For decades, researchers worked together to assemble a complete copy of the molecular instructions for a human — a map of the was finished in 2000, but with several missing pieces. Even when a complete , their work was not finished. A single reference genome can’t incorporate known genetic variations, such as the variants for the gene determining whether a person has a , making it less useful for detecting disease or finding cures for people from some backgrounds than others. For the past three years, we have been part of an international collaboration with 119 scientists across 60 institutions, called the .
”, published in Nature, this group is announcing the completion of the first human pangenome reference. The pangenome combines 47 individual genome reference sequences and better represents the genomic diversity of global populations. Building on Google’s deep learning technologies and (CNNs) and
Using graphs to build pangenomes
In the typical analysis workflow for high-throughput DNA sequencing, a then estimates where those pieces best fit relative to the single, linear human reference sequence. Next, variant caller software identifies the unique parts of the individual’s sequence relative to the reference.
But because humans carry a diverse set of sequences, sections that are present in an individual’s DNA but are not in the reference genome can’t be analyzed. One study of 910 African individuals found that a total of , which are powerful for genomics because they can represent the sequences of many people simultaneously, which is needed to create a pangenome. Nodes in a graph genome contain the known set of sequences in a population, and paths through those nodes compactly describe the unique sequences of an individual’s DNA.
| (SNV), insertions, or deletions. Illustration credit Darryl Leja, |
| Actual graph genome for the and and , which reads larger pieces of the genome (10,000 to millions of DNA characters long) at a time, are essential to the creation of high quality reference sequences because larger pieces can be stitched together into assembled genomes more easily than the short pieces read out by earlier technologies. , an open-source CNN variant caller framework that analyzes the short-read sequencing evidence of local regions of the genome. However, we were able to re-train DeepVariant to yield |
| Training and evaluation schematic for DeepVariant. |
We next teamed up with researchers at the University of California, Santa Cruz (UCSC) for another category, with a single nucleotide variants (SNVs) accuracy that matched short-read sequencing. This work has been used to ’s effort that generated (with many shared members) to scale those practices.
With a set of high-quality human reference genomes on the horizon, developing methods that could use those assemblies grew in importance. We worked to adapt DeepVariant to use the pangenome developed by the consortium. In partnership with UCSC, we built an end-to-end analysis workflow for . The use of the pangenome allows many previously missed variants to be correctly identified.
| (a gene with variants associated with cardiac arrhythmias and |
Improving pangenome sequences using transformers
Just as new sequencing technologies enabled new pangenome approaches, new informatics technologies enabled improvements for sequencing methods. Google adapted transformer architectures from analysis of human language to genome sequences to develop
data. Because PacBio sequencing provides the primary sequence information for the 47 genome assemblies, we could apply DeepConsensus to improve those assemblies. With application of DeepConsensus, consortium members .” Read our post on the Keyword Blog to learn more about the human pangenome reference announcement.Acknowledgements
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