This work has received funding from the European Union&#39;s Horizon 2020 research and innovation programme under grant agreement No. 643476 (COMPARE).

NOTE: List of software tools to be used: usearch v11.0.667; muscle v3.8.1551; porechop 0.2.4; cutadapt 2.5; minimap2 2.16-r922; samtools 1.9; trimmomatic 0.39; bbmap 38.33; spades v3.13.1; kma-1.2.8
1. Primer design
<ol>
	<li>Start with downloading or retrieving a set of relevant reference whole genome sequences from public or private data collections. For instance, retrieve all full length USUV genomes (taxid64286) from the NCBI database22. USUV encodes a genome of around 11,000 nucleotides so only retrieve the sequences with a sequence length of 8,000-12,000 nucleotides. Do this using the following search entry: 
	- taxid64286[Organism:noexp] AND 8000[SLEN]:12000[SLEN].
	<ol>
		<li>Click on Send to | Complete Record | File; use Format = FASTA and create the File.</li>
	</ol>
	</li>
	<li>To downsize the set of reference sequences, remove duplicate sequences or sequences with over 99% nucleotide identity from the dataset. Do this using the cluster fast option from usearch23. On the command line enter: 
	- usearch -cluster_fast All_USUV.fasta -id 0.99 -centroids All_USUV_dedup.fasta</li>
	<li>To generate the primers, sequences need to be aligned. This is done using MUSCLE24. On the command line enter: 
	- muscle -in All_USUV_dedup.fasta -out All_USUV_dedup_aligned.fasta -log log_muscle.txt 
	NOTE: It is essential to manually inspect the alignment to check for discrepancies. These can be manually corrected if needed and the ends can be trimmed according to the length of most whole genome sequences.</li>
	<li>Primal is used to make a draft selection of the primers which can be used for full length amplicon sequencing19. Upload the alignment to the primal website (<a href="http://primal.zibraproject.org/" target="_blank">http://primal.zibraproject.org/</a>) and select the preferred amplicon length and overlap length between the different amplicons. Go to primal.zibraproject.org, fill in the Scheme name, upload the aligned fasta file, select the amplicon length, overlap size, and generate the scheme.</li>
	<li>Align the complete set of available complete USUV sequences (not the downsized or deduplicated set). On the command line enter: 
	- muscle -in All_USUV.fasta -out All_USUV_aligned.fasta -log log_muscle.txt 
	NOTE: Map the generated primers against the complete alignment (do not use the deduplicated alignment), manually correct errors and include a maximum of 5 degenerative primer positions.</li>
</ol>
2. Multiplex PCR
<ol>
	<li>Perform the multiplex PCR using the designed primers and nanopore and Illumina sequencing. The multiplex PCR for USUV was performed as previous described19,21.</li>
	<li>Perform basecalling with flip-flop version 3.0.6.6+9999d81.</li>
</ol>
3. Data analysis to generate consensus sequences from nanopore data
<ol>
	<li>Several samples can be multiplexed on a single nanopore sequencing run. After performing the sequence run, demultiplex the nanopore data. Use Porechop25 for this. To prevent contamination and enhance accuracy, use the require_two_barcodes flag. On the command line enter: 
	- porechop -i Run_USUV.fastq -o Run_USUV_demultiplex --require_two_barcodes</li>
	<li>After demultiplexing, remove primer sequences (indicated in the file Primers_Usutu.fasta in both orientations) using cutadapt26. In addition, remove sequences with a length shorter than 75 nucleotides. The primers have to be removed since they can introduce artificial biases in the consensus sequence. On the command line enter: 
	- cutadapt -b file:Primers_USUV.fasta -o BC01_trimmed.fastq BC01.fastq -m 75</li>
	<li>Demultiplexed sequence reads can be mapped against a panel of distinct reference strains using minimap227 and a consensus sequence can be generated using samtools28. Follow the example below which shows the procedure of a reference-based alignment and the consensus sequence generation of one sample: BC01. On the command line enter: 
	- minimap2 -ax map-ont Random_Refs_USUV.fasta BC01_trimmed.fastq &#62; BC01.bam 
	- samtools sort BC01.bam &#62; BC01_sorted.bam 
	- bcftools mpileup -Ou -f Random_Refs_USUV.fasta BC01_sorted.bam | bcftools call -mv -Oz -o BC01.vcf.gz 
	- bcftools index BC01.vcf.gz 
	- cat Random_Refs_USUV.fasta | bcftools consensus BC01.vcf.gz &#62; BC01_consensus.fasta</li>
	<li>For reference-based alignments it is essential that a closely related reference sequence is used. Therefore, perform a BlastN search with the generated consensus sequence to identify the closest reference strain. After that, repeat the reference-based alignment with the closest reference strain as reference (step 3.3 and 3.4). On the command line enter: 
	- minimap2 -ax map-ont Ref_USUV_BC01.fasta BC01_trimmed.fastq &#62; BC01_ref.bam 
	- samtools sort BC01_ref.bam &#62; BC01_sorted_ref.bam 
	- bcftools mpileup -Ou -f Ref_USUV_BC01.fasta BC01_sorted_ref.bam | bcftools call -mv -Oz -o BC01_ref.vcf.gz 
	- bcftools index BC01_ref.vcf.gz 
	- cat Ref_USUV_BC01.fasta | bcftools consensus BC01_ref.vcf.gz &#62; BC01_ref_consensus.fasta</li>
</ol>
4. Analysis of the Illumina data
<ol>
	<li>These sequences are automatically demultiplexed after sequencing. Reads can be quality controlled using trimmomatic29. For paired-end Illumina sequences, use the commonly used cut-off median PHRED score of 33 and a minimal read length of 75 to get accurate, high quality reads. On the command line enter: 
	- trimmomatic PE -phred33 9_S9_L001_R1_001.fastq.gz 9_S9_L001_R2_001.fastq.gz 9_1P.fastq 9_1U.fastq 9_2P.fastq 9_2U.fastq LEADING:3 TRAILING:3 SLIDINGWINDOW:3:15 MINLEN:75</li>
	<li>Remove primers (indicated in the file Primers_Usutu.fasta in both orientations), since they can introduce artificial biases, using cutadapt26. In addition, remove sequences with a length shorter than 75 nucleotides using the commands below. On the command line enter: 
	- cutadapt -b o 9_1P_trimmed.fastq -p 9_2P_trimmed.fastq 9_1P.fastq 9_2P.fastq -m 75</li>
	<li>Before de novo assembly, the sequence reads can be normalized for an even coverage across the genome. This is essential since de novo assemblers like SPAdes take the read coverage into account when assembling sequence reads. Normalize reads to a read coverage of 50 using BBNorm from the BBMap package30. On the command line enter: 
