A new profiling approach for DNA sequences based on the nucleotides' physicochemical features for accurate analysis of SARS-CoV-2 genomes.

BACKGROUND: The prevalence of the COVID-19 disease in recent years and its widespread impact on mortality, as well as various aspects of life around the world, has made it important to study this disease and its viral cause. However, very long sequences of this virus increase the processing time, complexity of calculation, and memory consumption required by the available tools to compare and analyze the sequences. RESULTS: We present a new encoding method, named PC-mer, based on the k-mer and physic-chemical properties of nucleotides. This method minimizes the size of encoded data by around 2 k times compared to the classical k-mer based profiling method. Moreover, using PC-mer, we designed two tools: 1) a machine-learning-based classification tool for coronavirus family members with the ability to recive input sequences from the NCBI database, and 2) an alignment-free computational comparison tool for calculating dissimilarity scores between coronaviruses at the genus and species levels. CONCLUSIONS: PC-mer achieves 100% accuracy despite the use of very simple classification algorithms based on Machine Learning. Assuming dynamic programming-based pairwise alignment as the ground truth approach, we achieved a degree of convergence of more than 98% for coronavirus genus-level sequences and 93% for SARS-CoV-2 sequences using PC-mer in the alignment-free classification method. This outperformance of PC-mer suggests that it can serve as a replacement for alignment-based approaches in certain sequence analysis applications that rely on similarity/dissimilarity scores, such as searching sequences, comparing sequences, and certain types of phylogenetic analysis methods that are based on sequence comparison.

Elucidation of the pathway for biosynthesis of saponin adjuvants from the soapbark tree.

The Chilean soapbark tree (Quillaja saponaria) produces soap-like molecules called QS saponins that are important vaccine adjuvants. These highly valuable compounds are sourced by extraction from the bark, and their biosynthetic pathway is unknown. Here, we sequenced the Q. saponaria genome. Through genome mining and combinatorial expression in tobacco, we identified 16 pathway enzymes that together enable the production of advanced QS pathway intermediates that represent a bridgehead for adjuvant bioengineering. We further identified the enzymes needed to make QS-7, a saponin with excellent therapeutic properties and low toxicity that is present in low abundance in Q. saponaria bark extract. Our results enable the production of Q. saponaria vaccine adjuvants in tobacco and open the way for new routes to access and engineer natural and new-to-nature immunostimulants.

Low prevalence of influenza viruses and predominance of A(H3N2) virus with respect to SARS-CoV-2 during the 2021-2022 season in Bulgaria.

Social distancing, mask-wearing, and travel restrictions during the COVID-19 pandemic have significantly impacted the spread of influenza viruses. The objectives of this study were to analyze the pattern of influenza virus circulation with respect to that of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Bulgaria during the 2021-2022 season and to perform a phylogenetic/molecular analysis of the hemagglutinin (HA) and neuraminidase (NA) sequences of representative influenza strains. Influenza infection was confirmed using real-time reverse transcription polymerase chain reaction in 93 (4.2%) of the 2193 patients with acute respiratory illness tested wherein all detected viruses were subtyped as A(H3N2). SARS-CoV-2 was identified in 377 (24.3%) of the 1552 patients tested. Significant differences in the incidence of influenza viruses and SARS-CoV-2 were found between individual age groups, outpatients/inpatients, and in the seasonal distribution of cases. Two cases of coinfections were identified. In hospitalized patients, the Ct values of influenza viruses at admission were lower in adults aged ≥65 years (indicating higher viral load) than in children aged 0-14 years (p < 0.05). In SARS-CoV-2-positive inpatients, this association was not statistically significant. HA genes of all A(H3N2) viruses analyzed belonged to subclade 3C.2a1b.2a. The sequenced viruses carried 11 substitutions in HA and 5 in NA, in comparison to the vaccine virus A/Cambodia/e0826360/2020, including several substitutions in the HA antigenic sites B and C. This study revealed extensive changes in the typical epidemiology of influenza infection, including a dramatic reduction in the number of cases, diminished genetic diversity of circulating viruses, changes in age, and seasonal distribution of cases.

