09-11, 09:15–09:30 (Europe/Berlin), Main conference room - MW 0350
DNA barcodes and their decoding are at the core of high-throughput molecular biology. Large-scale assays such as pooled CRISPR screens and their single-cell manifestations are pushing the boundaries of what is accurately feasible with current barcode decoding technology, as we ever demand the capability to identify more perturbations, more cells, more omics, more insights in the same assay. Despite the critical nature of the decoding task, few methods exist that permit accurate, flexible and versatile decoding in the more demanding of these applications. Current state-of-the-art is many tools for individual tasks, often with severe limitations in their applicability, and often with inflexible vendor-specific solutions. Here, we propose a principled information theory-based barcode decoding software solution that addresses a wide range of decoding tasks in a unified framework, including demanding applications. Our approach can handle flexible error models, including those encountered in long-read sequencing such as Nanopore, and supports decoding arbitrary and non-trivial constructions of multiple barcodes, linkers, UMIs, and logical combinations thereof. Finally, we present a graphical user interface for clear, user-friendly specification of each barcode decoding task.
DNA barcodes and their decoding are at the core of high-throughput molecular biology. Large-scale assays such as pooled CRISPR screens and their single-cell manifestations are pushing the boundaries of what is accurately feasible with current barcode decoding technology, as we ever demand the capability to identify more perturbations, more cells, more omics, more insights in the same assay. Despite the critical nature of the decoding task, few methods exist that permit accurate, flexible and versatile decoding in the more demanding of these applications. Current state-of-the-art is many tools for individual tasks, often with severe limitations in their applicability, and often with inflexible vendor-specific solutions. Here, we propose a principled information theory-based barcode decoding software solution that addresses a wide range of decoding tasks in a unified framework, including demanding applications. Our approach can handle flexible error models, including those encountered in long-read sequencing such as Nanopore, and supports decoding arbitrary and non-trivial constructions of multiple barcodes, linkers, UMIs, and logical combinations thereof. Finally, we present a graphical user interface for clear, user-friendly specification of each barcode decoding task.
No previous knowledge expected
John Hawkins is an AI Health Innovation Cluster Postdoctoral Fellow at the European Molecular Biology Laboratory (EMBL) working in the labs of Lars Steinmetz and Oliver Stegle. He earned his Ph.D. in Computational Science, Engineering, and Mathematics at The University of Texas at Austin in the labs of Ilya Finkelstein and Bill Press. His interests are in developing high accuracy DNA barcodes and decoding software, and applying this technology for the creation of higher throughput and more informative CRISPR perturbation screens.