False Positive Calls Research Articles

Validation of a Customized Bioinformatics Pipeline for a Clinical Next-Generation Sequencing Test Targeting Solid Tumor–Associated Variants

Bioinformatic analysis is an integral and critical part of clinical next-generation sequencing. It is especially challenging for some pipelines to consistently identify insertions and deletions. We present the validation of an open source tumor amplicon pipeline (OTA-pipeline) for clinical next-generation sequencing targeting solid tumor-associated variants. Raw data generated from 557 TruSight Tumor 26 samples and in silico data were analyzed by the OTA-pipeline and legacy pipeline and compared. Discrepant results were confirmed by orthogonal methods. The OTA-pipeline reported 22 variants that were not detected by the previously validated pipeline, including seven synonymous or intronic single-nucleotide variants, five single-nucleotide variants at frequency <5%, one insertion, and nine deletions. Variant allele frequencies reported by the two pipelines were highly concordant, although a few significant discrepancies were present. Analysis of in silico FASTQ files demonstrated a higher sensitivity of detecting complex insertions and deletions with the OTA-pipeline. The higher sensitivity came at a cost, because false-positive calls were increased in difficult-to-sequence regions. However, these calls were all flagged by our strand bias filter, distinguishing them from true variants. Our validation process provides a model for laboratories that want to establish an in-house bioinformatics pipeline for clinical next-generation sequencing.

Mapping Late Leaf Spot Resistance in Peanut (Arachis hypogaea) Using QTL-seq Reveals Markers for Marker-Assisted Selection.

Late leaf spot (LLS; Cercosporidium personatum) is a major fungal disease of cultivated peanut (Arachis hypogaea). A recombinant inbred line population segregating for quantitative field resistance was used to identify quantitative trait loci (QTL) using QTL-seq. High rates of false positive SNP calls using established methods in this allotetraploid crop obscured significant QTLs. To resolve this problem, robust parental SNPs were first identified using polyploid-specific SNP identification pipelines, leading to discovery of significant QTLs for LLS resistance. These QTLs were confirmed over 4 years of field data. Selection with markers linked to these QTLs resulted in a significant increase in resistance, showing that these markers can be immediately applied in breeding programs. This study demonstrates that QTL-seq can be used to rapidly identify QTLs controlling highly quantitative traits in polyploid crops with complex genomes. Markers identified can then be deployed in breeding programs, increasing the efficiency of selection using molecular tools.Key Message: Field resistance to late leaf spot is a quantitative trait controlled by many QTLs. Using polyploid-specific methods, QTL-seq is faster and more cost effective than QTL mapping.

Unique, dual-indexed sequencing adapters with UMIs effectively eliminate index cross-talk and significantly improve sensitivity of massively parallel sequencing

BackgroundSample index cross-talk can result in false positive calls when massively parallel sequencing (MPS) is used for sensitive applications such as low-frequency somatic variant discovery, ancient DNA investigations, microbial detection in human samples, or circulating cell-free tumor DNA (ctDNA) variant detection. Therefore, the limit-of-detection of an MPS assay is directly related to the degree of index cross-talk.ResultsCross-talk rates up to 0.29% were observed when using standard, combinatorial adapters, resulting in 110,180 (0.1% cross-talk rate) or 1,121,074 (0.29% cross-talk rate) misassigned reads per lane in non-patterned and patterned Illumina flow cells, respectively. Here, we demonstrate that using unique, dual-matched indexed adapters dramatically reduces index cross-talk to ≤1 misassigned reads per flow cell lane. While the current study was performed using dual-matched indices, using unique, dual-unrelated indices would also be an effective alternative.ConclusionsFor sensitive downstream analyses, the use of combinatorial indices for multiplexed hybrid capture and sequencing is inappropriate, as it results in an unacceptable number of misassigned reads. Cross-talk can be virtually eliminated using dual-matched indexed adapters. These results suggest that use of such adapters is critical to reduce false positive rates in assays that aim to identify low allele frequency events, and strongly indicate that dual-matched adapters be implemented for all sensitive MPS applications.

Use of RAPTR-SV to Identify SVs from Read Pairing and Split Read Signatures.

