Types Of Sequence Data Research Articles

Unraveling the lncRNA-miRNA-mRNA Regulatory Network Involved in Poplar Coma Development through High-Throughput Sequencing.

Poplar coma, the fluff-like appendages of seeds originating from the differentiated surface cells of the placenta and funicle, aids in the long-distance dispersal of seeds in the spring. However, it also poses hazards to human safety and causes pollution in the surrounding environment. Unraveling the regulatory mechanisms governing the initiation and development of coma is essential for addressing this issue comprehensively. In this study, strand-specific RNA-seq was conducted at three distinct stages of coma development, revealing 1888 lncRNAs and 52,810 mRNAs. The expression profiles of lncRNAs and mRNAs during coma development were analyzed. Subsequently, potential target genes of lncRNAs were predicted through co-localization and co-expression analyses. Integrating various types of sequencing data, lncRNA-miRNA-TF regulatory networks related to the initiation of coma were constructed. Utilizing identified differentially expressed genes encoding kinesin and actin, lncRNA-miRNA-mRNA regulatory networks associated with the construction and arrangement of the coma cytoskeleton were established. Additionally, relying on differentially expressed genes encoding cellulose synthase, sucrose synthase, and expansin, lncRNA-miRNA-mRNA regulatory networks related to coma cell wall synthesis and remodeling were developed. This study not only enhances the comprehension of lncRNA but also provides novel insights into the molecular mechanisms governing the initiation and development of poplar coma.

Assessing parameter efficient methods for pre-trained language model in annotating scRNA-seq data

Annotating cell types of single-cell RNA sequencing (scRNA-seq) data is crucial for studying cellular heterogeneity in the tumor microenvironment. Recently, large-scale pre-trained language models (PLMs) have achieved significant progress in cell-type annotation of scRNA-seq data. This approach effectively addresses previous methods' shortcomings in performance and generalization. However, fine-tuning PLMs for different downstream tasks demands considerable computational resources, rendering it impractical. Hence, a new research branch introduces parameter-efficient fine-tuning (PEFT). This involves optimizing a few parameters while leaving the majority unchanged, leading to substantial reductions in computational expenses. Here, we utilize scBERT, a large-scale pre-trained model, to explore the capabilities of three PEFT methods in scRNA-seq cell type annotation. Extensive benchmark studies across several datasets demonstrate the superior applicability of PEFT methods. Furthermore, downstream analysis using models obtained through PEFT showcases their utility in novel cell type discovery and model interpretability for potential marker genes. Our findings underscore the considerable potential of PEFT in PLM-based cell type annotation, presenting novel perspectives for the analysis of scRNA-seq data.

Annotating microbial functions with ProkFunFind.

Analyzing microbial genomes has become an essential part of microbiology research, giving valuable insights into the functions and evolution of microbial species. Identifying genes of interest and assigning putative annotations to those genes is a central task in genome analysis, and a plethora of tools and approaches have been developed for this task. The ProkFunFind tool was developed to bridge the gap between these various annotation approaches, providing a flexible and customizable search approach to annotate microbial functions. ProkFunFind is designed around hierarchical definitions of biological functions, where individual genes can be identified using heterogeneous search terms consisting of sequences, profile hidden Markov models, protein domains, and orthology groups. This flexible and customizable search approach allows for searches to be tailored to specific biological functions, and the search results are output in multiple formats to facilitate downstream analyses. The utility of the ProkFunFind search tool was demonstrated through its application in searching for bacterial flagella, which are complex organelles composed of multiple genes. Overall, ProkFunFind provides an accessible and flexible way to integrate multiple types of annotation and sequence data while annotating biological functions in microbial genomes.IMPORTANCEGenome sequencing and analysis are increasingly important parts of microbiology, providing a way to predict metabolic functions, identify virulence factors, and understand the evolution of microbes. The expanded use of genome sequencing has also brought an abundance of search and annotation methods, but integrating the information from these different methods can be challenging and is often done through ad hoc approaches. To bridge the gap between different types of annotations, we developed ProkFunFind, a flexible and customizable search tool incorporating multiple search approaches and annotation types to annotate microbial functions. We demonstrated the utility of ProkFunFind by searching for gene clusters encoding flagellar genes using a combination of different annotation types and searches. Overall, ProkFunFind provides a reproducible and flexible way to identify gene clusters of interest, facilitating the meaningful analysis of new and existing microbial genomes.