	- bbmap/bbnorm.sh target=50 in=9_1P_trimmed.fastq in2=9_2P_trimmed.fastq out=Sample9_FW_norm.fastq out2=Sample9_RE_norm.fastq</li>
	<li>The normalized reads are de novo assembled using SPAdes31. Default settings are used for the assembly using all different kmers (21, 33, 55, 77, 99 and 127). On the command line enter: 
	- spades.py -k 21,33,55,77,99,127 -o Sample9 -1 Sample9.qc.f.fq -2 Sample9.qc.r.fq</li>
	<li>Map the QC reads against the obtained consensus sequence using minimap2 and programs like Geneious, Bioedit or Ugene to curate the alignment. It is important to check the beginning and the end of the contig.
	<ol>
		<li>Align the QC reads against the obtained consensus sequencing using minimap2.</li>
		<li>Import the alignment in Geneious/Bioedit/UGene.</li>
		<li>Manually inspect, correct and curate especially the beginning and the end of the genome.</li>
	</ol>
	</li>
</ol>
5. Determining the required read coverage to compensate for the error profile in nanopore sequencing using Illumina data as gold standard
<ol>
	<li>Select sequence reads mapping to one amplicon, in this case amplicon 26. Subsequently, map the nanopore reads against this amplicon using minimap2. Use Samtools to select only the reads mapping to amplicon 26 and to convert the bam file into fastq. On the command line enter: 
	- minimap2 -ax map-ont -m 150 Amplicon26.fasta BC01_trimmed.fastq &#62; BC01.bam 
	- samtools view -b -F 4 BC01.bam &#62; BC01_mapped.bam 
	- samtools bam2fq BC01_mapped.bam | seqtk seq - -&#62; BC01_mapped.fastq</li>
	<li>Randomly select subsets of for instance 200 sequence reads one thousand times. For example, changing it to 10 will result in the random selection of one thousand times a subset of 10 sequence reads. The script is provided as Supplementary File 1. On the command line enter: 
	- python Random_selection.py</li>
	<li>All randomly selected sequence reads are aligned to amplicon 26. Use KMA32 to map the sequence reads and to immediately generate a consensus sequence. Use optimized settings for nanopore sequencing, indicated by the -bcNano flag. On the command line enter: 
	- kma index -i Amplicon26.fasta 
	- for file in random_sample*; do 
	- sampleID=${file%.fastq} 
	- kma -i ${sampleID}.fastq -o ${sampleID} -t_db Amplicon26.fasta -mem_mode -mp 5 -mrs 0.0 -bcNano 
	- done</li>
	<li>Inspect the generated consensus sequences on the command line using: 
	- cat *.fsa &#62; All_genomes.fsa 
	- minimap2 -ax map-ont Amplicon26.fasta All_genomes.fsa &#62; All_genomes.bam 
	- samtools sort All_genomes.bam &#62; All_genomes_sorted.bam 
	- samtools stats All_genomes_sorted.bam &#62; stats.txt
	<ol>
		<li>The error rate is displayed in the stats.txt under the heading error rate #mismatches / bases mapped. Display it on the screen with the following command: 
		- grep ^SN stats.txt | cut -f 2-</li>
		<li>The amount of indels is displayed under the heading #Indels per cycle. Display it on the screen with the following command: 
		- grep ^IC stats.txt | cut -f 2-</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Faria, N. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Establishment+and+cryptic+transmission+of+Zika+virus+in+Brazil+and+the+Americas.">Establishment and cryptic transmission of Zika virus in Brazil and the Americas.</a> Nature. 546 (7658), 406-410 (2017).</li><li>Bonaldo, M. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome+analysis+of+yellow+fever+virus+of+the+ongoing+outbreak+in+Brazil+reveals+polymorphisms.">Genome analysis of yellow fever virus of the ongoing outbreak in Brazil reveals polymorphisms.</a> Mem&#243;rias do Instituto Oswaldo Cruz. 112 (6), 447-451 (2017).</li><li>Faria, N. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genomic+and+epidemiological+monitoring+of+yellow+fever+virus+transmission+potential.">Genomic and epidemiological monitoring of yellow fever virus transmission potential.</a> bioRxiv. , 299842 (2018).</li><li>Magi, A., Giusti, B., Tattini, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Characterization+of+MinION+nanopore+data+for+resequencing+analyses.">Characterization of MinION nanopore data for resequencing analyses.</a> Briefings in Bioinformatics. 18 (6), bbw077 (2016).</li><li>Rang, F. J., Kloosterman, W. P., de Ridder, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=From+squiggle+to+basepair:+computational+approaches+for+improving+nanopore+sequencing+read+accuracy.">From squiggle to basepair: computational approaches for improving nanopore sequencing read accuracy.</a> Genome Biology. 19 (1), 90 (2018).</li><li>. Nanopore Store, R10 flow cells Available from: <a target="_blank" href="https://store.nanoporetech.com/flowcells/spoton-flow-cell-mk-i-r10.html">https://store.nanoporetech.com/flowcells/spoton-flow-cell-mk-i-r10.html</a> (2019)</li><li>Wick, R. R., Judd, L. M., Holt, K. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Performance+of+neural+network+basecalling+tools+for+Oxford+Nanopore+sequencing.">Performance of neural network basecalling tools for Oxford Nanopore sequencing.</a> Genome Biology. 20 (1), 129 (2019).</li><li>. GitHub - nanoporetech/flappie: Flip-flop basecaller for Oxford Nanopore reads Available from: <a target="_blank" href="https://github.com/nanoporetech/flappie">https://github.com/nanoporetech/flappie</a> (2019)</li><li>L&#252;hken, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Distribution+of+Usutu+Virus+in+Germany+and+Its+Effect+on+Breeding+Bird+Populations.">Distribution of Usutu Virus in Germany and Its Effect on Breeding Bird Populations.</a> Emerging Infectious Diseases. 23 (12), 1994-2001 (2017).</li><li>Cadar, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Widespread+activity+of+multiple+lineages+of+Usutu+virus,+Western+Europe,+2016.">Widespread activity of multiple lineages of Usutu virus, Western Europe, 2016.</a> Eurosurveillance. 22 (4), (2017).</li><li>Becker, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Epizootic+emergence+of+Usutu+virus+in+wild+and+captive+birds+in+Germany.">Epizootic emergence of Usutu virus in wild and captive birds in Germany.