High-throughput short read sequencing technologies are still the leading cost-effective means of assessing variation in individual samples. Unfortunately, while such technologies are eminently capable of detecting single nucleotide polymorphisms (SNP) and small insertions and deletions, the detection of large copy number variants (CNV) with these technologies is prone to numerous false positives. CNV detection tools that incorporate multiple variant signals and exclude regions of systemic bias in the genome tend to reduce the probability of false positive calls and therefore represent the best means of ascertaining true CNV regions. To this end, we provide instructions and details on the use of the RAPTR-SV CNV detection pipeline, which is a tool that incorporates read-pair and split-read signals to identify high confidence CNV regions in a sequenced sample. By combining two different structural variant (SV) signals in variant calling, RAPTR-SV enables the easy filtration of artifact CNV calls from large datasets.

Genomic Sequence Variation Analysis by Resequencing.

Whole-genome resequencing is a method for determining the differences between individuals and a reference genome. The experiments are performed by sequencing the individuals, aligning generated reads to a common reference and discovering variation within the data set by analysis of the alignment with software tools. When correlated with phenotypic information, sites of causative genomic variation may be putatively assigned.While the analysis is generally straightforward, there are many nuances, and we aim to help you understand how to generate an initial result, sift through it to identify likely candidates for a phenotype of interest, and flag false positive calls.

Detection of Cross-Sample Contamination in Multiple Myeloma Samples and Sequencing Data.

The increasing applicability and sensitivity of next generation sequencing methods exacerbate one of the main issues in the molecular biology laboratory, namely cross-sample contamination. This type of contamination, which could massively increase the rate of false-positive calls in sequencing experiments, can originate at each step during the processing of multiple myeloma samples, such as CD138-selection of tumor cells, RNA and DNA isolation or the processing of sequencing libraries. Here we describe a Droplet Digital PCR (ddPCR) method and a simple bioinformatic solution for the detection of contamination in patient's samples and derived sequencing data, which are based on the same principle: detection of alternative alleles for single-nucleotide polymorphisms (SNPs) that are homozygous according to the control (germ line) sample.

Abstract CN08-03: Liquid biopsies for the management of cancer patient treatment and for early detection of cancer

Abstract Molecular profiling to aid treatment selection is becoming routine in major cancer centers across the globe. Liquid biopsies for this purpose are increasing because of the challenges that tumor biopsies can present, especially for particular tumor types (e.g., pancreas and lung cancer), for patients with multiple metastases, and when serial sampling is needed to monitor tumor evolution and acquired drug resistance. In Manchester, we have pioneered this approach within the TARGET protocol to support selection of the most appropriate phase I trial for patients at the Christie Hospital. Within the TARGET study, we are profiling DNA from tumor biopsies (archival and/or fresh) and circulating tumor DNA. I will describe the TARGET feasibility study with results from the first 100 patients that focused on the development of our robust ctDNA workflow and bioinformatics pipeline, how ctDNA data correlate with those derived from tumor biopsy, and the matching of molecular profiles with actionable alterations to available trials. I will also describe our biweekly Molecular Tumor Board and the current initiative to provide a "virtual tumor board," where board members can access and discuss laboratory and clinical data remotely. Our ongoing and future goals are to expand TARGET to a further 450 Christie Hospital patients and across the CRUK Experimental Medicines Centres North (ECMC North), bringing opportunities for patients to travel to the most appropriate trial for them across the region. I will then discuss how we are approaching the harder challenge of liquid biopsy for the early detection of cancer, focusing on lung cancer where earlier detection that results in surgery with curative intent would transform patient outcomes. The subject cohort study that will maximize the success of liquid biopsy discovery for lung cancer is termed the "hard to reach, high risk" and typically reside in socially deprived areas, only seeking medical attention when their cancer is at an advanced and usually incurable stage. To address this challenge, we have worked closely with the University Hospital of South Manchester to set up a parallel blood test discovery program alongside the Manchester Community Lung Health Study. Subjects receive invitations from their GP to attend a free Lung Health Check in their communities (in shopping mall carparks), part of which for many subjects is a CT scan in a purpose-built van. This summer (and ongoing) we have collected &amp;gt;600 subject blood samples in vans parked adjacent to the CT scanner van. Samples are brought to our laboratory ED for processing and storage prior to analysis of circulating tumor cells (CTCs) using new technology platforms that examine every cell within the sample, and of ctDNA. Liquid biopsy results will be mapped to CT scan results and with clinical follow-up. Last summer, the CT scan data led to earlier detection of lung cancer cases allowing curative-intent surgery, but this is expensive and generates false-positive calls. Unlike liquid biopsy, multiple repeat CT scanning cannot be offered. We are therefore asking whether liquid biopsies that provide a molecular confirmation of cancer (single CTC or ctDNA profiling) can identify those subjects with a positive CT scan who have a cancer diagnosis and whether liquid biopsies yield earlier detection of cancer. Citation Format: Caroline Dive. Liquid biopsies for the management of cancer patient treatment and for early detection of cancer [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr CN08-03.