Ntsm: an alignment-free, ultra-low-coverage, sequencing technology agnostic, intraspecies sample comparison tool for sample swap detection.

Due to human error, sample swapping in large cohort studies with heterogeneous data types (e.g., mix of Oxford Nanopore Technologies, Pacific Bioscience, Illumina data, etc.) remains a common issue plaguing large-scale studies. At present, all sample swapping detection methods require costly and unnecessary (e.g., if data are only used for genome assembly) alignment, positional sorting, and indexing of the data in order to compare similarly. As studies include more samples and new sequencing data types, robust quality control tools will become increasingly important. The similarity between samples can be determined using indexed k-mer sequence variants. To increase statistical power, we use coverage information on variant sites, calculating similarity using a likelihood ratio-based test. Per sample error rate, and coverage bias (i.e., missing sites) can also be estimated with this information, which can be used to determine if a spatially indexed principal component analysis (PCA)-based prescreening method can be used, which can greatly speed up analysis by preventing exhaustive all-to-all comparisons. Because this tool processes raw data, is faster than alignment, and can be used on very low-coverage data, it can save an immense degree of computational resources in standard quality control (QC) pipelines. It is robust enough to be used on different sequencing data types, important in studies that leverage the strengths of different sequencing technologies. In addition to its primary use case of sample swap detection, this method also provides information useful in QC, such as error rate and coverage bias, as well as population-level PCA ancestry analysis visualization.

Performance analysis of conventional and AI-based variant callers using short and long reads

BackgroundThe accurate detection of variants is essential for genomics-based studies. Currently, there are various tools designed to detect genomic variants, however, it has always been a challenge to decide which tool to use, especially when various major genome projects have chosen to use different tools. Thus far, most of the existing tools were mainly developed to work on short-read data (i.e., Illumina); however, other sequencing technologies (e.g. PacBio, and Oxford Nanopore) have recently shown that they can also be used for variant calling. In addition, with the emergence of artificial intelligence (AI)-based variant calling tools, there is a pressing need to compare these tools in terms of efficiency, accuracy, computational power, and ease of use.ResultsIn this study, we evaluated five of the most widely used conventional and AI-based variant calling tools (BCFTools, GATK4, Platypus, DNAscope, and DeepVariant) in terms of accuracy and computational cost using both short-read and long-read data derived from three different sequencing technologies (Illumina, PacBio HiFi, and ONT) for the same set of samples from the Genome In A Bottle project. The analysis showed that AI-based variant calling tools supersede conventional ones for calling SNVs and INDELs using both long and short reads in most aspects. In addition, we demonstrate the advantages and drawbacks of each tool while ranking them in each aspect of these comparisons.ConclusionThis study provides best practices for variant calling using AI-based and conventional variant callers with different types of sequencing data.

Resolving the phylogeny of Thladiantha (Cucurbitaceae) with three different target capture pipelines

BackgroundDespite recent advances, reliable tools to simultaneously handle different types of sequencing data (e.g., target capture, genome skimming) for phylogenomics are still scarce. Here, we evaluate the performance of the recently developed pipeline Captus in comparison with the well-known target capture pipelines HybPiper and SECAPR. As test data, we analyzed newly generated sequences for the genus Thladiantha (Cucurbitaceae) for which no well-resolved phylogeny estimate has been available so far, as well as simulated reads derived from the genome of Arabidopsis thaliana.ResultsOur pipeline comparisons are based on (1) the time needed for data assembly and locus extraction, (2) locus recovery per sample, (3) the number of informative sites in nucleotide alignments, and (4) the topology of the nuclear and plastid phylogenies. Additionally, the simulated reads derived from the genome of Arabidopsis thaliana were used to evaluate the accuracy and completeness of the recovered loci. In terms of computation time, locus recovery per sample, and informative sites, Captus outperforms HybPiper and SECAPR. The resulting topologies of Captus and SECAPR are identical for coalescent trees but differ when trees are inferred from concatenated alignments. The HybPiper phylogeny is similar to Captus in both methods. The nuclear genes recover a deep split of Thladiantha in two clades, but this is not supported by the plastid data.ConclusionsCaptus is the best choice among the three pipelines in terms of computation time and locus recovery. Even though there is no significant topological difference between the Thladiantha species trees produced by the three pipelines, Captus yields a higher number of gene trees in agreement with the topology of the species tree (i.e., fewer genes in conflict with the species tree topology).