A Multiplex PCR and DNA-Sequencing Workflow on Serum for the Diagnosis and Species Identification for Invasive Aspergillosis and Mucormycosis.

There has been significant increase in the use of molecular tools for the diagnosis of invasive aspergillosis (IA) and mucormycosis. However, their range of detection may be too limited as species diversity and coinfections are increasing. Here, we aimed to evaluate a molecular workflow based on a new multiplex PCR assay detecting the whole Aspergillus genus and the Mucorales order followed by a species-specific PCR or a DNA-sequencing approach for IA and/or mucormycosis diagnosis and species identification on serum. Performances of the MycoGENIE Aspergillus spp./Mucorales spp. duplex PCR kit were analyzed on a broad range of fungal strains and on sera from high-risk patients prospectively over a 12-month period. The kit allowed the detection of nine Aspergillus species and 10 Mucorales (eight genera) strains assessed. No cross-reactions between the two targets were observed. Sera from 744 patients were prospectively analyzed, including 35 IA, 16 mucormycosis, and four coinfections. Sensitivity varies from 85.7% (18/21) in probable/proven IA to 28.6% (4/14) in COVID-19-associated pulmonary aspergillosis. PCR-positive samples corresponded to 21 A. fumigatus, one A. flavus, and one A. nidulans infections. All the disseminated mucormycosis were positive in serum (14/14), including the four Aspergillus coinfections, but sensitivity fell to 33.3% (2/6) in localized forms. DNA sequencing allowed Mucorales identification in serum in 15 patients. Remarkably, the most frequent species identified was Rhizomucor pusillus (eight cases), whereas it is barely found in fungal culture. This molecular workflow is a promising approach to improve IA and mucormycosis diagnosis and epidemiology.

Description of the novel HLA-C allele, HLA-C*12:376, identified in a deceased COVID-19 patient.

The new allele HLA-C*12:376 showed one nonsynonymous nucleotide difference compared with the C*12:03:01:01 allele in codon 30.

AccuVIR: an ACCUrate VIRal genome assembly tool for third-generation sequencing data.

MOTIVATION: RNA viruses tend to mutate constantly. While many of the variants are neutral, some can lead to higher transmissibility or virulence. Accurate assembly of complete viral genomes enables the identification of underlying variants, which are essential for studying virus evolution and elucidating the relationship between genotypes and virus properties. Recently, third-generation sequencing platforms such as Nanopore sequencers have been used for real-time virus sequencing for Ebola, Zika, coronavirus disease 2019, etc. However, their high per-base error rate prevents the accurate reconstruction of the viral genome. RESULTS: In this work, we introduce a new tool, AccuVIR, for viral genome assembly and polishing using error-prone long reads. It can better distinguish sequencing errors from true variants based on the key observation that sequencing errors can disrupt the gene structures of viruses, which usually have a high density of coding regions. Our experimental results on both simulated and real third-generation sequencing data demonstrated its superior performance on generating more accurate viral genomes than generic assembly or polish tools. AVAILABILITY AND IMPLEMENTATION: The source code and the documentation of AccuVIR are available at https://github.com/rainyrubyzhou/AccuVIR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PredictION: a predictive model to establish the performance of Oxford sequencing reads of SARS-CoV-2.

The optimization of resources for research in developing countries forces us to consider strategies in the wet lab that allow the reuse of molecular biology reagents to reduce costs. In this study, we used linear regression as a method for predictive modeling of coverage depth given the number of MinION reads sequenced to define the optimum number of reads necessary to obtain >200X coverage depth with a good lineage-clade assignment of SARS-CoV-2 genomes. The research aimed to create and implement a model based on machine learning algorithms to predict different variables (e.g., coverage depth) given the number of MinION reads produced by Nanopore sequencing to maximize the yield of high-quality SARS-CoV-2 genomes, determine the best sequencing runtime, and to be able to reuse the flow cell with the remaining nanopores available for sequencing in a new run. The best accuracy was -0.98 according to the R squared performance metric of the models. A demo version is available at https://genomicdashboard.herokuapp.com/.

Targeted genomic sequencing with probe capture for discovery and surveillance of coronaviruses in bats.

