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Paul Jannis Zurek

Philipp Knyphausen

Katharina Neufeld

Ahir Pushpanath

Florian Hollfelder

UMI-linked nanopore consensus sequencing (UMIC-seq) of highly similar gene variants

Accurate consensus sequences of nanopore reads are commonly generated for genome assemblies, where every sequencing read represents a unique fragment that overlaps with many others, facilitating stacking for accurate consensus generation1,2. By contrast, libraries in directed protein evolution comprise rather closely related members. Here the sequence diversity in randomized gene libraries is typically low: protein variants with a few or even a single point mutation can be functionally different. An erroneous raw third-generation sequencing read cannot reliably be assigned to its template, when multiple template candidates differing only in few point mutations are possible. This inability to assign reads to any one parental molecule therefore renders consensus-based ‘polishing’ from unlinked reads impossible. Similar samples in which individual members differ merely in a few nucleotides can also be found in e.g. immune repertoires, metagenomic 16S amplicons or medical diagnostics, calling for a suitably high-quality sequencing solution. Here, we introduce an accurate nanopore consensus sequencing workflow based on a bioinformatic sequence link between descendant reads and their origin molecule by introducing random DNA barcodes (i.e. unique molecular identifiers, UMIs) prior to amplification for sequencing. UMIs are random sequence tags most commonly used to retain information on true molecule numbers before PCR amplification. Specifically, the distinct template identity marked by the UMI enables confident identification and quantification of PCR duplicates3. This method has been applied to achieve unbiased counting of molecules for expression data in RNA-seq experiments4,5 and the accurate quantification of rare mutations6,7. Here, we leverage UMI-tags to assign erroneous nanopore reads to their origin molecule, facilitating clustering for accurate consensus formation even when starting with a pool of highly similar sequences (e.g. a library of gene variants in protein evolution containing only point mutations) that could not be reliably distinguished in the ordinary nanopore sequencing output.The UMIs have to be highly diverse to allow faithful assignment of raw, low-accuracy nanopore reads to the corresponding variant. Based on our experience, 50 bp UMIs are chosen to enable high efficiency clustering at comparatively high nanopore sequencing error rates. The UMIs are incorporated using primers in two PCR cycles, with deliberately low amplification so that potential PCR bias is reduced. UMI-variant complexity is constricted by a transformation step into cells, because an exact number of molecules is represented in the number of transformant colonies, directly combining selective amplification and control over molecule count in a straightforward molecular biology step. Finally, DNA is straightforwardly isolated from individual colonies and sequenced using current standard nanopore amplicon protocols. UMI clustering is performed with a fast, ‘greedy’ agglomerative algorithm (see the scripts available at <a href="https://github.com/fhlab/UMIC-seq" rel="noopener noreferrer" target="_blank">https://github.com/fhlab/UMIC-seq</a>)8 due to the immense number of sequence comparisons that need to be performed, limiting the use of most conventional all-vs-all comparisons for clustering. Potential mutations are identified for each cluster via signal-level analysis with nanopolish1,9, using the parental gene sequence as a reference.

Nuclease-free water (Ambion) Plasmid DNA (Pool of variants in pASK-IBA63b+) Acceptor plasmid (pRSFDuet-1) Primers (Supplementary Table S1) Q5 High-Fidelity 2X Master Mix (NEB) Gibson Assembly Master Mix (NEB) FastDigest KpnI and BamHI Phosphate Buffered Saline (PBS) AMPure XP SPRI beads (Beckman Coulter)

Library preparation:Zymo Clean &amp; Concentrator-5 (Zymo Research)High-efficiency electrocompetent E. coli cells (e.g. E. cloni 10G ELITE, Lucigen, #60051-1)GeneJET Plasmid Miniprep Kit (ThermoFischer Scientific)SQK-LSK109 sequencing kit and flow cell (Oxford Nanopore Technologies, ONT)Generation of consensus sequences:Unix-based operating system: MacOS, Windows Subsystem for Linux, Ubuntu or similar. Python (version &gt; 3.6) Python packages &nbsp;&nbsp;&nbsp;scikit-bio, scikit-allel, biopython Nanopolish (version &gt; 10.1) NanoPlot and NanoFilt Porechop &nbsp;&nbsp;&nbsp;Porechop can be used to demultiplex reads. As it is no longer officially maintained, demultiplexing was &nbsp;&nbsp;&nbsp;also incorporated into the UMIC-seq scripts.