PhyBWT2: phylogeny reconstruction via eBWT positional clustering

BackgroundMolecular phylogenetics studies the evolutionary relationships among the individuals of a population through their biological sequences. It may provide insights about the origin and the evolution of viral diseases, or highlight complex evolutionary trajectories. A key task is inferring phylogenetic trees from any type of sequencing data, including raw short reads. Yet, several tools require pre-processed input data e.g. from complex computational pipelines based on de novo assembly or from mappings against a reference genome. As sequencing technologies keep becoming cheaper, this puts increasing pressure on designing methods that perform analysis directly on their outputs. From this viewpoint, there is a growing interest in alignment-, assembly-, and reference-free methods that could work on several data including raw reads data.ResultsWe present phyBWT2, a newly improved version of phyBWT (Guerrini et al. in 22nd International Workshop on Algorithms in Bioinformatics (WABI) 242:23–12319, 2022). Both of them directly reconstruct phylogenetic trees bypassing both the alignment against a reference genome and de novo assembly. They exploit the combinatorial properties of the extended Burrows-Wheeler Transform (eBWT) and the corresponding eBWT positional clustering framework to detect relevant blocks of the longest shared substrings of varying length (unlike the k-mer-based approaches that need to fix the length k a priori). As a result, they provide novel alignment-, assembly-, and reference-free methods that build partition trees without relying on the pairwise comparison of sequences, thus avoiding to use a distance matrix to infer phylogeny. In addition, phyBWT2 outperforms phyBWT in terms of running time, as the former reconstructs phylogenetic trees step-by-step by considering multiple partitions, instead of just one partition at a time, as previously done by the latter.ConclusionsBased on the results of the experiments on sequencing data, we conclude that our method can produce trees of quality comparable to the benchmark phylogeny by handling datasets of different types (short reads, contigs, or entire genomes). Overall, the experiments confirm the effectiveness of phyBWT2 that improves the performance of its previous version phyBWT, while preserving the accuracy of the results.

Characterization of a strain-specific CD-1 reference genome reveals potential inter- and intra-strain functional variability

BackgroundCD-1 is an outbred mouse stock that is frequently used in toxicology, pharmacology, and fundamental biomedical research. Although inbred strains are typically better suited for such studies due to minimal genetic variability, outbred stocks confer practical advantages over inbred strains, such as improved breeding performance and low cost. Knowledge of the full genetic variability of CD-1 would make it more useful in toxicology, pharmacology, and fundamental biomedical research.ResultsWe performed deep genomic DNA sequencing of CD-1 mice and used the data to identify genome-wide SNPs, indels, and germline transposable elements relative to the mm10 reference genome. We used multiple genome-wide sequencing data types and previously published CD-1 SNPs to validate our called variants. We used the called variants to construct a strain-specific CD-1 reference genome, which we show can improve mappability and reduce experimental biases from genome-wide sequencing data derived from CD-1 mice. Based on previously published ChIP-seq and ATAC-seq data, we find evidence that genetic variation between CD-1 mice can lead to alterations in transcription factor binding. We also identified a number of variants in the coding region of genes which could have effects on translation of genes.ConclusionsWe have identified millions of previously unidentified CD-1 variants with the potential to confound studies involving CD-1. We used the identified variants to construct a CD-1-specific reference genome, which can improve accuracy and reduce bias when aligning genomics data derived from CD-1 mice.

