Understanding and controlling for sample and platform biases in NGS assays
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What is the impact of assay failure in your laboratory and how do you monitor for it? The advancement of next-generation sequencing has provided invaluable resources to researchers in multiple industries and disciplines, and will be a major driver during the personalized medicine revolution that is upon us. However, while the cost of generating sequencing data continues to decrease this does not take into account the significant costs associated with the infrastructure and expertise that are required to develop a robust, routine NGS pipeline. Specifically, as predicted by Sboner, et al in 2011, the cost of the sequencing portion of the experiment continues to decrease and the costs associated with upfront experimental design and downstream analysis dominate the cost of each assay. This is true whether you are performing a pre-clinical R&D project, and perhaps even more so for clinical assays. In the paper, the authors note the unpredictable and considerable ‘human time’ spent on the upstream design and downstream analysis. Here at Horizon, we aim to develop tools that help researchers and clinicians optimize these workflows to make NGS more reliable and ultimately, more affordable by streamlining these resource intensive areas.
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Understanding and controlling for sample and platform biases in NGS assays
- 1. HORIZON DISCOVERY Understanding and Controlling for Sample and Platform Biases in NGS Assays
- 2. 2 For Research Use Only What is the impact of assay failure in your laboratory and how do you monitor for it?
- 3. 3 For Research Use Only Clinical Application of Next Generation Sequencing Using just one sample, one workflow can test for mutation status across multiple genes
- 4. 4 For Research Use Only The Sources of Variability in the Next Generation Sequencing Workflow
- 5. 5 For Research Use Only Quantitative Multiplex 0 5 10 15 20 25 30 BRAF V600E KIT D816V EGFR ΔE746 - A750 EGFR L858R EGFR T790M EGFR G719S KRAS G13D KRAS G12D NRAS Q61K PIK3CA H1047R PIK3CA E545K Horizon Partner A Partner B Partner C AmpliSeq Panel in three laboratories
- 6. 6 For Research Use Only Next-Generation Sequencing Introduction Also known as high-throughput or massively-parallel sequencing • Allows us to address questions that require a lot of data • Has been applied to scientific questions across industries • Pharma • Biotech • Biofuels • Agriculture • Food Science • Archeology • Medicine • Personalized Medicine
- 7. 7 For Research Use Only Next-Generation Sequencing Introduction
- 8. 8 For Research Use Only RNA transcriptomics DNA metagenomics And more… Next-Generation Sequencing Introduction DNA epigenomics DNA resequencing DNA de novo assembly
- 9. 9 For Research Use Only RNA transcriptomics DNA metagenomics And more… Next-Generation Sequencing Introduction DNA epigenomics DNA resequencing DNA de novo assembly We will focus on: • Biological Sample • Library Preparation • Sequencing Platform • Informatics Pipeline o View our previous webinar for more on informatics
- 10. 10 For Research Use Only
- 11. 11 For Research Use Only NGS Workflow – Reference Materials
- 12. 12 For Research Use Only Source of Error: Biological Sample Potential Sources of bias/error include: • User errors o Exogeneous DNA contamination o Mislabelling • Heterogenous sample o Non-tumor cells o Mixed-cell populations (xenografts) • Limited sample availability o Low Quantity • Degradation/fragmentation o FFPE o cfDNA
- 13. 13 For Research Use Only Formalin Compromised DNA Reference Standards • Multiple formats for Quantitative Multiplex Reference Standard • 11 validated positive mutations • Frequency range: 24%-1% • HD-C749 (Formalin-Compromised DNA) – (mild formalin treatment, low-level degraded) • Lanes 2 and 4 on right • HD-C751 (Formalin-Compromised DNA) – (harsh formalin treatment, highly degraded) • Lanes 3 and 5 on right The Quantitative Multiplex also comes in the following formats: • HD701 (DNA) – high molecular weight DNA extracted directly from cells • HD200 (FFPE) - mild-formalin fixation, embedded in paraffin once extracted shows little degradation Genomic DNA Tapescreen assay [bp] 1 2 3 4 5
- 14. 14 For Research Use Only Formalin-Compromised Multiplex Reference Standard HD-C751 HD-C749 How does formalin treatment affect downstream analysis? Amplification bias may not be detected without appropriate controls.