MICRA: an automatic pipeline for fast characterization of microbial genomes from high-throughput sequencing data

The increase in available sequence data has advanced the field of microbiology; however, making sense of these data without bioinformatics skills is still problematic. We describe MICRA, an automatic pipeline, available as a web interface, for microbial identification and characterization through reads analysis. MICRA uses iterative mapping against reference genomes to identify genes and variations. Additional modules allow prediction of antibiotic susceptibility and resistance and comparing the results of several samples. MICRA is fast, producing few false-positive annotations and variant calls compared to current methods, making it a tool of great interest for fully exploiting sequencing data.

A straightforward assay to evaluate DNA integrity and optimize next-generation sequencing for clinical diagnosis in oncology

Next generation sequencing (NGS) has become an informative tool to guide cancer treatment and conduce a personalized approach in oncology. The biopsy collected for pathologic analysis is usually stored as formalin-fixed paraffin-embedded (FFPE) blocks and then availed for molecular diagnostic, resulting in DNA molecules that are invariably fragmented and chemically modified. In an attempt to improve NGS based diagnostics in oncology we developed a straightforward DNA integrity assessment assay based on qPCR, defining clear parameters to whether NGS sequencing results is accurate or when it should be analyzed with caution.We performed DNA extraction from 12 tumor samples from diverse tissues and accessed DNA integrity by straightforward qPCR assays. In order to perform a cancer panel NGS sequencing, DNA library preparation was performed using RNA capture baits. Reads were aligned to the reference human genome and mutation calls were further validated by Sanger sequencing. Results obtained by the DNA integrity assays correlated to the efficiency of the pre-capture library preparation in up to 0.94 (Pearson's test). Moreover, sequencing results showed that poor integrity DNA leads to high rates of false positive mutation calls, specially C:G>T:A and C:G>A:T.Poor quality FFPE DNA samples are prone to generating false positive mutation calls. These are especially perilous in cases in which subclonal populations are expected, such as in advance disease, since it could lead clinicians to erroneous conclusions and equivocated conduct.

Ultraaccurate genome sequencing and haplotyping of single human cells

Accurate detection of variants and long-range haplotypes in genomes of single human cells remains very challenging. Common approaches require extensive in vitro amplification of genomes of individual cells using DNA polymerases and high-throughput short-read DNA sequencing. These approaches have two notable drawbacks. First, polymerase replication errors could generate tens of thousands of false-positive calls per genome. Second, relatively short sequence reads contain little to no haplotype information. Here we report a method, which is dubbed SISSOR (single-stranded sequencing using microfluidic reactors), for accurate single-cell genome sequencing and haplotyping. A microfluidic processor is used to separate the Watson and Crick strands of the double-stranded chromosomal DNA in a single cell and to randomly partition megabase-size DNA strands into multiple nanoliter compartments for amplification and construction of barcoded libraries for sequencing. The separation and partitioning of large single-stranded DNA fragments of the homologous chromosome pairs allows for the independent sequencing of each of the complementary and homologous strands. This enables the assembly of long haplotypes and reduction of sequence errors by using the redundant sequence information and haplotype-based error removal. We demonstrated the ability to sequence single-cell genomes with error rates as low as 10-8 and average 500-kb-long DNA fragments that can be assembled into haplotype contigs with N50 greater than 7 Mb. The performance could be further improved with more uniform amplification and more accurate sequence alignment. The ability to obtain accurate genome sequences and haplotype information from single cells will enable applications of genome sequencing for diverse clinical needs.

Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-seq data.

The main application of ChIP-seq technology is the detection of genomic regions that bind to a protein of interest. A large part of functional genomics’ public catalogs is based on ChIP-seq data. These catalogs rely on peak calling algorithms that infer protein-binding sites by detecting genomic regions associated with more mapped reads (coverage) than expected by chance, as a result of the experimental protocol's lack of perfect specificity. We find that GC-content bias accounts for substantial variability in the observed coverage for ChIP-seq experiments and that this variability leads to false-positive peak calls. More concerning is that the GC effect varies across experiments, with the effect strong enough to result in a substantial number of peaks called differently when different laboratories perform experiments on the same cell line. However, accounting for GC content bias in ChIP-seq is challenging because the binding sites of interest tend to be more common in high GC-content regions, which confounds real biological signals with unwanted variability. To account for this challenge, we introduce a statistical approach that accounts for GC effects on both nonspecific noise and signal induced by the binding site. The method can be used to account for this bias in binding quantification as well to improve existing peak calling algorithms. We use this approach to show a reduction in false-positive peaks as well as improved consistency across laboratories.

Abstract 565: Analytical performance of TruSight® Tumor 170 in the detection of gene fusions and splice variants using RNA from formalin-fixed, paraffin-embedded (FFPE) solid tumor samples

Abstract Recent studies have highlighted the importance of gene fusions and splice variants in solid tumor profiling1. Next-generation sequencing can be an effective means of detecting these alterations in FFPE samples using RNA rather than DNA, as a single chimeric RNA transcript could result from numerous alterations in DNA2. To that end, Illumina developed TruSight® Tumor 1703, a comprehensive, hybrid capture-based NGS assay targeting 170 key cancer genes. Along with a DNA workflow, the assay includes a RNA workflow for the identification of splice variants and gene fusions. Following sequencing on the NextSeq® or HiSeq® instruments, TruSight® Tumor 170 offers an analytical pipeline which initiates variant calling. These algorithms were first optimized against the simulated read data from &amp;gt;350 fusions and splice variants reported in the RNA content of the gene panel. A hybrid approach of read alignment and assembly was used to enhance the fusion calling sensitivity. Deliberate filters were designed to reduce false positive calling from sequence homologs, polymerase read-through, or FFPE artifacts. For splice variant calling, a panel of FFPE non-cancerous samples were used to capture false positive mutation calls. With endogenous RNA splicing in cellular physiology, exon-boundary probes were added in the hybrid capture to enhance enrichment efficiency. To the best of our knowledge, there is not yet a standard definition for the limit of detection (LoD) in detecting gene fusions and splice variants from NGS data. We propose to define the LoD of a fusion calling and splice variant NGS panel as the lowest molecule count of a chimeric transcript that could be reliably detected with a sufficient number of supporting sequencing reads. To determine the LoD of TruSight® Tumor 170 using this definition, we mixed cell lines expressing a panel of known fusions and splice variants to measure the copy number of each chimeric transcript. Using these samples we examined the ability of the assay to confidently detect the alterations using 40 ng of RNA input. To demonstrate the analytical sensitivity and specificity of this NGS based assay, we compiled a panel of 49 mixed samples and validated the molecule count to be near the LoD of 5 copies per ng RNA input by PCR. The sensitivity was &amp;gt;98% for fusions and 100% for splice variants. For understanding the limit of blank (LoB) of the assay, another panel of 40 samples not harboring fusions and splice variants was also assessed by TruSight® Tumor 170. These samples demonstrated a ~97% specificity for fusion calling and &amp;gt;95% specificity for splice variant calling. These results indicate that the TruSight® Tumor 170 panel analysis can identify lowly expressed fusions and splice variants from a small amount of compromised RNA from solid tumor samples at high analytical sensitivity and specificity. 1 Klijn et al. (2015) 2 Maher et al. (2009) 3 For Research Use Only. Citation Format: Tingting Du, June Snedecor, Jennifer S. LoCoco, Xiao Chen, Laurel Ball, Allan Castaneda, Danny Chou, Katie Clark, Brian Crain, Anthony Daulo, Manh Do, Sarah Dumm, Yonmee Han, Mike Havern, Chia-Ling Hsieh, Tingting Jiang, Suzanne Johansen, Scott Lang, Rachel Liang, Jaime McLean, Yousef Nassiri, Austin Purdy, Jason Rostron, Jennifer Silhavy, Natasha Talago, Li Teng, Kevin Wu, Clare Zlatkov, Chen Zhao, Ali Kuraishy, Karen Gutekunst, Sohela De Rozieres, Matthew Friedenberg, Anne C. Jager, Han-Yu Chuang. Analytical performance of TruSight® Tumor 170 in the detection of gene fusions and splice variants using RNA from formalin-fixed, paraffin-embedded (FFPE) solid tumor samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 565. doi:10.1158/1538-7445.AM2017-565