A high fidelity approach to assembling the complex Borrelia genome

BackgroundBacteria of the Borrelia burgdorferi sensu lato (s.l.) complex can cause Lyme borreliosis. Different B. burgdorferi s.l. genospecies vary in their host and vector associations and human pathogenicity but the genetic basis for these adaptations is unresolved and requires completed and reliable genomes for comparative analyses. The de novo assembly of a complete Borrelia genome is challenging due to the high levels of complexity, represented by a high number of circular and linear plasmids that are dynamic, showing mosaic structure and sequence homology. Previous work demonstrated that even advanced approaches, such as a combination of short-read and long-read data, might lead to incomplete plasmid reconstruction. Here, using recently developed high-fidelity (HiFi) PacBio sequencing, we explored strategies to obtain gap-free, complete and high quality Borrelia genome assemblies. Optimizing genome assembly, quality control and refinement steps, we critically appraised existing techniques to create a workflow that lead to improved genome reconstruction.ResultsDespite the latest available technologies, stand-alone sequencing and assembly methods are insufficient for the generation of complete and high quality Borrelia genome assemblies. We developed a workflow pipeline for the de novo genome assembly for Borrelia using several types of sequence data and incorporating multiple assemblers to recover the complete genome including both circular and linear plasmid sequences.ConclusionOur study demonstrates that, with HiFi data and an ensemble reconstruction pipeline with refinement steps, chromosomal and plasmid sequences can be fully resolved, even for complex genomes such as Borrelia. The presented pipeline may be of interest for the assembly of further complex microbial genomes.

ViralVectors: compact and scalable alignment-free virome feature generation.

The amount of sequencing data for SARS-CoV-2 is several orders of magnitude larger than any virus. This will continue to grow geometrically for SARS-CoV-2, and other viruses, as many countries heavily finance genomic surveillance efforts. Hence, we need methods for processing large amounts of sequence data to allow for effective yet timely decision-making. Such data will come from heterogeneous sources: aligned, unaligned, or even unassembled raw nucleotide or amino acid sequencing reads pertaining to the whole genome or regions (e.g., spike) of interest. In this work, we propose ViralVectors, a compact feature vector generation from virome sequencing data that allows effective downstream analysis. Such generation is based on minimizers, a type of lightweight "signature" of a sequence, used traditionally in assembly and read mapping - to our knowledge, the first use minimizers in this way. We validate our approach on different types of sequencing data: (a) 2.5M SARS-CoV-2 spike sequences (to show scalability); (b) 3K Coronaviridae spike sequences (to show robustness to more genomic variability); and (c) 4K raw WGS reads sets taken from nasal-swab PCR tests (to show the ability to process unassembled reads). Our results show that ViralVectors outperforms current benchmarks in most classification and clustering tasks. Graphical Abstract showing the all steps of proposed approach. We start by collecting the sequence-based data. Then Data cleaning and preprocessing is applied. After that, we generate the feature embeddings using minimizer based approach. Then Classification and clustering algorithms are applied on the resultant data and predictions are made on the test set.

A weighted distance-based approach for deriving consensus tumor evolutionary trees.

The acquisition of somatic mutations by a tumor can be modeled by a type of evolutionary tree. However, it is impossible to observe this tree directly. Instead, numerous algorithms have been developed to infer such a tree from different types of sequencing data. But such methods can produce conflicting trees for the same patient, making it desirable to have approaches that can combine several such tumor trees into a consensus or summary tree. We introduce The Weighted m-Tumor Tree Consensus Problem (W-m-TTCP) to find a consensus tree among multiple plausible tumor evolutionary histories, each assigned a confidence weight, given a specific distance measure between tumor trees. We present an algorithm called TuELiP that is based on integer linear programming which solves the W-m-TTCP, and unlike other existing consensus methods, allows the input trees to be weighted differently. On simulated data we show that TuELiP outperforms two existing methods at correctly identifying the true underlying tree used to create the simulations. We also show that the incorporation of weights can lead to more accurate tree inference. On a Triple-Negative Breast Cancer dataset, we show that including confidence weights can have important impacts on the consensus tree identified. An implementation of TuELiP and simulated datasets are available at https://bitbucket.org/oesperlab/consensus-ilp/src/main/.

Effect of Different Types of Sequence Data on Palaeognath Phylogeny.

Palaeognathae consists of five groups of extant species: flighted tinamous (1) and four flightless groups: kiwi (2), cassowaries and emu (3), rheas (4), and ostriches (5). Molecular studies supported the groupings of extinct moas with tinamous and elephant birds with kiwi as well as ostriches as the group that diverged first among the five groups. However, phylogenetic relationships among the five groups are still controversial. Previous studies showed extensive heterogeneity in estimated gene tree topologies from conserved nonexonic elements, introns, and ultraconserved elements. Using the noncoding loci together with protein-coding loci, this study investigated the factors that affected gene tree estimation error and the relationships among the five groups. Using closely related ostrich rather than distantly related chicken as the outgroup, concatenated and gene tree-based approaches supported rheas as the group that diverged first among groups (1)-(4). Whereas gene tree estimation error increased using loci with low sequence divergence and short length, topological bias in estimated trees occurred using loci with high sequence divergence and/or nucleotide composition bias and heterogeneity, which more occurred in trees estimated from coding loci than noncoding loci. Regarding the relationships of (1)-(4), the site patterns by parsimony criterion appeared less susceptible to the bias than tree construction assuming stationary time-homogeneous model and suggested the clustering of kiwi and cassowaries and emu the most likely with ∼40% support rather than the clustering of kiwi and rheas and that of kiwi and tinamous with 30% support each.

