The clinical outcome of COVID-19 is strongly associated with microbiome dynamics in the upper respiratory tract.

OBJECTIVES: The respiratory tract is the portal of entry for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although a variety of respiratory pathogens other than SARS-CoV-2 have been associated with severe cases of COVID-19 disease, the dynamics of the upper respiratory microbiota during disease the course of disease, and how they impact disease manifestation, remain uncertain. METHODS: We collected 349 longitudinal upper respiratory samples from a cohort of 65 COVID-19 patients (cohort 1), 28 samples from 28 recovered COVID-19 patients (cohort 2), and 59 samples from 59 healthy controls (cohort 3). All COVID-19 patients originated from the earliest stage of the epidemic in Wuhan. Based on a modified clinical scale, the disease course was divided into five clinical disease phases (pseudotimes): "Healthy" (pseudotime 0), "Incremental" (pseudotime 1), "Critical" (pseudotime 2), "Complicated" (pseudotime 3), "Convalescent" (pseudotime 4), and "Long-term follow-up" (pseudotime 5). Using meta-transcriptomics, we investigated the features and dynamics of transcriptionally active microbes in the upper respiratory tract (URT) over the course of COVID-19 disease, as well as its association with disease progression and clinical outcomes. RESULTS: Our results revealed that the URT microbiome exhibits substantial heterogeneity during disease course. Two clusters of microbial communities characterized by low alpha diversity and enrichment for multiple pathogens or potential pathobionts (including Acinetobacter and Candida) were associated with disease progression and a worse clinical outcome. We also identified a series of microbial indicators that classified disease progression into more severe stages. Longitudinal analysis revealed that although the microbiome exhibited complex and changing patterns during COVID-19, a restoration of URT microbiomes from early dysbiosis toward more diverse status in later disease stages was observed in most patients. In addition, a group of potential pathobionts were strongly associated with the concentration of inflammatory indicators and mortality. CONCLUSION: This study revealed strong links between URT microbiome dynamics and disease progression and clinical outcomes in COVID-19, implying that the treatment of severe disease should consider the full spectrum of microbial pathogens present.

GDmicro: classifying host disease status with GCN and deep adaptation network based on the human gut microbiome data.

MOTIVATION: With advances in metagenomic sequencing technologies, there are accumulating studies revealing the associations between the human gut microbiome and some human diseases. These associations shed light on using gut microbiome data to distinguish case and control samples of a specific disease, which is also called host disease status classification. Importantly, using learning-based models to distinguish the disease and control samples is expected to identify important biomarkers more accurately than abundance-based statistical analysis. However, available tools have not fully addressed two challenges associated with this task: limited labeled microbiome data and decreased accuracy in cross-studies. The confounding factors, such as the diet, technical biases in sample collection/sequencing across different studies/cohorts often jeopardize the generalization of the learning model. RESULTS: To address these challenges, we develop a new tool GDmicro, which combines semi-supervised learning and domain adaptation to achieve a more generalized model using limited labeled samples. We evaluated GDmicro on human gut microbiome data from 11 cohorts covering 5 different diseases. The results show that GDmicro has better performance and robustness than state-of-the-art tools. In particular, it improves the AUC from 0.783 to 0.949 in identifying inflammatory bowel disease. Furthermore, GDmicro can identify potential biomarkers with greater accuracy than abundance-based statistical analysis methods. It also reveals the contribution of these biomarkers to the host's disease status. AVAILABILITY AND IMPLEMENTATION: https://github.com/liaoherui/GDmicro.

Salmonella enterica serovar Choleraesuis vector outperforms alum as an adjuvant, increasing a cross-protective immune response against Glaesserella parasuis.