Public health emergencies like SARS, MERS, and COVID-19 have prioritized surveillance of zoonotic coronaviruses, resulting in extensive genomic characterization of coronavirus diversity in bats. Sequencing viral genomes directly from animal specimens remains a laboratory challenge, however, and most bat coronaviruses have been characterized solely by PCR amplification of small regions from the best-conserved gene. This has resulted in limited phylogenetic resolution and left viral genetic factors relevant to threat assessment undescribed. In this study, we evaluated whether a technique called hybridization probe capture can achieve more extensive genome recovery from surveillance specimens. Using a custom panel of 20,000 probes, we captured and sequenced coronavirus genomic material in 21 swab specimens collected from bats in the Democratic Republic of the Congo. For 15 of these specimens, probe capture recovered more genome sequence than had been previously generated with standard amplicon sequencing protocols, providing a median 6.1-fold improvement (ranging up to 69.1-fold). Probe capture data also identified five novel alpha- and betacoronaviruses in these specimens, and their full genomes were recovered with additional deep sequencing. Based on these experiences, we discuss how probe capture could be effectively operationalized alongside other sequencing technologies for high-throughput, genomics-based discovery and surveillance of bat coronaviruses.

Lightweight Pattern Matching Method for DNA Sequencing in Internet of Medical Things.

An area of medical science, that is, gaining prominence, is DNA sequencing. Genetic mutations responsible for the disease have been detected using DNA sequencing. The research is focusing on pattern identification methodologies for dealing with DNA-sequencing problems relating to various applications. A few examples of such problems are alignment and assembly of short reads from next generation sequencing (NGS), comparing DNA sequences, and determining the frequency of a pattern in a sequence. The approximate matching of DNA sequences is also well suited for many applications equivalent to the exact matching of the sequence since the DNA sequences are often subject to mutation. Consequently, recognizing pattern similarity becomes necessary. Furthermore, it can also be used in virtually every application that calls for pattern matching, for example, spell-checking, spam filtering, and search engines. According to the traditional approach, finding a similar pattern in the case where the sequence length is l s and the pattern length is l p occurs in O (l s ∗l p ). This heavy processing is caused by comparing every character of the sequence repeatedly with the pattern. The research intended to reduce the time complexity of the pattern matching by introducing an approach named "optimized pattern similarity identification" (OPSI). This methodology constructs a table, entitled "shift beyond for avoiding redundant comparison" (SBARC), to bypass the characters in the texts that are already compared with the pattern. The table pertains to the information about the character distance to be skipped in the matching. OPSI discovers at most spots of similar patterns occur in the sequence (by ignoring è mismatches). The experiment resulted in the time complexity identified as O (l s . è). In comparison to the size of the pattern, the allowed number of mismatches will be much smaller. Aspects such as scalability, generalizability, and performance of the OPSI algorithm are discussed. In comparison with the hamming distance-based approximate pattern matching algorithm, the proposed algorithm is found to be 69% more efficient.

kmer2vec: A Novel Method for Comparing DNA Sequences by word2vec Embedding.

The comparison of DNA sequences is of great significance in genomics analysis. Although the traditional multiple sequence alignment (MSA) method is popularly used for evolutionary analysis, optimally aligning k sequences becomes computationally intractable when k increases due to the intrinsic computational complexity of MSA. Despite numerous k-mer alignment-free methods being proposed, the existing k-mer alignment-free methods may not truly capture the contextual structures of the sequences. In this study, we present a novel k-mer contextual alignment-free method (called kmer2vec), in which the sequence k-mers are semantically embedded to word2vec vectors, an essential technique in natural language processing. Consequently, the method converts each DNA/RNA sequence into a point in the word2vec high-dimensional space and compares DNA sequences in the space. Because the word2vec vectors are trained from the contextual relationship of k-mers in the genomes, the method may extract valuable structural information from the sequences and reflect the relationship among them properly. The proposed method is optimized on the parameters from word2vec training and verified in the phylogenetic analysis of large whole genomes, including coronavirus and bacterial genomes. The results demonstrate the effectiveness of the method on phylogenetic tree construction and species clustering. The method running speed is much faster than that of the MSA method, especially the phylogenetic relationships constructed by the kmer2vec method are more accurate than the conventional k-mer alignment-free method. Therefore, this approach can provide new perspectives for phylogeny and evolution and make it possible to analyze large genomes. In addition, we discuss special parameterization in the k-mer word2vec embedding construction. An effective tool for rapid SARS-CoV-2 typing can also be derived when combining kmer2vec with clustering methods.