Highly accurate long reads are crucial for realizing the potential of biodiversity genomics

BackgroundGenerating the most contiguous, accurate genome assemblies given available sequencing technologies is a long-standing challenge in genome science. With the rise of long-read sequencing, assembly challenges have shifted from merely increasing contiguity to correctly assembling complex, repetitive regions of interest, ideally in a phased manner. At present, researchers largely choose between two types of long read data: longer, but less accurate sequences, or highly accurate, but shorter reads (i.e., >Q20 or 99% accurate). To better understand how these types of long-read data as well as scale of data (i.e., mean length and sequencing depth) influence genome assembly outcomes, we compared genome assemblies for a caddisfly, Hesperophylax magnus, generated with longer, but less accurate, Oxford Nanopore (ONT) R9.4.1 and highly accurate PacBio HiFi (HiFi) data. Next, we expanded this comparison to consider the influence of highly accurate long-read sequence data on genome assemblies across 6750 plant and animal genomes. For this broader comparison, we used HiFi data as a surrogate for highly accurate long-reads broadly as we could identify when they were used from GenBank metadata.ResultsHiFi reads outperformed ONT reads in all assembly metrics tested for the caddisfly data set and allowed for accurate assembly of the repetitive ~ 20 Kb H-fibroin gene. Across plants and animals, genome assemblies that incorporated HiFi reads were also more contiguous. For plants, the average HiFi assembly was 501% more contiguous (mean contig N50 = 20.5 Mb) than those generated with any other long-read data (mean contig N50 = 4.1 Mb). For animals, HiFi assemblies were 226% more contiguous (mean contig N50 = 20.9 Mb) versus other long-read assemblies (mean contig N50 = 9.3 Mb). In plants, we also found limited evidence that HiFi may offer a unique solution for overcoming genomic complexity that scales with assembly size.ConclusionsHighly accurate long-reads generated with HiFi or analogous technologies represent a key tool for maximizing genome assembly quality for a wide swath of plants and animals. This finding is particularly important when resources only allow for one type of sequencing data to be generated. Ultimately, to realize the promise of biodiversity genomics, we call for greater uptake of highly accurate long-reads in future studies.

The K-mer antibiotic resistance gene variant analyzer (KARGVA).

Characterization of antibiotic resistance genes (ARGs) from high-throughput sequencing data of metagenomics and cultured bacterial samples is a challenging task, with the need to account for both computational (e.g., string algorithms) and biological (e.g., gene transfers, rearrangements) aspects. Curated ARG databases exist together with assorted ARG classification approaches (e.g., database alignment, machine learning). Besides ARGs that naturally occur in bacterial strains or are acquired through mobile elements, there are chromosomal genes that can render a bacterium resistant to antibiotics through point mutations, i.e., ARG variants (ARGVs). While ARG repositories also collect ARGVs, there are only a few tools that are able to identify ARGVs from metagenomics and high throughput sequencing data, with a number of limitations (e.g., pre-assembly, a posteriori verification of mutations, or specification of species). In this work we present the k-mer, i.e., strings of fixed length k, ARGV analyzer - KARGVA - an open-source, multi-platform tool that provides: (i) an ad hoc, large ARGV database derived from multiple sources; (ii) input capability for various types of high-throughput sequencing data; (iii) a three-way, hash-based, k-mer search setup to process data efficiently, linking k-mers to ARGVs, k-mers to point mutations, and ARGVs to k-mers, respectively; (iv) a statistical filter on sequence classification to reduce type I and II errors. On semi-synthetic data, KARGVA provides very high accuracy even in presence of high sequencing errors or mutations (99.2 and 86.6% accuracy within 1 and 5% base change rates, respectively), and genome rearrangements (98.2% accuracy), with robust performance on ad hoc false positive sets. On data from the worldwide MetaSUB consortium, comprising 3,700+ metagenomics experiments, KARGVA identifies more ARGVs than Resistance Gene Identifier (4.8x) and PointFinder (6.8x), yet all predictions are below the expected false positive estimates. The prevalence of ARGVs is correlated to ARGs but ecological characteristics do not explain well ARGV variance. KARGVA is publicly available at https://github.com/DataIntellSystLab/KARGVA under MIT license.

