Solving the mysteries of bioscience
Foundational Science Fuels Breakthroughs
Inspiring Next-Generation Scientists
The team develops computational pipelines and methods to analyze sequencing data, characterize transgenic mice, and characterize lineage relationships and biological signaling between cells across various tissues
The Seattle Hub for Synthetic Biology is a collaboration between Allen Institute, Chan Zuckerberg Initiative and the University of Washington.
Goals and Approach
The Read Team at the Seattle Hub for Synthetic Biology is working to advance the field of in vivo genomic recording by developing computational pipelines and methods to characterize lineage relationships and biological signaling between cells across various tissues. It aims to develop open, reproducible, and scalable bioinformatics workflows for the analysis of recording experiments. From these workflows and analyses, the team seeks to understand and characterize the mechanisms underlying prime editing mediated genomic recording to build the most informative and capable genomic recorder. Through this work, researchers hope to advance the field’s understanding in this area and unlock a new era of in vivo genomic recording.
Computational algorithms to analyze genomic recording next-generation sequencing data.
Developing open, reproducible, and scalable bioinformatics workflows for the data processing and analysis of prime editing mediated recording experiments in both mouse embryonic stem cell cultures and mouse models. The data going into the pipelines includes short and long read next-generation sequencing data, from both bulk and single-cell genomics experiments
From data generated in the lab, the Read Team tracks cell lineages and biological signaling events by reading out the identity and sequential order of DNA barcodes that mark these events. By integrating many synthetic DNA TAPE constructs into cells, the team has a large capacity to record cell lineage and biological signaling over many generations of cell divisions. The datasets generated will enable the development of novel computational approaches towards whole organism lineage tracing