- 15. 15 For Research Use Only Formalin-Compromised Multiplex Reference Standard Variant Expected Ratio “Acceptable Range” Determined Ratio Batch 1 Determined Ratio Batch 2 Determined Ratio Batch 3 Determined Ratio Batch 1 Determined Ratio Batch 2 Determined Ratio Batch 3 EGFR G719S 25% 22.1%-27% 23.4% 23.8% 23.4% 24.1% 22.7% 23.2% PI3KCA H1047R 18% 14%-21% 19.6% 20.0% 18.8% 20.7% 20.4% 20.7% KRAS G13D 15% 12%-18% 13.8% 14.8% 12.9% 15.3% 17.8% 14.0% NRAS Q61K 13% 10%-15% 10.4% 10.1% 12.0% 12.8% 13.5% 13.2% BRAF V600E 11% 8.6%-12.8% 12.4% 12.5% 11.9% 12.3% 11.6% 12.7% PI3KCA E545K 9% 7.2%-10.8% 8.0% 8.1% 8.8% 10.7% 13.1% 13.0% KIT D816V 10% 8%-12% 10.5% 10.2% 10.2% 10.5% 21.9% 20.1% KRAS G12D 6% 4.8%-7.2% 5.9% 6.0% 5.3% 7.2% 5.9% 7.2% EGFR L858R 3% 2.1%-3.9% 3.2% 3.3% 3.3% 3.4% 4.3% 3.5% EGFR ∆E746- A750 2% 1.4%-2.6% 1.9% 2.0% 1.9% 1.9% 3.3% 3.2% EGFR T790M 1% 0.7%-1.3% 1.3% 1.3% 1.0% 1.1% 1.6% 1.2% HD-C749 HD-C751
- 16. 16 For Research Use Only Bias/Errors in Library Preparation Robasky, K. et al. The role of replicates for error mitigation in next-generation sequencing. Nature Rev. Genet. 15, 56-62 (2014).
- 17. 17 For Research Use Only Sequencing Library Preparation Enrichment options: • whole-genome (not enriched) • whole-exome capture • custom capture • capture-based panels • off-the-shelf amplicon panels • custom amplicon panels Goal: Use a reference standard that reflects your actual sample.
- 18. 18 For Research Use Only Source of Error: Library Preparation Errors arising from sequencing library preparation include: • Uneven sequencing coverage • Sequence changes • Length biasing/preferential amplification • Primer bias Mispriming Multiple Displacement Amplification (MDA) Incorporation of errors From NuGEN
- 19. 19 For Research Use Only Variant Type Mutation Expected Fractional Abundance (%) or CNV: SNV High GC GNA11 Q209L 5.6 SNV High GC AKT1 E17K 5.6 SNV Low GC KRAS G13D 5.6 SNV Low GC Pi3Ka E545K 5.6 Long Insertion EGFR V769 ins 5.6 Long Deletion EGFR (delE746-A750) 5.3 Fusion ROS1 translocation 5.6 Fusion RET translocation 5.6 CNV MET amplification 4.5 x amplification CNV MYC amplification 9.5 x amplification SNP EGFR_G719S 5.3 Short Deletion MET_p.V237fs 4.8* SNV High GC NOTCH1_p.P668S 5.0 Short Deletion FLT3_p.S985fs 5.6 Short Deletion BRCA2_p.A1689fs 5.6 Short Deletion FBXW7_p.G667fs 5.6 Structural Multiplex Reference Standard *This product is part of our early access program. It is the responsibility of the individual laboratory to determine expected results specific to its assay.
- 20. 20 For Research Use Only Bias/Errors in Library Preparation Robasky, K. et al. The role of replicates for error mitigation in next-generation sequencing. Nature Rev. Genet. 15, 56-62 (2014).
- 21. 21 For Research Use Only Platform Bias – Overview 3 Common Platforms: Common sources of bias/error include: • User error Sample overloading • Machine failure Laser, hard drive, software, fluidics failures • Nucleotide malfunction Fluorophore quenching, nucleotide damage, signal overlap • Sequence context errors High GC content, low-complexity regions, homopolymers • Dephasing Incomplete extension, addition of multiple nucleotides
- 22. 22 For Research Use Only Platform Bias – Illumina Images from Illumina.
- 23. 23 For Research Use Only Platform Bias – Illumina Images from Illumina.