KARAJ: An Efficient Adaptive Multi-Processor Tool to Streamline Genomic and Transcriptomic Sequence Data Acquisition.

Here we developed KARAJ, a fast and flexible Linux command-line tool to automate the end-to-end process of querying and downloading a wide range of genomic and transcriptomic sequence data types. The input to KARAJ is a list of PMCIDs or publication URLs or various types of accession numbers to automate four tasks as follows; firstly, it provides a summary list of accessible datasets generated by or used in these scientific articles, enabling users to select appropriate datasets; secondly, KARAJ calculates the size of files that users want to download and confirms the availability of adequate space on the local disk; thirdly, it generates a metadata table containing sample information and the experimental design of the corresponding study; and lastly, it enables users to download supplementary data tables attached to publications. Further, KARAJ provides a parallel downloading framework powered by Aspera connect which reduces the downloading time significantly.

HapCUT2: A Method for Phasing Genomes Using Experimental Sequence Data.

Rapid advances in high-throughput DNA sequencing technologies have enabled variant discovery from whole-genome sequencing (WGS) datasets; however linking variants on a chromosome together into haplotypes, also known as haplotype phasing, remains difficult. Human genomes are diploid and haplotype phasing is crucial for the complete interpretation and analysis of genetic variation.Hapcut2 ( https://github.com/vibansal/HapCUT2 ) is an open-source software for phasing diploid genomes using sequence data generated using different sequencing technologies and experimental methods. In this article, we give an overview of the algorithm used by Hapcut2 and describe how to use Hapcut2 for haplotype phasing of individual genomes using different types of sequence data.

TeaPVs: a comprehensive genomic variation database for tea plant (Camellia sinensis)

Genome variation not only plays an important role in plant phenotypic modeling and adaptive evolution, but also enhances population genetic diversity and regulates gene expression. The tea tree (Camellia sinensis) has a large genome (~ 3.0 Gb), making the identification of genome-wide variants time-consuming and expensive. With the continuous publication of a large number of different types of population sequencing data, there is a lack of an open platform to integrate these data and identify variants in the tea plant genome.To integrate the genetic variation confidence in the tea plant population genome, 238 whole-genome resequencing, 213 transcriptome sequencing, and 96 hybrid F1 individuals with a total of more than 20 Tb were collected for mutation site identification. Based on these variations information, we constructed the first tea tree variation web service database TeaPVs (http://47.106.184.91:8025/ and http://liushang.top:8025/). It supports users to search all SNP, Indel, SV mutations and SSR/Polymorphic SSR sequences by location or gene ID. Furthermore, the website also provides the functions of gene expression search of different transcriptome, sequence blast, sequence extraction of CDS and mutation loci, etc.The features of the TeaPVs database make it a comprehensive tea plant genetic variation bioinformatics platform for researchers, and will also be helpful for revealing new functional mutations in the tea plant genome and molecular marker-assisted breeding.

Assessment of Genetic Diversity of Noroviruses Circulating in Temporary Accommodation Centers for Refugees in the Rostov Region in 2022 Using the NoroNetRus Online Software

