and their distal regulatory elements, which frequently host disease-related genetic mutations. This random-forest based algorithm can lean unique patterns from multiple orthogonal platforms and can discover previously missed chromatin interactions. Further, this software can dramatically reduce sequencing depths by more than ten folds, and can still effectively capture the meaningful chromatin loops. This feature also means significantly reduced sequencing cost, which is one of the major bottlenecks for the study of 3D genome organization. As majority of the human diseases are related with genetic mutations in the non-protein-coding part of the genome, this work can potentially identify such elements and reveal target for personalized therapy. [Salameh et al. Nat. Comms. 2020]
Using long-reads sequencing and single techniques to study model animal system: The zebrafish has been widely used in the study of human disease, as ~70% of the protein-coding genes are conserved between the two species. However, the annotation of functional control elements in the zebrafish genome has been poor and there are still many errors in the current zebrafish genome assembly. In this work, we generated the most comprehensive map of transcriptomes, cis-regulatory elements, methylomes and 3D genome organization in zebrafish. We also performed single-cell ATAC-seq in zebrafish brain, which delineated 25 different clusters of cell types. By combining multipe long-read DNA sequencing techniques and Hi-C, we assembled the sex-determining chromosome 4 de novo. Overall, our work provides a great resource for the functional annotation of vertebrate genomes and the study of evolutionarily conserved elements. [Yang et al. Nature 2020]
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1. Hongbo Yang, Yu Luan, Tingting Liu, Hyung Joo Lee, Li Fang, Yanli Wang, Xiaotao Wang, Bo Zhang, Qiushi Jin, Khai Chung Ang, Xiaoyun Xing, Juan Wang, Jie Xu, Fan Song, Iyyanki Sriranga, Chachrit Khunsriraksakul, Tarik Salameh, Daofeng Li, Mayank N. K. Choudhary, Jacek Topczewski, Kai Wang, Glenn S. Gerhard, Ross C. Hardison, Ting Wang, Keith C. Cheng, Feng Yue. "A map of cis-regulatory elements and 3D genome structures in zebrafish." Nature 588, 337–343, 2020.
2. Tarik J Salameh, Xiaotao Wang, Fan Song, Bo Zhang, Sage M. Wright, Chachrit Khunsriraksakul, Feng Yue. "A supervised learning framework for chromatin loop detection in genome-wide contact maps." [Manuscript], [Download predicted loops]. Nature Communications 11:3428, 2020.
3. Jesse Dixon, Jie Xu, Vishnu Dileep, Ye Zhan, Fan Song, Victoria T. Le, Galip Gurkan Yardimci, Abhijit Chakraborty, Ferhat Ay, William Stafford Noble, Job Dekker, David M. Gilbert, Feng Yue. "Integrative Framework For Detecting Structural Variations In Cancer Genomes." Nature Genetics 50, 1388-1398, 2018.
4. Grant RA, Morales-Nebreda L, Markov NS, Swaminathan S, Querrey M, Guzman ER, Abbott DA, Donnelly HK, Donayre A, Goldberg IA, Klug ZM, Borkowski N, Lu Z, Kihshen H, Politanska Y, Sichizya L, Kang M, Shilatifard A, Qi C, Lomasney JW, Argento AC, Kruser JM, Malsin ES, Pickens CO, Smith SB, Walter JM, Pawlowski AE, Schneider D, Nannapaneni P, Abdala-Valencia H, Bharat A, Gottardi CJ, Budinger GRS, Misharin AV, Singer BD, Wunderink RG, NU SCRIPT Study Investigators. “Circuits between infected macrophages and T cells in SARS-CoV-2 pneumonia.” Nature. 2021 Feb; 590(7847): 635-641.
5. Wei X, Yang J, Adair SJ, Ozturk H, Kuscu C, Lee KY, Kane WJ, O'Hara PE, Liu D, Demirlenk YM, Habieb AH, Yilmaz E, Dutta A, Bauer TW, Adli M. “Targeted CRISPR screening identifies PRMT5 as synthetic lethality combinatorial target with gemcitabine in pancreatic cancer cells.” Proc. Natl. Acad. Sci. USA. 2020 Nov; 117(45): 28068-28079.
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Look out for next month's spotlight Center: Bioethics and Medical Humanities with Dr. Michelson!
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