Simultaneous co-infection with Omicron (B.1.1.529) and Delta (21A/478K.V1) SARS-CoV-2 variants confirmed by whole genome sequencing.

We reported herein a simultaneous co-identification with Omicron (B.1.1.529) and Delta (21A/478K.V1) SARS-CoV-2 variants, confirmed by whole genome sequencing in an 83-year-old French patient.

Using generative adversarial networks for genome variant calling from low depth ONT sequencing data.

Genome variant calling is a challenging yet critical task for subsequent studies. Existing methods almost rely on high depth DNA sequencing data. Performance on low depth data drops a lot. Using public Oxford Nanopore (ONT) data of human being from the Genome in a Bottle (GIAB) Consortium, we trained a generative adversarial network for low depth variant calling. Our method, noted as LDV-Caller, can project high depth sequencing information from low depth data. It achieves 94.25% F1 score on low depth data, while the F1 score of the state-of-the-art method on two times higher depth data is 94.49%. By doing so, the price of genome-wide sequencing examination can reduce deeply. In addition, we validated the trained LDV-Caller model on 157 public Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) samples. The mean sequencing depth of these samples is 2982. The LDV-Caller yields 92.77% F1 score using only 22x sequencing depth, which demonstrates our method has potential to analyze different species with only low depth sequencing data.

A metagenomic DNA sequencing assay that is robust against environmental DNA contamination.

Metagenomic DNA sequencing is a powerful tool to characterize microbial communities but is sensitive to environmental DNA contamination, in particular when applied to samples with low microbial biomass. Here, we present Sample-Intrinsic microbial DNA Found by Tagging and sequencing (SIFT-seq) a metagenomic sequencing assay that is robust against environmental DNA contamination introduced during sample preparation. The core idea of SIFT-seq is to tag the DNA in the sample prior to DNA isolation and library preparation with a label that can be recorded by DNA sequencing. Any contaminating DNA that is introduced in the sample after tagging can then be bioinformatically identified and removed. We applied SIFT-seq to screen for infections from microorganisms with low burden in blood and urine, to identify COVID-19 co-infection, to characterize the urinary microbiome, and to identify microbial DNA signatures of sepsis and inflammatory bowel disease in blood.

Origins and Evolution of Seasonal Human Coronaviruses.

Four seasonal human coronaviruses (sHCoVs) are endemic globally (229E, NL63, OC43, and HKU1), accounting for 5-30% of human respiratory infections. However, the epidemiology and evolution of these CoVs remain understudied due to their association with mild symptomatology. Using a multigene and complete genome analysis approach, we find the evolutionary histories of sHCoVs to be highly complex, owing to frequent recombination of CoVs including within and between sHCoVs, and uncertain, due to the under sampling of non-human viruses. The recombination rate was highest for 229E and OC43 whereas substitutions per recombination event were highest in NL63 and HKU1. Depending on the gene studied, OC43 may have ungulate, canine, or rabbit CoV ancestors. 229E may have origins in a bat, camel, or an unsampled intermediate host. HKU1 had the earliest common ancestor (1809-1899) but fell into two distinct clades (genotypes A and B), possibly representing two independent transmission events from murine-origin CoVs that appear to be a single introduction due to large gaps in the sampling of CoVs in animals. In fact, genotype B was genetically more diverse than all the other sHCoVs. Finally, we found shared amino acid substitutions in multiple proteins along the non-human to sHCoV host-jump branches. The complex evolution of CoVs and their frequent host switches could benefit from continued surveillance of CoVs across non-human hosts.

Using deep learning to detect digitally encoded DNA trigger for Trojan malware in Bio-Cyber attacks.

