We thank the participants in the Children&#39;s Oncology Group AAML1531 study and the Nurses&#39; Health Study for their contributions in the form of patient samples. This work was funded by the National Institutes of Health (UM1 CA186107, RO1 CA49449 and RO1 CA149445), the Children&#39;s Discovery Institute of Washington University and St. Louis Children&#39;s Hospital (MC-II-2015-461), and Eli Seth Matthews Leukemia Foundation.

1. Targeted Error-corrected Sequencing for DNA
<ol>
	<li>PCR amplification of genomic fragments of interest.
	<ol>
		<li>Use a high-fidelity DNA polymerase to amplify the amplicons (Materials Table, Item 1). Amplify the PCR reaction with the following conditions in a thermal cycler: 30 s at 98&#160;&#176;C; 18&#8211;40 cycles of 10 s at 98&#160;&#176;C, 30 s at 66&#160;&#176;C, and 30 s at 72&#160;&#176;C; 2 min at 72&#160;&#176;C; hold at 4&#160;&#176;C.</li>
		<li>Purify the PCR products with paramagnetic beads (Materials Table, Item 2). Add the PCR reaction to the beads in a 1: 1.8 ratio (PCR reaction volume: bead volume) according to the manufacturer&#8217;s protocol. Elute with 20 &#181;L of ddH2O.</li>
		<li>Quantify concentration of DNA (Materials Table, Item 3) to determine final concentration of DNA.</li>
		<li>Run an aliquot of DNA on a 2% agarose gel (Materials Table, Item 4) to confirm the size of the amplicons. 
		NOTE: Alternatively, researchers can opt to perform a Bioanalyzer analysis on the PCR products to determine the size of amplified genomic fragments as well as the concentration of the products.</li>
	</ol>
	</li>
	<li>Sequencing adapter annealing
	<ol>
		<li>Obtain i7 adapters (Materials Table, Item 5). Use them as they are provided for subsequent steps.</li>
		<li>Purchase 16N i5 adapters commercially with the following oligo sequence (Materials Table Item 6): AATGATACGGCGACCACCGAGATCTACAC(N1:25252525)(N1)(N1)(N1)(N1)(N1)(N1)(N1)(N1)(N1)(N1)(N1)(N1)(N1)(N1)(N1)ACACTCTTTCCCTACACGACGCTCTTCCGATCT 
		NOTE: The 16N i5 adapters replace the standard i5 adapters and they are adapters with a string of 16 random-nucleotide to facilitate ECS.</li>
		<li>Make 16N i5 adapter working solution: 40 &#181;L of 100 &#181;M 16N i5 adapter stock, 10 &#181;L of TE buffer, and 10 &#181;L of 500 &#181;M NaCl solution.</li>
		<li>Aliquot 7.5 &#181;L of the i5 working solution prepared in Step 1.2.3 into separate PCR wells.</li>
		<li>Add 5 &#181;L of sample-specific i7 adapter into corresponding wells.</li>
		<li>Incubate at 95&#160;&#176;C for 5 min then cool by 1&#160;&#176;C every 30 s to 4&#160;&#176;C in a thermal cycler.</li>
		<li>Hold at 4&#160;&#176;C.</li>
	</ol>
	</li>
	<li>End-repair &#38; dA-tailing of libraries 
	NOTE: In parallel with adapter annealing, one can perform end repair and dA-tailing on the PCR amplicons from Step 1.1. Following completion of these steps, ligation of annealed adapters from Step 1.2 onto the end repaired and dA-tailed PCR amplicons is performed. Following adapter ligation, the ECS library construction is complete.
	<ol>
		<li>Begin with at most 1 &#181;g of starting DNA (minimum ~200 ng)</li>
		<li>Perform end-repair and dA-tail on amplicons (Materials Table, Item 7).
		<ol>
			<li>Add 3.0 &#181;L of End Prep Enzyme Mix and 6.5 &#181;L of End Repair Buffer.</li>
			<li>Incubate the mix for 30 min at 20&#160;&#176;C, then for 30 min at 65&#160;&#176;C and hold at 4&#160;&#176;C.</li>
		</ol>
		</li>
		<li>Perform ligation on the annealed adapters (Materials Table, Item 8).
		<ol>
			<li>Add 2.5 &#181;L of the annealed adapters from Step 2, 15 &#181;L of Blunt/TA Ligase Mastermix, and 1 &#181;L of Ligation enhancer.</li>
			<li>Incubate the mix for 15 min at 20&#160;&#176;C, then for 15 min at 37&#160;&#176;C.</li>
		</ol>
		</li>
		<li>Clean up libraries with magnetic beads (Materials Table Item 2): Add the PCR reaction to beads in a modified 1: 0.75 ratio (PCR reaction volume: magnetic bead volume):
		<ol>
			<li>Pipette 62.6 &#181;L of magnetic bead solution into the 83.5 &#181;L of PCR products from Step 1.2.7.</li>
			<li>Transfer the mixture to a 1.5 mL low binding tube.</li>
			<li>Mix thoroughly by pipetting up and down at least 10 times.</li>
			<li>Leave the mixture to stand at room temperature for 5 minutes.</li>
			<li>Place the tube onto a magnetic holder. Incubate for 2 minutes at room temperature or until supernatant is clear.</li>
			<li>Remove supernatant.</li>
			<li>Wash the beads with 200 &#181;L of 70% ethanol.</li>
			<li>Incubate for 30 s. Remove ethanol.</li>
			<li>Repeat ethanol wash step once.</li>
			<li>Air-dry the beads.</li>
			<li>Elute with 20 &#181;L of ddH2O. 
			NOTE: This modification in PCR reaction to magnetic bead ratio will preferentially remove DNA fragments that are smaller than 200 bp.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Quantification by droplet digital PCR 
	NOTE: Precise mutation quantification requires strict observance of the number of molecules of each library that are loaded onto the sequencer. To achieve this, quantifying the number of molecules for individual libraries per unit volume is performed using the QX200 droplet digital PCR (ddPCR) platform &#8212; quantitative PCR is an alternative option. Following ddPCR analysis, the readout will specify the number of molecules per &#181;L per library.
	<ol>
		<li>Dilute ECS libraries 1:1,000 by incrementally diluting by a factor of 10 in PCR strip-tubes.</li>
		<li>Prepare the following mastermix for ddPCR in 1.5 mL tube: 10 &#181;L of PCR Mix (Materials Table, Item 9), 0.2 &#181;L of P5 Primer, 0.2 &#181;L of P7 Primer, 5 &#181;L of ECS cleaned-up product from Step 1.4.1., and 4.5 &#181;L of ddH2O.</li>
		<li>Aliquot 20 &#181;L of the mastermix into each sample well making sure there are multiples of 8.
		<ol>
			<li>Aliquot 70 &#181;L of droplet generation oil (Materials Table, Item 10) into each oil well. Cover the cassette with a rubber gasket.</li>
		</ol>
		</li>
		<li>Make droplets using the droplet generator (Materials Table, Item 11).</li>
		<li>Using a multichannel pipette, load the droplets generated in Step 1.4.4 into a PCR plate ensuring that the pipetting of the sample is done slowly over a span of 5 seconds to avoid shearing the DNA.</li>
		<li>Amplify the signal in the droplets for 40 cycles in a thermal cycler using the following conditions: 5 min at 95&#160;&#176;C; 40 cycles of 30 s at 95&#160;&#176;C, 1 min at 63&#160;&#176;C; 5 min at 4&#160;&#176;C, 5 min at 90&#160;&#176;C; and then hold at 4&#160;&#176;C.</li>
		<li>Prepare ddPCR template droplet reader machine (Materials Table, Item 11). Ensure specification for parameters for Absolute Quantification and using the QX200 ddPCR Eva Green Supermix.</li>
		<li>Once ddPCR analysis is complete, make sure to set the same divisive threshold across all samples.</li>
		<li>Using the concentration readout from the QX200 Droplet Reader, aliquot the appropriate volume to introduce the desired number of molecules into subsequent step.</li>
	</ol>
	</li>
	<li>PCR amplification of the libraries for sequencing
	<ol>
		<li>Prepare the following mastermix for the desired number of molecules from Step 1.4.9: 25 &#181;L of Q5 Mastermix (Materials Table, Item 1), 2.5 &#181;L of P5 Primer (10 &#181;M), 2.5 &#181;L of P7 Primer (10 &#181;M), X &#181;L of DNA, 20-X &#181;L of ddH2O.</li>
		<li>Amplify the libraries from Step 1.5.1 in a thermal cycler using the following conditions: 30 s at 98&#160;&#176;C; 20 cycles of 10 s at 98&#160;&#176;C, 30 s at 63&#160;&#176;C, 30 s at 72&#160;&#176;C; 2 min at 72&#160;&#176;C; and then hold at 4&#160;&#176;C.</li>
		<li>Clean up libraries with magnetic beads (Materials Table, Item 2): Add the PCR reaction to magnetic beads in a modified 1: 0.75 ratio (PCR reaction volume: magnetic bead volume).
		<ol>
			<li>Pipette 37.5 &#181;L of magnetic bead solution into the 50 &#181;L PCR products from Step 1.5.2.</li>
			<li>Transfer the mixture to a 1.5 mL low binding tube.</li>
			<li>Mix thoroughly by pipetting up and down at least 10 times.</li>
			<li>Leave the mixture to stand at room temperature for 5 min.</li>
			<li>Place the tube onto a magnetic holder. Incubate for 2 minutes at room temperature or until supernatant is clear.</li>
			<li>Remove supernatant.</li>
