Individual Sequences Research Articles

ОПТИМІЗАЦІЯ ПСЕВДОВИПАДКОВИХ ПОСЛІДОВНОСТЕЙ ГОЛДА В СИСТЕМАХ ЗВ’ЯЗКУ

The article considers the method of optimization of Gold’s pseudo-random sequences for communication systems. At the same time, the peculiarity of the generation of the specified sequences is taken into account, namely that they can be generated only with a specific maximum number N (for example, 9, 33, 65, etc.). But in practice, there are often cases when a much smaller number of them is needed, i.e. there are excess PVPs. The problem arises, which sequences are more appropriate to choose as redundant, if they are not equivalent. The analysis of the structure of correlation functions, both periodic and aperiodic, showed that their structures differ significantly, both in terms of the number of side petals and their level. As a result, among the existing array of sequences, less effective ones were found, which should be used as redundant ones. An algorithm was developed for optimization based on data on the correlation functions of individual sequences. It is shown that the performed optimization leads to an increase in the immunity of broadband systems in which sequence data is used, and a decrease in the probability of deterioration of the immunity. Based on the proposed criteria, a quantitative assessment of the effectiveness of the performed optimization was carried out.

An individual sequencing control strategy for three-phase four-leg inverter under unbalanced loads

Under the influence of the unbalanced load, the negative and zero sequence components in the output voltage of the three-phase four-leg voltage source inverter(TPFL-VSI) will cause asymmetry voltage. This paper designs an individual sequencing control for the three-phase four-leg inverter to address this problem. The second-order generalized integrator is adopted to extract the current’s positive and negative sequence components. The virtual oscillator control(VOC) controls the positive sequence component, and the negative sequence component is suppressed by designing the virtual impedance and the multiple proportional resonance controller. The proportional resonance control is used to control the zero-sequence voltage of the fourth bridge arm. Considering that voltage deviation is caused by the access of negative sequence virtual impedance and load step, the secondary voltage compensation strategy is further added in the individual sequencing control to improve the droop relationship in the VOC and effectively solve the problem of system voltage instability. Ultimately, a TPFL-VSI model is built in Matlab/Simulink, and simulation results verify the feasibility and effectiveness of the proposed control strategy under different conditions.

Practical Guide to Interpreting Cardiac Magnetic Resonance in Patients with Cardiac Masses.

It is common for a cardiac mass to be discovered accidentally during an echocardiographic examination. Following the relief of a cardiac mass, being able to evaluate and characterize it using non-invasive imaging methods is critical. Echocardiography, computed tomography (CT), cardiac magnetic resonance imaging (CMR), and positron emission tomography (PET) are the main imaging modalities used to evaluate cardiac masses. Although multimodal imaging often allows for a better assessment, CMR is the best technique for the non-invasive characterization of tissues, as the different MR sequences help in the diagnosis of cardiac masses. This article provides detailed descriptions of each CMR sequence employed in the evaluation of cardiac masses, underlining the potential information it can provide. The description in the individual sequences provides useful guidance to the radiologist in performing the examination.

Genetic variants and altered expression of SERPINF1 confer disease susceptibility in patients with otosclerosis.

Otosclerosis (OTSC) is a focal and diffuse bone disorder of the human middle ear characterized by abnormal bone growth and deposition at the stapes' footplate. This hinders the transmission of acoustic waves to the inner ear leading to subsequent conductive hearing loss. The plausible convections for the disease are genetic and environmental factors with yet an unraveled root cause. Recently, exome sequencing of European individuals with OTSCrevealed rare pathogenic variants in the Serpin Peptidase Inhibitor, Clade F (SERPINF1)gene. Here, we sought to investigate the causal variants of SERPINF1 in the Indian population. The gene and protein expression was also evaluated in otosclerotic stapes to ameliorate our understanding of the potential effect of this gene in OTSC. A total of 230 OTSC patients and 230 healthy controls were genotyped by single-strand conformational polymorphism and Sanger sequencing methods. Bycomparing the case controls, we identified five rare variants (c.72 C > T, c.151 G > A, c.242 C > G, c.823 A > T, and c.826 T > A) only in patients. Four variants c.390 T > C (p = 0.048), c.440-39 C > T (p = 0.007), c.643 + 9 G > A (p = 0.035), and c.643 + 82 T > C (p = 0.005) were found to be significantly associated with the disease. Down-regulation of SERPINF1 transcript level in otosclerotic stapes was quantified by qRT-PCR, ddPCR and further validated by in situ hybridization. Similarly, reduced protein expression was observed by immunohistochemistry and immunofluorescence in otosclerotic stapes that corroborate with immunoblotting of patients' plasma samples. Our findings identified that SERPINF1 variants are associated with the disease. Furthermore, reduced expression of SERPINF1 in otosclerotic stapes might contribute to OTSC pathophysiology.