- 24. 24 For Research Use Only Platform Bias – Ion Torrent Illustration: James Provost http://spectrum.ieee.org/biomedical/devices/the-gene-machine-and-me ErrorRate Homopolymer length
- 25. 25 For Research Use Only Platform Bias – PacBio Single Molecule Real Time (SMRT) Sequencing Image from PacBio.
- 26. 26 For Research Use Only Platform Bias – How can replicates help? DNA samples from blood and saliva were sequenced on two different platforms — Illumina and Complete Genomics — which resulted in 88.1% concordance of single-nucleotide variants (SNVs) across replicates. Cross Platform Replicates
- 27. 27 For Research Use Only Value of Replicates – Biological and Technical Robasky, K. et al. The role of replicates for error mitigation in next-generation sequencing. Nature Rev. Genet. 15, 56-62 (2014). R = replicates
- 28. 28 For Research Use Only Value of Technical Replicates – Process Noise Platform QX100 Droplet Digital PCR (Internal QC) Ampliseq Cancer Hotspot Panel v2* Gene Mutation Specification Observed mutant ratio, % COV BRAF V600E 10.5 10.2 10.3 0.01 KIT D816V 10.0 10.4 10.1 0.01 EGFR ΔE746 - A750 2.0 2.0 Not detected - EGFR L858R 3.0 2.7 2.4 0.07 EGFR T790M 1.0 0.9 Not detected - EGFR G719S 24.5 24.4 24.8 0.01 KRAS G13D 15.0 16.1 15.5 0.03 KRAS G12D 6.0 5.0 5.1 0.03 NRAS Q61K 12.5 12.8 12.6 0.01 PIK3CA H1047R 17.5 18.6 17.9 0.01 PIK3CA E545K 9.0 8.9 8.8 0.01 *Average of 8 runs, average coverage 2000x Quantitative Multiplex Reference Standard Available as gDNA or FFPE ready for extraction
- 29. 29 For Research Use Only NGS Workflow
- 30. 30 For Research Use Only Source of Error: Bioinformatics http://www.horizondx.com/bioinformatics-webinar.html
- 31. 31 For Research Use Only Next-Generation Sequencing – Wrap up
- 32. 32 Horizon Discovery – Your Partners in Personalized Medicine Powering Genomic Research and Translational Medicine, from Sequence to Treatment Horizon’s mission is to be a fully integrated life science company that provides enabling products, services and research programs to clients engaged at every stage of the healthcare continuum from sequence to treatment
- 33. 33 Horizon’s Range of Products/Services
- 34. 34 Horizon’s Range of Products/Services
- 35. 35 For Research Use Only Routinely monitor the performance of your workflows and assays with independent external controls What extraction and quantification methods are you using? What is the limit of detection of your workflow? Is the impact of formalin treatment interesting to you? What is the impact of assay failure in your laboratory and how do you monitor for it?
- 36. 36 For Research Use Only How to Test the Robustness and Sensitivity of your Workflow and Assay Structural Standard DNA Sample Complexity Sample Features QMRS DNA and FFPE GIAB FFPE Gene-Specific Multiplex DNA and FFPE Tru-Q DNA
- 37. Your Horizon Contact: t + 44 (0)1223 655580 f + 44 (0)1223 655581 e info@horizondiscovery.com w www.horizondiscovery.com Horizon Discovery, 7100 Cambridge Research Park, Waterbeach, Cambridge, CB25 9TL, United Kingdom Natalie LaFranzo, PhD US Customer/Technical Support Scientist n.lafranzo@horizondiscovery.com +1-844-655-7800
Notes de l'éditeur
- The advancement of next-generation sequencing has provided invaluable resources to researchers in multiple industries and disciplines, and will be a major driver during the personalized medicine revolution that is upon us. However, while the cost of generating sequencing data continues to decrease as demonstrated in the figure here from the NIH, this does not take into account the significant costs associated with the infrastructure and expertise that are required to develop a robust, routine NGS pipeline.
