Sequencing Errors Research Articles

Effects of error, chimera, bias, and GC content on the accuracy of amplicon sequencing.

Amplicon sequencing of targeted genes is the predominant approach to estimate the membership and structure of microbial communities. However, accurate reconstruction of community composition is difficult due to sequencing errors, and other methodological biases and effective approaches to overcome these challenges are essential. Using a mock community of 33 phylogenetically diverse strains, this study evaluated the effect of GC content on sequencing results and tested different approaches to improve overall sequencing accuracy while characterizing the pros and cons of popular amplicon sequence data processing approaches. The sequencing results from this study can serve as a benchmarking data set for future algorithmic improvements. Furthermore, the new insights on sequencing error, chimera formation, and GC bias from this study will help enhance the quality of amplicon sequencing studies and support the development of new data analysis approaches.

Hybrid-hybrid correction of errors in long reads with HERO

Although generally superior, hybrid approaches for correcting errors in third-generation sequencing (TGS) reads, using next-generation sequencing (NGS) reads, mistake haplotype-specific variants for errors in polyploid and mixed samples. We suggest HERO, as the first “hybrid-hybrid” approach, to make use of both de Bruijn graphs and overlap graphs for optimal catering to the particular strengths of NGS and TGS reads. Extensive benchmarking experiments demonstrate that HERO improves indel and mismatch error rates by on average 65% (27∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document}95%) and 20% (4∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document}61%). Using HERO prior to genome assembly significantly improves the assemblies in the majority of the relevant categories.

Hybridized gated recurrent unit with variational mode decomposition and an error compensation mechanism for multi-step-ahead monthly rainfall forecasting.

Highly accurate monthly rainfall predictions can provide early warnings for rain-related disasters, such as floods and droughts, and allow governments to make timely decisions. This paper proposes a two-phase error compensation model based on a gated recurrent unit (GRU), variational mode decomposition (VMD), and error compensation mechanism (ECM) (GRU-VMD-ECM) for accurate multi-step-ahead monthly rainfall forecasts. In the first phase, the GRU model is used to make an initial monthly rainfall prediction, and the error series is extracted. In the second phase, the error series is decomposed into eight subseries using the VMD method. Each subseries is then input into the GRU model to build different forecasting models. These predicted error sequences are added to the initial prediction results to obtain the final forecast. The model's performance is tested using six evaluation indicators based on Beijing's monthly rainfall data from 1951 to 2018. The results show that the error compensation mechanism significantly improved the prediction accuracy, particularly in the Nash-Sutcliffe efficiency (NSE) of single-step-ahead prediction which recorded a substantial increase of 281.16% from 0.259981 to 0.990944, as well as a decrease in root mean square error (RMSE) from 2.257580 to 0.249746. Furthermore, the GRU-VMD-ECM model outperforms the RF, GRU-CNN, and VMD-GRU models in terms of precision across all forecasting horizons. These findings highlight the potential of the GRU-VMD-ECM model in providing highly accurate monthly rainfall predictions for early warnings and informed decision-making by governments.

Improving the filtering of false positive single nucleotide variations by combining genomic features with quality metrics.

Technical errors in sequencing or bioinformatics steps and difficulties in alignment at some genomic sites result in false positive (FP) variants. Filtering based on quality metrics is a common method for detecting FP variants, but setting thresholds to reduce FP rates may reduce the number of true positive variants by overlooking the more complex relationships between features. The goal of this study is to develop a machine learning-based model for identifying FPs that integrates quality metrics with genomic features and with the feature interpretability property to provide insights into model results. We propose a random forest-based model that utilizes genomic features to improve identification of FPs. Further examination of the features shows that the newly introduced features have an important impact on the prediction of variants misclassified by VEF, GATK-CNN, and GARFIELD, recently introduced FP detection systems. We applied cost-sensitive training to avoid errors in misclassification of true variants and developed a model that provides a robust mechanism against misclassification of true variants while increasing the prediction rate of FP variants. This model can be easily re-trained when factors such as experimental protocols might alter the FP distribution. In addition, it has an interpretability mechanism that allows users to understand the impact of features on the model's predictions. The software implementation can be found at https://github.com/ideateknoloji/FPDetect.

Correcting modification-mediated errors in nanopore sequencing by nucleotide demodification and reference-based correction

The accuracy of Oxford Nanopore Technology (ONT) sequencing has significantly improved thanks to new flowcells, sequencing kits, and basecalling algorithms. However, novel modification types untrained in the basecalling models can seriously reduce the quality. Here we reports a set of ONT-sequenced genomes with unexpected low quality due to novel modification types. Demodification by whole-genome amplification significantly improved the quality but lost the epigenome. We also developed a reference-based method, Modpolish, for correcting modification-mediated errors while retaining the epigenome when a sufficient number of closely-related genomes is publicly available (default: top 20 genomes with at least 95% identity). Modpolish not only significantly improved the quality of in-house sequenced genomes but also public datasets sequenced by R9.4 and R10.4 (simplex). Our results suggested that novel modifications are prone to ONT systematic errors. Nevertheless, these errors are correctable by nucleotide demodification or Modpolish without prior knowledge of modifications.