Reference-Free Plant Disease Detection Using Machine Learning and Long-Read Metagenomic Sequencing.

Surveillance for early disease detection is crucial to reduce the threat of plant diseases to food security. Metagenomic sequencing and taxonomic classification have recently been used to detect and identify plant pathogens. However, for an emerging pathogen, its genome may not be similar enough to any public genome to permit reference-based tools to identify infected samples. Also, in the case of point-of care diagnosis in the field, database access may be limited. Therefore, here we explore reference-free detection of plant pathogens using metagenomic sequencing and machine learning (ML). We used long-read metagenomes from healthy and infected plants as our model system and constructed k-mer frequency tables to test eight different ML models. The accuracy in classifying individual reads as coming from a healthy or infected metagenome were compared. Of all models, random forest (RF) had the best combination of short run-time and high accuracy (over 0.90) using tomato metagenomes. We further evaluated the RF model with a different tomato sample infected with the same pathogen or a different pathogen and a grapevine sample infected with a grapevine pathogen and achieved similar performances. ML models can thus learn features to successfully perform reference-free detection of plant diseases whereby a model trained with one pathogen-host system can also be used to detect different pathogens on different hosts. Potential and challenges of applying ML to metagenomics in plant disease detection are discussed. IMPORTANCE Climate change may lead to the emergence of novel plant diseases caused by yet unknown pathogens. Surveillance for emerging plant diseases is crucial to reduce their threat to food security. However, conventional genomic based methods require knowledge of existing plant pathogens and cannot be applied to detecting newly emerged pathogens. In this work, we explored reference-free, meta-genomic sequencing-based disease detection using machine learning. By sequencing the genomes of all microbial species extracted from an infected plant sample, we were able to train machine learning models to accurately classify individual sequencing reads as coming from a healthy or an infected plant sample. This method has the potential to be integrated into a generic pipeline for a meta-genomic based plant disease surveillance approach but also has limitations that still need to be overcome.

Direct sequencing of total Saccharomyces cerevisiae tRNAs by LC-MS/MS.

Among RNAs, transfer RNAs (tRNAs) contain the widest variety of abundant posttranscriptional chemical modifications. These modifications are crucial for tRNAs to participate in protein synthesis, promoting proper tRNA structure and aminoacylation, facilitating anticodon:codon recognition, and ensuring the reading frame maintenance of the ribosome. While tRNA modifications were long thought to be stoichiometric, it is becoming increasingly apparent that these modifications can change dynamically in response to the cellular environment. The ability to broadly characterize the fluctuating tRNA modification landscape will be essential for establishing the molecular level contributions of individual sites of tRNA modification. The locations of modifications within individual tRNA sequences can be mapped using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). In this approach, a single tRNA species is purified, treated with ribonucleases, and the resulting single-stranded RNA products are subject to LC-MS/MS analysis. The application of LC-MS/MS to study tRNAs is limited by the necessity of analyzing one tRNA at a time, because the digestion of total tRNA mixtures by commercially available ribonucleases produces many short digestion products unable to be uniquely mapped back to a single site within a tRNA. We overcame these limitations by taking advantage of the highly structured nature of tRNAs to prevent the full digestion by single-stranded RNA-specific ribonucleases. Folding total tRNA prior to digestion allowed us to sequence Saccharomyces cerevisiae tRNAs with up to 97% sequence coverage for individual tRNA species by LC-MS/MS. This method presents a robust avenue for directly detecting the distribution of modifications in total tRNAs.