</a> PLoS ONE. 7 (2), (2012).</li><li>Diagne, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Usutu+Virus+Isolated+from+Rodents+in+Senegal.">Usutu Virus Isolated from Rodents in Senegal.</a> Viruses. 11 (2), 181 (2019).</li><li>Bakonyi, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Usutu+virus+infections+among+blood+donors,+Austria,+July+and+August+2017+–+Raising+awareness+for+diagnostic+challenges.">Usutu virus infections among blood donors, Austria, July and August 2017 – Raising awareness for diagnostic challenges.</a> Eurosurveillance. 22 (41), (2017).</li><li>Zaaijer, H. L., Slot, E., Molier, M., Reusken, C. B. E. M., Koppelman, M. H. G. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Usutu+virus+infection+in+Dutch+blood+donors.">Usutu virus infection in Dutch blood donors.</a> Transfusion. , trf.15444 (2019).</li><li>Cadar, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Blood+donor+screening+for+West+Nile+virus+(WNV)+revealed+acute+Usutu+virus+(USUV)+infection,+Germany,+September+2016.">Blood donor screening for West Nile virus (WNV) revealed acute Usutu virus (USUV) infection, Germany, September 2016.</a> Eurosurveillance. 22 (14), 30501 (2017).</li><li>Pierro, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detection+of+specific+antibodies+against+West+Nile+and+Usutu+viruses+in+healthy+blood+donors+in+northern+Italy,+2010–2011.">Detection of specific antibodies against West Nile and Usutu viruses in healthy blood donors in northern Italy, 2010–2011.</a> Clinical Microbiology and Infection. 19 (10), E451-E453 (2013).</li><li>Pecorari, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=First+human+case+of+Usutu+virus+neuroinvasive+infection,+Italy,+August-September+2009.">First human case of Usutu virus neuroinvasive infection, Italy, August-September 2009.</a> Euro surveillance: bulletin européen sur les maladies transmissibles = European Communicable Disease Bulletin. 14 (50), (2009).</li><li>Simonin, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Human+Usutu+Virus+Infection+with+Atypical+Neurologic+Presentation,+Montpellier,+France,+2016.">Human Usutu Virus Infection with Atypical Neurologic Presentation, Montpellier, France, 2016.</a> Emerging Infectious Diseases. 24 (5), 875-878 (2018).</li><li>Quick, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiplex+PCR+method+for+MinION+and+Illumina+sequencing+of+Zika+and+other+virus+genomes+directly+from+clinical+samples.">Multiplex PCR method for MinION and Illumina sequencing of Zika and other virus genomes directly from clinical samples.</a> Nature Protocols. 12 (6), 1261-1276 (2017).</li><li>Quick, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Real-time,+portable+genome+sequencing+for+Ebola+surveillance.">Real-time, portable genome sequencing for Ebola surveillance.</a> Nature. 530 (7589), 228-232 (2016).</li><li>Oude Munnink, B. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Towards+high+quality+real-time+whole+genome+sequencing+during+outbreaks+using+Usutu+virus+as+example.">Towards high quality real-time whole genome sequencing during outbreaks using Usutu virus as example.</a> Infection, Genetics and Evolution. 73, 49-54 (2019).</li><li>Benson, D. A., Karsch-Mizrachi, I., Lipman, D. J., Ostell, J., Sayers, E. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GenBank.">GenBank.</a> Nucleic Acids Research. 38 (Database issue), D46-D51 (2010).</li><li>Edgar, R. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Search+and+clustering+orders+of+magnitude+faster+than+BLAST.">Search and clustering orders of magnitude faster than BLAST.</a> Bioinformatics. 26 (19), 2460-2461 (2010).</li><li>Edgar, R. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MUSCLE:+multiple+sequence+alignment+with+high+accuracy+and+high+throughput.">MUSCLE: multiple sequence alignment with high accuracy and high throughput.</a> Nucleic Acids Research. 32 (5), 1792-1797 (2004).</li><li>. GitHub - rrwick/Porechop: adapter trimmer for Oxford Nanopore reads Available from: <a target="_blank" href="https://github.com/rrwick/porechop">https://github.com/rrwick/porechop</a> (2018)</li><li>Martin, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cutadapt+removes+adapter+sequences+from+high-throughput+sequencing+reads.">Cutadapt removes adapter sequences from high-throughput sequencing reads.</a> EMBnet.journal. 17 (1), 10 (2011).</li><li>Li, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Minimap2:+pairwise+alignment+for+nucleotide+sequences.">Minimap2: pairwise alignment for nucleotide sequences.</a> Bioinformatics. 34 (18), 3094-3100 (2018).</li><li>Li, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Sequence+Alignment/Map+format+and+SAMtools.">The Sequence Alignment/Map format and SAMtools.</a> Bioinformatics. 25 (16), 2078-2079 (2009).</li><li>Bolger, A. M., Lohse, M., Usadel, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Trimmomatic:+a+flexible+trimmer+for+Illumina+sequence+data.">Trimmomatic: a flexible trimmer for Illumina sequence data.</a> Bioinformatics (Oxford, England). 30 (15), 2114-2120 (2014).</li><li>. BBMap download | SourceForge.net Available from: <a target="_blank" href="https://sourceforge.net/projects/bbmap/">https://sourceforge.net/projects/bbmap/</a> (2019)</li><li>Bankevich, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SPAdes:+a+new+genome+assembly+algorithm+and+its+applications+to+single-cell+sequencing.">SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing.</a> Journal of Computational Biology. 19 (5), 455-477 (2012).</li><li>Clausen, P. T. L. C., Aarestrup, F. M., Lund, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rapid+and+precise+alignment+of+raw+reads+against+redundant+databases+with+KMA.">Rapid and precise alignment of raw reads against redundant databases with KMA.</a> BMC Bioinformatics. 19 (1), 307 (2018).</li><li>Brinkmann, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Proficiency+Testing+of+Virus+Diagnostics+Based+on+Bioinformatics+Analysis+of+Simulated+In+Silico+High-Throughput+Sequencing+Data+Sets.">Proficiency Testing of Virus Diagnostics Based on Bioinformatics Analysis of Simulated In Silico High-Throughput Sequencing Data Sets.</a> Journal of Clinical Microbiology. 57 (8), (2019).</li></ol>