The changing face of circulating tumor DNA (ctDNA) profiling: Factors that shape the landscape of methodologies, technologies, and commercialization.

Liquid biopsies, in particular the profiling of circulating tumor DNA (ctDNA), have long held promise as transformative tools in cancer precision medicine. Despite a prolonged incubation phase, ctDNA profiling has recently experienced a strong wave of development and innovation, indicating its imminent integration into the cancer management toolbox. Various advancements in mutation-based ctDNA analysis methodologies and technologies have greatly improved sensitivity and specificity of ctDNA assays, such as optimized preanalytics, size-based pre-enrichment strategies, targeted sequencing, enhanced library preparation methods, sequencing error suppression, integrated bioinformatics and machine learning. Moreover, research breakthroughs have expanded the scope of ctDNA analysis beyond hotspot mutational profiling of plasma-derived apoptotic, mono-nucleosomal ctDNA fragments. This broader perspective considers alternative genetic features of cancer, genome-wide characterization, classical and newly discovered epigenetic modifications, structural variations, diverse cellular and mechanistic ctDNA origins, and alternative biospecimen types. These developments have maximized the utility of ctDNA, facilitating landmark research, clinical trials, and the commercialization of ctDNA assays, technologies, and products. Consequently, ctDNA tests are increasingly recognized as an important part of patient guidance and are being implemented in clinical practice. Although reimbursement for ctDNA tests by healthcare providers still lags behind, it is gaining greater acceptance. In this work, we provide a comprehensive exploration of the extensive landscape of ctDNA profiling methodologies, considering the multitude of factors that influence its development and evolution. By illuminating the broader aspects of ctDNA profiling, the aim is to provide multiple entry points for understanding and navigating the vast and rapidly evolving landscape of ctDNA methodologies, applications, and technologies.

SAKE: Strobemer-assisted k-mer extraction.

K-mer-based analysis plays an important role in many bioinformatics applications, such as de novo assembly, sequencing error correction, and genotyping. To take full advantage of such methods, the k-mer content of a read set must be captured as accurately as possible. Often the use of long k-mers is preferred because they can be uniquely associated with a specific genomic region. Unfortunately, it is not possible to reliably extract long k-mers in high error rate reads with standard exact k-mer counting methods. We propose SAKE, a method to extract long k-mers from high error rate reads by utilizing strobemers and consensus k-mer generation through partial order alignment. Our experiments show that on simulated data with up to 6% error rate, SAKE can extract 97-mers with over 90% recall. Conversely, the recall of DSK, an exact k-mer counter, drops to less than 20%. Furthermore, the precision of SAKE remains similar to DSK. On real bacterial data, SAKE retrieves 97-mers with a recall of over 90% and slightly lower precision than DSK, while the recall of DSK already drops to 50%. We show that SAKE can extract more k-mers from uncorrected high error rate reads compared to exact k-mer counting. However, exact k-mer counters run on corrected reads can extract slightly more k-mers than SAKE run on uncorrected reads.

Single-Cell Dissection Reveals a Distinct Origin of Small Paroxysmal Nocturnal Hemoglobinuria Clones in Immune Aplastic Anemia and Healthy Individuals