Cracking the Floral Quartet Code: How Do Multimers of MIKCC-Type MADS-Domain Transcription Factors Recognize Their Target Genes?

MADS-domain transcription factors (MTFs) are involved in the control of many important processes in eukaryotes. They are defined by the presence of a unique and highly conserved DNA-binding domain, the MADS domain. MTFs bind to double-stranded DNA as dimers and recognize specific sequences termed CArG boxes (such as 5'-CC(A/T)6GG-3') and similar sequences that occur hundreds of thousands of times in a typical flowering plant genome. The number of MTF-encoding genes increased by around two orders of magnitude during land plant evolution, resulting in roughly 100 genes in flowering plant genomes. This raises the question as to how dozens of different but highly similar MTFs accurately recognize the cis-regulatory elements of diverse target genes when the core binding sequence (CArG box) occurs at such a high frequency. Besides the usual processes, such as the base and shape readout of individual DNA sequences by dimers of MTFs, an important sublineage of MTFs in plants, termed MIKCC-type MTFs (MC-MTFs), has evolved an additional mechanism to increase the accurate recognition of target genes: the formation of heterotetramers of closely related proteins that bind to two CArG boxes on the same DNA strand involving DNA looping. MC-MTFs control important developmental processes in flowering plants, ranging from root and shoot to flower, fruit and seed development. The way in which MC-MTFs bind to DNA and select their target genes is hence not only of high biological interest, but also of great agronomic and economic importance. In this article, we review the interplay of the different mechanisms of target gene recognition, from the ordinary (base readout) via the extravagant (shape readout) to the idiosyncratic (recognition of the distance and orientation of two CArG boxes by heterotetramers of MC-MTFs). A special focus of our review is on the structural prerequisites of MC-MTFs that enable the specific recognition of target genes.

Chapter 12. Well sequence stratigraphy interpretation methodology

Abstract This chapter describes the methodology employed to recognize depositional sequences in well data, involving the integration of wireline logs, biostratigraphy, lithostratigraphy (and lithological interpretations), seismic and sedimentology (particularly facies analysis), with an emphasis on a pragmatic approach to sequence recognition in North Sea Jurassic–lowermost Cretaceous well successions. Wireline log profiles characterizing individual depositional sequences or parts of sequences are illustrated by reference to key North Sea well sections.

Cell state-dependent chromatin targeting in NUT carcinoma.

Aberrant transcriptional programming and chromatin dysregulation are common to most cancers. Whether by deranged cell signaling or environmental insult, the resulting oncogenic phenotype is typically manifested in transcriptional changes characteristic of undifferentiated cell growth. Here we analyze targeting of an oncogenic fusion protein, BRD4-NUT, composed of 2 normally independent chromatin regulators. The fusion causes the formation of large hyperacetylated genomic regions or megadomains, mis-regulation of c-MYC, and an aggressive carcinoma of squamous cell origin. Our previous work revealed largely distinct megadomain locations in different NUT carcinoma patient cell lines. To assess whether this was due to variations in individual genome sequences or epigenetic cell state, we expressed BRD4-NUT in a human stem cell model and found that megadomains formed in dissimilar patterns when comparing cells in the pluripotent state with the same cell line following induction along a mesodermal lineage. Thus, our work implicates initial cell state as the critical factor in the locations of BRD4-NUT megadomains. These results, together with our analysis of c-MYC protein-protein interactions in a patient cell line, are consistent with a cascade of chromatin misregulation underlying NUT carcinoma.

Quantitative haplotype-resolved analysis of mitochondrial DNA heteroplasmy in Human single oocytes, blastoids, and pluripotent stem cells.