The authors have nothing to disclose.

Nanopore sequencing is constantly evolving and therefore there is a need for methods to monitor the error rate. Here, we describe a workflow to monitor the error rate of the nanopore sequencer. This can be useful after the release of a new flow cell, or if new releases of the basecalling are released. However, this can also be useful for users who want to set-up and validate their own sequencing protocol.
Different software and alignment tools can yield different results33. In this manuscript, we aimed to use freely available software packages which are commonly used, and which have clear documentation. In some cases, preference might be given to commercial tools, which generally have a more user-friendly interfaces but have to be paid for. In the future, this method can be applied to the same sample in case big modifications in sequence technology or basecalling software are introduced Preferentially this should be done after each update of the basecaller or flowcell, however given the speed of the current developments this can be also been done only after major updates.
The reduction in the error rate in sequencing allows for a higher number of samples to be multiplexed. Thereby, nanopore sequencing is getting closer to replacing conventional real time PCRs for diagnostic assays, which is already the case for influenza virus diagnostics. In addition, the reduction of the error rate increases the usability of this technique sequencing, for instance for the determination of minor variants and for high-throughput unbiased metagenomic sequencing.
A critical step in the protocol is that close, reliable reference sequences need to be available. The primers are based on the current knowledge about virus diversity and might need to be updated every once in a while. Another critical point when setting up an amplicon-based sequencing approach is the balancing of the primer concentration to get an even balance in amplicon depth. This enables the multiplexing of more samples on a sequence run and results in a significant cost reduction.

