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Real-time mapping of nanopore raw signals

Zhang, H and Li, H and Jain, C and Cheng, H and Au, KF and Li, H and Aluru, S (2021) Real-time mapping of nanopore raw signals. In: Bioinformatics, 37 . I477-I483.

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Official URL: https://doi.org/10.1093/bioinformatics/btab264


Motivation: Oxford Nanopore Technologies sequencing devices support adaptive sequencing, in which undesired reads can be ejected from a pore in real time. This feature allows targeted sequencing aided by computational methods for mapping partial reads, rather than complex library preparation protocols. However, existing mapping methods either require a computationally expensive base-calling procedure before using aligners to map partial reads or work well only on small genomes. Results: In this work, we present a new streaming method that can map nanopore raw signals for real-time selective sequencing. Rather than converting read signals to bases, we propose to convert reference genomes to signals and fully operate in the signal space. Our method features a new way to index reference genomes using k-d trees, a novel seed selection strategy and a seed chaining algorithm tailored toward the current signal characteristics. We implemented the method as a tool Sigmap. Then we evaluated it on both simulated and real data and compared it to the state-of-the-art nanopore raw signal mapper Uncalled. Our results show that Sigmap yields comparable performance on mapping yeast simulated raw signals, and better mapping accuracy on mapping yeast real raw signals with a 4.4� speedup. Moreover, our method performed well on mapping raw signals to genomes of size >100 Mbp and correctly mapped 11.49 more real raw signals of green algae, which leads to a significantly higher F1-score (0.9354 versus 0.8660). © 2021 The Author(s). Published by Oxford University Press.

Item Type: Journal Article
Publication: Bioinformatics
Publisher: Oxford University Press
Additional Information: The copyright for this article belongs to Authors
Keywords: algorithm; DNA sequence; genome; high throughput sequencing; nanopore; software, Algorithms; Genome; High-Throughput Nucleotide Sequencing; Nanopores; Sequence Analysis, DNA; Software
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 20 Nov 2021 11:33
Last Modified: 20 Nov 2021 11:33
URI: http://eprints.iisc.ac.in/id/eprint/69873

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