Abstract 5690: Optimized molecular barcoding enables accurate targeted mutation detection in circulating cell-free DNA (cfDNA)

Abstract The evaluation of cfDNA allows novel approaches to noninvasive detection of actionable alterations, resistance mechanisms, and tumor monitoring in patients with cancer. Importantly, tumor-specific DNA fragments represent a small minority of the cfDNA and can be obscured by false positive (FP) variants introduced by chemical damage and sequencer error. To address this, we improved key processes in the design of NGS libraries, including a new molecular barcoding approach, that maximize molecular recovery while eliminating spurious variants. We engineered a set of Illumina sequencing chemistry compatible adaptors incorporating unique molecular identifiers (barcodes) enabling reconstruction of the sequence of both strands of the original DNA molecule. These barcodes incorporate a number of key design improvements as compared to published methodologies, which enhance sequencer cluster density, thereby increasing library diversity and molecular recovery. Our new design identified both chemical and sequencer errors, reducing incorrect base calls to rates below 5e-7. We validated our methodology for use in cfDNA using both dilution experiments and patient blood samples with known oncogenic alterations via a custom capture panel targeting actionable genomic alterations in a 55kb region. By identifying the molecular origin of each read, we found that the sensitivity of detection obtained from barcoded libraries followed ideal binomial sampling expectations. We obtained an average molecular depth of 1,000 molecules per site from the plasma extracted from a single blood collection tube, which corresponded to an 80% sensitivity of detection of known oncogenic single-nucleotide and indel mutations at 0.15% mutant allele frequency (MAF) in cfDNA with no FP calls. Furthermore, we successfully detected known gene-fusions at 0.5%, and amplifications (&amp;gt;10 copies) down to 1% MAF. We designed and validated a custom-engineered error-correcting sequencing adapters, ideal for broad range of applications requiring high accuracy detection of ultra-low frequency alterations. Note: This abstract was not presented at the meeting. Citation Format: Carlo G. Artieri, Kyle A. Beauchamp, Valentina S. Vysotskaia, Noah C. Welker, Eric A. Evans, Clement Chu, Haluk Tezcan, Imran S. Haque. Optimized molecular barcoding enables accurate targeted mutation detection in circulating cell-free DNA (cfDNA) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5690. doi:10.1158/1538-7445.AM2017-5690

Abstract 5360: DNA repair increases sequencing accuracy without altering actual mutation frequency in clinical samples