Fast developments in third generation sequence technologies allows us to move forward towards close to real-time sequencing during viral outbreaks. This timely availability of genetic information can be useful to determine the origin and evolution of viral pathogens. Gold standards in the fields of next generation sequencing however, are still the second-generation sequencers. These techniques rely on specific and time-consuming techniques like clonal amplification during an emulsion PCR or clonal bridge amplification. The third-generation sequencers are cheaper, hand-held and come with simplified library preparation methodologies. Especially the small size of the sequence device and the low purchase price makes it an interesting candidate for deployable, fieldable sequencing. This could for instance be seen during the Ebola virus outbreak in Sierra Leone and during the ongoing arbovirus outbreak investigations in Brazil1,2,3. However, the reported high error rate4 might limit the applications for which nanopore sequencing can be used.
Nanopore sequencing is evolving quickly. New products are available in the market on a regular basis. Examples of this are for instance the 1D squared kits which enables sequencing of both strands of the DNA molecule, thereby boosting the accuracy of the called bases5 and the development of the R10 flow cell which measures the change in current at two different instances in the pore6. In addition, improved bio-informatic tools like improvements in basecalling will improve the accuracy of basecalling7. One of the most frequently used basecallers, (e.g., Albacore), has been updated at least 12 times in a 9-month time period5. Recently, the manufacturer also released a novel basecaller called flip-flop, which is implemented in the default nanopore software8. Together, all of these improvements will lead to more accurate sequences and will decrease the error rate of the nanopore sequencer.
Usutu virus (USUV) is a mosquito-borne arbovirus of the family Flaviviridae and it has a positive-stranded RNA genome of around 11,000 nucleotides. USUV mainly affects great grey owls and blackbirds9,10, although other bird species are also susceptible to USUV infection11. Recently, USUV was also identified in rodents and shrews although their potential role in transmission of the virus remains unknown12. In humans, asymptomatic infections have been described in blood donors13,14,15,16 while USUV infections also have been reported to be associated with encephalitis or meningo-encephalitis17,18. In the Netherlands, USUV was first detected in wild birds in 201610 and in asymptomatic blood donors in 201814. Since the initial detection of USUV, outbreaks have been reported during the subsequent years and surveillance, including whole genome sequencing, is currently ongoing to monitor the emerge and spread of an arbovirus in a previously na&#239;ve population.
Similar to what has been described for other viruses, such as Ebola virus, Zika virus and yellow fever virus3,19,20, we have developed a primer set to sequence full length USUV21. This polymerase chain reaction (PCR)-based approach allows for the recovery of full length USUV genomes from highly host-contaminated sample types like brain samples in samples up to a Ct value of around 32. Benefits of an amplicon-based sequencing approach are a higher sensitivity compared to metagenomic sequencing and a higher specificity. Limitations of using an amplicon-based approach are that the sequences should be similar in order to design primers fitting all strains and that primers are designed on our current knowledge about the virus diversity.
Given the constant developments and improvements in third generation sequencing, there is a need to evaluate the error rate of the sequencer on a regular basis. Here, we describe a method to evaluate the performance of nanopore directly against Illumina sequencing using USUV as an example. This method is applied to sequences generated with the latest R10 flow cell and basecalling is performed with the latest version of the flip-flop basecaller.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Agencourt AMPure XP beads</td>
			<td>Beckman Coulter</td>
			<td>A63881</td>
			<td></td>
		</tr>
		<tr>
			<td>dNTPs</td>
			<td>Qiagen</td>
			<td>201900</td>
			<td></td>
		</tr>
		<tr>
			<td>FLO-MIN106 R10 flowcell</td>
			<td>Nanopore</td>
			<td>R10 flowcell</td>
			<td></td>
		</tr>
		<tr>
			<td>KAPA Hyperplus libarary preparation kit</td>
			<td>Roche</td>
			<td>7962436001</td>
			<td></td>
		</tr>
		<tr>
			<td>Library Loading Bead Kit</td>
			<td>Nanopore</td>
			<td>EXP-LLB001</td>
			<td></td>
		</tr>
		<tr>
			<td>Ligation Sequencing Kit 1D</td>
			<td>Nanopore</td>
			<td>SQK-LSK109</td>
			<td></td>
		</tr>
		<tr>
			<td>Native Barcoding Kit 1D 1-12</td>
			<td>Nanopore</td>
			<td>EXP-NBD103</td>
			<td></td>
		</tr>
		<tr>
			<td>Native Barcoding Kit 1D 13-24</td>
			<td>Nanopore</td>
			<td>EXP-NBD104</td>
			<td></td>
		</tr>
		<tr>
			<td>NEB Blunt/TA Ligase Master Mix</td>
			<td>NEB</td>
			<td>M0367S</td>
			<td></td>
		</tr>
		<tr>
			<td>NEB Next Quick Ligation Module</td>
			<td>NEB</td>
			<td>E6056</td>
			<td></td>
		</tr>
		<tr>
			<td>NEB Next Ultra II End Repair / dA-Tailing Module</td>
			<td>NEB</td>
			<td>E7546S</td>
			<td></td>
		</tr>
		<tr>
			<td>Protoscript II Reverse Transcriptase</td>
			<td>NEB</td>
			<td>M0368X</td>
			<td></td>
		</tr>
		<tr>
			<td>Q5 High-Fidelity polymerase</td>
			<td>NEB</td>
			<td>M0491</td>
			<td></td>
		</tr>
		<tr>
			<td>Qubit dsDNA HS Assay kit</td>
			<td>Thermo Fisher</td>
			<td>Q32851</td>
			<td></td>
		</tr>
		<tr>
			<td>Random Primers</td>
			<td>Promega</td>
			<td>C1181</td>
			<td></td>
		</tr>
		<tr>
			<td>RNAsin Ribonuclease Inhibitor</td>
			<td>Promega</td>
			<td>N2111</td>
			<td></td>
		</tr>
	</tbody>
</table>

validating whole genome nanopore sequencing, using usutu virus as an example

Whole genome sequencing can be used to characterize and to trace viral outbreaks. Nanopore-based whole genome sequencing protocols have been described for several different viruses. These approaches utilize an overlapping amplicon-based approach which can be used to target a specific virus or group of genetically related viruses. In addition to confirmation of the virus presence, sequencing can be used for genomic epidemiology studies, to track viruses and unravel origins, reservoirs and modes of transmission. For such applications, it is crucial to understand possible effects of the error rate associated with the platform used. Routine application in clinical and public health settings require that this is documented with every important change in the protocol. Previously, a protocol for whole genome Usutu virus sequencing on the nanopore sequencing platform was validated (R9.4 flowcell) by direct comparison to Illumina sequencing. Here, we describe the method used to determine the required read coverage, using the comparison between the R10 flow cell and Illumina sequencing as an example.