This article uses Deep Learning technologies to safeguard DNA sequencing against Bio-Cyber attacks. We consider a hybrid attack scenario where the payload is encoded into a DNA sequence to activate a Trojan malware implanted in a software tool used in the sequencing pipeline in order to allow the perpetrators to gain control over the resources used in that pipeline during sequence analysis. The scenario considered in the paper is based on perpetrators submitting synthetically engineered DNA samples that contain digitally encoded IP address and port number of the perpetrator's machine in the DNA. Genetic analysis of the sample's DNA will decode the address that is used by the software Trojan malware to activate and trigger a remote connection. This approach can open up to multiple perpetrators to create connections to hijack the DNA sequencing pipeline. As a way of hiding the data, the perpetrators can avoid detection by encoding the address to maximise similarity with genuine DNAs, which we showed previously. However, in this paper we show how Deep Learning can be used to successfully detect and identify the trigger encoded data, in order to protect a DNA sequencing pipeline from Trojan attacks. The result shows nearly up to 100% accuracy in detection in such a novel Trojan attack scenario even after applying fragmentation encryption and steganography on the encoded trigger data. In addition, feasibility of designing and synthesizing encoded DNA for such Trojan payloads is validated by a wet lab experiment.

Benchmarking and Assessment of Eight De Novo Genome Assemblers on Viral Next-Generation Sequencing Data, Including the SARS-CoV-2.

Viral genomics has become crucial in clinical diagnostics and ecology, not to mention to stem the COVID-19 pandemic. Whole-genome sequencing (WGS) is pivotal in gaining an improved understanding of viral evolution, genomic epidemiology, infectious outbreaks, pathobiology, clinical management, and vaccine development. Genome assembly is one of the crucial steps in WGS data analyses. A series of different assemblers has been developed with the advent of high-throughput next-generation sequencing (NGS). Various studies have reported the evaluation of these assembly tools on distinct datasets; however, these lack data from viral origin. In this study, we performed a comparative evaluation and benchmarking of eight de novo assemblers: SOAPdenovo, Velvet, assembly by short sequences (ABySS), iterative De Bruijn graph assembler (IDBA), SPAdes, Edena, iterative virus assembler, and VICUNA on the viral NGS data from distinct Illumina (GAIIx, Hiseq, Miseq, and Nextseq) platforms. WGS data of diverse viruses, that is, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), dengue virus 3, human immunodeficiency virus 1, hepatitis B virus, human herpesvirus 8, human papillomavirus 16, rhinovirus A, and West Nile virus, were utilized to assess these assemblers. Performance metrics such as genome fraction recovery, assembly lengths, NG50, N50, contig length, contig numbers, mismatches, and misassemblies were analyzed. Overall, three assemblers, that is, SPAdes, IDBA, and ABySS, performed consistently well, including for genome assembly of SARS-CoV-2. These assembly methods should be considered and recommended for future studies of viruses. The study also suggests that implementing two or more assembly approaches should be considered in viral NGS studies, especially in clinical settings. Taken together, the benchmarking of eight de novo genome assemblers reported in this study can inform future public health and ecology research concerning the viruses, the COVID-19 pandemic, and viral outbreaks.

Case report of Curtobacterium isolated from a catheter tip sample misidentified as Cronobacter.

The Curtobacterium genus is a member of the family Microbacteriaceae, and Curtobacterium species are recognized as plant pathogens. The aim of this study was to investigate a dubious result of species identification for an infection located on a catheter tip of a patient with Covid-19. A strain isolated from a catheter tip sample, identified by VITEK® 2 as Cronobacter spp., was submitted to polyphasic analysis: Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) using VITEK® MS, real-time polymerase chain reaction targeting dnaG gene, and 16S rRNA full gene Sanger sequencing analysis for confirmation. The strain presented negative result using qPCR and could not identified by MALDI-TOF MS. 16S rRNA full gene Sanger sequencing analysis identified the strain as Curtobacterium spp. The Gram-variable characteristic (Gram-negative instead of Gram-positive) of the isolated strain was the responsible for the misidentification by VITEK® 2 and VITEK® MS did not identify the strain. 16S rRNA full gene sequencing analysis identified the strain as Curtobacterium genus, but other complementary techniques are necessary to identify at species level.