Abstract Introduction Paroxysmal nocturnal hemoglobinuria (PNH) clones are characterized by the absence of cell surface glycosylphosphatidylinositol (GPI)-anchors owing to somatic mutations in the PIGA gene on the X chromosome. Large PNH clones derived from PIGA mutant multipotent progenitors (MPPs) result in clinically significant hemolysis. In immune aplastic anemia (AA), the PNH clone represents immune escape from T cell-mediated pathogenesis and is usually small (<1%), sometimes likely derived from lineage-committed progenitors (LCPs), with detectable GPI-anchor deficient (PNH-type) cell populations only in granulocytes and monocytes. Small PNH-type cells are also present in healthy individuals at a much lower frequency (<0.003%) than in AA patients. The nature of the small PNH-type cells is not fully understood because of the difficulties in analyzing rare cell populations. Methods In this study, we developed a method to detect PIGA mutations within a small population of cells at single-cell resolution. PNH-type cells were enriched from peripheral blood leukocytes using magnetic microbeads and monoclonal antibodies specific for GPI-anchored proteins. The enriched cell fraction was then subjected to fluorescence-activated cell sorting after staining with lineage-specific markers and fluorescein-labelled proaerolysin. We sorted a limited number (100 cells in males and 50 cells in females) of PNH-type granulocytes (PNH-Gs) and T cells (PNH-Ts) as well as wild-type cells into separate tubes, and their PIGA genes were directly amplified by multiplex PCR without a DNA extraction procedure for subsequent deep nucleotide sequencing. To ensure the specificity of PIGA mutation detection, we set cut-off thresholds of 2% for single-nucleotide substitutions and 1% for indels, in addition to the thresholds based on base-position-specific error rates in 10 sets of 2000 wild-type cells, as previously reported (Zaimoku et al. Blood 2021). Results In 19 sets of 50-100 wild-type cells from 13 individuals, we detected only 7 mutations with variant allele frequencies (VAFs) ranging from 2.1% to 4.2% (Figure 1). Three of these were synonymous mutations, and the remaining 4 were missense mutations, suggesting that these mutations may be true passenger mutations rather than sequencing errors. In all 6 healthy individuals, small PNH-type cells exhibited a polyclonal pattern. We detected numerous small, likely inactivating, PIGA mutations in PNH-Gs, with a median of 11 (range, 6-18) mutations per 100 cells. In 3 individuals assessed twice, approximately half of the PIGA mutations were detected in both sets of PNH-Gs, whereas the remaining mutations were unique to each set. Similarly, PNH-Ts from 4 healthy individuals showed polyclonality, with a median of 14 (range, 6-22) mutations per 100 cells. Interestingly, PIGA mutations were not shared between PNH-Gs and PNH-Ts, except for 2 splice site mutations with VAFs <10%. The polyclonal pattern was further confirmed by Sanger sequencing, which successfully detected 7 unique PIGA mutations in 5 sets of 5 PNH-type cells (instead of 100 cells) in a healthy donor, whereas no mutations were detected in 3 sets of 5 wild-type cells, except for 1 synonymous mutation. In contrast, small PNH-Gs of 5 AA patients, with frequencies of 0.02%, 0.08%, 0.09%, 0.2%, and 0.5% of total granulocytes, were primarily derived from single PIGA mutant clones, which accounted for 90%-99% of PNH-G populations. In another patient with AA, PNH-Gs with a frequency of 3.3% were derived from 2 dominant clones. Notably, the dominant PIGA mutations in the PNH-Gs of AA patients were all detectable in PNH-Ts, even in 2 patients whose PNH-Ts were not detectable (<0.003%) and in 3 patients whose percentages of PNH-Ts were 0.004%, 0.02%, and 0.08% before microbead-based enrichment. Discussion Our novel sequencing approach provides insight into the clonality and lineage distribution of small PNH-type cells without in vitro culture manipulation. In healthy individuals, most PNH clones originate from LCPs, where many cell divisions occur, thereby providing opportunities for the acquisition of PIGA mutations. In immune AA, only PIGA mutations acquired at the MPP level lead to clonal proliferation, strongly suggesting that the immune pathogenesis of AA targets MPPs, rather than LCPs. A clonality analysis of small PNH-type cells thus helps diagnose the immune pathogenesis of AA.

A Novel and Cost-Efficient Measurable Residual Disease (MRD) Detection Assay Using a Single Molecule Molecular Inversion Probe-Based Next Generation Sequencing (NGS) Assay Predicts Overall Survival in Patients with Acute Myeloid Leukemia Undergoing Allogeneic Stem Cell Transplantation

Introduction Relapse remains the main reason for poor outcomes of patients (pts) with acute myeloid leukemia (AML). Allogeneic hematopoietic stem cell transplantation (HSCT) is the preferred post-remission treatment in pts at high risk of relapse. The presence of measurable residual disease (MRD) after intensive frontline treatment or before or after HSCT has emerged as an important predictor of relapse. Various methods are used for MRD detection, including flow cytometry and molecular assays detecting individual gene mutations (mut) such as NPM1mut. Next generation sequencing (NGS)-based assays covering multiple genes commonly mutated in AML may allow sensitive MRD detection in a broad range of pts with diverse genetic characteristics. Methods We established a targeted, error corrected NGS MRD assay utilizing single-molecule molecular inversion probes (smMIPs). The smMIP protocol uses a hybridization-capture approach to selectively enrich and amplify both DNA strands of 92 regions in 32 genes in a single reaction. Unique molecular identifiers (UMIs) facilitate computational correction of sequencing errors to enable reliable detection of variants ≥0.5% variant allele frequency (VAF), with the additional advantage of relatively low reagent and sequencing costs of < $100 per sample. The smMIP assay was used to evaluate MRD status on pre-HSCT bone marrow samples from 98 AML pts who received an HSCT in first (n=83) or second (n=15) complete remission with or without count recovery. Median age at HSCT was 59 (range 20-75) years, 42% were female; 70% had de novo AML, 19% had an antecedent myeloid neoplasm and 11% had AML post cytotoxic therapy. European LeukemiaNet (ELN)-2022 risk groups at diagnosis were favorable in 11%, intermediate in 33%, adverse in 20% and unknown in 22%. All pts had received intensive induction chemotherapy. Twenty-nine percent received a myeloablative conditioning regimen (MAC), while 23% received reduced intensity conditioning (RIC) and 48% a non-myeloablative (NMA) conditioning regimen. Donors were matched related (18%), haploidentical (6%), or matched (55%) or mismatched (21%) unrelated. The median follow-up for pts alive was 34.8 months. Results Overall, we detected 190 variants in 71 (72%) of 98 pre-HSCT remission samples. The most commonly affected genes were DNMT3A (in 37% of pts), TET2 (21%), PPM1D (12%), IDH2 (10%) and TP53 (5%). Twenty-nine percent of pts had only variants affecting the clonal hematopoiesis-related genes DNMT3A, TET2 or ASXL1 (‘DTA‘), while 44% of patients had ≥1 variant involving a non-DTA gene. MRD-positive pts, defined as those with ≥1 non-DTA variant, had similar baseline and HSCT-related characteristics (includingage, sex, ELN risk group, de novo vs. secondary AML) compared to pts without non-DTA variants. Overall survival (OS) was significantly inferior in NGS MRD-positive pts compared to those with no non-DTA variant (Figure A, 5-year OS, 49% vs. 85%, P=.011). MRD-positive pts also had a non-significant higher cumulative incidence of relapse at 5 years (46% vs. 24%, P=.14), while non-relapse mortality was similar in both groups (P=.72). We next performed multivariable analyses, considering variables associated with OS at an univariable P<.10 (MRD status, patient sex, therapy-related AML, conditioning intensity), and using stepwise backward variable selection to identify factors significantly associated with OS (Figure B). In the final multivariable model, pre-HSCT MRD detection remained associated with inferior OS (hazard ratio 2.51; 95% confidence interval, 1.07 - 5.91; P=.035). Female sex associated with favorable OS, while myeloablative conditioning (MAC) showed a borderline significant association with superior OS. Of note, we did not detect an interaction between conditioning intensity and detection of MRD with regard to OS, i.e. the prognostic association of MRD detection was not different between pts receiving myeloablative or less-intensive conditioning. However, this analysis was limited by the small patient number. Conclusion Detection of MRD by targeted NGS using the smMIPs approach provides prognostic information in pts undergoing allogeneic HSCT, including those without established molecular markers such as NPM1mut. This method is applicable to a large proportion of AML pts and is cost-efficient for broad clinical use. S.M.K. and T.H., and M.J. and K.H.M. contributed equally to this abstract.