Recently, a new version of the flow cell version (R10) was released and offered improvements to the basecaller used to convert the electronic current signal to DNA sequences (so-called flip-flop basecaller). Therefore, we have re-sequenced USUV from brain tissue of an USUV-positive owl which was previously sequenced on a R9.4 flow cell and on an Illumina Miseq instrument21. Here, we described the method used to determine the required read coverage for reliable consensus calling by direct comparison to Illumina sequencing.
Using the newer flow cell in combination with the basecaller flip-flop we show that a read coverage of 40x results in identical results as compared to Illumina sequencing. A read coverage of 30x results in an error rate of 0.0002% which corresponds to one error in every 585,000 nucleotides sequenced, while a read coverage of 20x results in one error in every 63,529 nucleotides sequenced. A read coverage of 10x results in one error in every 3,312 nucleotides sequenced, meaning that over three nucleotides per full USUV genome are being called wrong. With a read coverage above 30x, no indels were observed. A read coverage of 20x resulted in the detection of one indel position while a read coverage of 10x resulted in indels in 29 positions. An overview of the error rate using different read coverage cut-offs is shown in Table 1.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Coverage</td>
			<td>Errors iteration 1</td>
			<td>Error rate iteration 1</td>
			<td>Indels:</td>
			<td>Errors iteration 2</td>
			<td>Error rate iteration 2</td>
			<td>Indels:</td>
			<td>Errors iteration 3</td>
			<td>Error rate iteration 3</td>
			<td>Indels:</td>
		</tr>
		<tr>
			<td>10&#215;</td>
			<td>100</td>
			<td>0.0274%</td>
			<td>4</td>
			<td>116</td>
			<td>0.0297%</td>
			<td>18</td>
			<td>110</td>
			<td>0.0282%</td>
			<td>7</td>
		</tr>
		<tr>
			<td>20&#215;</td>
			<td>4</td>
			<td>0.0010%</td>
			<td>0</td>
			<td>6</td>
			<td>0.0015%</td>
			<td>1</td>
			<td>7</td>
			<td>0.0018%</td>
			<td>0</td>
		</tr>
		<tr>
			<td>30x</td>
			<td>2</td>
			<td>0.0005%</td>
			<td>0</td>
			<td>0</td>
			<td>0.0000%</td>
			<td>0</td>
			<td>0</td>
			<td>0.0000%</td>
			<td>0</td>
		</tr>
		<tr>
			<td>40x</td>
			<td>0</td>
			<td>0.0000%</td>
			<td>0</td>
			<td>0</td>
			<td>0.0000%</td>
			<td>0</td>
			<td>0</td>
			<td>0.0000%</td>
			<td>0</td>
		</tr>
		<tr>
			<td>50&#215;</td>
			<td>0</td>
			<td>0.0000%</td>
			<td>0</td>
			<td>0</td>
			<td>0.0000%</td>
			<td>0</td>
			<td>0</td>
			<td>0.0000%</td>
			<td>0</td>
		</tr>
	</tbody>
</table>
Table 1: Overview of the error rate of nanopore sequencing. Each iteration represents one thousand random samples.
Supplementary File 1: Random selection. <a href="https://www.jove.com/files/ftp_upload/60906/Random_selection.zip" target="_blank">Please click here to view this file (Right click to download).</a>

Watch this Scientific Journal Video about Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example at JoVE.com

Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example

We previously validated a protocol for amplicon-based whole genome Usutu virus (USUV) sequencing on a nanopore sequencing platform. Here, we describe the methods used in more detail and determine the error rate of the nanopore R10 flow cell.

Whole genome sequencing can be used to characterize and to trace viral outbreaks. Nanopore-based whole genome sequencing protocols have been described for ...

validating-whole-genome-nanopore-sequencing-using-usutu-virus-as-an

Research

JoVE Journal

Genetics

8.6K Views.  Erasmus University Medical Center. We previously validated a protocol for amplicon-based whole genome Usutu virus (USUV) sequencing on a nanopore sequencing platform. Here, we describe the methods used in more detail and determine the error rate of the nanopore R10 flow cell.

Video: Validating Whole Genome Nanopore Sequencing, using Usutu Virus as an Example

Mapping RNA-RNA Interactions Globally Using Biotinylated Psoralen

Knowing how RNAs interact with themselves and with others is key to understanding RNA based gene regulation in the cell. While examples of RNA-RNA interactions such as microRNA-mRNA interactions have been shown to regulate gene expression, the full extent to which RNA interactions occur in the cell is still unknown. Previous methods to study RNA interactions have primarily focused on subsets of RNAs that are interacting with a particular protein or RNA species. Here, we detail a method named Sequencing of Psoralen crosslinked, Ligated, and Selected Hybrids (SPLASH) that allows genome-wide capture of RNA interactions in vivo in an unbiased manner. SPLASH utilizes in vivo crosslinking, proximity ligation, and high throughput sequencing to identify intramolecular and intermolecular RNA base-pairing partners globally. SPLASH can be applied to different organisms including bacteria, yeast and human cells, as well as diverse cellular conditions to facilitate the understanding of the dynamics of RNA organization under diverse cellular contexts. The entire experimental SPLASH protocol takes about 5 days to complete and the computational workflow takes about 7 days to complete.

Here, we detail the method of Sequencing of Psoralen crosslinked, Ligated, and Selected Hybrids (SPLASH), which enables genome-wide mapping of intramolecular and intermolecular RNA-RNA interactions in vivo. SPLASH can be applied to study RNA interactomes of organisms including yeast, bacteria and humans.

Knowing how RNAs interact with themselves and with others is key to understanding RNA based gene regulation in the cell. While examples of RNA-RNA ...

Generation of Native Chromatin Immunoprecipitation Sequencing Libraries for Nucleosome Density Analysis

We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) experimental protocol compatible with a Gaussian mixture distribution based analysis methodology (nucleosome density ChIP-seq; ndChIP-seq) that enables the generation of combined measurements of micrococcal nuclease (MNase) accessibility with histone modification genome-wide. Nucleosome position and local density, and the posttranslational modification of their histone subunits, act in concert to regulate local transcription states. Combinatorial measurements of nucleosome accessibility with histone modification generated by ndChIP-seq allows for the simultaneous interrogation of these features. The ndChIP-seq methodology is applicable to small numbers of primary cells inaccessible to cross-linking based ChIP-seq protocols. Taken together, ndChIP-seq enables the measurement of histone modification in combination with local nucleosome density to obtain new insights into shared mechanisms that regulate RNA transcription within rare primary cell populations.