- Specifically, as predicted by Sboner, et al in 2011, the cost of the sequencing portion of the experiment continues to decrease and the costs associated with upfront experimental design and downstream analysis dominate the cost of each assay. This is true whether you are performing a pre-clinical R&D project, and perhaps even more so for clinical assays. In the paper, the authors note the unpredictable and considerable ‘human time’ spent on the upstream design and downstream analysis. Here at Horizon, we aim to develop tools that help researchers and clinicians optimize these workflows to make NGS more reliable and ultimately, more affordable by streamlining these resource intensive areas.
- Depending on the different genetic material you are starting with, and by following specific experimental workflows, NGS can be applied to many applications, as shown here. For today’s webinar, we will focus on DNA resequencing applications, which implies the data generated will be compared to an existing reference sequence (such as the human genome) to identify onco-relevant mutations including single-nucleotide variants, insertions-deletions, copy number variants and translocations.
- And as Jonathan showed earlier, when we think about the full NGS workflow for resequencing applications, there are many molecular manipulations that must occur to generate the fully analyzed data set. In each of these steps, bias and error can introduce variability. We’re going to focus on the variability and sources of error in 3 of these points: The Biological Sample itself The molecular steps of preparing a sequencing library, And specific biases associated with some of the commonly used Sequencing Platforms I want to mention that while we won’t go through this today, if you’re interested in learning more about the Bioinformatics analysis, you should check out the recording of our previous webinar saved on the web.
- And a great reference for further reading is a fantastic perspective article from Nature Reviews – Genetics titled, “The Role of Replicates for Error Mitigation in next-generation sequencing”. I’ll refer back to the ideas presented in this paper later in the webinar.
- In each laboratory, a researcher or clinician should consider the impact that bias and error have on their own assays. And, as requested during the attendee poll in our previous webinar, I’ll highlight how known reference standards have been shown to be vital in better understanding and combating variability to ensure that NGS assays are accurate and reproducible. These points are particularly important when we consider patient samples of low-quantity, low-quality, and especially when the goal is detecting low allelic frequency variants. Furthermore, in order for NGS technology to evolve and allow us to better discern haplotype phasing and determine differences between two dysfunctional gene copies, we will need to have a good understanding of the error rates and limitations of the pipelines we are using.
- First we’ll focus on the sources of error associated with the biological sample itself. Some errors, such as mislabelling or sample mix-ups are due to human error and may be difficult to predict. While these are a bit of an ‘unknown-unknown’, running a known, predictable standard alongside your own samples can at least help to introduce a checkpoint in your data! Biological variability, such as tumor heterogeneity and mixed-cell populations, add additional variability to the sample. In this case, use of biological replicates can help identify true positive variants in a cell population. We’ll discuss this in more detail later. Limited sample availability requires that we amplify the DNA by PCR in order to achieve sufficient material for sequencing. The degree of amplification required will depend on the library preparation approach you select. Finally, there’s also the degree of fragmentation, whether inherent to the sample, such as in cfDNA or due to the preservation method such as in FFPE samples.
- Fragmented materials, such as cell-free DNA or formalin-compromised DNA may perform differently in an NGS assay due to variability in primer binding, amplification, or exclusion during size selection as a result of the high fragmentation. Horizon offers genetically-equivalent multiplex reference standards in varying formats, from high molecular weight or high DNA Integrity Number to formalin-compromised and fragmented DNA with low DINs, to allow researchers to evaluate how fragmentation affects detection. The Quantitative Multiplex Range includes the Formalin-Compromised DNA format in which cells are treated with either a mild or severe formalin intensity treatment. Use of these reference standards together as a set can help you to investigate the effect of formalin on your workflow starting from the quantification and library preparation stage. These standards have characterised fragmentation patterns, and have been quantified using the Qubit assay to prevent overestimation which has been demonstrated with the Nanodrop assay. We have targeted defined allelic frequencies and with the comparison between the mildly treated sample and the severely treated sample you can investigate the effect of formalin on the allelic frequency determination.
- When we compare HD-C749 and HD-C751, which are standards derived from genetically-equivalent cell lines, the only variable we are changing is the intensity of the formalin treatment. You can see in the plot here, collected during our internal droplet digital PCR validations that the Ct values for these two standards differ – both in their quantitative value, but also in their reproducibility. This does not mean however that the HD-C751 is performing ‘poorly’, but rather it is a important reference for better understanding what expectations one should have for their own biological samples that have been treated with formalin. Importantly, the impact of this variability in amplification will be dependent on the library approach that you’re using.