The adjuvant and/or vector significantly affect a vaccine's efficacy. Although traditional adjuvants such as alum have contributed to vaccine development, deficiencies in the induction of cellular and mucosal immunity have limited their further promotion. Salmonella vectors have unique advantages for establishing cellular and mucosal immunity due to mucosal pathways of invasion and intracellular parasitism. In addition, Salmonella vectors can activate multiple innate immune pathways, thereby promoting adaptive immune responses. In this work, the attenuated Salmonella enterica serovar Choleraesuis (S. Choleraesuis) vector rSC0016 was used to deliver the conserved protective antigen HPS_06257 of Glaesserella parasuis (G. parasuis), generating a novel recombinant strain rSC0016(pS-HPS_06257). The rSC0016(pS-HPS_06257) can express and deliver the HPS_06257 protein to the lymphatic system of the host. In comparison to HPS_06257 adjuvanted with alum, rSC0016(pS-HPS_06257) significantly increased TLR4 and TLR5 activation in mice as well as the levels of proinflammatory cytokines. In addition, rSC0016 promoted a greater degree of maturation in bone marrow-derived dendritic cells (BMDCs) than alum. The specific humoral, mucosal, and cellular immune responses against HPS_06257 in mice immunized with rSC0016(pS-HPS_06257) were significantly higher than those of HPS_06257 adjuvanted with alum. HPS_06257 delivered by the S. Choleraesuis vector induces a Th1-biased Th1/Th2 mixed immune response, while HPS adjuvanted with alum can only induce a Th2-biased immune response. HPS_06257 adjuvanted with alum only causes opsonophagocytic activity (OPA) responses against a homologous strain (G. parasuis serotype 5, GPS5), whereas rSC0016(pS-HPS_06257) could generate cross-OPA responses against a homologous strain and a heterologous strain (G. parasuis serotype 12, GPS12). Ultimately, HPS_06257 adjuvanted with alum protected mice against lethal doses of GPS5 challenge by 60 % but failed to protect mice against lethal doses of GPS12. In contrast, mice immunized with rSC0016(pS-HPS_06257) had 100 % or 80 % survival when challenged with lethal doses of GPS5 or GPS12, respectively. Altogether, the S. Choleraesuis vector rSC0016 could potentially generate an improved innate immune response and an improved adaptive immunological response compared to the traditional alum adjuvant, offering a novel concept for the development of a universal G. parasuis vaccine.

Albumin-to-fibrinogen ratio is an independent prognostic parameter in de novo non-M3 acute myeloid leukemia.

Inflammation and nutrition related proteins participate in the development of acute myeloid leukemia (AML). It has been reported that the albumin-to-fibrinogen ratio (AFR) could serve as a prognostic indicator in patients with malignancy, but the precise relevance of AML is unclear. This study aimed to evaluate the effect of AFR on survival prognosis in patients with AML. We analyzed 227 patients newly diagnosed with non-M3 AML. AFR was calculated as albumin divided by fibrinogen. Based on the cutoff point from X-tile program, patients were divided into AFR-high (38.8%) and AFR-low (61.2%) groups. AFR-low group showed a poorer complete remission rate (P < 0.001) and median time to relapse (P = 0.026), while the mortality was higher (P = 0.009) than AFR-high ones. According to the log-rank test, AFR-low group had shorter OS (P < 0.001) and DFS (P = 0.034). Multivariate analysis identified AFR, ELN risk, bone marrow transplant, and hemoglobin as independent prognostic variables associated with OS. A visualized nomogram for predicting OS was performed. The C-index (0.75), calibration plots, and decision curve analyses of new model showed better discrimination, calibration, and net benefits than the ELN risk model. The time-dependent receiver operating characteristic (ROC) curve of 1-, 2-, and 3-year also functioned well (AUC, 0.81, 0.93 and 0.90, respectively). Our study provided a comprehensive view of AFR which could be an independent prognostic indicator in AML patients. The prognostic model utilized readily available information from ordinary clinical practice to improve predictive performance, identify risks, and assist in therapeutic decision-making.

LncRNA-Top: Controlled deep learning approaches for lncRNA gene regulatory relationship annotations across different platforms.

By soaking microRNAs (miRNAs), long non-coding RNAs (lncRNAs) have the potential to regulate gene expression. Few methods have been created based on this mechanism to anticipate the lncRNA-gene relationship prediction. Hence, we present lncRNA-Top to forecast potential lncRNA-gene regulation relationships. Specifically, we constructed controlled deep-learning methods using 12417 lncRNAs and 16127 genes. We have provided retrospective and innovative views among negative sampling, random seeds, cross-validation, metrics, and independent datasets. The AUC, AUPR, and our defined precision@k were leveraged to evaluate performance. In-depth case studies demonstrate that 47 out of 100 projected top unknown pairings were recorded in publications, supporting the predictive power. Our additional software can annotate the scores with target candidates. The lncRNA-Top will be a helpful tool to uncover prospective lncRNA targets and better comprehend the regulatory processes of lncRNAs.

PhaGenus: genus-level classification of bacteriophages using a Transformer model.

