Bo Li - Principal Scientist, Cellular and Tissue Genomics, Computational Sciences

Bo Li

Principal Scientist, Cellular and Tissue Genomics, Computational Sciences

Postdoc Mentor
Years at Genentech
Awards & Honors

I joined Genentech in August 2021 as a Senior Scientist and then as a Principal Scientist in 2022. My research focuses on large-scale single-cell multi-omics and spatial omics data analysis and tool development. Before joining Genentech, I was an Assistant Professor of Medicine (tenure-track) at Harvard Medical School, an Assistant Investigator & Director of Bioinformatics and Computational Biology at the Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, and an Associate Member of the Broad Institute of MIT and Harvard. My representative works include a) RSEM, a popular RNA-Seq transcript quantification tool that is cited over 15,000 times (Google Scholar) and adopted by big consortia such as TCGA and ENCODE; b) Cumulus, the first comprehensive cloud-based single-cell RNA-Seq data analysis framework. I completed two postdoctoral trainings with Dr. Lior Pachter at UC Berkeley and Dr. Aviv Regev at the Broad Institute.

Postdoctoral Mentor

It takes me over 10 years to transit from a hardcore computer scientist, whose main focus is novel tool development to a computational biologist, who appreciate both data-driven science discovery and innovative tool development. The two postdoc trainings I received from Drs. Lior Pachter and Aviv Regev are essential for making this transition happen. As a Postdoc mentor, I hope to help my trainees to advance their scientific career, just like how my postdoc advisers helped me before. Collaborating with colleagues from Cellular and Tissue Genomics, AI/ML and the Translational Genomics program, my trainees will have opportunities to work on exciting translational research by applying cutting edge single-cell multiomics & spatial omics technologies and the state-of-the-art deep learning algorithms.

Featured Publication

Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq

Nat. Methods 2020; 17:793-798

Li B†, Gould J, Yang Y, Sarkizova S, Tabaka M, Ashenberg O, Rosen Y, Slyper M, Kowalczyk MS, Villani AC, Tickle T, Hacohen N, Rozenblatt-Rosen O†, Regev A†.

COVID-19 tissue atlases reveal SARS-CoV-2 pathology and cellular targets

Nature 2021; 595(7865):107-113

Delorey TM*, Ziegler CGK*, Heimberg G*, Normand R*, Yang Y*, Segerstolpe A*, Abbondanza D*, Fleming SJ*, Subramanian A*, Montoro DT*, Jagadeesh KA*, Dey KK*, Sen P*, Slyper M*, Pita-Juárez YH*, Phillips D*, Biermann J*, Bloom-Ackermann Z, Barkas N, Ganna A, Gomez J, Melms JC, Katsyv I, Normandin E, Naderi P, Popov YV, Raju SS, Niezen S, Tsai LTY, Siddle KJ, Sud M, Tran VM, Vellarikkal SK, Wang Y, Amir-Zilberstein L, Atri DS, Beechem J, Brook OR, Chen J, Divakar P, Dorceus P, Engreitz JM, Essene A, Fitzgerald DM, Fropf R, Gazal S, Gould J, Grzyb J, Harvey T, Hecht J, Hether T, Jané-Valbuena J, Leney-Greene M, Ma H, McCabe C, McLoughlin DE, Miller EM, Muus C, Niemi M, Padera R, Pan L, Pant D, Pe'er C, Pfiffner-Borges J, Pinto CJ, Plaisted J, Reeves J, Ross M, Rudy M, Rueckert EH, Siciliano M, Sturm A, Todres E, Waghray A, Warren S, Zhang S, Zollinger DR, Cosimi L, Gupta RM, Hacohen N, Hibshoosh H, Hide W, Price AL, Rajagopal J, Tata PR, Riedel S, Szabo G, Tickle TL, Ellinor PT^, Hung D^, Sabeti PC^, Novak R^, Rogers R^, Ingber DE^, Jiang ZG^, Juric D^, Babadi M^, Farhi SL^, Izar B^, Stone JR^, Vlachos IS^†, Solomon IH^, Ashenberg O^, Porter CBM^, Li B^, Shalek AK^†, Villani AC^†, Rozenblatt-Rosen O^†, Regev A^†

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

BMC Bioinform. 2011; 12:323. Citation:14,273 (Google Scholar)

Li B, Dewey CN.

^ co-senior authors

† co-corresponding authors