Maternal mitochondria are the sole source of mtDNA for every cell of the offspring. Heteroplasmic mtDNA mutations inherited from the oocyte are a common cause of metabolic diseases and associated with late-onset diseases. However, the origin and dynamics of mtDNA heteroplasmy remain unclear. We used our individual Mitochondrial Genome sequencing (iMiGseq) technology to study mtDNA heterogeneity, quantitate single nucleotide variants (SNVs) and large structural variants (SVs), track heteroplasmy dynamics, and analyze genetic linkage between variants at the individual mtDNA molecule level in single oocytes and human blastoids. Our study presented the first single-mtDNA analysis of the comprehensive heteroplasmy landscape in single human oocytes. Unappreciated levels of rare heteroplasmic variants well below the detection limit of conventional methods were identified in healthy human oocytes, of which many are reported to be deleterious and associated with mitochondrial disease and cancer. Quantitative genetic linkage analysis revealed dramatic shifts of variant frequency and clonal expansions of large SVs during oogenesis in single-donor oocytes. iMiGseq of a single human blastoid suggested stable heteroplasmy levels during early lineage differentiation of naïve pluripotent stem cells. Therefore, our data provided new insights of mtDNA genetics and laid a foundation for understanding mtDNA heteroplasmy at early stages of life.

Interdisciplinary approaches to predicting evolutionary biology.

Interdisciplinary approaches to predicting evolutionary biology.

35 Novel statistical methods to unlock the clinical potential of liquid biopsy sampling

OBJECTIVES/GOALS: Decoding the origins of cell-free DNA (cfDNA) released from dying cells in a liquid biopsy sample (e.g. blood) can provide insight into the dynamic, organism-wide changes reflective of health and disease state. Making cfDNA an ideal target for genomic monitoring of disease-related changes. METHODS/STUDY POPULATION: Methylome-wide sequencing (WGBS) data present unique statistical challenges. To this end, we developed a novel statistical method using an Expectation-Maximization algorithm to decode the cellular origins of cfDNA fragments in liquid biopsies. Our flexible, probabilistic method leverages the co-regulation of neighboring CpG sites on the individual sequencing read to facilitate tissue of origin analysis, as opposed to prior methods that focus on the methylation rate of a single CpG site. We assess the performance of our model in various simulated settings and apply our model to an important clinical example in which we are able to detect early off-target tissue damage from radiation therapy via minimally invasive blood draws. RESULTS/ANTICIPATED RESULTS: We found our model more effective at capturing the range of biologically plausible methylation patterns on cfDNA read fragments compared to prior models that use single CpG sites. We also show our model is robust to high levels of noise inherent with WGBS data. We demonstrate the accuracy of cell-type proportion estimation on in-silico mixed cfDNA samples from real WGBS data. Finally, we use our model in a clinical application. We detect significant (p < 0.05) increases in cellular contributions from lung and cardiac tissue in breast cancer patients (n=15) undergoing radiation therapy compared to baseline. We also detect novel signals of radiation induced toxicity to the liver in right-sided breast cancer patients (n=8) receiving radiation treatment compared to matched left-sided breast cancer patients (n=7). DISCUSSION/SIGNIFICANCE: Here we address an unmet need in developing novel statistical methodologies that can handle the unique complexities of methylated cfDNA obtained from liquid biopsy samples. We also demonstrate the far-ranging clinical utility of serial liquid biopsy sampling to complement and advance standards of clinical care in oncology and other pathologies.

Single-cell individual full-length mtDNA sequencing by iMiGseq uncovers unexpected heteroplasmy shifts in mtDNA editing.

The ontogeny and dynamics of mtDNA heteroplasmy remain unclear due to limitations of current mtDNA sequencing methods. We developed individual Mitochondrial Genome sequencing (iMiGseq) of full-length mtDNA for ultra-sensitive variant detection, complete haplotyping, and unbiased evaluation of heteroplasmy levels, all at the individual mtDNA molecule level. iMiGseq uncovered unappreciated levels of heteroplasmic variants in single cells well below the conventional NGS detection limit and provided accurate quantitation of heteroplasmy level. iMiGseq resolved the complete haplotype of individual mtDNA in single oocytes and revealed genetic linkage of de novo mutations. iMiGseq detected sequential acquisition of detrimental mutations, including large deletions, in defective mtDNA in NARP/Leigh syndrome patient-derived induced pluripotent stem cells. iMiGseq identified unintended heteroplasmy shifts in mitoTALEN editing, while showing no appreciable level of unintended mutations in DdCBE-mediated mtDNA base editing. Therefore, iMiGseq could not only help elucidate the mitochondrial etiology of diseases, but also evaluate the safety of various mtDNA editing strategies.