			<li>Wash the beads with 200 &#181;L of 70% ethanol.</li>
			<li>Incubate for 30 s. Remove ethanol.</li>
			<li>Repeat ethanol wash step once.</li>
			<li>Air-dry the beads.</li>
			<li>Elute with 20 &#181;L of ddH2O.</li>
		</ol>
		</li>
		<li>Run an aliquot of DNA on a 2% agarose gel to confirm the size of the amplicons.</li>
		<li>Quantify concentration of DNA (Materials Table, Item 3) to determine concentration of the separate ECS libraries.</li>
		<li>Pool the libraries in equimolar amounts. 
		NOTE: For example, researchers can pool eight libraries in an equimolar group4 with 4 million starting molecules for sequencing using a sequencing platform which outputs up to 400 million reads. Conservatively, it is recommended to use an average of ten raw reads for error-correction per molecules. This would take up 360 million reads (4 million molecules * 8 libraries * 10 reads for error correction). With 4 million unique molecules per library, researchers can expect to get a theoretical mean consensus read coverage of 7042x per amplicon (4 million/568 amplicons from the gene panel).</li>
		<li>Quantify concentration of DNA (Materials Table, Item 3) to determine concentration of pooled ECS library.</li>
		<li>Submit the pooled ECS library at roughly 4 nM.</li>
		<li>Provide the following sequencing settings to Illumina sequencing platforms (MiSeq, HiSeq or NextSeq): 2x144 paired-end reads, 8 cycles Index 1 and 16 cycles Index 2.</li>
	</ol>
	</li>
</ol>
2. Gene Panels with Error-corrected Sequencing of DNA
<ol>
	<li>Hybridization of oligos from gene panels 
	NOTE: In this step, one will construct sequencing libraries using a modified Illumina TruSight or TruSeq protocol to incorporate the UMIs (Materials Table, Item 17).
	<ol>
		<li>Hybridize oligos onto genomic fragment following manufacturer&#8217;s protocol. Use 250 ng of DNA (or any desired amount of starting material).</li>
		<li>Remove unbound oligos following manufacturer&#8217;s protocol.</li>
		<li>Perform extension-ligation following manufacturer&#8217;s protocol. 
		NOTE: Modifications to the manufacturer&#8217;s protocol begin below.</li>
	</ol>
	</li>
	<li>Incorporation of i5 and i7 Adapters via PCR
	<ol>
		<li>Prepare the PCR mastermix by pipetting the following reagents into a tube of appropriate volume size: 37.5 &#181;L of Q5 Mastermix (Materials Table, Item 1), 6 &#181;L of 10 &#181;M 16N i5 adapters (detailed in Method 1, Step 1.2.2), 6 &#181;L of i7 adapters (Use different i7 adapters for separate samples for multiplexing), and 22 &#181;L of extension-ligation solution with beads from Step 2.1.3. 
		NOTE: The Q5 Mastermix replaces the polymerase mastermix provided by Illumina. The Q5 polymerase amplifies the genomic fragment with higher fidelity and fewer introduced errors.</li>
		<li>Run PCR program on a thermal cycler using the following parameters: 30&#160;s at 98&#160;&#176;C, 4&#8211;6 cycles of 10 s at 98&#160;&#176;C, 30 s at 66&#160;&#176;C, 30&#160;s at 72&#160;&#176;C; 2 min at 72&#160;&#176;C, and then hold at 4&#160;&#176;C. 
		NOTE: The number of cycles depends on the panel size. From our experience, a 4-cycle PCR is sufficient if the gene panel has about 1,500 different pairs of gene specific oligos, whereas a panel with 500&#8211;600 pairs of oligos requires 6 cycles of PCR.</li>
		<li>Clean up PCR reactions with magnetic beads (Materials Table, Item 2): Add the PCR reaction to magnetic beads in a modified 1 PCR reaction: 0.75 magnetic bead ratio:
		<ol>
			<li>Pipette 56.25&#160;&#181;L of magnetic bead solution into the 75 &#181;L of PCR products from Step 2.2.2.</li>
			<li>Transfer the mixture to a 1.5 mL low binding tube.</li>
			<li>Mix thoroughly by pipetting up and down at least 10 times.</li>
			<li>Leave the mixture to stand at room temperature for 5 min.</li>
			<li>Place the tube onto a magnetic holder. Incubate for 2 min at room temperature or until supernatant is clear.</li>
			<li>Remove supernatant.</li>
			<li>Wash the beads with 200&#160;&#181;L of 70% ethanol.</li>
			<li>Incubate for 30 s. Remove ethanol.</li>
			<li>Repeat ethanol wash step once.</li>
			<li>Air-dry the beads.</li>
			<li>Elute with 20&#160;&#181;L of ddH2O.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Quantify libraries using QX200 ddPCR platform. 
	<ol>
		<li>Follow Step 1.4 in Method 1. 
		NOTE: 4 million molecules were normalized per sample library4 in the representative result (Figure 2) in order to obtain a theoretical mean of 7,042 uniquely indexed molecules (4 million divided by 568 gene-specific oligos).</li>
	</ol>
	</li>
	<li>Amplify and normalize libraries for sequencing.
	<ol>
		<li>Amplify the desired number of molecules using the following mastermix for the final PCR totaling 50&#160;&#181;L: 25&#160;&#181;L of Q5 Mastermix, 2&#160;&#181;L of P5 Primer (1&#160;&#181;M), 2&#160;&#181;L of P7 Primer (1 &#181;M), and 21&#160;&#181;L of DNA molecules.</li>
		<li>Run PCR program on a thermal cycler using the following parameter: 30 s at 98&#160;&#176;C; 16 cycles of 10 s at 98&#160;&#176;C, 30 s at 66&#160;&#176;C, 30 s at 72&#160;&#176;C; 2 min at 72&#160;&#176;C; and then hold at 4&#160;&#176;C.</li>
		<li>Clean up sequencing libraries using magnetic beads (Materials Table, Item 2): Add the PCR reaction to magnetic beads in a modified 1 PCR reaction: 0.75 magnetic bead ratio:
		<ol>
			<li>Pipette 37.5 &#181;L of magnetic bead solution into the 50 &#181;L PCR products from Step 2.4.2.</li>
			<li>Transfer the mixture to a 1.5 mL low binding tube.</li>
			<li>Mix thoroughly by pipetting up and down at least 10 times.</li>
			<li>Leave the mixture to stand at room temperature for 5 min.</li>
			<li>Place the tube onto a magnetic holder. Incubate for 2 min at room temperature or until supernatant is clear.</li>
			<li>Remove supernatant.</li>
			<li>Wash the beads with 200 &#181;L of 70% ethanol.</li>
			<li>Incubate for 30 s. Remove ethanol.</li>
			<li>Repeat ethanol wash step once.</li>
			<li>Air-dry the beads.</li>
			<li>Elute with 20&#160;&#181;L of ddH2O.</li>
		</ol>
		</li>
		<li>Run an aliquot of eluted DNA (~3 &#181;L) on a 2% agarose gel to confirm the size of the amplicons.</li>
		<li>Quantify concentration of DNA (Materials Table, Item 3) to determine concentration of the separate ECS libraries.</li>
		<li>Pool the libraries in equimolar amounts. Refer to Method 1 Step 1.5.6. and also Discussion for more details on pooling.</li>
		<li>Submit the pooled ECS library at roughly 4 nM.</li>
		<li>Provide the following sequencing settings to Illumina sequencing platforms (MiSeq, HiSeq or NextSeq): 2x144 paired-end reads, 8 cycles Index 1 and 16 cycles Index 2.</li>
	</ol>
	</li>
	<li>ECS Bioinformatic Processing and Analysis
	<ol>
		<li>Obtain the sample-demultiplexed reads from the sequencer or perform demultiplexing of raw sequence reads into different samples using i7 adapter sequences bioinformatically with a custom script.</li>
		<li>Trim off the first 30 nucleotides of each demultiplexed read to remove oligo sequences from the gene panel.</li>
		<li>Align reads that share the same UMIs to one another to form read families. 
		NOTE: Researchers can use UMI-aware software such as MAGERI13 to extract read families. No hamming distance was allowed within the UMI sequence in this experiment to increase the specificity of the method.</li>
		<li>Perform de-duplication and error-correction using the following recommended parameters.
		<ol>
			<li>Use &#8805;5 read pairs in the same read family. A minimum of three read pairs is recommended.</li>
			<li>Compare nucleotide at every position across all reads in the same read family, and generate a consensus nucleotide if there is at least 90% concordance among the reads for the particular nucleotide. Call an N if there is less than 90% agreement for the nucleotide position.</li>
			<li>Discard consensus reads that have &#62;10% of the total number of consensus nucleotides being called as N.</li>
		</ol>
		</li>
		<li>Align all retained consensus reads locally to either hg19 or hg38 human reference genome using researcher&#8217;s preferred aligner(s) such as Bowtie2 and BWA.</li>
		<li>Process aligned reads with Mpileup using parameters &#8211;BQ0 &#8211;d 10,000,000,000,000 to remove coverage thresholds to ensure a proper pileup output regardless of VAF.</li>