Error-Corrected Next-Generation Sequencing Provides a Comprehensive Overview of the Subclonal Mutation Landscape and Its Prognostic Implications in Juvenile Myelomonocytic Leukemia

Introduction: Juvenile myelomonocytic leukemia (JMML) is a rare pediatric myelodysplastic/myeloproliferative neoplasm characterized by mutations in genes activating the RAS pathway ( PTPN11, NF1, NRAS, KRAS, and CBL). Approximately 20% of patients with JMML have secondary mutations in SETBP1 and/or JAK3, and recent droplet digital PCR (ddPCR) analyses revealed that mutations in these two genes, including very rare mutations (variant allele frequency [VAF] < 0.05) that are difficult to detect by conventional next-generation sequencing (NGS), are associated with poor prognoses. Meanwhile, the landscape of low VAF subclonal mutations of other genes in patients with JMML has not been adequately evaluated. Therefore, we utilized error-corrected NGS, which can reduce the systematic sequencing error rate of conventional NGS by incorporating molecular barcodes (MBCs). Methods: We performed error-corrected NGS in 93 patients with JMML and 11 patients with JMML-like myeloproliferative disorder with Noonan syndrome (NS/MPD) using a small panel associated with myeloid malignancies. Samples for error-corrected NGS were prepared using a SureSelect XT HS2 DNA Library Preparation Kit (Agilent Technologies), including unique dual index and duplex MBCs. A unique MBC is incorporated into each DNA fragment, and the incorporated MBC can differentiate true and false duplications, allowing for highly accurate sequencing that can detect mutations with VAF > 0.001. We used the HiSeq X platform (Illumina) and SureCall v.4.2 application (Agilent Technologies) for analysis. In addition, DNA methylation analysis was performed on the same cohort using the Infinium HumanMethylation 450K BeadChip and/or Digital Restriction Enzyme Analysis of Methylation as previously described (Murakami et al. 2018;131:1576-1586, Kitazawa et al. Blood Adv. 2021;5:5507-5518). This study was approved by the Ethics Committee of the Nagoya University Graduate School of Medicine. Results: In the cohort of 104 patients with JMML or NS/MPD, 160 mutations were identified, of which 30 (19%) had VAF < 0.05 (eight in JAK3, seven in NF1, five in ASXL1, three in NRAS, two each in PTPN11 and SOS1, and one each in SETBP1, SH2B3, and ZRSR2). Subclonal mutations in SETBP1 p.D868N and JAK3 p.R657Q identified by error-corrected NGS were confirmed using ddPCR, and consistent results were obtained for both assays (R 2 = 0.9977 and 0.9919 respectively). Of 93 patients with JMML, 30 (32%) had subclonal or clonal secondary mutations, whereas no such mutations were detected in patients with NS/MPD ( P = 0.031). Of 104 patients, 23 (22%) had secondary mutations with VAF ≥ 0.05 (Major group), 7 (7%) only had secondary mutations with VAF < 0.05 (Minor group), and the remaining 74 (71%) had no secondary mutations (Negative group). We assessed the associations of subclonal mutations detected by error-corrected NGS with the DNA methylation profile, the most important prognostic factor in JMML. Whereas 22 of 23 (96%) and 5 of 7 (71%) patients in the Major and Minor groups, respectively, had high methylation (HM) profiles, only 20 of 74 (27%) patients in the Negative group featured HM profiles ( P < 0.001). The 4-year overall survival (OS) rates were significantly lower in the Major (52.2% [95% confidence interval {CI} = 30.5%-70.0%], P = 0.01) and Minor groups (42.9% [95% CI = 9.8%-73.4%], P = 0.03) than in the Negative group (76.3% [95% CI = 64.6%-84.5%]). In a subgroup analysis of 30 patients with secondary mutations, patients with secondary mutations in RAS pathway genes (n = 18) had significantly lower 4-year OS rates than those with secondary mutations in other genes (n = 12; 32.4% [95% CI = 12.7%-54.0%] vs. 75.0% [95% CI: 40.8%-91.2%], P = 0.047). Conclusions: We successfully performed error-corrected NGS to assess comprehensive subclonal secondary mutational profiles, including very low VAF variants. The presence of subclonal mutations, particularly in RAS pathway genes, was associated with poor OS. These findings provide important information for appropriate risk stratification, which will contribute to the implementation of precision medicine for patients with JMML.

