Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
X
X
poster

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.


VIEW
poster
Single-Cell Multi-Omic Correlation of Single Nucleotide Variants, Copy Number Variation, and Surface Epitopes for Clonal Profiling of Myeloma
Adam Sciambi; Indira Krishnan; Ben Geller; Daniel Mendoza; Chenchen Yang; Charlie Murphy; Cedric Dos Santos; Vivek S. Chopra; Habib Hamidi; Michael Nixon; Yann Nouet; Todd Druley; Herve Avet-Loiseau
poster
Decoding the mosaicism of genome editing with single-cell multi-omics analysis
Saurabh Gulaiti
poster
Single-Cell Multi-Omic Correlation of Single Nucleotide Variants, Copy Number Variation, and Surface Epitopes for Clonal Profiling of Myeloma
Adam Sciambi, Cedric Dos Santos, Vivek S. Chopra, Habib Hamidi, Michael Nixon, Yann Nouet, Todd Druley, Herve Avet-Loiseau
poster
A Multiomic, Single-Cell Measurable Residual Disease (scMRD) Assay For Phasing DNA Mutations and Surface Immunophenotypes
Charlie Murphy
REQUEST QUOTE