		<li>Filter out positions with less than 1000x consensus read coverage. 
		NOTE: The researcher determines the minimum coverage for each nucleotide position arbitrarily, it is recommended to have at least 500x consensus read coverage for downstream analysis.</li>
		<li>Use binomial distribution to call single nucleotide variants (SNPs) in retained data from Step 2.5.7 with the following parameters. The binomial statistic will be based on a genomic position-specific error model. Each genomic position is modeled independently after summing out the error rates of all samples for that particular position. Following the example: 
		Probability of nucleotide profile at a given genomic position, p 
		&#8721; Variant RF2 &#8721; Total RFs 
		= 26/255505 
		= 0.000101759 
		Binomial probability of 24 variant RFs out of 35911 total RFs, P(X &#8805; x) in sample K 
		= 1 - binomial(24, 35911, 0.000101759) 
		= 2.26485E-13 
		NOTE: For each genomic position queried, there would be three possible mutational changes (i.e.,A&#62;T, A&#62;C, A&#62;G), and each of which would be represented as background artifact. Somatic events that are significantly different from the background after Bonferroni correction are retained. In the example shown in Table 1, the number of tests performed was 11, hence a Bonferroni corrected p-value &#8804;0.00454545 (0.05/11) was required to call an event as statistically significant.</li>
		<li>Somatic events are required to be present in both replicates from the same specimen; otherwise, regard them as false positives.</li>
	</ol>
	</li>
</ol>
<img alt="Table 1" src="/files/ftp_upload/57509/57509table1.jpg" /> 
Table 1: Example demonstrating the way to construct a position-specific binomial error model.
3. Error-corrected Sequencing of RNA
<ol>
	<li>In addition to assessing for mutations at the DNA level, integrate ECS with various targeted RNA sequencing panels to detect rare or low abundance transcript at the RNA level. By combining ECS with the off-the-shelf Qiagen RNA sequencing panels, we demonstrated digital quantification of gene expression for transcripts with as few as ten copies without a need for normalization against a housekeeping gene. The UMIs required for error-correction have been integrated into the panel.
	<ol>
		<li>Perform total RNA extraction (Materials Table, Item 20).</li>
		<li>Carry out ECS-RNA library preparation according to manufacturer&#8217;s protocol (Materials Table, Item 19).</li>
		<li>Perform bioinformatics pipeline according to Step 2.5.1&#8211;2.5.6. of Method 2 outlined in the previous section. After Step 2.5.6, the number of aligned consensus reads per gene represents the expression level of the gene without the need for gene length normalization.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Hoang, M. L., 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-wide+quantification+of+rare+somatic+mutations+in+normal+tissues+using+massively+parallel+sequencing.">Genome-wide quantification of rare somatic mutations in normal tissues using massively parallel sequencing.</a> Proceedings of the National Academy of Sciences USA. 113, 9846-9851 (2016).</li><li>O'Roak, B. 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=Sporadic+autism+exomes+reveal+a+highly+interconnected+protein+network+of+de+novo+mutations.">Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations.</a> Nature. 485, 246-250 (2012).</li><li>Young, A. L., 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=Quantifying+ultra-rare+pre-leukemic+clones+via+targeted+error-corrected+sequencing.">Quantifying ultra-rare pre-leukemic clones via targeted error-corrected sequencing.</a> Leukemia. 29 (7), 1608-1611 (2015).</li><li>Young, A. L., Challen, G. A., Birmann, B. M., Druley, T. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clonal+hematopoiesis+harbouring+AML-associated+mutations+is+ubiquitous+in+healthy+adults.">Clonal hematopoiesis harbouring AML-associated mutations is ubiquitous in healthy adults.</a> NatureCommunications. 7, 12484 (2016).</li><li>Patel, J. P., 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=Prognostic+relevance+of+integrated+genetic+profiling+in+acute+myeloid+leukemia.">Prognostic relevance of integrated genetic profiling in acute myeloid leukemia.</a> New England Journal of Medicine. 366, 1079-1089 (2012).</li><li>Shendure, J., Ji, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Next-generation+DNA+sequencing.">Next-generation DNA sequencing.</a> Nature Biotechnology. 26 (10), 1135-1145 (2008).</li><li>Kohlmann, 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=Monitoring+of+residual+disease+by+next-generation+deep-sequencing+of+RUNX1+mutations+can+identify+acute+myeloid+leukemia+patients+with+resistant+disease.">Monitoring of residual disease by next-generation deep-sequencing of RUNX1 mutations can identify acute myeloid leukemia patients with resistant disease.</a> Leukemia. 28, 129-137 (2014).</li><li>Luthra, 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=Next-generation+sequencing-based+multigene+mutational+screening+for+acute+myeloid+leukemia+using+MiSeq:+applicability+for+diagnostics+and+disease+monitoring.">Next-generation sequencing-based multigene mutational screening for acute myeloid leukemia using MiSeq: applicability for diagnostics and disease monitoring.</a> Haematologica. 99, 465-473 (2014).</li><li>Kinde, I., Wu, J., Papadopoulos, N., Kinzler, K. W., Vogelstein, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Detection+and+quantification+of+rare+mutations+with+massively+parallel+sequencing.">Detection and quantification of rare mutations with massively parallel sequencing.</a> Proceedings of the National Academy of Sciences USA. 108 (23), 9530-9535 (2011).</li><li>Schmitt, 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=Detection+of+ultra-rare+mutations+by+next-generation+sequencing.">Detection of ultra-rare mutations by next-generation sequencing.</a> Proceedings of the National Academy of Sciences USA. 109 (36), 14508-14513 (2012).</li><li>Vander Heiden, J. 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=pRESTO:+a+toolkit+for+processing+high-throughput+sequencing+raw+reads+of+lymphocyte+receptor+repertoires.">pRESTO: a toolkit for processing high-throughput sequencing raw reads of lymphocyte receptor repertoires.</a> Bioinformatics. 30 (13), 1930-1932 (2014).</li><li>Newman, A. 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=Integrated+digital+error+suppression+for+improved+detection+of+circulating+tumor+DNA.">Integrated digital error suppression for improved detection of circulating tumor DNA.</a> NatureBiotechnology. 34, 547-555 (2016).</li><li>Shugay, 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=MAGERI:+Computational+pipeline+for+molecular-barcoded+targeted+resequencing.">MAGERI: Computational pipeline for molecular-barcoded targeted resequencing.</a> PLOSComputationalBiology. 13 (5), e1005480 (2017).</li><li>Wong, T. 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=Role+of+TP53+mutations+in+the+origin+and+evolution+of+therapy-related+acute+myeloid+leukaemia.">Role of TP53 mutations in the origin and evolution of therapy-related acute myeloid leukaemia.</a> Nature. 518, 552-555 (2014).</li><li>Krimmel, J. 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=Ultra-deep+sequencing+detects+ovarian+cancer+cells+in+peritoneal+fluid+and+reveals+somatic+TP53+mutations+in+noncancerous+tissues.">Ultra-deep sequencing detects ovarian cancer cells in peritoneal fluid and reveals somatic TP53 mutations in noncancerous tissues.</a> Proceedings of the National Academy of Sciences USA. 113 (21), 6005-6010 (2016).</li><li>Phallen, 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=Direct+detection+of+early-stage+cancers+using+circulating+tumor+DNA.">Direct detection of early-stage cancers using circulating tumor DNA.</a> ScienceTranslationalMedicine. 9, eaan2415 (2017).</li><li>Egorov, E. S., 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=Quantitative+profiling+of+immune+repertoires+for+minor+lymphocyte+counts+using+unique+molecular+identifiers.">Quantitative profiling of immune repertoires for minor lymphocyte counts using unique molecular identifiers.</a> The Journal of Immunology. 194 (12), 6155-6163 (2015).</li></ol>