New Pattern of Emerging Somatic Mutations in Optimal Responders Following Tyrosine Kinase Inhibitor Therapy in Chronic Myeloid Leukemia Patients Evidenced from Mutational Kinetic Analysis Based on Pairwise Comparison

Introduction Recent advances in genomics in chronic myeloid leukemia (CML) patients (pts) provide new insights in understanding somatic mutation (SM) and their prognostic implications following tyrosine kinase inhibitor (TKI) therapy. DNMT3A, TET2, or ASXL1 (DTA) mutations are most frequently detected following TKI therapy and have adverse prognosis. Five longitudinal patterns of changes in SM after TKI therapy were suggested with its clinical significance (Kim, Blood 2017): Pattern 1, persistent mutation burden despite optimal TKI response; Pattern 2, emerging mutation which correlates with TKI resistance/CML progression; Pattern 3, mutational clearance with mixed clinical outcomes; and Pattern 4 and 5 having some mutation in germline control as well as CML cells. Clonal evolution (CE) in Philadelphia negative (Ph -) clone is found in up to 10% of CML pts who respond to TKI therapy optimally but develop +8 or -7 changes in Ph - clone. This is diagnosed only by metaphase cytogenetic test on bone marrow samples. Alternatively, fluorescence in situ hybridization (FISH) test can be used. With recent advances in next-generation sequencing (NGS), we hypothesize that SM profiling can capture a clone carrying a SM in the Ph - population. We sequenced and analyzed mutation profiles in paired samples taken prior to TKI therapy and at longitudinal follow-up. Patients and methods The present study adopted single molecule-tagging and molecular inversion probe (smMIP)-based sequencing methods to analyze the DNA extracted from the mononuclear cell fraction of peripheral blood samples collected from CML pts at initial diagnosis and during follow-up. While conventional NGS has a relatively high intrinsic sequencing error rate and is limited in detecting mutations with a variant allele frequency (VAF) of less than 2%, smMIP-based sequencing overcomes this issue by tracking all sequence reads from a single strand and can detect mutations with a limit of detection up to 0.1%. The in-house CML-specific smMIP panel used in this study included 40 genes with 332 amplicon probes covering genes involved in epigenetic modifiers, activation signaling, myeloid transcription factor, spliceosome, tumor suppressor, cohesion, and miscellaneous functions. To evaluate kinetics of mutations, we calculated doubling time of mutations. Using the mutational VAFs from 2 time points, t1 and t2, the doubling time was calculated according to the following equation: = (-2 -1) log(2) /log(-2) - log(-1), where VAF1 and VAF2 is the VAF of the mutant at time points t1 and t2, respectively. Results We performed targeted deep sequencing on a total of 119 serial samples from 51 CML pts (median of 405.5 days following TKI therapy). 41 pts had an optimal response, 7 were non-responders, and 3 were non-evaluable for response. Pattern 2 was observed in 1 of 7 non-responders. In 12 pts pattern 3 (mutation clearance with mixed clinical outcomes) was observed. 22 pts carried 36 mutations. At baseline 9 pts had somatic mutations. Emergence of somatic mutations in optimal responders was observed in 13 out of total 51 (25%) pts, with DNMT3A (n=5), TET2 (n=4), ASXL1 (n=4), and EZH2 (n=2). While JAK2, SF3B1, U2AF1, PHF6, TP53, BRAF, and CBL were observed in one patient each (Figure 1). Particularly, those with emerging DTA mutation were all optimal responders to TKI therapy. This pattern of mutational change is novel and has never been described before. The median doubling time was 77 days (range 21-1909 days) in the group with emergence of somatic mutation in follow-up samples. The doubling time was 56.1, 93.7, 74.8 days in DNMT3A, ASXL1, and TET2 mutations, respectively. Based on kinetics of mutations (doubling time), to capture 10-fold increase (i.e. 1 log increase) of certain mutation, NGS profile can be monitored every 6-7 months. This pattern of emergence of mutations (mostly DTA mutation) in pts responding optimally to TKI therapy is new and does not fit into the five patterns mentioned in our previous paper (Kim, Blood 2017). Thus, we propose this sixth novel pattern called “Emerging mutational clones in CML pts responding optimally to TKI therapy”. Conclusion We suggest a new pattern of emerging mutations in optimal responders to TKI therapy, which could derive from expansion of Ph- clone carrying some clones with emerging mutations. Longitudinal NGS monitoring in CML patients is feasible with frequency every 6-7 months.