Abstract Targeted cancer therapy based on genomic alterations can be remarkably effective. Currently, cancer genome profiling using next generation sequencing (NGS) is routinely applied in cancer care to guide personalized treatment. The accuracy of this profiling directly impacts therapeutic choices and the outcomes of patient care. We previously showed that false positive variants are abundant and can account for a major fraction of identified somatic variations in publicly available datasets (doi: http://dx.doi.org/10.1101/070334). These false positive variants show signs of mutagenic DNA damage. We further demonstrated that enzymatic DNA repair increases sequencing quality by lowering damage-induced background noise. Therefore, enzymatic DNA repair has the potential to improve sequencing accuracy, avoiding incorrect somatic variant calls and consequently reducing incorrect diagnostic conclusions. In this study, we investigated whether enzymatic DNA repair introduces any bias to NGS libraries using analysis by droplet digital PCR (ddPCR) and deep sequencing. DNA Reference Standards containing multiple common cancer mutations (Horizon Discovery, Inc.) were spiked into formalin-fixed paraffin-embedded (FFPE) DNA isolated from tumor samples from different tissue types at defined frequencies (0.5-10% quantified by ddPCR). Genotyping of the FFPE DNA ensured that they were free of any of the spiked-in mutations. After DNA repair and library preparation, mutation frequencies were quantified by ddPCR, and compared to the mutation levels in input DNA and control libraries without repair. Deep sequencing of 151 cancer genes including these spike-ins showed no difference in mutation frequency for the spiked-in mutations between the control and repair groups. However, the number of false positive variant calls was reduced in the repair group. Our data demonstrates that DNA repair significantly increases sequencing accuracy without altering the frequency of actual mutations in tumor samples. Citation Format: Pingfang Liu, Lixin Chen, Laurence Ettwiller, Eileen Dimalanta, Theodore B. Davis, Thomas C. Evans. DNA repair increases sequencing accuracy without altering actual mutation frequency in clinical samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5360. doi:10.1158/1538-7445.AM2017-5360

Abstract 3732: Analytical performance of TruSight® Tumor 170 on small nucleotide variations and gene amplifications using DNA from formalin-fixed, paraffin-embedded (FFPE) solid tumor samples

Abstract Expanding the paradigm of solid tumor profiling from single-gene testing to comprehensive panels presents many challenges. One such challenges is the ability of these panels to detect genetic alterations from FFPE samples, where the DNA is of low abundance and often heavily compromised. Despite these challenges, next-generation sequencing (NGS) offers the ability to assess multiple variants simultaneously in an ever-expanding list of relevant tumor genes. To that end, Illumina developed a comprehensive, hybrid capture-based NGS assay targeting 170 key cancer genes that is FFPE optimized. The assay consists of a DNA workflow for the identification of single and multiple nucleotide variants (SNVs, MNVs), small insertions and deletions (indels), gene amplifications, as well as a RNA workflow for the identification of splice variants and gene fusions. Following sequencing on the NextSeq® or HiSeq® instruments, the analytical pipeline initiates variant calling. The DNA aligner and variant callers were first optimized against the simulated read data from &amp;gt;40,000 COSMIC[1] mutations reported in the exons of the 170 genes. To reduce false positive variant calling due to systematic errors, each variant call was evaluated against its locus specific background error distribution. This distribution was compiled from a panel of FFPE normal samples and was also used to normalize against systematic bias in read coverage to increase the accuracy of amplification calling. Furthermore, gene amplification calling was improved by the addition of enhancer probes to the hybrid capture pool. The analytical sensitivity and specificity of TruSight® Tumor 170* was assessed on a large collection of FFPE samples and reference material. A panel of 72 cancer samples, including multiple tissue types, reference standards, and cell line and FFPE mixes were used to evaluate the limit of detection. The samples contained 533 SNVs, 80 indels including deletions up to 30 base pairs and insertions up to 31 base pairs, 4 MNVs, and 31 gene amplifications, characterized by orthogonal testing methods. Using 40 ng DNA input, detection sensitivity of the &amp;gt;1000 variants (including replicates) tested at variant allele frequencies down to ~5% was at 99.6%, while detection sensitivity of gene amplifications as low as 1.45x to 2.2x was at 98%. For limit of blank samples, a panel of 24 normal samples was used. Again using 40 ng DNA input, we show &amp;gt;99% specificity for small variant calling and &amp;gt;95% specificity for gene amplification calling. These data demonstrates the TruSight® Tumor 170 is able to detect multiple variant types within a single sample at low nucleic acid input, while exhibiting high analytical sensitivity and specificity for low allele fraction detection. [1] Forbes, et al. (2015) *For Research Use Only. Not for use in diagnostic procedures. Citation Format: Danny Chou, Xiao Chen, Austin Purdy, Li Teng, Byron Luo, Chen Zhao, Laurel Ball, Allan Castaneda, Katie Clark, Brian Crain, Anthony Daulo, Manh Do, Tingting Du, Sarah Dumm, Yonmee Han, Michael Havern, Chia-Ling Hsieh, Tingting Jiang, Suzanne Johansen, Scott Lang, Rachel Liang, Jennifer S. LoCoco, Jaime McLean, Yousef Nassiri, Jason Rostron, Jennifer Silhavy, June Snedecor, Natasha Talago, Kevin Wu, Clare Zlatkov, Ali Kuraishy, Karen Gutekunst, Sohela De Rozieres, Matthew Friedenberg, Han-Yu Chuang, Anne C. Jager. Analytical performance of TruSight® Tumor 170 on small nucleotide variations and gene amplifications using DNA from formalin-fixed, paraffin-embedded (FFPE) solid tumor samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3732. doi:10.1158/1538-7445.AM2017-3732

