M
Mandana Arbab
Researcher at Broad Institute
Publications - 14
Citations - 979
Mandana Arbab is an academic researcher from Broad Institute. The author has contributed to research in topics: Cas9 & Genome editing. The author has an hindex of 8, co-authored 11 publications receiving 530 citations. Previous affiliations of Mandana Arbab include Brigham and Women's Hospital & Utrecht University.
Papers
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Journal ArticleDOI
Predictable and precise template-free CRISPR editing of pathogenic variants
Max W. Shen,Mandana Arbab,Jonathan Y. Hsu,Daniel Worstell,Sannie J. Culbertson,Olga Krabbe,Olga Krabbe,Christopher A. Cassa,Christopher A. Cassa,David R. Liu,David R. Liu,David R. Liu,David K. Gifford,Richard I. Sherwood,Richard I. Sherwood +14 more
TL;DR: This study establishes an approach for precise, template-free genome editing using a machine-learning algorithm to predict the spectrum of CRISPR–Cas9-nuclease-mediated DNA repair outcomes at human genomic target sites.
Journal ArticleDOI
Continuous evolution of SpCas9 variants compatible with non-G PAMs.
Shannon M. Miller,Tina Wang,Tina Wang,Tina Wang,Peyton B. Randolph,Peyton B. Randolph,Peyton B. Randolph,Mandana Arbab,Mandana Arbab,Mandana Arbab,Max W. Shen,Tony P. Huang,Tony P. Huang,Tony P. Huang,Zaneta Matuszek,Gregory A. Newby,Gregory A. Newby,Gregory A. Newby,Holly A. Rees,Holly A. Rees,Holly A. Rees,David R. Liu,David R. Liu,David R. Liu +23 more
TL;DR: Three new SpCas9 variants that collectively recognize NRNH PAMs are reported that enable targeting of most NR PAM sequences and substantially reduce the fraction of genomic sites that are inaccessible by Cas9-based methods.
Journal ArticleDOI
Determinants of Base Editing Outcomes from Target Library Analysis and Machine Learning
Mandana Arbab,Mandana Arbab,Mandana Arbab,Max W. Shen,Beverly Mok,Beverly Mok,Beverly Mok,Christine D. Wilson,Christine D. Wilson,Christine D. Wilson,Żaneta Matuszek,Christopher A. Cassa,Christopher A. Cassa,David R. Liu,David R. Liu,David R. Liu +15 more
TL;DR: This work characterized sequence-activity relationships of cytosine and adenine base editors and used the resulting outcomes to train BE-Hive, a machine learning model that accurately predicts base editing genotypic outcomes and engineer novel CBE variants that modulate editing outcomes.
Journal ArticleDOI
Efficient C•G-to-G•C base editors developed using CRISPRi screens, target-library analysis, and machine learning.
Luke W. Koblan,Mandana Arbab,Mandana Arbab,Mandana Arbab,Max W. Shen,Jeffrey A. Hussmann,Andrew V. Anzalone,Andrew V. Anzalone,Andrew V. Anzalone,Jordan L. Doman,Jordan L. Doman,Jordan L. Doman,Gregory A. Newby,Gregory A. Newby,Gregory A. Newby,Dian Yang,Beverly Mok,Beverly Mok,Beverly Mok,Joseph M. Replogle,Albert Xu,Tyler A Sisley,Jonathan S. Weissman,Britt Adamson,David R. Liu,David R. Liu,David R. Liu +26 more
TL;DR: In this article, a suite of engineered C•G-to-G•C base editors (CGBEs) with machine learning models were used to enable efficient, high-purity C• G-toG-C base editing.
Journal ArticleDOI
Cloning-free CRISPR.
Mandana Arbab,Sharanya Srinivasan,Sharanya Srinivasan,Tatsunori Hashimoto,Niels Geijsen,Richard I. Sherwood +5 more
TL;DR: Self-cloning CRISPR/Cas9 technology substantially lowers the bar on mouse and human transgenesis and enables efficient generation of gene knockouts at approximately one-sixth the cost of plasmid-based sgRNA construction with only 2 hr of preparation for each targeted site.