1.&nbsp;Loman, N. J., Quick, J. &amp; Simpson, J. T. A complete bacterial genome assembled de novo using only nanopore sequencing data. Nat. Methods 12, 733–735 (2015).2.&nbsp;Vaser, R., Sovic, I., Nagarajan, N. &amp; Sikic, M. Fast and accurate de novo genome assembly from long uncorrected reads. Genome Res. gr.214270.116 (2017) doi:10.1101/gr.214270.116.3. McCloskey, M. L., Stöger, R., Hansen, R. S. &amp; Laird, C. D. Encoding PCR Products with Batch-stamps and Barcodes. Biochem. Genet. 45, 761–767 (2007).4.&nbsp;Kivioja, T. et al. Counting absolute numbers of molecules using unique molecular identifiers. Nat. Methods 9, 72–74 (2012).5.&nbsp;Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2014).6.&nbsp;Kinde, I., Wu, J., Papadopoulos, N., Kinzler, K. W. &amp; Vogelstein, B. Detection and quantification of rare mutations with massively parallel sequencing. Proc. Natl. Acad. Sci. 108, 9530–9535 (2011).7.&nbsp;Schmitt, M. W. et al. Detection of ultra-rare mutations by next-generation sequencing. Proc. Natl. Acad. Sci. U. S. A. 109, 14508–14513 (2012).8. Zurek, P. J., Knyphausen, P., Neufeld, K., Pushpanath, A. &amp; Hollfelder, F. UMI-linked consensus sequencing enables phylogenetic analysis of directed evolution. Github Fhlab UMIC-Seq (2020) doi:10.5281/zenodo.4055319.9.&nbsp;Quick, J. et al. Real-time, portable genome sequencing for Ebola surveillance. Nature 530, 228–232 (2016).

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Here we present a straightforward unique molecular identifier (UMI)-linked nanopore consensus sequencing workflow (UMIC-seq), resulting in cost-effective and accurate long-read sequencing of amplicons. Short random molecular barcodes (i.e. unique molecular identifiers, UMIs) are attached to a pool of gene variants prior to nanopore sequencing to enable reliable clustering and the generation of accurate consensus sequences, even when starting from highly similar gene variants (e.g. a library of point mutants in directed protein evolution) that could not be reliably distinguished in the ordinary nanopore sequencing output.&nbsp;

UMI-linked nanopore consensus sequencing (UMIC-seq) of highly similar gene variants

Biochemistry

Molecular biology

Biological techniques

Computational biology and bioinformatics

Method Article

Paul Jannis Zurek, Philipp Knyphausen, Katharina Neufeld, Ahir Pushpanath, Florian Hollfelder

Paul Jannis Zurek

University of Cambridge

ORCiD: https://orcid.org/0000-0002-7049-0813

Philipp Knyphausen

Katharina Neufeld

Ahir Pushpanath

Johnson Matthey

Florian Hollfelder

University of Cambridge

Corresponding Author

ORCiD: https://orcid.org/0000-0002-1367-6312

DOI:

10.21203/rs.3.pex-1177/v1

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nanopore sequencing, consensus sequences, amplicon sequencing, directed evolution, protein engineering

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10.1038/s41467-020-19687-9

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Paul Jannis Zurek, Philipp Knyphausen, Katharina Neufeld, Ahir Pushpanath, Florian Hollfelder

Paul Jannis Zurek

University of Cambridge

ORCiD: https://orcid.org/0000-0002-7049-0813

Philipp Knyphausen

Katharina Neufeld

Ahir Pushpanath

Johnson Matthey

Florian Hollfelder

University of Cambridge

Corresponding Author

ORCiD: https://orcid.org/0000-0002-1367-6312

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10.21203/rs.3.pex-1177/v1

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