Initial proof-of-concept work suggests strong potential to further improve PacBio’s highly accurate HiFi Sequencing with Google deep learning technologies
PacBio, a leading developer of high-quality, highly accurate sequencing platforms, announced a research collaboration with Google. Under the terms of the collaboration, PacBio will explore the use of Google’s genomic analysis, machine learning and algorithm development tools to further improve PacBio’s already highly accurate variant calls for HiFi sequencing runs, unlocking more insights from PacBio sequencing data.
The collaboration builds on previous research from PacBio and Google. A recent publication on the work available on bioRxiv, Deep Consensus: Gap-Aware Sequence Transformers for Sequence Correction, yielded improvements in variant calling and suggested that Google’s DeepConsensus machine learning tool is capable of increasing the yield of 99.9 percent accurate HiFi reads by as much as another 27 percent per instrument run.
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“By collaborating with Google, PacBio will ultimately help our customers further improve the quality and output of HiFi data by reducing the number of sequencing passes needed to achieve high confidence in base accuracy,” said Christian Henry, President and CEO of PacBio. “These improvements in accuracy and data analysis have the potential to enable more customers to experience the benefits of long-read sequencing as part of their research and translational projects, ultimately making a positive impact on implementing genomics in precision health.”
PacBio hopes to improve the utility and overall value of HiFi data specifically for applications such as whole genome sequencing (WGS), full-length isoform, and targeted sequencing applications by integrating Google’s deep learning technology into its future product releases.
“Collaborating with PacBio presents the opportunity to help unlock more of the value in highly accurate genomic data, which will enable the research community to further improve their understanding of biology,” said Andrew Carroll, Genomics Product Lead for Health AI at Google. “This is a strong step forward in working with the genomics community to maximize the value of genomic data.”
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