Mixture detection with Demixtify

The de facto genetic markers of forensics are short tandem repeats (STRs). There are many analytical tools designed to work with STRs, including techniques for analyzing and assessing DNA mixtures. In contrast, the nascent field of forensic genetic genealogy often relies on biallelic single nucleotide polymorphisms (SNPs). Tools designed for the forensic assessment of SNPs are somewhat lacking, especially for DNA mixtures. In this paper we introduce Demixtify, a program that detects DNA mixtures using biallelic SNPs. Demixtify is quite powerful; highly imbalanced mixtures can be detected (≤1:99, considering in silico and in vitro mixtures) when coverage is ample. Demixtify can also detect mixtures in low coverage (∼1×) samples (when the mixture is relatively balanced). Demixtify includes an empirical estimator of sequence error that is specific to the markers assayed, making it especially relevant to the forensic community. Orthogonal techniques are also developed to characterize in vitro mixtures, as well as samples thought to be single source, and the results of these approaches serve to validate the techniques presented.

Evaluation of 10 Different Pipelines for Bacterial Single-Nucleotide Variant Detection

Abstract Bacterial genome sequencing is a powerful technique for studying the genetic diversity and evolution of microbial populations. However, the detection of genomic variants from sequencing data is challenging due to the presence of contamination, sequencing errors and multiple strains within the same species. Several bioinformatics tools have been developed to address these issues, but their performance and accuracy have not been systematically evaluated. In this study, we compared 10 variant detection pipelines using 18 simulated and 17 real datasets of high-throughput sequences from a bundle of representative bacteria. We assessed the sensitivity of each pipeline under different conditions of coverage, simulation and strain diversity. We also demonstrated the application of these tools to identify consistent mutations in a 30-time repeated sequencing dataset of Staphylococcus hominis. We found that HaplotypeCaller, but not Mutect2, from the GATK tool set showed the best performance in terms of accuracy and robustness. CFSAN and Snippy performed not as well in several simulated and real sequencing datasets. Our results provided a comprehensive benchmark and guidance for choosing the optimal variant detection pipeline for high-throughput bacterial genome sequencing data.

IoT-based compensation and control of power theft in weak distribution system

This paper presents a prototype model of real-time rampant power theft detection and compensating the required power current components through switching of distribution static compensator (DSTATCOM) using IoT (Internet of Things) based microcontroller via the GSM module. In this case study, an Internet of Things (IoT) application is used to wirelessly transmit data of the difference of smart meters through intelligent current sensors with ESP8266 module. Transmitted pulse sequences find out the derivatives of functions at discrete points through weight function. A weighting function includes power theft detection information in the pulse sequence series form. It computes the unknown sample of pulse in the line that identified the power theft and the quality of the power supply. The sequence error of the of energy meter units communicates microcontroller through the IoT. Microcontroller communicate to μ-PMU to addressed power quality error with a GPS receiver in the line. Based on weight function an algorithm has been developed for switching DSTATCOM for compensation of power quality. The reference and feedback quantities’ resultant errors are sampled within a time rate of the weight function. This control approach is applied with dynamic load to analyze the power quality issues. The results are implemented in MATLAB Simulink and validated experimentally for power theft detection and its compensation in low voltage distribution system.

Analytical Validation of a Duplex Sequencing AML Assay with a Limit of Detection Below 0.01% VAF for SNVs and Indels