Multimethod Longitudinal HIV Drug Resistance Analysis in Antiretroviral-Therapy-Naive Patients.

The global intensification of antiretroviral therapy (ART) can lead to increased rates of HIV drug resistance (HIVDR) mutations in treated and also in ART-naive patients. ART-naive HIV-1-infected patients from Cameroon were subjected to a multimethod HIVDR analysis using amplification-refractory mutation system (ARMS)-PCR, Sanger sequencing, and longitudinal next-generation sequencing (NGS) to determine their profiles for the mutations K103N, Y181C, K65R, M184V, and T215F/Y. We processed 66 ART-naive HIV-1-positive patients with highly diverse subtypes that underlined the predominance of CRF02_AG and the increasing rate of F2 and other recombinant forms in Cameroon. We compared three resistance testing methods for 5 major mutation sites. Using Sanger sequencing, the overall prevalence of HIVDR mutations was 7.6% (5/66) and included all studied mutations except K65R. Comparing ARMS-PCR with Sanger sequencing as a reference, we obtained a sensitivity of 100% (5/5) and a specificity of 95% (58/61), caused by three false-positive calls with ARMS-PCR. For 32/66 samples, we obtained NGS data and we observed two additional mismatches made up of minority variants (7% and 18%) that might not be clinically relevant. Longitudinal NGS analyses revealed changes in HIVDR mutations in all five positive subjects that could not be attributed to treatment. In one of these cases, superinfection led to the temporary masking of a resistant virus. HIVDR mutations can be sensitively detected by ARMS-PCR and sequencing methods with comparable performances. Longitudinal changes in HIVDR mutations have to be considered even in the absence of treatment.

TIDDIT, an efficient and comprehensive structural variant caller for massive parallel sequencing data

Reliable detection of large structural variation ( > 1000 bp) is important in both rare and common genetic disorders. Whole genome sequencing (WGS) is a technology that may be used to identify a large proportion of the genomic structural variants (SVs) in an individual in a single experiment. Even though SV callers have been extensively used in research to detect mutations, the potential usage of SV callers within routine clinical diagnostics is hindered by high computational costs, usage of non-standard output format, and limited support for the various sequencing platforms and libraries. Another well known, but not well-addressed problem is the large number of benign variants and reference errors present in the human genome that further complicates analysis. Here we present TIDDIT, a time efficient variant caller, that uses discordant read pairs as well as the depth of coverage and split reads to detect and classify a large spectrum of SVs. As part of the software suite, TIDDIT also includes a database functionality that enables filtering for rare variants and reduces the number of false positive calls and background noise. Benchmarked against five state-of-the-art SV callers, TIDDIT performs at an equal/superior level while using only 2 CPU hours per sample. Thanks to its speed, sensitivity, flexibility and ability to easily detect variants on a wide range of WGS library types, TIDDIT solves many of the problems that are currently hindering the utilization of WGS for SV calling in clinical settings.

