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Announcing the new Tapestri Solution for Solid Tumor Oncology Research. Learn More
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Improvements in variant calling sensitivity and specificity in single-cell DNA sequencing using deep learning


Manimozhi Manivannan

Through single-cell sequencing technologies, it is now possible to interrogate thousands of cells in a single experiment for genetic variability. Single-cell DNA platforms like Tapestri are still susceptible to errors from polymerase incorporations, structure-induced template switching, PCR mediated recombination in the workflow, or DNA-damage. Errors from sequencing could propagate from cluster amplification, cycle sequencing, or image analysis. Altogether, these errors can be divided into substitutions, insertions, and deletion errors, which range from 0.5% to 2%, depending on the sequencer. This makes rare variant and minimal residual disease detection challenging. To address these challenges, deep learning models have been developed to correct the errors, reduce false-positive rates, and predict true variants.


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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
poster
Multimodal Analysis of DNA and Proteins in Single Cells
Prithvi Singh, Dalia Dhingra, Saurabh Parikh, Adam Sciambi, Aik Ooi
poster
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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