Introduction: It is obvious that effective investigation of norovirus outbreaks is impossible without the use of methods enabling differentiation of pathogen genotypes, the principal of which is the determination of the type of capsid and polymerase based on sequencing data. Yet, unstable operation of international services for assessment of norovirus genomes from a number of IP addresses located in the Russian Federation has been noted recently, which may affect the efficiency and promptness of inves- tigation of norovirus outbreaks in our country. Objective: To develop domestic software for genotyping of noroviruses and further assessment of genetic diversity of norovi- ruses identified in the year 2022 in the Rostov Region. Materials and methods: The materials for the study were stool samples from 210 patients with symptoms of gastrointestinal infec- tions from temporary accommodation centers for refugees and children’s centers. The study population included patients aged 1 month to 74 years with symptoms of acute bowel infections. Detection of the pathogen RNA was carried out using a reagent kit AmpliSens® OKI screen-FL (Moscow, Russia). Fragment sequencing was performed using a genomic analyzer SeqStudio (Thermo Fisher, USA). The authors’ software was developed in the Java and Python programming languages. Results: We developed a domestic NoroNetRus software allowing determination of the type of capsid and polymerase of noroviruses based on sequencing data. The software is free of charge and available online at http://antiplague.ru/noronetrus. The fragment analysis of VP1 gene sequences for 25 norovirus samples showed that they belonged to three different genotypes (GII.4 Sydney, GII.10, and GII.17), and the genotype GII.10, in its turn, was distributed between two different clusters. Our findings indicate the existence of at least four different sources of infection. Conclusion. The data obtained indicate the circulation of at least four different genovariants of the causative agent of norovirus infection in the Rostov Region in 2022.

Best genome sequencing strategies for annotation of complex immune gene families in wildlife.

The biodiversity crisis and increasing impact of wildlife disease on animal and human health provides impetus for studying immune genes in wildlife. Despite the recent boom in genomes for wildlife species, immune genes are poorly annotated in nonmodel species owing to their high level of polymorphism and complex genomic organisation. Our research over the past decade and a half on Tasmanian devils and koalas highlights the importance of genomics and accurate immune annotations to investigate disease in wildlife. Given this, we have increasingly been asked the minimum levels of genome quality required to effectively annotate immune genes in order to study immunogenetic diversity. Here we set out to answer this question by manually annotating immune genes in 5 marsupial genomes and 1 monotreme genome to determine the impact of sequencing data type, assembly quality, and automated annotation on accurate immune annotation. Genome quality is directly linked to our ability to annotate complex immune gene families, with long reads and scaffolding technologies required to reassemble immune gene clusters and elucidate evolution, organisation, and true gene content of the immune repertoire. Draft-quality genomes generated from short reads with HiC or 10× Chromium linked reads were unable to achieve this. Despite mammalian BUSCOv5 scores of up to 94.1% amongst the 6 genomes, automated annotation pipelines incorrectly annotated up to 59% of manually annotated immune genes regardless of assembly quality or method of automated annotation. Our results demonstrate that long reads and scaffolding technologies, alongside manual annotation, are required to accurately study the immune gene repertoire of wildlife species.

An asparagine metabolism-based classification reveals the metabolic and immune heterogeneity of hepatocellular carcinoma

Introduction and objectiveshepatocellular carcinoma (HCC) is the major form of liver cancer with a poor prognosis. Amino acid metabolism has been found to alter in cancers and contributes to malignant progression. However, the asparagine metabolism status and relevant mechanism in HCC were barely understood.MethodsBy conducting consensus clustering and the least absolute shrinkage and selection operator regression of HCC samples from three cohorts, we classified the HCC patients into two subtypes based on asparagine metabolism level. The Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analyses and Gene Set Enrichment Analysis of the differentially expressed genes between two subgroups were conducted. Immune cell infiltration was evaluated using CIBERSORT algorithm. The prognostic values of genes were analyzed by univariate and multivariate cox regression, ROC curve and Kaplan–Meier survival estimate analyses. Cell types of sing-cell RNA sequencing (scRNA-seq) data were clustered utilizing UMAP method.ResultsHCC patients with higher asparagine metabolism level have worse prognoses. Moreover, we found the distinct energy metabolism patterns, DNA damage response (DDR) pathway activating levels, drug sensitivities to DDR inhibitors, immune cell compositions in the tumor microenvironment and responses to immune therapy between two subgroups. Further, we identified a potential target gene, glutamic-oxaloacetic transaminase 2 (GOT2). GOT2 downregulation was associated with worse HCC prognosis and increased infiltration of T regulatory cells (Tregs). ScRNA-seq revealed the GOT2 downregulation in cancer stem cells compared with HCC cells.ConclusionsTaken together, HCC subtype which is more reliant on asparagine and glutamine metabolism has a worse prognosis, and a core gene of asparagine metabolism GOT2 is a potential prognostic marker and therapeutic target of HCC. Our study promotes the precision therapy of HCC and may improve patient outcomes.