The challenge of detecting indels in bacterial genomes from short-read sequencing data

We tested the capabilities of four different software tools to detect insertions and deletions (indels) in a bacterial genome on the basis of short sequencing reads. We included tools applying the gapped-alignment (VarScan, FreeBayes) or split-read (Pindel) methods, respectively, and a combinatorial approach with local de-novo assembly (ScanIndel). Tests were performed with 151-basepair, paired-end sequencing reads simulated from a bacterial (Clostridioides difficile R20291) genome sequence with predefined indels (indel length, 1–2321bp). Results achieved with the different tools varied widely, and the specific sensitivity and false-discovery rates strongly depended on indel size. All tools tested were able to detect short indels (≤29 basepairs) at sensitivities close to 100%, albeit Pindel reported up to 20% false calls. In contrast, gapped-alignment and split-read tools failed to recover large proportions of long indels (>29bp) even at 120-fold coverage, and again, Pindel produced significant numbers of false-positive calls. Outstandingly, ScanIndel detected and reconstructed 97% of long indels on average (95% confidence intervals, 88%–99%) and, at the same time, produced negligible amounts of false calls. Hence, the combinatorial approach implemented in ScanIndel was able to recover the positions, types and sequences of indels with excellent sensitivity and false-discovery rate, by encompassing the full indel length spectrum present in the datasets.

New Approaches on Quantification of Campylobacter jejuni in Poultry Samples: The Use of Digital PCR and Real-time PCR against the ISO Standard Plate Count Method.

Campylobacteriosis is the most frequently reported bacterial food-borne illness in the European Union and contaminated broiler meat is considered the most important source of infection in humans. The aim of the present study was to evaluate real-time PCR (qPCR) and digital PCR (dPCR) for quantification of Campylobacter jejuni in 75 broiler neck-skin samples collected from a poultry slaughterhouse, and to compare them with the ISO 10272-2 standard plate count method. For qPCR standard curve, C. jejuni-negative neck-skin samples were spiked with C. jejuni suspension with a known number of bacterial cells. The observed CFU/g values by qPCR correlated greatly with the expected values and qPCR showed good performance with the reliable limit of detection (rLOD) and limit of quantification (LOQ) of three and 31 target copies per reaction, respectively. However, both rLOD (1219 CFU/g) and LOQ (12,523 CFU/g) were beyond the EFSA-proposed critical limit of 500–1,000 CFU/g of neck skin. Although C. jejuni cell counts were ≤1,000 CFU/g in only 7/75 samples by plate counting, they were ≤LOQ in 60/75 and ≤rLOD in 26/75 (≤1,000 CFU/g in 24/75) samples by qPCR. A strong and statistically significant correlation was observed between qPCR and dPCR. Both PCR-based methods correlated significantly with the plate count method; however, the correlation was moderate. Using the Bland–Altman analysis, an average agreement was noted between all three methods, although with a large standard deviation. A significant bias toward overestimation in dPCR was observed, probably due to the relatively high number of false positive calls. The linear dynamic range was comparable in both PCR-based methods; however, qPCR proved to be more suitable for routine use. In the future, the establishment of a reliable molecular quantification of C. jejuni in poultry samples showing a wide range of contamination levels down to the proposed critical limit is needed to enable time- and cost-effective surveillance throughout all stages in the food production chain. As both rLOD and LOQ were beyond this limit, a modification of the procedure is suggested to include less sample dilution prior to DNA extraction to enable PCR-based quantification of C. jejuni at the proposed microbiological criteria.

GATK hard filtering: tunable parameters to improve variant calling for next generation sequencing targeted gene panel data

BackgroundNGS technology represents a powerful alternative to the standard Sanger sequencing in the context of clinical setting. The proprietary software that are generally used for variant calling often depend on preset parameters that may not fit in a satisfactory manner for different genes.GATK, which is widely used in the academic world, is rich in parameters for variant calling. However the self-adjusting parameter calibration of GATK requires data from a large number of exomes. When these are not available, which is the standard condition of a diagnostic laboratory, the parameters must be set by the operator (hard filtering). The aim of the present paper was to set up a procedure to assess the best parameters to be used in the hard filtering of GATK. This was pursued by using classification trees on true and false variants from simulated sequences of a real dataset data.ResultsWe simulated two datasets, with different coverages, including all the sequence alterations identified in a real dataset according to their observed frequencies. Simulated sequences were aligned with standard protocols and then regression trees were built up to identify the most reliable parameters and cutoff values to discriminate true and false variant calls. Moreover, we analyzed flanking sequences of region presenting a high rate of false positive calls observing that such sequences present a low complexity make up.ConclusionsOur results showed that GATK hard filtering parameter values can be tailored through a simulation study based-on the DNA region of interest to ameliorate the accuracy of the variant calling.