The authors have nothing to disclose.

Here, we demonstrate a suite of error-corrected sequencing protocols that can be easily implemented to study mutations with low VAFs in different diseases. The most important factor is the incorporation of UMIs with each molecule before sequencing as they enable error-correction of the raw reads. The methods described here allow researchers to incorporate customized UMIs to both commercially available gene panels and self-designed gene-specific oligos.
Standard NGS protocol precludes the detection of mutations with VAF below 2% due to the sequencing error rate, and this limits the application of NGS in studies where the detection of rare variants is crucial. By circumventing the standard NGS error rate, ECS enables sensitive detection of these raw variants. For instance, detection of pathogenic mutations when these mutations first arise (therefore having low VAF) is imperative to inform early intervention of the disease14,15. In leukemia research, the detection of minimal residual disease (residual leukemic cells post-treatment) informs risk stratification and could be used to inform treatment options in a manner that binary flow cytometric assessments cannot. In addition, ECS is applicable to detect circulating tumor nucleic acid and to evaluate metastatic potential in solid tumor patients by assessing for the presence/absence as well as the variant burden of certain mutations that are characteristics of the primary tumor16.
As demonstrated in Table 1, the power of using binomial distribution-based position-specific error model to call variants depends largely on the number of sequenced libraries as well as the depth of sequencing used to build the error model. The robustness of the error model increases with higher number of samples and more sequencing depth. It is recommended to use at least 10 sequenced samples with an average of error-corrected read coverage of 3000x per sample to build an error profile for each sample. The position-specific approach is similar to MAGERI, but instead of using an aggregate error rate for all six different substitution types (A&#62;C/T&#62;G, A&#62;G/T&#62;C, A&#62;T/T&#62;A, C&#62;A/G&#62;T, C&#62;G/G&#62;C, C&#62;T/G&#62;A)13, we model each substitution independently at every position. For instance, an error rate of C&#62;T at a given genomic position is different from another position. Our approach also takes into account a sequencing batch effect, as the base substitution rate observed in one sequencing run might be different from another run. Hence it is important to model each position for all substitution types especially when samples from different sequencing runs are pooled to build the model.
An important consideration when designing an ECS experiment is the desired detection threshold. The beauty of NGS studies is that they can be easily scaled in terms of genes/targets of interest, detection threshold (dictated by depth of sequencing), and number of individuals queried. For example, if the researchers are interested to find rare mutations in two amplicons with a detection threshold of 0.0001, they can pool maximally 75 samples in a single sequencing run using MiSeq V2 chemistry which outputs up to 15 million reads (2 amplicons * 10,000 molecules * 10 reads for error-correction * 75 samples = 15 million sequencing reads). Researchers can vary the number of molecules going into sequencing or the number of pooled samples in a single sequencing run to adjust the detection threshold. In our studies, we aimed to find mutations with a detection threshold of 0.0001 VAF (1:10,000) using the Illumina gene panel. We routinely use 250 ng of starting DNA to ensure that sufficient molecules are captured in order to achieve the aforementioned detection threshold. Researchers can opt to start with lower amount of DNA (50 ng is recommended) if the desired detection limit is &#62;0.001 VAF.
As the UMIs are appended onto the i5 indexes, sequencing settings have to be amended accordingly. For example, we used 16 N UMIs, and the sequencing settings were 2x144 paired end reads, 8 cycles of Index 1 and 16 cycles of index 2 as opposed to the usual 8 cycles of Index 2. The increase in Index 2 cycle is compensated by a decrease in the total number of cycles allocated to the reads. If researchers opt to use 12N UMIs10,17, the settings should be changed to 12 cycles of Index 2.
This UMI-based sequencing method is optimized to correct for sequencing errors. It remains suboptimal in dealing with PCR jackpotting, which is an issue for all amplification-based method. We performed rounds of post-sequencing and post-bioinformatics validation using ddPCR, and we hardly detect any false positives due to PCR jackpotting. Nonetheless, it is recommended that researchers conduct the experiments using high fidelity polymerase to ensure low amplification errors.