- This is further exemplified when we consider the allelic frequencies we observe during this droplet digital PCR. When we compare 3 matched lots of HD-C749 and HD-C751, where Batch 1 for each is derived from an identical cell line mixture, we can observe quantitatively the effect the intensity of formalin treatment has on our variability. Here, we observe that for all lots of HD-C749 the detected allelic frequencies fit within the “acceptable range” we use for non-formalin treated samples. However, in matched lots, where the actual frequencies are biologically identical but the cells have been subjected to more intense formalin treatment, some of the variants fall outside our benchmark for non-formalin treated samples. Interestingly, these are not all at low allelic frequencies, but rather are likely dependent on the genomic context of that region. I want to emphasize that these results do not indicate that HD-C751 is not “performing properly”. Rather, HD-C751 provides us with realistic expectations and allows us to better understand the variability that we will observe in formalin-treated samples.
- As I mentioned earlier, the impact of your sample variability is affected by the approach you take in preparing your sample for sequencing.
- While our knowledge of the human genome has been significantly enhanced since the completion of the Human Genome Project, there are still many regions that are either not well understood, or are repetitive. As a result, it’s often preferred to interrogate a reduced representation of the genome, focusing specifically on the exonic regions or smaller targeted regions of interest with short, 100-300 bp sequencing reads. Given the high-throughput nature of NGS, multiple samples may be multiplexed on a single sequencing run, making each assay more affordable and efficient. And so, if we think about the primary ways of approaching targeted sequencing, we consider first, solution-based enrichment using probes to capture specific regions, and second, targeted PCR to generate short amplicons. (CLICK) Reference standards can play an integral role during protocol optimization of sequencing library preparation. It’s ideal to have a standard that mimics the concentration, genomic diversity, and fragmentation level of your actual clinical sample. Ensuring there are no competing regions for probe or primer binding, and that the input quantity, quality, concentration yields the optimal library output can be achieved with the appropriate reference standard. If we consider again the formalin-compromised reference standards, we could compare whether it would be beneficial to use a “PCR-free” library to eliminate amplification biases, or better understand how the formalin treatment affects amplicon generation during library preparation. And as we’ve discussed, Horizon’s reference standards are all derived from genomic DNA from human cell lines, making them more realistic than plasmid or synthetic standards.
- Parameters that you may want to evaluate at this stage of your assay optimization include how the library protocol performs in providing adequate sequencing coverage in your regions of interest, whether the PCR or amplification steps introduce unwanted sequence changes, whether certain regions are preferentially amplified, and the many potential effects of primer bias. You may also have the option of moving to a PCR-free library kit, or introducing molecular barcodes such as in NuGEN Single Primer Enrichment Technology.
- Importantly, if you wish to detect some of the more difficult and rare mutations such as larger insertions or deletions, copy number variants and translocations, you’ll want to evaluate whether this is feasible with your library protocol and overall pipeline. We’ve combined each of these types of variants in our early access HD-753 Structural Multiplex DNA Reference Standard so that you may use 1 sample, with well-defined genomic complexity to test your workflow. Importantly, this standard also allows you to evaluate the genomic context with regard to GC content, which we’ll find is important both in terms of the library protocol but also the platform itself.
- And so, as the next stage in the NGS process, we must also consider the bias associated with the platform itself. There are additional molecular steps that occur on the machine in the actual data generation step of the assay.
- There are three platforms that are most commonly used in laboratories across the world. These are the Illumina series including the MiSeq, NextSeq, HiSeq and X-Ten/X-Five, the Ion Torrent Personal Genome Machine (PGM) and Ion Proton, and the PacBio platform. Each has advantages in terms of read length, output, multiplexing, and versatility. Across the board, there are platform biases that may occur. There are again user errors, where the platform is overloaded and cannot perform optimally, which results in poor quality data. Machine failure and reagent issues are also ubiquitous across all platforms. However, there are also platform-specific biases due to differences in chemistries.