We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) methodology for the generation of sequence datasets suitable for a nucleosome density ChIP-seq analytical framework integrating micrococcal nuclease (MNase) accessibility with histone modification measurements.

We present a modified native chromatin immunoprecipitation sequencing (ChIP-seq) experimental protocol compatible with a Gaussian mixture distribution ...

G2-seq: A High Throughput Sequencing-based Technique for Identifying Late Replicating Regions of the Genome

Numerous techniques have been developed to follow the progress of DNA replication through the S phase of the cell cycle. Most of these techniques have been directed toward elucidation of the location and timing of initiation of genome duplication rather than its completion. However, it is critical that we understand regions of the genome that are last to complete replication, because these regions suffer elevated levels of chromosomal breakage and mutation, and they have been associated with both disease and aging. Here we describe how we have extended a technique that has been used to monitor replication initiation to instead identify those regions of the genome last to complete replication. This approach is based on a combination of flow cytometry and high throughput sequencing. Although this report focuses on the application of this technique to yeast, the approach can be used with any cells that can be sorted in a flow cytometer according to DNA content.

We describe a technique for combining flow cytometry and high throughput sequencing to identify late replicating regions of the genome.

Numerous techniques have been developed to follow the progress of DNA replication through the S phase of the cell cycle. Most of these techniques have ...

Profiling DNA Replication Timing Using Zebrafish as an In Vivo Model System

DNA replication timing is an important cellular characteristic, exhibiting significant relationships with chromatin structure, transcription, and DNA mutation rates. Changes in replication timing occur during development and in cancer, but the role replication timing plays in development and disease is not known. Zebrafish were recently established as an in vivo model system to study replication timing. Here is detailed the protocols for using the zebrafish to determine DNA replication timing. After sorting cells from embryos and adult zebrafish, high-resolution genome-wide DNA replication timing patterns can be constructed by determining changes in DNA copy number through analysis of next generation sequencing data. The zebrafish model system allows for evaluation of the replication timing changes that occur in vivo throughout development, and can also be used to assess changes in individual cell types, disease models, or mutant lines. These methods will enable studies investigating the mechanisms and determinants of replication timing establishment and maintenance during development, the role replication timing plays in mutations and tumorigenesis, and the effects of perturbing replication timing on development and disease.

Profiling DNA Replication Timing Using Zebrafish as an In Vivo Model System

Zebrafish were recently used as an in vivo model system to study DNA replication timing during development. Here is detailed the protocols for using zebrafish embryos to profile replication timing. This protocol can be easily adapted to study replication timing in mutants, individual cell types, disease models, and other species.

DNA replication timing is an important cellular characteristic, exhibiting significant relationships with chromatin structure, transcription, and DNA ...

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been used to analyze the spectrum of clonal mutations in malignancy. Though far more efficient than traditional Sanger methods, NGS struggles with identifying rare clonal and subclonal mutations due to its high error rate of ~0.5&#8211;2.0%. Thus, standard NGS has a limit of detection for mutations that are &#62;0.02 variant allele fraction (VAF). While the clinical significance for mutations this rare in patients without known disease remains unclear, patients treated for leukemia have significantly improved outcomes when residual disease is &#60;0.0001 by flow cytometry. In order to mitigate this artefactual background of NGS, numerous methods have been developed. Here we describe a method for Error-corrected DNA and RNA Sequencing (ECS), which involves tagging individual molecules with both a 16 bp random index for error-correction and an 8 bp patient-specific index for multiplexing. Our method can detect and track clonal mutations at variant allele fractions (VAFs) two orders of magnitude lower than the detection limit of NGS and as rare as 0.0001 VAF.

Next-generation sequencing (NGS) is a powerful tool for genomic characterization that is limited by the high error rate of the platform (~0.5&#8211;2.0%). We describe our methods of error-corrected sequencing that allow us to obviate the NGS error rate and detect mutations at variant allele fractions as rare as 0.0001.

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been ...

Ultralow Input Genome Sequencing Library Preparation from a Single Tardigrade Specimen

Tardigrades are microscopic animals that enter an ametabolic state called anhydrobiosis when facing desiccation and can return to their original state when water is supplied. The genomic sequencing of microscopic animals such as tardigrades risks bacterial contamination that sometimes leads to erroneous interpretations, for example, regarding the extent of horizontal gene transfer in these animals. Here, we provide an ultralow input method to sequence the genome of the tardigrade, Hypsibius dujardini, from a single specimen. By employing rigorous washing and contaminant exclusion along with an efficient extraction of the 50 ~ 200 pg genomic DNA from a single individual, we constructed a library sequenced with a DNA sequencing instrument. These libraries were highly reproducible and unbiased, and an informatics analysis of the sequenced reads with other H. dujardini genomes showed a minimal amount of contamination. This method can be applied to unculturable tardigrades that could not be sequenced using previous methods.

Contamination during the genomic sequencing of microscopic organisms remains a large problem. Here, we show a method to sequence the genome of a tardigrade from a single specimen with as little as 50 pg of genomic DNA without whole genome amplification to minimize the risk of contamination.

Tardigrades are microscopic animals that enter an ametabolic state called anhydrobiosis when facing desiccation and can return to their original state ...