Sensitive, specific, precise, and accurate detection of low frequency mutations in acute myeloid leukemia (AML) is critical to assess measurable residual disease (MRD). Next generation sequencing (NGS) assays are powerful tools for this, permitting interrogation of thousands of base positions across genomic regions of interest. However, PCR and sequencing errors in these assays limit accurate mutation detection at variant allele frequencies (VAF) below approximately 1%. Duplex sequencing (DS), an error-corrected NGS (ecNGS) method, greatly reduces such errors by relating original top and bottom DNA strands to make double stranded consensus sequences, thus enabling sensitive, specific, accurate, and precise detection of variants below 0.01% VAF. Here, we report analytical validation of an updated DuplexSeq™ AML assay, a 36-gene panel informed by 2022 European LeukemiaNet (ELN) recommendations combined with kitted library preparation reagents and a cloud-based analysis pipeline. The assay uniformly targets regions that harbor AML-associated mutations: 82% of targeted regions exhibit sequencing depth >80% of the panel-wide mean and >99.5% of targeted regions exhibit depth >20% of the panel-wide mean. These analytical studies also validate an improved “v2 chemistry” version of a DS library preparation kit. This improved chemistry provides an enzymatic fragmentation step that replaces time-consuming mechanical fragmentation methods while increasing the yield of duplex data for a given input mass of DNA sample and eliminating damage that results from mechanical fragmentation. A contrived human genomic DNA sample carrying 26 variants targeted by the panel, DNA extracted from AML-positive peripheral blood (PB) and bone marrow aspirate (BMA) specimens, and DNA extracted from healthy normal PB specimens were used as test samples to execute limit of detection (LoD), limit of blank (LoB), accuracy, linearity, and repeatability & reproducibility studies. In this assay a duplex depth threshold of ≥20,000x is applied; ≥91.8% of the 42,605 genomic base positions targeted by the panel are consistently (in 95% of analyzed libraries) above this threshold. The LoD study assayed samples carrying variants from 0.37% to 0.00055% VAF. The assay demonstrated excellent sensitivity, with variants down to 0.00055% VAF detected. A probit model was used to estimate the VAF at which 95% of variants would be detected, establishing an LoD of 0.008% VAF for single-nucleotide variants (SNVs) and insertions and deletions (indels) (see Figure 1). The LoB study assessed the background detection rate in DNA samples derived from healthy normal specimens. In >95% of sites surveyed, no variants were observed, establishing an assay LoB of 0 (0% VAF). Accuracy of the assay was determined by interrogating PB and BMA samples from AML-positive and -negative donors along with contrived test samples. Results were compared to data acquired via orthogonal methods. For variants above the LoD of 0.008% VAF, accuracy was high, with overall percent agreement (OPA) of 98.3%, positive percent agreement (PPA) of 100.0%, and negative percent agreement (NPA) of 98.3%. Data were linear from 0.0016% VAF to 96.1% VAF with R 2 = 0.95. All 6 expected FLT3-ITD variants, and all 5 expected NPM1 insertions were detected by the assay. Precision study data showed that the assay was highly repeatable, with OPA of 97.5%, PPA of 98.0%, and NPA of 97.3%. Reproducibility across 2 operators, 2 lots of reagents, and 3 independent runs of library preparation was also very high, with OPA of 97.5%, PPA of 98.8%, and NPA of 96.9%. Current ELN guidelines provisionally define NGS-MRD positivity as ≥0.1% VAF, but recent evidence suggests lower frequency variants may be informative. With an LoD of 0.008% VAF for SNVs and indels, and an LoB of 0, this novel AML assay comprising an updated gene panel coupled with improved duplex sequencing chemistry and software represents a highly sensitive, specific, accurate, and precise ecNGS assay for detecting variants associated with AML MRD.

Optimal Reference for DNA Synthesis

In recent years, DNA has emerged as a potentially viable storage technology. DNA synthesis, which refers to the task of writing the data into DNA, is perhaps the most costly part of existing storage systems. Consequently, the high cost and low throughput limit the practical use of available DNA synthesis technologies. It has been found that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">homopolymer run</i> (i.e., the repetition of the same nucleotide) is a major factor affecting the synthesis and sequencing errors. Recently, [26] raised and studied the coding problem for efficient synthesis for DNA-based storage systems. Among other things, they studied the maximal code size under synthesis constraints. In [29], the authors studied the role of batch optimization in reducing the cost of large-scale DNA synthesis, for a given pool <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> of random quaternary strings of fixed length. This problem is related to the problem posed in [26] which can be viewed as the opposite side of the coin. Instead of seeking the largest code in which every codeword can be synthesized in a certain amount of time, they asked what is the average synthesis time of a randomly chosen string. Following the lead of [29], in this paper, we take a step forward towards the theoretical understanding of DNA synthesis, and study the homopolymer run of length <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> ≥ 1. Specifically, we are given a set of DNA strands <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</i> , randomly drawn from a Markovian distribution modeling a general homopolymer run length constraint, that we wish to synthesize. For this problem, we derive asymptotically tight high probability lower and upper bounds on the cost of DNA synthesis, for any <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> ≥ 1. Our bounds imply that, perhaps surprisingly, the periodic sequence ACGT is asymptotically optimal in the sense of achieving the smallest possible cost. Our main technical contribution is the representation of the DNA synthesis process as a certain constrained system, for which string techniques can be applied.

16 Set-Shifting as a Predictor of Adaptive Functioning in Individuals with Acquired Brain Injury

Objective:Cognitive flexibility, typically measured using neuropsychological tasks of set-shifting, has been associated with mental and physical health, social relationships, resilience, and overall quality of life (Diamond, 2013; Chen et al., 2014; Davis et al., 2010; de Abreu et al., 2014; Genet et al., 2011). Previous research has found conflicting results regarding the relationship between set-shifting and various measures of functional outcomes in individuals with traumatic brain injury (Allanson et al., 2017). The present study examined the relationship between cognitive flexibility and adaptive functioning in individuals with acquired brain injuries (ABI).Participants and Methods:Participants in this research are adults (n = 116) with severe, chronic ABI who completed a neuropsychological evaluation through Bancroft Neurorehab between 2012-2022. Participants ranged in age from 20.4 - 67.8 years (M = 45.8). Individuals included in data analysis completed Trails A and B, Wide Range Achievement Test, Fourth Edition (WRAT-4) Word Reading, and Texas Functional Living Scale (TFLS). Set-shifting ability was measured using Trails B and adaptive functioning was measured using the TFLS. Word reading ability, measured using the WRAT-4, was included as a covariate to account for the impact of word reading difficulties on Trails B performance.Results:A simple linear regression was conducted to examine if Trails B T-score (M = 24.7) and WRAT-4 Word Reading Standard Score (M = 87.8) predicted TFLS Total T-score (M = 35.8). The overall regression model was statistically significant (R2 = .351, F(2, 113) = 32.0, p &lt; .001). It was found that lower performances on Trails B (ß = .272, p &lt;.001) and WRAT-4 Word Reading (ß = .189, p &lt;.001) both significantly predicted a lower TFLS Total T-score.Conclusions:Set-shifting and word reading ability significantly predicted the overall adaptive functioning score on the TFLS which adds to a body of literature that suggests that the ability to think and behave flexibly affects functional aspects of everyday living. These findings are consistent with previous literature regarding the association between cognitive flexibility and adaptive functioning in the general population, and these results add to the growing body of research on cognitive flexibility in individuals with brain injury. Clinicians may use an individual’s set-shifting performance to estimate and further assess potential difficulties in completing activities of daily living. This information may assist in subsequent treatment planning and identifying treatment goals of cognitive rehabilitation consistent with rehabilitation psychology’s goals of increasing levels of adaptive functioning and quality of life (Division 22 of the American Psychological Association, n.d.). Future research may examine if certain domains of adaptive functioning are more or less affected by impairments in cognitive flexibility. Future research may also examine patterns of set-shifting performance, such as sequencing errors vs. set-loss errors, associated with specific areas of insult.

