| Literature DB >> 34146472 |
Wuming Gong1, Alejandro A Granados2, Jingyuan Hu3, Matthew G Jones4, Ofir Raz5, Irepan Salvador-Martínez6, Hanrui Zhang7, Ke-Huan K Chow2, Il-Youp Kwak8, Renata Retkute9, Alidivinas Prusokas10, Augustinas Prusokas11, Alex Khodaverdian12, Richard Zhang12, Suhas Rao12, Robert Wang12, Phil Rennert13, Vangala G Saipradeep14, Naveen Sivadasan14, Aditya Rao14, Thomas Joseph14, Rajgopal Srinivasan14, Jiajie Peng15, Lu Han15, Xuequn Shang15, Daniel J Garry1, Thomas Yu16, Verena Chung16, Michael Mason16, Zhandong Liu3, Yuanfang Guan7, Nir Yosef12, Jay Shendure17, Maximilian J Telford6, Ehud Shapiro5, Michael B Elowitz2, Pablo Meyer18.
Abstract
The recent advent of CRISPR and other molecular tools enabled the reconstruction of cell lineages based on induced DNA mutations and promises to solve the ones of more complex organisms. To date, no lineage reconstruction algorithms have been rigorously examined for their performance and robustness across dataset types and number of cells. To benchmark such methods, we decided to organize a DREAM challenge using in vitro experimental intMEMOIR recordings and in silico data for a C. elegans lineage tree of about 1,000 cells and a Mus musculus tree of 10,000 cells. Some of the 22 approaches submitted had excellent performance, but structural features of the trees prevented optimal reconstructions. Using smaller sub-trees as training sets proved to be a good approach for tuning algorithms to reconstruct larger trees. The simulation and reconstruction methods here generated delineate a potential way forward for solving larger cell lineage trees such as in mouse.Entities:
Keywords: C. elegans; CRISPR; M. musculus; benchmarking; cell lineage tracing; crowdsourcing; intmemoir; lineage reconstruction; machine learning; simulation
Mesh:
Year: 2021 PMID: 34146472 DOI: 10.1016/j.cels.2021.05.008
Source DB: PubMed Journal: Cell Syst ISSN: 2405-4712 Impact factor: 10.304