Sequence-Dependent Interaction of the Human Papillomavirus E2 Protein with the DNA Elements on Its DNA Replication Origin.

The human papillomavirus (HPV) E2 protein is essential for regulating the initiation of viral DNA replication as well as the regulation of transcription of certain HPV-encoded genes. Its ability to recognize and bind to its four recognition sequences in the viral origin is a key step in the initiation of HPV DNA replication. Thus, understanding the mechanism of DNA binding by E2 protein and the unique roles played by individual DNA sequence elements of the replication origin is essential. We have purified the recombinant full-length HPV type 11 E2 protein. Quantitative DNA binding analysis indicated E2 protein bound all four DNA binding sites with reasonably high affinities but with distinct preferences. It bound its cognate binding sites 1, 2, and 4 with higher affinities, but bound binding site 3 with lower affinity. Analysis of binding to these sites unraveled multiple sequence elements that appeared to influence E2 binding affinity and target discrimination, including the sequence of spacer region, flanking sequences, and proximity of E2 binding sites. Thermodynamic analysis indicated hydrophobic interaction in the protein-DNA complex formation. Our studies indicate a large multi-protein complex formation on the HPV-origin DNA, likely due to reasonably high binding affinities as well as intrinsic oligomerization propensity of E2 dimers.

Known sequence features explain half of all human gene ends.

Cleavage and polyadenylation (CPA) sites define eukaryotic gene ends. CPA sites are associated with five key sequence recognition elements: the upstream UGUA, the polyadenylation signal (PAS), and U-rich sequences; the CA/UA dinucleotide where cleavage occurs; and GU-rich downstream elements (DSEs). Currently, it is not clear whether these sequences are sufficient to delineate CPA sites. Additionally, numerous other sequences and factors have been described, often in the context of promoting alternative CPA sites and preventing cryptic CPA site usage. Here, we dissect the contributions of individual sequence features to CPA using standard discriminative models. We show that models comprised only of the five primary CPA sequence features give highest probability scores to constitutive CPA sites at the ends of coding genes, relative to the entire pre-mRNA sequence, for 59% of all human genes. U1-hybridizing sequences provide a small boost in performance. The addition of all known RBP RNA binding motifs to the model increases this figure to only 61%, suggesting that additional factors beyond the core CPA machinery have a minimal role in delineating real from cryptic sites. To our knowledge, this high effectiveness of established features to predict human gene ends has not previously been documented.

SUP: a probabilistic framework to propagate genome sequence uncertainty, with applications.

Genetic sequencing is subject to many different types of errors, but most analyses treat the resultant sequences as if they are known without error. Next generation sequencing methods rely on significantly larger numbers of reads than previous sequencing methods in exchange for a loss of accuracy in each individual read. Still, the coverage of such machines is imperfect and leaves uncertainty in many of the base calls. In this work, we demonstrate that the uncertainty in sequencing techniques will affect downstream analysis and propose a straightforward method to propagate the uncertainty. Our method (which we have dubbed Sequence Uncertainty Propagation, or SUP) uses a probabilistic matrix representation of individual sequences which incorporates base quality scores as a measure of uncertainty that naturally lead to resampling and replication as a framework for uncertainty propagation. With the matrix representation, resampling possible base calls according to quality scores provides a bootstrap- or prior distribution-like first step towards genetic analysis. Analyses based on these re-sampled sequences will include a more complete evaluation of the error involved in such analyses. We demonstrate our resampling method on SARS-CoV-2 data. The resampling procedures add a linear computational cost to the analyses, but the large impact on the variance in downstream estimates makes it clear that ignoring this uncertainty may lead to overly confident conclusions. We show that SARS-CoV-2 lineage designations via Pangolin are much less certain than the bootstrap support reported by Pangolin would imply and the clock rate estimates for SARS-CoV-2 are much more variable than reported.

Characterization of three novel HLA alleles: HLA-B*44:481:02, HLA-DQB1*03:338N, and HLA-DQB1*06:467.

Three novel alleles were characterized by next-generation sequencing in Russian individuals.

