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Announcing the new Tapestri Solution for Solid Tumor Oncology Research. Learn More
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Amplicon design algorithm for single cell targeted DNA sequencing using machine learning


Shu Wang

High throughput single cell DNA targeted sequencing enables the detection of rare mutations in cells and the identification of subclones defined by co-occurrence of mutations. The big challenge with multiplex sequencing at the single cell level is the non-uniform amplification of targeted regions during PCR. This results in an inadequate coverage of mutations of interest in the panel and hence makes genotyping challenging. To address this challenge, a machine learning engine was developed to optimize amplicon design for uniform amplification by making reliable performance prediction.


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poster
Leveraging Single-cell DNA Sequencing for In-depth Characterization of Cell and Gene Therapies
Jacqueline Marin, Benjamin Schroeder, Shu Wang, Daniel Mendoza, Adam Sciambi, Brittany Enzmann
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Multimodal Analysis of DNA and Proteins in Single Cells
Prithvi Singh, Dalia Dhingra, Saurabh Parikh, Adam Sciambi, Aik Ooi
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Precise Measurement of Transduction Efficiency at Single-Cell Resolution for Cell and Gene Therapy Development
Khushali Patel
poster
Enabling single cell analysis of copy number variation in breast cancer
Jacqueline Marin
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