As we age, exposure to mutagens and stochastic errors during cell division result in the accumulation of somatic aberrations in the genome, and this underlies the fundamental pathogenesis of malignant transformation, neuro-developmental diseases, pediatric disorders and normal aging1,2. Somatic mutations with disease-driving potential are important diagnostic and prognostic biomarkers for early detection and risk management3,4,5. In order to better understand physiologic clonogenesis, which will inform clinical and research decisions, the accurate quantification and characterization of these mutations is of primary importance. Next-generation sequencing (NGS) is currently used to study clonal mutations in heterogeneous DNA samples; however, NGS is limited to identifying mutations at &#62;0.02 variant allele fraction (VAF) &#8212; due to the inherent error-rate of 0.5&#8211;2.0% of the sequencing platforms6,7,8. As a result, tracking diagnostically and prognostically significant somatic variants at lower VAF cannot be achieved using standard NGS.
Recently, various methods have been developed in order to circumvent the error rate of NGS8,9,10,11. These methods utilize molecular tagging, which enables error correction after sequencing. Each molecule or genomic fragment in the sequencing library is tagged with a random Unique Molecular Identifier (UMI) that is specific to that molecule. The UMIs are constructed by permutations of a string of randomized nucleotides (8&#8211;16 N). A second sample-specific barcode is also integrated into the workflow that enables multiplexing multiple samples into the same NGS sequencing run. PCR amplification is performed on the molecularly tagged library, and subsequently the library is sent for sequencing. During library preparation, it is expected that errors will be randomly introduced to the genomic fragment during PCR amplification and sequencing8. To remove random sequencing errors, raw sequencing reads are grouped according to the UMI. Artifacts from sequencing are not expected to be present in all reads with the same UMI at the same genomic position due to the stochastic nature of introduction, whereas a true variant will be faithfully amplified and sequenced in all reads that share the same UMI. The artifacts are bioinformatically removed. Here, we describe three methods of Error-corrected Sequencing (ECS) optimized in the laboratory for DNA to identify single nucleotide variants (SNVs) and small insertion-deletions (Indels), and for RNA to facilitate quantification of gene expression below the NGS error threshold.
The first method describes a way to look for rare somatic event using gene specific primers designed by researchers. Prior to library preparation, researchers should design primers to target the fragments of interest. We used the web-app Primer3 (http://bioinfo.ut.ee/primer3-0.4.0/). Amplicons of 200&#8211;250 bp are ideal for polymerase chain reaction (PCR) as these will, once UMIs have been incorporated, generate overlapping paired-end reads with 150 bp paired-end reads. The optimal primer design conditions to be used are: Minimum primer size = 19; Optimum primer size = 25; Maximum primer size = 30; Minimum Tm = 64 &#176;C; Optimum Tm = 70 &#176;C; Maximum Tm = 74 &#176;C; Maximum Tm difference = 5 &#176;C; Minimum GC content = 45; Maximum GC content = 80; Number to return = 20; Maximum 3&#39; end stability = 100.
In Method 2, we describe a method combining the ECS-DNA protocol with Illumina chemistry to survey for clonal SNVs and small Indels as rare as 0.0001 VAF using commercially available gene panels that include hundreds of amplicons. We have used the TruSight Myeloid Sequencing Panel (Illumina) for our experiment and designed an expanded panel to include additional genes of interest for pediatric myeloid diseases. These panels have not offered unique molecular identifiers (UMIs) that would facilitate error correction, so we have added our own adapter strategy to these panels. ECS should work equally well with any of other panels designed to enrich for genes associated with different diseases. Following DNA isolation and subsequent quantification from the tissue or sample of interest, it is recommended to have at least 500 ng of stock DNA per specimen. We routinely make a single sequencing library using 250 ng of DNA in order to capture as much unique genomic fragment as possible for downstream reads de-duplication and VAF calculation. An optional replicate sequencing library can be made with the remaining 250 ng of DNA. We always make two replicate libraries per specimen, and we consider only those events detected independently in both replicates as true positives. We also implemented a genomic position-specific binomial error model to increase the accuracy of variant calling4,13.
Lastly, we describe a method coupling ECS to RNA sequencing for transcript quantification using off-the-shelf QIAseq Targeted RNA panels (Qiagen). The UMIs required for de-duplication and error correction have been incorporated in the kits, and researchers can make libraries following manufacturer&#39;s recommendations. Bioinformatically, researchers can follow the pipeline outlined for ECS-DNA, which will be explained in detail in the PROTOCOL section.

<table><tbody><tr><td>Q5 High Fidelity Hot Start Master Mix</td><td>New England BioLabs</td><td>M0492S</td><td></td></tr><tr><td>Agencourt AMPure XP</td><td>Beckman Coulter</td><td>A63880</td><td></td></tr><tr><td>Qubit dsDNA HS Assay Kit</td><td>Thermo Fisher Scientific</td><td>Q32854</td><td></td></tr><tr><td>SYBR Safe DNA Gel Stain</td><td>Thermo Fisher Scientific</td><td>S33102</td><td></td></tr><tr><td>Truseq Custom Amplicon Index Kit</td><td>Illumina</td><td>FC-130-1003</td><td></td></tr><tr><td>UMI i5 adapter sequences</td><td>Integrated DNA Technologies</td><td>-</td><td></td></tr><tr><td>NEBNext Ultra End Repair/dA-Tailing Module</td><td>New England BioLabs</td><td>E7442S</td><td></td></tr><tr><td>NEBNext Ultra II Ligation Module</td><td>New England BioLabs</td><td>E7595S</td><td></td></tr><tr><td>QX200 ddPCR EvaGreen Supermix</td><td>Bio-Rad</td><td>1864034</td><td></td></tr><tr><td>QX200 Droplet Generation Oil for EvaGreen</td><td>Bio-Rad</td><td>1864005</td><td></td></tr><tr><td>QX200 Droplet Digital PCR System</td><td>Bio-Rad</td><td>1864001</td><td></td></tr><tr><td>ddPCR 96-Well Plates</td><td>Bio-Rad</td><td>12001925</td><td></td></tr><tr><td>DG8 Cartridges for QX200/QX100 Droplet Generator</td><td>Bio-Rad</td><td>1864008</td><td></td></tr><tr><td>DG8 Gaskets for QX200/QX100 Droplet Generator</td><td>Bio-Rad</td><td>1863009</td><td></td></tr><tr><td>Bioanalyzer</td><td>Agilent Genomics</td><td>G2939BA</td><td></td></tr><tr><td>TapeStation</td><td>Agilent Genomics</td><td>G2991AA</td><td></td></tr><tr><td>TruSight Myeloid Sequencing Panel</td><td>Illumina</td><td>FC-130-1010</td><td></td></tr><tr><td>Bowtie 2</td><td>Johns Hopkins University</td><td>-</td><td></td></tr><tr><td>Customized QIAseq Targeted RNA Panel</td><td>Qiagen</td><td>-</td><td></td></tr><tr><td>Rneasy Plus Mini Kit (50)</td><td>Qiagen</td><td>74134</td><td></td></tr></tbody></table>

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.