Exome sequencing in individuals with congenital anomalies of the kidney and urinary tract (CAKUT): a single-center experience

Individuals with congenital anomalies of the kidney and urinary tract (CAKUT) show a broad spectrum of malformations. CAKUT can occur in an isolated fashion or as part of a syndromic disorder and can lead to end-stage kidney failure. A monogenic cause can be identified in ~12% of affected individuals. This study investigated a single-center CAKUT cohort analyzed by exome sequencing (ES). Emphasis was placed on the question whether diagnostic yield differs between certain CAKUT phenotypes (e.g., bilateral kidney affection, unilateral kidney affection or only urinary tract affection). 86 unrelated individuals with CAKUT were categorized according to their phenotype and analyzed by ES to identify a monogenic cause. Prioritized variants were rated according to the recommendations of the American College of Medical Genetics and Genomics and the Association for Clinical Genomic Science. Diagnostic yields of different phenotypic categories were compared. Clinical data were collected using a standardized questionnaire. In the study cohort, 7/86 individuals had a (likely) pathogenic variant in the genes PAX2, PBX1, EYA1, or SALL1. Additionally, in one individual, a 17q12 deletion syndrome (including HNF1B) was detected. 64 individuals had a kidney affection, which was bilateral in 36. All solved cases (8/86, 9%) had bilateral kidney affection (diagnostic yield in subcohort: 8/36, 22%). Although the diagnostic yield in CAKUT cohorts is low, our single-center experience argues, that, in individuals with bilateral kidney affection, monogenic burden is higher than in those with unilateral kidney or only urinary tract affection.

Performance comparisons between clustering models for reconstructing NGS results from technical replicates.

To improve the performance of individual DNA sequencing results, researchers often use replicates from the same individual and various statistical clustering models to reconstruct a high-performance callset. Here, three technical replicates of genome NA12878 were considered and five model types were compared (consensus, latent class, Gaussian mixture, Kamila-adapted k-means, and random forest) regarding four performance indicators: sensitivity, precision, accuracy, and F1-score. In comparison with no use of a combination model, i) the consensus model improved precision by 0.1%; ii) the latent class model brought 1% precision improvement (97%-98%) without compromising sensitivity (= 98.9%); iii) the Gaussian mixture model and random forest provided callsets with higher precisions (both >99%) but lower sensitivities; iv) Kamila increased precision (>99%) and kept a high sensitivity (98.8%); it showed the best overall performance. According to precision and F1-score indicators, the compared non-supervised clustering models that combine multiple callsets are able to improve sequencing performance vs. previously used supervised models. Among the models compared, the Gaussian mixture model and Kamila offered non-negligible precision and F1-score improvements. These models may be thus recommended for callset reconstruction (from either biological or technical replicates) for diagnostic or precision medicine purposes.

Cytogenetic and Molecular Characterization of Eigenmannia aff. desantanai (Gymnotiformes: Sternopygidae): A First Report of System of Sex Chromosomes ZW1W2/ZZ in Gymnotiformes.

Gymnotiformes a monophyletic group of fish endemic to the Neotropics, represent an important component of the freshwater ichthyofauna that presents relevant taxonomic problems. Thus, in view of the morphological complexity involving Eigenmannia (Gymnotiformes) fish species, this study aimed to characterize Eigenmannia aff. desantanai of the upper Paraguay River basin through cytogenetic and molecular analyses, to help in the correct identification and delimitation of species. This study reports a multiple sex system of the type ZW1W2/ZZ, with 2n = 31 for females and 2n = 30 for males. A single pair of chromosomes carrying the nucleolar organizing regions (NORs) was detected. The heterochromatin was colocated in NOR sites and mainly located in the centromeric regions of chromosomes. Besides that, individual sequences COI from the specimens of E. aff. desantanai were obtained, totalizing three haplotypes. The distance p between the haplotypes in E. aff. desantanai, ranged from 0.2% to 7.1%. Species delimitation tests indicated the existence of two possible operational taxonomic units of E. aff. desantanai. Thus, this study reports a new multiple sex system in Gymnotiformes and these specimens previously identified as E. aff. desantanai may belong to two distinct species.