- Illumina’s chemistry makes use of clonal amplification to generated “clusters” of short fragments as shown in the figure on the left, and then fluorescent detection of sequencing by synthesis. The terminal base is deprotected, and all 4 fluorescently-labelled nucleotides are flowed over the flow cell, along with a polymerase. Following single base incorporation, the flow cell is imaged to detect what base was introduced in each “cluster”. Then the process repeats for a certain number of cycles, which determines the read length. The data from each cluster is considered 1 read. In this approach, there are a few places where errors can occur. First, you may have substitution errors that arise when incorrect bases are incorporated during clonal amplification (which may be propagated). Additionally, Illumina sequencing has been found to have a sequence-specific error profile, postulated to result from single-strand DNA folding or sequencing specific differences in enzyme preferences.
- For more information on this, check out the paper by Nakamura, et al.
- In Ion Torrent sequencing, we again are using sequencing by synthesis. Fragments are prepared using emulsion PCR to generate a clonal set of fragments which make be deposited into a microwell on the sequencing chip. Rather than making use of labelled nucleotides and optics during synthesis, individual nulceotides are flowed over the chip individually (with a polymerase), and the release of positively-charged protons are electronically detected by the sensor in each well. The number of released hydrogen ions, and therefore signal, is proportional to the number of nucleotides incorporated. With this platform, if repeats of the same nucleotide, also know as homopolymers, are being sequenced, then multiple hydrogren ions are released in a single cycle. This results in an increased electronic signal, which can be difficult to quantify for long repeats. This limitation is also shared by other technologies that detect single nucleotide incorporations, such as pyrosequencing in the Roche 454 platform. This homopolymer bias results in insertion and deletion errors in the sequencing data.
- The PacBio platform also uses a modified version of sequencing by synthesis. However, the difference lies in that a single polymerase enzyme is in each well, and therefore single-molecules are sequenced, which is why PacBio sequencing is also called, “Single Molecule Real-Time or SMRT Sequencing”. The active polymerase is immobilized at the bottom of what’s known as a zero-mode waveguide or ZMW, and fluorescently-labeled nucleotides diffuse into the ZMW chamber. The illumination is directed to the bottom of the ZMW, and the nucleotide held by the polymerase prior to incorporation emits an extended signal that identifies the base being incorporated. When a nucleotide is incorporated by the polymerase, the fluorophore is cleaved and diffuses away. The specific base is called based on the fluorescent tag detected, and the software generates a set of reads for each molecule. PacBio’s SMRT sequencing touts impressive read lengths – averaging 10-15 kb in length. However, if the fluorescent signal is not detected due to quenching or non-labeled nucleotides, false insertions and deletions may be incorporated as sequencing errors. Due to the nature of the single-molecule detection of this technology, these errors are more prevalent than other technologies (as compared to Illumina or Ion Torrent where ‘clusters’ or droplets of molecules are sequenced) and this results in the error rate of the PacBio machine being inappropriate for detection of low alleleic frequencies, as they may be difficult to disambiguate from the noise. Furthermore, the output of the PacBio machine is not yet at the same level as the Ion Torrent and Illumina sequencers, but continues to improve. However, the PacBio does show impressive resistance to GC bias. As shown in the plots on the right. The top plots show the relative coverage GC-bias for Illumina MiSeq, Ion Torrent PGM, and Pacific Biosciences RS on the P. falciparum (19% GC), E. coli (51%), and R. sphaeroides (69%) genomes (Table 2, data sets 1 to 9). Unbiased coverage would be represented by a horizontal line at a relative coverage = 1 , which is shown here as a black dashed line. ---- http://www.pacificbiosciences.com/products/smrt-technology/ http://www.pacificbiosciences.com/pdf/Poster_Genotyping_and_Variation_Discovery_in_Human_Data.pdf
- We see that Pacific Biosciences coverage levels are the least biased, followed by Illumina, although all technologies exhibit error-rate biases in high- and low-GC regions and at long homopolymer runs. By combining data from two technologies, one can reduce coverage bias if the biases in the component technologies are complementary and of similar magnitude. In the data presented here, cross platform replicates were compared. DNA samples from blood and saliva were sequenced on two different platforms — Illumina and Complete Genomics — which resulted in 88.1% concordance of single-nucleotide variants (SNVs) across replicates. In another study, sequencing on three platforms — Illumina, Roche 454 and SOLiD — showed 64.7% concordance.