With Targeted Error-Corrected Sequencing for DNA, we have performed a proof of principle experiment diluting mutant patient DNA in commercial genomic DNA. The patient had a mutation in GATA1 (chrX:48650264, C&#62;G) with original VAF of 0.19. We demonstrate in Figure 1 that ECS is quantitative to a level of 1:10,000 for single nucleotide variant.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57509/57509fig1.jpg" /> 
Figure 1: Dilution series of GATA1 SNV demonstrating that ECS is quantitative to the level of 1:10,000. <a href="https://www.jove.com/files/ftp_upload/57509/57509fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
We also show that the ECS-DNA reliably detects rare clonal mutations in genes recurrently in adult acute myeloid leukemia (AML) in healthy elderly individuals4. We obtained buffy coat samples from 20 healthy individuals in the Nurse&#39;s Health Study banked roughly ~10 years apart. We applied the ECS-DNA panel protocol on these samples. For this experiment, we adapted the Illumina TruSight Myeloid Sequencing Panel that consists of 568 amplicons (more information on gene list on https://www.illumina.com/products/by-type/clinical-research-products/trusight-myeloid.html) and sequenced 80 libraries from 20 individuals (2 collections at different time points, 2 replicates per individual per time point) using Illumina NextSeq platform, which generated an average of 47.7 million paired-end reads and an average of 3.4 million error-corrected consensus sequences per library4. The mean nucleotide coverage per library was roughly 6,000x (3.4 millions divided by 568). For each sample, we constructed a position-specific error profile using sequenced libraries that are not from the same sample. We found 109 clonal somatic mutations that were present in both replicates of at least one collection time point. These mutations have VAF ranging from 0.0003&#8211;0.1451. We selected 21 mutations with known COSMIC representations, and validated all 21 mutations in one or two collection time point(s) using ddPCR (n = 34, Figure 2, adapted from Young et al. 20164).
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/57509/57509fig2.jpg" /> 
Figure 2: Mutations identified by ECS were verified via ddPCR with highly concordant VAFs. (n=34, modified from Young et al. 20164). <a href="https://www.jove.com/files/ftp_upload/57509/57509fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
With respect to error-corrected expression level using ECS-RNA protocol, we customized a gene panel using QIAseq chemistry that consists of 416 genes known to be associated with various cancers (adapted from QIAseq Human Cancer Transcriptome panel), and we amplified the most commonly expressed exon of a given gene (Gene list in Supplementary Material 1). We sequenced the libraries using Illumina MiSeq platform in paired-end format that gave an average of 8.3 million reads per library, and we managed to capture an average of 0.417 million error-corrected consensus sequences. We showed that the expression level of low abundance transcript (&#60;1,000 transcript count in 50 ng of total RNA) is highly reproducible between replicates (data point n = 300, Figure 3). Validation by ddPCR (six selected genes of varying degree of expression) demonstrated that the expression level of genes had been correctly captured by the ECS protocol without the need for normalization.
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/57509/57509fig3.jpg" /> 
Figure 3: Top, correlation of transcript counts by ECS-RNA between replicates of the same sample (n = 300). Bottom, transcript counts identified by ECS were verified by ddPCR (n = 6). <a href="https://www.jove.com/files/ftp_upload/57509/57509fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Rare Event Detection Using Error-corrected DNA and RNA Sequencing at JoVE.com

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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 ...

rare-event-detection-using-error-corrected-dna-and-rna-sequencing

Research

JoVE Journal

Genetics

12.0K Views.  Washington University School of Medicine. Volgende-generatie rangschikken (NGS) is een krachtig hulpmiddel voor genomische karakterisering die wordt beperkt door het hoge foutenpercentage van het platform (~0.5–2.0%). Wij beschrijven onze methoden van fout-gecorrigeerde sequentie die ons toelaten te ondervangen van het NGS foutenpercentage en mutaties op variant allel breuken zo zeldzaam als 0,0001 detecteren.

Video: Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Wild-type Blocking PCR Combined with Direct Sequencing as a Highly Sensitive Method for Detection of Low-Frequency Somatic Mutations

Accurate detection and identification of low frequency mutations can be problematic when assessing residual disease after therapy, screening for emerging resistance mutations during therapy, or when patients have few circulating tumor cells. Wild-type blocking PCR followed by sequencing analysis offers high sensitivity, flexibility, and simplicity as a methodology for detecting these low frequency mutations. By adding a custom designed locked nucleic acid oligonucleotide to a new or previously established conventional PCR based sequencing assay, sensitivities of approximately 1 mutant allele in a background of 1,000 WT alleles can be achieved (1:1,000). Sequencing artifacts associated with deamination events commonly found in formalin fixed paraffin embedded tissues can be partially remedied by the use of uracil DNA glycosylase during extraction steps. The optimized protocol here is specific for detecting MYD88 mutation, but can serve as a template to design any WTB-PCR assay. Advantages of the WTB-PCR assay over other commonly utilized assays for the detection of low frequency mutations including allele specific PCR and real-time quantitative PCR include fewer occurrences of false positives, greater flexibility and ease of implementation, and the ability to detect both known and unknown mutations.

Wild-type blocking PCR followed by direct sequencing offers a highly sensitive method of detection for low frequency somatic mutations in a variety of sample types.

Accurate detection and identification of low frequency mutations can be problematic when assessing residual disease after therapy, screening for emerging ...

Assessment of DNA Contamination in RNA Samples Based on Ribosomal DNA

One method extensively used for the quantification of gene expression changes and transcript abundances is reverse-transcription quantitative real-time PCR (RT-qPCR). It provides accurate, sensitive, reliable, and reproducible results. Several factors can affect the sensitivity and specificity of RT-qPCR. Residual genomic DNA (gDNA) contaminating RNA samples is one of them. In gene expression analysis, non-specific amplification due to gDNA contamination will overestimate the abundance of transcript levels and can affect the RT-qPCR results. Generally, gDNA is detected by qRT-PCR using primer pairs annealing to intergenic regions or an intron of the gene of interest. Unfortunately, intron/exon annotations are not yet known for all genes from vertebrate, bacteria, protist, fungi, plant, and invertebrate metazoan species.
Here we present a protocol for detection of gDNA contamination in RNA samples by using ribosomal DNA (rDNA)-based primers. The method is based on the unique features of rDNA: their multigene nature, highly conserved sequences, and high frequency in the genome. Also as a case study, a unique set of primers were designed based on the conserved region of ribosomal DNA (rDNA) in the Poaceae family. The universality of these primer pairs was tested by melt curve analysis and agarose gel electrophoresis. Although our method explains how rDNA-based primers can be applied for the gDNA contamination assay in the Poaceae family,&#160;it could be easily used to other prokaryote and eukaryote species

Here, we present a protocol for tracing genomic DNA (gDNA) contamination in RNA samples. The presented method utilizes primers specific for the internal transcribed spacer region (ITS) of ribosomal DNA (rDNA) genes. The method is suited for reliable and sensitive detection of DNA contamination in most eukaryotes and prokaryotes.

One method extensively used for the quantification of gene expression changes and transcript abundances is reverse-transcription quantitative real-time ...

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

The analysis of circulating tumor DNA (ctDNA) using next-generation sequencing (NGS) has become a valuable tool for the development of clinical oncology. However, the application of this method is challenging due to its low sensitivity in analyzing the trace amount of ctDNA in the blood. Furthermore, the method may generate false positive and negative results from this sequencing and subsequent analysis. To improve the feasibility and reliability of ctDNA detection in the clinic, here we present a technique which enriches rare mutations for sequencing, Enrich Rare Mutation Sequencing (ER-Seq). ER-Seq can distinguish a single mutation out of 1 x 107 wild-type nucleotides, which makes it a promising tool to detect extremely low frequency genetic alterations and thus will be very useful in studying disease heterogenicity. By virtue of the unique sequencing adapter&#39;s ligation, this method enables an efficient recovery of ctDNA molecules, while at the same time correcting for errors bidirectionally (sense and antisense). Our selection of 1021 kb probes enriches the measurement of target regions that cover over 95% of the tumor-related driver mutations in 12 tumors. This cost-effective and universal method enables a uniquely successful accumulation of genetic data. After efficiently filtering out background error, ER-seq can precisely detect rare mutations. Using a case study, we present a detailed protocol demonstrating probe design, library construction, and target DNA capture methodologies, while also including the data analysis workflow. The process to carry out this method typically takes 1-2 days.

This manuscript describes a technique for detecting mutations of low frequency in ctDNA, ER-Seq. This method is differentiated by its unique use of two-directional error correction, a special background filter, and efficient molecular acquirement.