Amplification of Near Full-length HIV-1 Proviruses for Next-Generation Sequencing

The Full-Length Individual Proviral Sequencing (FLIPS) assay is an efficient and high-throughput method designed to amplify and sequence single, near full-length (intact and defective), HIV-1 proviruses. FLIPS allows determination of the genetic composition of integrated HIV-1 within a cell population. Through identifying defects within HIV-1 proviral sequences that arise during reverse transcription, such as large internal deletions, deleterious stop codons/hypermutation, frameshift mutations, and mutations/deletions in cis acting elements required for virion maturation, FLIPS can identify integrated proviruses incapable of replication. The FLIPS assay can be utilized to identify HIV-1 proviruses that lack these defects and are&#160;therefore potentially replication-competent. The FLIPS protocol involves: lysis of HIV-1 infected cells, nested PCR of near full-length HIV-1 proviruses (using primers targeted to the HIV-1 5&#39; and 3&#39; LTR), DNA purification and quantification, library preparation for Next-generation Sequencing (NGS), NGS, de novo assembly of proviral contigs, and a simple process of elimination for identifying replication-competent proviruses. FLIPS provides advantages over traditional methods designed to sequence integrated HIV-1 proviruses, such as single-proviral sequencing. FLIPS amplifies and sequences near full-length proviruses enabling replication competency to be determined, and also uses fewer amplification primers, preventing the consequences of primer mismatches. FLIPS is a useful tool for understanding the genetic landscape of integrated HIV-1 proviruses, especially within the latent reservoir, however, its utilization can extend to any application in which the genetic composition of integrated HIV-1 is required.

Full-length individual proviral sequencing (FLIPS) provides an efficient and high-throughput method for the amplification and sequencing of single, near full-length (intact and defective) HIV-1 proviruses and allows for determination of their potential replication-competency. FLIPS overcomes limitations of previous assays designed to sequence the latent HIV-1 reservoir.

The Full-Length Individual Proviral Sequencing (FLIPS) assay is an efficient and high-throughput method designed to amplify and sequence single, near ...

Genome Editing in Mammalian Cell Lines using CRISPR-Cas

The clustered regularly interspaced short palindromic repeats (CRISPR) system functions naturally in bacterial adaptive immunity, but has been successfully repurposed for genome engineering in many different living organisms. Most commonly, the wildtype CRISPR associated 9 (Cas9) or Cas12a endonuclease is used to cleave specific sites in the genome, after which the DNA double-stranded break is repaired via the non-homologous end joining (NHEJ) pathway or the homology-directed repair (HDR) pathway depending on whether a donor template is absent or present respectively. To date, CRISPR systems from different bacterial species have been shown to be capable of performing genome editing in mammalian cells. However, despite the apparent simplicity of the technology, multiple design parameters need to be considered, which often leave users perplexed about how best to carry out their genome editing experiments. Here, we describe a complete workflow from experimental design to identification of cell clones that carry desired DNA modifications, with the goal of facilitating successful execution of genome editing experiments in mammalian cell lines. We highlight key considerations for users to take note of, including the choice of CRISPR system, the spacer length, and the design of a single-stranded oligodeoxynucleotide (ssODN) donor template. We envision that this workflow will be useful for gene knockout studies, disease modeling efforts, or the generation of reporter cell lines.

CRISPR-Cas is a powerful technology to engineer the complex genomes of plants and animals. Here, we detail a protocol to efficiently edit the human genome using different Cas endonucleases. We highlight important considerations and design parameters to optimize editing efficiency.

The clustered regularly interspaced short palindromic repeats (CRISPR) system functions naturally in bacterial adaptive immunity, but has been ...

Sequencing of mRNA from Whole Blood using Nanopore Sequencing

Sequencing in remote locations and resource-poor settings presents unique challenges. Nanopore sequencing can be successfully used under such conditions, and was deployed to West Africa during the recent Ebola virus epidemic, highlighting this possibility. In addition to its practical advantages (low cost, ease of equipment transport and use), this technology also provides fundamental advantages over second-generation sequencing approaches, particularly the very long read length, ability to directly sequence RNA, and real-time availability of data. Raw read accuracy is lower than with other sequencing platforms, which represents the main limitation of this technology; however, this can be partially mitigated by the high read depth generated. Here, we present a field-compatible protocol for sequencing of the mRNAs encoding for Niemann-Pick C1, which is the cellular receptor for ebolaviruses. This protocol encompasses extraction of RNA from animal blood samples, followed by RT-PCR for target enrichment, barcoding, library preparation, and the sequencing run itself, and can be easily adapted for use with other DNA or RNA targets.

Nanopore sequencing is a novel technology that allows cost-effective sequencing in remote locations and resource-poor settings. Here, we present a protocol for sequencing of mRNAs from whole blood that is compatible with such conditions.

Sequencing in remote locations and resource-poor settings presents unique challenges. Nanopore sequencing can be successfully used under such conditions, ...

Cell Surface Receptor Identification Using Genome-Scale CRISPR/Cas9 Genetic Screens

Intercellular communication mediated by direct interactions between membrane-embedded cell surface receptors is crucial for the normal development and functioning of multicellular organisms. Detecting these interactions remains technically challenging, however. This manuscript describes a systematic genome-scale CRISPR/Cas9 knockout genetic screening approach that reveals cellular pathways required for specific cell surface recognition events. This assay utilizes recombinant proteins produced in a mammalian protein expression system as avid binding probes to identify interaction partners in a cell-based genetic screen. This method can be used to identify the genes necessary for cell surface interactions detected by recombinant binding probes corresponding to the ectodomains of membrane-embedded receptors. Importantly, given the genome-scale nature of this approach, it also has the advantage of not only identifying the direct receptor but also the cellular components that are required for the presentation of the receptor at the cell surface, thereby providing valuable insights into the biology of the receptor.

This manuscript describes a genome-scale cell-based screening approach to identify extracellular receptor-ligand interactions.

Intercellular communication mediated by direct interactions between membrane-embedded cell surface receptors is crucial for the normal development and ...