Adaptive Evolution of the Fox Coronavirus Based on Genome-Wide Sequence Analysis.

Purpose: To report the first complete fox coronavirus (CoV) genome sequence obtained through genome-wide amplifications and to understand the adaptive evolution of fox CoV. Methods: Anal swab samples were collected from 35 foxes to detect the presence of CoV and obtain the virus sequence. Phylogenetic analysis was conducted using MrBayes. The possibility of recombination within these sequences was assessed using GARD. Analysis of the levels of selection pressure experienced by these sequences was assessed using methods on both the PAML and Data Monkey platforms. Results: Of the 35 samples, two were positive, and complete genome sequences for the viruses were obtained. Phylogenetic analysis, using Bayesian methods, of these sequences, together with other CoV sequences, revealed that the fox CoV sequences clustered with canine coronavirus (CCoV) sequences, with sequences from other carnivores more distantly related. In contrast to the feline, ferret and mink CoV sequences that clustered into species-specific clades, the fox CoV fell within the CCoV clade. Minimal evidence for recombination was found among the sequences. A total of 7, 3, 14, and 2 positively selected sites were identified in the M, N, S, and 7B genes, respectively, with 99, 111, and 581 negatively selected sites identified in M, N, and S genes, respectively. Conclusion: The complete genome sequence of fox CoV has been obtained for the first time. The results suggest that the genome sequence of fox CoV may have experienced adaptive evolution in the genes replication, entry, and virulence. The number of sites in each gene that experienced negative selection is far greater than the number that underwent positive selection, suggesting that most of the sequence is highly conserved and important for viral survive. However, positive selection at a few sites likely aided these viruses to adapt to new environments.

Complete genome sequence of a novel porcine hemagglutinating encephalomyelitis virus strain identified in South Korea.

Porcine hemagglutinating encephalomyelitis virus (PHEV) is a member of the subgenus Embecovirus of the genus Betacoronavirus, and it is ubiquitously distributed in most pig-farming countries worldwide with low clinical incidence. Here, we report the full-length genome sequence and molecular characterization of a novel PHEV strain identified in diarrheic neonates in South Korea. The complete genome of the Korean PHEV strain GNU-2113 was sequenced and analyzed to characterize PHEV circulating in South Korea. The GNU-2113 genome was determined to be 29,982 nucleotides in length, with large unique deletions in the regions encoding nonstructural protein 3 and NS2. It was found to share 95.1-96.9% sequence identity with other global strains. Genetic and phylogenetic analysis indicated that the GNU-2113 strain is distantly related to the existing PHEV genotypes, implying that the virus appears to undergo substantial evolution under endemic pressure. This study provides important information about the genetic diversity of PHEV circulating subclinically in swine herds, which may ensure viral fitness in the enzootic environment.

High-Integrity Sequencing of Spike Gene for SARS-CoV-2 Variant Determination.

For tiling of the SARS-CoV-2 genome, the ARTIC Network provided a V4 protocol using 99 pairs of primers for amplicon production and is currently the widely used amplicon-based approach. However, this technique has regions of low sequence coverage and is labour-, time-, and cost-intensive. Moreover, it requires 14 pairs of primers in two separate PCRs to obtain spike gene sequences. To overcome these disadvantages, we proposed a single PCR to efficiently detect spike gene mutations. We proposed a bioinformatic protocol that can process FASTQ reads into spike gene consensus sequences to accurately call spike protein variants from sequenced samples or to fairly express the cases of missing amplicons. We evaluated the in silico detection rate of primer sets that yield amplicon sizes of 400, 1200, and 2500 bp for spike gene sequencing of SARS-CoV-2 to be 59.49, 76.19, and 92.20%, respectively. The in silico detection rate of our proposed single PCR primers was 97.07%. We demonstrated the robustness of our analytical protocol against 3000 Oxford Nanopore sequencing runs of distinct datasets, thus ensuring high-integrity sequencing of spike genes for variant SARS-CoV-2 determination. Our protocol works well with the data yielded from versatile primer designs, making it easy to determine spike protein variants.