The analysis of circulating tumor DNA (ctDNA) using next-generation sequencing (NGS) has become a valuable tool for the development of clinical oncology. ...

Cancer Research

Genome-wide Surveillance of Transcription Errors in Eukaryotic Organisms

Accurate transcription is required for the faithful expression of genetic information. Surprisingly though, little is known about the mechanisms that control the fidelity of transcription. To fill this gap in scientific knowledge, we recently optimized the circle-sequencing assay to detect transcription errors throughout the transcriptome of Saccharomyces cerevisiae, Drosophila melanogaster, and Caenorhabditis elegans. This protocol will provide researchers with a powerful new tool to map the landscape of transcription errors in eukaryotic cells so that the mechanisms that control the fidelity of transcription can be elucidated in unprecedented detail.

This protocol provides researchers with a new tool to monitor the fidelity of transcription in multiple model organisms.

Accurate transcription is required for the faithful expression of genetic information. Surprisingly though, little is known about the mechanisms that ...

Detection and Monitoring of Tumor Associated Circulating DNA in Patient Biofluids

Complications associated with upfront and repeat surgical tissue sampling present the need for minimally invasive platforms capable of molecular sub-classification and temporal monitoring of tumor response to therapy. Here, we describe our dPCR-based method for the detection of tumor somatic mutations in cell free DNA (cfDNA), readily available in&#160;patient biofluids. Although limited in the number of mutations that can be tested for in each assay, this method provides a high level of sensitivity and specificity. Monitoring of mutation abundance, as calculated by MAF, allows for the evaluation of tumor response to therapy, thereby providing a much-needed supplement to radiographic imaging.

Here, we present a protocol to detect tumor somatic mutations in circulating DNA present in patient biological fluids (biofluids). Our droplet digital polymerase chain reaction (dPCR)-based method enables quantification of the tumor mutation allelic frequency (MAF), facilitating a minimally invasive complement to diagnosis and temporal monitoring of tumor response.

Complications associated with upfront and repeat surgical tissue sampling present the need for minimally invasive platforms capable of molecular ...

Naked-Eye Detection of Rare Point Mutations in DNA

The protocol describes a naked-eye colorimetric test for the detection of somatic point mutations in an excess of wild type DNA. The future foreseen application of the method is the identification of rare mutations in circulating cell-free DNA from liquid biopsies, with a relevance in cancer diagnostics and stratification of oncological patients for personalized therapy. As a proof of concept, the test has been designed to detect the BRAFV600E mutation in the BRAF gene, which is important to identify the sub-group of melanoma patients that can benefit from targeted therapies with BRAF inhibitors. However, this colorimetric test can be easily generalized to other somatic mutations of clinical relevance due to the use of universal detection probes, thus providing strong potential in oncological diagnostics.
The test detects 0.5% of BRAFV600E in an excess of BRAFWT DNA, which matches the sensitivity of some commercial instrumental assays. Such sensitivity is clinically relevant for diagnostic purposes, allowing the early identification of drug-sensitive patients. In contrast to commercial assays based on real-time PCR, this test requires minimal instrumentation and processing, as it can be performed on DNA amplified with a standard PCR (or isothermal techniques) and provides a naked-eye readout with a one-tube reaction of a few steps in only one hour. At present, the test has been used only on synthetic DNA samples. However, the latter have been designed to mimic a real sample amplified from circulating cell-free DNA, to favor the translation of the test to clinical diagnostics.

This protocol permits the naked-eye identification of point mutated DNA in a 200-fold excess of wild type DNA molecules, by exploiting gold nanoparticles and paramagnetic microparticles.

The protocol describes a naked-eye colorimetric test for the detection of somatic point mutations in an excess of wild type DNA. The future foreseen ...

Biochemistry

Optimizing Low-Frequency Mutation Detection with Wild-Type Blocking PCR

Wild-type Blocking PCR Combined with Sanger Sequencing for Detection of Low-frequency Somatic Mutation

When monitoring minimal residual disease (MRD) after tumor treatment, there are higher requirements of the lower limit of detection than when detecting for drug resistance mutations and circulating tumor cell mutations during therapy. Traditional Sanger sequencing has 5%-20% wild-type mutation detection, so its limit of detection cannot meet the corresponding requirements. The wild-type blocking technologies that have been reported to overcome this include blocker displacement amplification (BDA), non-extendable locked nucleic acid (LNA), hot-spot-specific probes (HSSP), etc. These technologies use specific oligonucleotide sequences to block wild-type or recognize wild-type and then combine this with other methods to prevent wild-type amplification and amplify mutant amplification, leading to characteristics like high sensitivity, flexibility, and convenience. This protocol uses BDA, a wild-type blocking PCR combined with Sanger sequencing, to optimize the detection of RHOA G17V low-frequency somatic mutations, and the detection sensitivity can reach 0.5%, which can provide a basis for MRD monitoring of angioimmunoblastic T-cell lymphoma.

Author Spotlight: Advancing the Detection of Low-Frequency Mutations in Cancer Tissues

This article introduces the application of a low-frequency detection method based on Sanger sequencing in angioimmunoblastic lymphoma. Provide a basis for applying this method to other diseases.

When monitoring minimal residual disease (MRD) after tumor treatment, there are higher requirements of the lower limit of detection than when detecting ...

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

As DNA sequencing technology has markedly advanced in recent years2, it has become increasingly evident that the amount of genetic variation between any two individuals is greater than previously thought3. In contrast, array-based genotyping has failed to identify a significant contribution of common sequence variants to the phenotypic variability of common disease4,5. Taken together, these observations have led to the evolution of the Common Disease / Rare Variant hypothesis suggesting that the majority of the "missing heritability" in common and complex phenotypes is instead due to an individual's personal profile of rare or private DNA variants6-8. However, characterizing how rare variation impacts complex phenotypes requires the analysis of many affected individuals at many genomic loci, and is ideally compared to a similar survey in an unaffected cohort. Despite the sequencing power offered by today's platforms, a population-based survey of many genomic loci and the subsequent computational analysis required remains prohibitive for many investigators.
To address this need, we have developed a pooled sequencing approach1,9 and a novel software package1 for highly accurate rare variant detection from the resulting data. The ability to pool genomes from entire populations of affected individuals and survey the degree of genetic variation at multiple targeted regions in a single sequencing library provides excellent cost and time savings to traditional single-sample sequencing methodology. With a mean sequencing coverage per allele of 25-fold, our custom algorithm, SPLINTER, uses an internal variant calling control strategy to call insertions, deletions and substitutions up to four base pairs in length with high sensitivity and specificity from pools of up to 1 mutant allele in 500 individuals. Here we describe the method for preparing the pooled sequencing library followed by step-by-step instructions on how to use the SPLINTER package for pooled sequencing analysis (<a href="http://www.ibridgenetwork.org/wustl/splinter" target="_blank">http://www.ibridgenetwork.org/wustl/splinter</a>). We show a comparison between pooled sequencing of 947 individuals, all of whom also underwent genome-wide array, at over 20kb of sequencing per person. Concordance between genotyping of tagged and novel variants called in the pooled sample were excellent. This method can be easily scaled up to any number of genomic loci and any number of individuals. By incorporating the internal positive and negative amplicon controls at ratios that mimic the population under study, the algorithm can be calibrated for optimal performance. This strategy can also be modified for use with hybridization capture or individual-specific barcodes and can be applied to the sequencing of naturally heterogeneous samples, such as tumor DNA.

Pooled DNA sequencing is a fast and cost-effective strategy to detect rare variants associated with complex phenotypes in large cohorts. Here we describe the computational analysis of pooled, next-generation sequencing of 32 cancer-related genes using the SPLINTER software package. This method is scalable, and applicable to any phenotype of interest.