Use of genome sequencing to resolve differences in gradient diffusion and agar dilution antimicrobial susceptibility testing performance of Neisseria gonorrhoeae isolates in Alberta, Canada.

Agar dilution is the gold standard method for phenotypic antimicrobial susceptibility testing (AST) for Neisseria gonorrhoeae. However, this method is laborious and requires expertise, so laboratories that perform N. gonorrhoeae AST may choose alternative methods such as disk diffusion and gradient diffusion. In this study, we retrospectively compare the performance of gradient diffusion to agar dilution for 2,394 unique N. gonorrhoeae isolates identified in Alberta from 2017 to 2020 against azithromycin, cefixime, ceftriaxone, ciprofloxacin, penicillin, and tetracycline. Genome sequencing was utilized to resolve discrepancies between AST methods, detect antimicrobial resistance markers, and identify trends between error rates and sequence types (STs) of isolates. Over 90% of N. gonorrhoeae isolates were susceptible to azithromycin, cefixime, and ceftriaxone, whereas decreased susceptibility was observed for ciprofloxacin, penicillin, and tetracycline. Categorical (CA) and essential agreement (EA) was poorest between the two methods for penicillin (CA: 86.02%; EA: 77.69%) and tetracycline (CA: 47.22%; EA: 55.96%); however, the low CA was primarily attributed to minor errors. Antimicrobial agents with errors outside of acceptable limits included azithromycin (very major error: 18.42%; major error: 7.73%) and tetracycline (very major error: 6.17%). Genome sequencing on a subset of isolates resolved 30.3% of the azithromycin major errors and confirmed the azithromycin or tetracycline very major errors. Significant associations between certain STs and error types for azithromycin and tetracycline were also identified. Overall, gradient diffusion compared well to agar dilution for cefixime, ceftriaxone, and ciprofloxacin, and genome sequencing was identified as a useful tool to arbitrate discrepant susceptibility testing results between gradient diffusion and agar dilution for N. gonorrhoeae.

Two‐order cooperative optimization of swarm control based on reinforcement learning

AbstractThis paper presents a study of the cooperative optimal swarm control problem for two‐order multi‐agent systems with partially unknown nonlinear functions. Unlike traditional approaches that consider a single error, this paper proposes to use multi‐order errors in the performance index function to achieve optimal control performance. Additionally, different proportional coefficients are assigned to illustrate the varying influences of each sequence error, and a two‐order cooperative (TOC)performance index function is designed. To address the influence of unknown nonlinear functions, a swarm control system based on sliding mode control with an actor‐critic network is constructed, which increases the applicability of the proposed method to a variety of dynamic models. Furthermore, to alleviate the computational pressure caused by the multi‐order errors in the TOC performance index function, a new reinforcement learning (RL)‐based sliding mode swarm controller is designed. The stability of the proposed controller is demonstrated using the Lyapunov function. Finally, the control model and control rate are applied to a quadrotor unmanned aerial vehicle system, and simulation results demonstrate that the multi‐agent systems can effectively achieve swarm control.Impact Statement: This paper proposes a reinforcement learning‐based sliding mode control strategy for the cooperative optimal swarm control problem, where the nonlinear functions of two‐order multi‐agent systems are only partially known. In addition, we also propose a cooperative performance index function, which takes into account multi‐order errors for optimizing the performance. This contribution is significant for research in sliding mode control strategies and error co‐optimization.

Analysis and Optimization of the K-Means Algorithm in Determining Course Scheduling

Preparation of course schedules is the core activity of learning and learning. This is felt by study programs, where assignments are arranged through lecturer assignments, to class determination and class schedule dissolution, therefore the right application is needed to be able to arrange schedules automatically, then by using the k-means method you can arrange schedules correctly , in designing this application requires a prototyping application development method which gives good results where the clusters formed reach 17 clusters where the course schedule is made with the results of the Means Sequence Error in cycle 1 which is 65.66% with an accuracy of 54.06%. so that it is continued in cycle 2 the MSE value is 87.55% with an accuracy of 84.03%. The K-Means algorithm can accurately determine course schedules in the faculties of computer science and information technology.