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bioRxiv · 10.1101/2022.09.07.506963

Exploring high-quality microbial genomes by assembly of linked-reads with high barcode specificity using deep learning

Zhang, Z.·Wang, H.·Yang, C.·Yufen, H.·Yue, Z.·Chen, Y.·Han, L.·Lu, A.·Fang, X.·Zhang, L.
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Abstract

Despite long-read sequencing enables to generate complete genomes of unculturable microbes, its high cost hinders its widespread application in large cohorts. An alternative method is to assemble short-reads with long-range connectivity, which can be a cost-effective way to generate high-quality microbial genomes. We developed Pangaea to improve metagenome assembly using short-reads with physical or virtual barcodes. It adopts a deep-learning-based binning algorithm to assemble the co-barcoded reads with similar sequence contexts and abundances to improve assemblies of high- and medium-abundance microbes. Pangaea also leverages a multi-thresholding reassembly strategy to refine assembly for low-abundance microbes. We benchmarked Pangaea with linked-reads and a combination of short- and long-reads from mock communities and human gut metagenomes. Pangaea achieved significantly higher contig continuity as well as more near-complete metagenome-assembled genomes (NCMAGs) than the existing assemblers. Pangaea was also observed to generate three complete and circular NCMAGs on the human gut microbiomes.

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Zhang, Z., Wang, H., Yang, C., Yufen, H., Yue, Z., Chen, Y., Han, L., Lu, A., Fang, X., Zhang, L.. 2022-09-09. Exploring high-quality microbial genomes by assembly of linked-reads with high barcode specificity using deep learning. https://doi.org/10.1101/2022.09.07.506963

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