As DNA sequencing technology has markedly advanced in recent years2, it has become increasingly evident that the amount of genetic variation between any ...

Biology

A Nonsequencing Approach for the Rapid Detection of RNA Editing

RNA editing is a process that leads to posttranscriptional sequence alterations in RNAs. Detection and quantification of RNA editing rely mainly on Sanger sequencing and RNA sequencing techniques. However, these methods can be costly and time-consuming. In this protocol, a portable microtemperature gradient gel electrophoresis (&#181;TGGE) system is used as a nonsequencing approach for the rapid detection of RNA editing. The process is based on the principle of electrophoresis, which uses high temperatures to denature nucleic acid samples as they move across a polyacrylamide gel. Across a range of temperatures, a DNA fragment forms a gradient of fully double-stranded DNA to partially separated strands and then to entirely separated single-stranded DNA. RNA-edited sites with distinct nucleotide bases produce different melting profiles in &#181;TGGE analyses. We used the &#181;TGGE-based approach to characterize the differences between the melting profiles of four edited RNA fragments and their corresponding nonedited (wild-type) fragments. Pattern Similarity Scores (PaSSs) were calculated by comparing the band patterns produced by the edited and nonedited RNAs and were used to assess the reproducibility of the method. Overall, the platform described here enables the detection of even single base mutations in RNAs in a straightforward, simple, and cost-effective manner. It is anticipated that this analysis tool will aid new molecular biology findings.

Rapid detection and reliable quantification of RNA editing events at a genomic scale remain challenging and currently rely on direct RNA sequencing methods. The protocol described here uses microtemperature gradient gel electrophoresis (&#181;TGGE) as a simple, quick, and portable method of detecting RNA editing.

RNA editing is a process that leads to posttranscriptional sequence alterations in RNAs. Detection and quantification of RNA editing rely mainly on Sanger ...

Bioengineering

16S rRNA Sequencing: A PCR-based Technique to Identify Bacterial Species

Source: Ewa Bukowska-Faniband1, Tilde Andersson1, Rolf Lood1 
1 Department of Clinical Sciences Lund, Division of Infection Medicine, Biomedical Center, Lund University, 221 00 Lund, Sweden
Planet Earth is a habitat for millions of bacterial species, each of which has specific characteristics. Identification of bacterial species is widely used in microbial ecology to determine biodiversity of environmental samples and medical microbiology to diagnose infected patients. Bacteria can be classified using conventional microbiology methods, such as microscopy, growth on specific media, biochemical and serological tests, and antibiotic sensitivity assays. In recent decades, molecular microbiology methods have revolutionized bacterial identification. A popular method is 16S ribosomal RNA (rRNA) gene sequencing. This method is not only faster and more accurate than conventional methods, but also allows identification of strains that are difficult to grow in laboratory conditions. Furthermore, differentiation of strains at the molecular level enables discrimination between phenotypically identical bacteria (1-4).
16S rRNA joins with a complex of 19 proteins to form a 30S subunit of the bacterial ribosome (5). It is encoded by the 16S rRNA gene, which is present and highly conserved in all bacteria due to its essential function in ribosome assembly; however, it also contains variable regions which may serve as fingerprints for particular species. These features have made the 16S rRNA gene an ideal genetic fragment to be used in identification, comparison, and phylogenetic classification of bacteria (6).
16S rRNA gene sequencing is based on the polymerase chain reaction (PCR) (7-8) followed by DNA sequencing (9). PCR is a molecular biology method used to amplify specific fragments of DNA through a series of cycles that include:
i) Denaturation of a double stranded DNA template 
ii) Annealing of primers (short oligonucleotides) that are complementary to the template 
iii) Extension of primers by the DNA polymerase enzyme, which synthesizes a new DNA strand
A schematic overview of the method is shown in Figure 1.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/10510/10510fig1.jpg" /> 
Figure 1: Schematic overview of the PCR reaction. <a href="https://www.jove.com/files/ftp_upload/10510/10510fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
There are several factors that are important for a successful PCR reaction, one of which is quality of the DNA template. Isolation of chromosomal DNA from bacteria can be performed using standard protocols or commercial kits. Special care should be taken to obtain DNA that is free of contaminants that can inhibit the PCR reaction.
Conserved regions of the 16S rRNA gene permit the design of universal primer pairs (one forward and one reverse) that can bind to and amplify the target region in any bacterial species. The target region can vary in size. While some primer pairs can amplify most of the 16S rRNA gene, others amplify only parts of it. Examples of commonly used primers are shown in Table 1 and their binding sites are depicted in Figure 2.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Primer name</td>
			<td>Sequence (5&#39;&#8594;3&#39;)</td>
			<td>Forward/ reverse</td>
			<td>Reference</td>
		</tr>
		<tr>
			<td>8F b)</td>
			<td>AGAGTTTGATCCTGGCTCAG</td>
			<td>forward</td>
			<td>-1</td>
		</tr>
		<tr>
			<td>27F</td>
			<td>AGAGTTTGATCMTGGCTCAG</td>
			<td>forward</td>
			<td>-10</td>
		</tr>
		<tr>
			<td>515F</td>
			<td>GTGCCAGCMGCCGCGGTAA</td>
			<td>forward</td>
			<td>-11</td>
		</tr>
		<tr>
			<td>911R</td>
			<td>GCCCCCGTCAATTCMTTTGA</td>
			<td>reverse</td>
			<td>-12</td>
		</tr>
		<tr>
			<td>1391R</td>
			<td>GACGGGCGGTGTGTRCA</td>
			<td>reverse</td>
			<td>-11</td>
		</tr>
		<tr>
			<td>1492R</td>
			<td>GGTTACCTTGTTACGACTT</td>
			<td>reverse</td>
			<td>-11</td>
		</tr>
	</tbody>
</table>
Table 1: Examples of standard oligonucleotides used in amplification of 16S rRNA genes a). 
a) The expected lengths of the PCR product generated using the different primer combinations can be estimated by calculating the distance between the binding sites for the forward and the reverse primer (see Figure 2), e.g. the size of the PCR product using primer pair 8F-1492R is ~1500 bp, and for primer pair 27F-911R ~900 bp. 
b) also known as fD1
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/10510/10510fig2.jpg" /> 
Figure 2: Representative figure of the 16S rRNA sequence and the primer-binding sites. Conserved regions are colored in grey and variable regions are filled with diagonal lines. To allow for the highest resolution, primer 8F and 1492R (name based on location on rRNA sequence) are used to amplify the whole sequence, allowing for the sequencing of several variable regions of the gene. <a href="https://www.jove.com/files/ftp_upload/10510/10510fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Cycling conditions for PCR (i.e. the temperature and time required for the DNA to be denatured, annealed with primers, and synthesized) are dependent on the type of polymerase that is used and the properties of the primers. It is recommended to follow the manufacturer&#39;s guidelines for a particular polymerase.
After the PCR program is completed, the products are analyzed by agarose gel electrophoresis. A successful PCR yields a single band of expected size. The product must be purified prior to sequencing to remove residual primers, deoxyribonucleotides, polymerase, and buffer which were present in the PCR reaction. The purified DNA fragments are usually sent for sequencing to commercial sequencing services; however, some institutions perform DNA sequencing at their own core facilities.
The DNA sequence is automatically generated from a DNA chromatogram by a computer and must be carefully checked for quality, as manual editing is sometimes needed. Following this step, the gene sequence is compared with sequences deposited in the 16S rRNA database. The regions of similarity are identified, and the most similar sequences are delivered.

16S rRNA Sequencing: Identifying Bacterial Species by PCR

Source: Ewa Bukowska-Faniband1, Tilde Andersson1, Rolf Lood1
1 Department of Clinical Sciences Lund, Division of Infection Medicine, Biomedical Center, ...

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

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