- Aside from cross-platform replicates, what other ways can replicates help mitigate error in NGS assays? Biological replicates, or samples that have been independently prepared under the same conditions from the same host (as shown above) and technical replicates, usually considered a repeat analysis of the exact same sample (sequencing library in the case of NGS) have significant utility during protocol optimization, validation, and even later during sample analysis. Biological and technical replicates can be used to assess the specificity and sensitivity of sequence variant-calling methods, “in a manner that is independent of the algorithms and the chemistry that are used to call the variants, thereby guiding the appropriate selection of quality score thresholds”.
- As I introduced earlier, technical replicates are usually considered a repeat analysis of the exact same sample. In the case of NGS sequencing, this would be running the same sequencing library on the same machine using the same parameters. By running these types of replicates during initial pipeline development, or when evaluating new protocols or sample types, you will obtain information on the degree of noise associated with your pipeline. However, most laboratories would prefer not to use precious patient samples for these types of upfront experiments. Horizon’s multiplex reference standards allow you to perform these experiments using a renewable, cost-effective resource. With these standards you can determine the sensitivity, specificity, and limit of detection of your particular protocol, avoiding false variant calls and assisting with data interpretation and confidence. Furthermore, Horizon’s reference standards are validated using a completely orthogonal technology, droplet digital PCR, which is crucial for understanding the bias in your sequencing protocol.
- To wrap up the NGS workflow, I’ll briefly mention the role bioinformatics analysis can play in introducing variability. We’ll discuss this in more detail during our June webinar, where we dig further into the role reference standards can play in optimizing your informatics workflow. For both of the benchtop sequencers, the researcher may use the on machine analysis software. However, depending on the goals or flexibility of the assay, a researcher may choose to use an external, off-the-shelf software package or custom-developed pipeline. These may vary in parameters such as alignment stringency, which is how closely the sequence must match the reference, or the threshold of coverage required to make a variant call, such as requiring that the position must be sequenced at coverage of 4x or greater. For parameters such as these, it's important to evaluate how changes to the pipeline affect the final data output.
- A great reference for understanding the role of bioinformatics in more detail is our previous webinar, which can be accessed from our website, under Scientific Support – Archived Webinars.
- And so, coming back to the figure I showed at the beginning of the discussion, if we can make use of reference standards to better refine molecular protocols and analytical pipelines, we can help improve the cost and utility of next generation sequencing. Once we’ve accomplished that, what’s left is the downstream analysis. As the authors note, “linking a genetic variation to a phenotype, especially a clinically relevant one, requires a lot of expertise and effort, first to identify highly confident variants; second, to estimate their functional impact; third, to select from among the functional ones those that are correlated to the phenotype. A team of bioinformaticians, statisticians, geneticists, biologists and physicians is required to translate the information in the primary data into useful knowledge to understand the impact of genomic variants in biological systems; this can often take weeks or months of extensive experimental validations using animal models or cell lines.” Here at Horizon, we are a multi-disciplinary team, with experts spanning the range of translational genomics. I’d like to now hand things off to Jonathan to briefly introduce how our team fits into the Horizon offerings and the other resources we have available.
- As I mentioned at the beginning of the webinar, it is useful to use different products to answer different questions whether that is the determination of the limit of detection or assessment of the robustness of the assay. One way of to classify the different Q-Seq references is based on the sample features and the sample complexity. If we begin with the Gene-Specific Multiplex, these cover several mutations within the KRAS or EGFR genes and coming from a single parental cell line background, this allows you to dilute with the matched wildtype and determine the limit of detection of your assay. To establish the specificity of this limit of detection, the Tru-Q product range could be used. These are more complex samples which cover 40 engineered mutations at 5%, 2.5% and 1.3%. Using these together, you can determine the level at which the mutation drops out and can compare this across a number of mutations within a selection of oncorelevant genes relevant to many targeted gene panels. Rotational standard, recommended by the New York State Guidelines. The quantitative multiplex provides a single reference standard with multiple formats allowing you to investigate multiple factors introducing variability. With the gDNA you can slot it straight into the quantification or library preparation step, with FFPE you can perform the DNA extraction step and monitor your entire workflow and finally with the formalin-compromised DNA format you can investigate the effect of formalin on your assay. The GIAB samples are well characterised genome samples as an FFPE section which are well-characterized through the PGP and scientific community. Finally, the structural standard we have recently introduced to the range as part of our Early Access product, provides a reference material that contains SNPs and small indels but in addition, copy number variants and translocations and larger indels.