MOTIVATION: Bacteriophages (phages for short), which prey on and replicate within bacterial cells, have a significant role in modulating microbial communities and hold potential applications in treating antibiotic resistance. The advancement of high-throughput sequencing technology contributes to the discovery of phages tremendously. However, the taxonomic classification of assembled phage contigs still faces several challenges, including high genetic diversity, lack of a stable taxonomy system and limited knowledge of phage annotations. Despite extensive efforts, existing tools have not yet achieved an optimal balance between prediction rate and accuracy. RESULTS: In this work, we develop a learning-based model named PhaGenus, which conducts genus-level taxonomic classification for phage contigs. PhaGenus utilizes a powerful Transformer model to learn the association between protein clusters and support the classification of up to 508 genera. We tested PhaGenus on four datasets in different scenarios. The experimental results show that PhaGenus outperforms state-of-the-art methods in predicting low-similarity datasets, achieving an improvement of at least 13.7%. Additionally, PhaGenus is highly effective at identifying previously uncharacterized genera that are not represented in reference databases, with an improvement of 8.52%. The analysis of the infants' gut and GOV2.0 dataset demonstrates that PhaGenus can be used to classify more contigs with higher accuracy.

A Bacterial mRNA-Lysis-Mediated Cargo Release Vaccine System for Regulated Cytosolic Surveillance and Optimized Antigen Delivery.

Engineered vector-based in vivo protein delivery platforms have made significant progress for both prophylactic and therapeutic applications. However, the lack of effective release strategies results in foreign cargo being trapped within the vector, restricting the provision of significant performance benefits and enhanced therapeutic results compared to traditional vaccines. Herein, the development of a Salmonella mRNA interferase regulation vector (SIRV) system is reported to overcome this challenge. The genetic circuits are engineered that (1) induce self-lysis to release foreign antigens into target cells and (2) activate the cytosolic surveillance cGAS-STING axis by releasing DNA into the cytoplasm. Delayed synthesis of the MazF interferase regulates differential mRNA cleavage, resulting in a 36-fold increase in the delivery of foreign antigens and modest activation of the inflammasome, which collectively contribute to the marked maturation of antigen-presenting cells (APCs). Bacteria delivering the protective antigen SaoA exhibits excellent immunogenicity and safety in mouse and pig models, significantly improving the survival rate of animals challenged with multiple serotypes of Streptococcus suis. Thus, the SIRV system enables the effective integration of various modular components and antigen cargos, allowing for the generation of an extensive range of intracellular protein delivery systems using multiple bacterial species in a highly efficient manner.

Maternal gestational weight gain and adverse pregnancy outcomes in non-diabetic women.

OBJECTIVE: This study investigated the relationship between maternal gestational weight gain (GWG) and the risk of adverse pregnancy outcomes in gestational diabetes mellitus (GDM)-negative pregnant women. METHODS: We did a retrospective cohort study between 1 July 2017, and 1 January 2020, at Women's Hospital, Zhejiang University School of Medicine. Firstly, pregnant women were divided into subgroups according to the entire GWG (inadequate GWG, adequate GWG, and excessive GWG) and GDM status (positive and negative) during pregnancy. Secondly, the whole population of pregnant women with GDM was used as a reference to evaluate the relationship between GWG and adverse pregnancy outcomes in GDM-negative pregnant women. Lastly, subgroup analysis was conducted based on pre-pregnancy body mass index (pp-BMI). RESULTS: A total of 30,910 pregnant women were analysed. Included pregnancy women were divided into three groups based on GWG: 7569 (24.49%) pregnancy women had inadequate GWG, 13088 (42.34%) had adequate GWG, and 10,253 (33.17%) had excessive GWG. In addition to preterm birth and small for gestational age (SGA), the incidence of macrosomia and large for gestational age (LGA) continues to increase from inadequate GWG to excessive GWG groups. Pregnant women without GDM who have excessive GWG are at higher risk of macrosomia and LGA than pregnant women with GDM. Moreover, this risk increased with increasing pp-BMI. Pregnant women without GDM with inadequate GWG were at risk of preterm birth regardless of pp-BMI. Only those with inadequate GWG and pp-BMI < 18.5 kg/m2 had an increased risk of SGA. CONCLUSIONS: In conclusion, inappropriate GWG is strongly associated with adverse pregnancy outcomes, even if they do not have GDM. Therefore, this population should receive attention and management before and during pregnancy.Impact StatementWhat is already known on this subject? Several studies have focused on the GDM population and the risk of adverse pregnancy outcomes, but few have focused on GDM-negative populations. This is because GDM-negative women are perceived to be "safe," leading to less focus on themselves, which can lead to subsequent excessive weight gain during pregnancy. Whether this factor increases the risk of adverse pregnancy outcomes in this population remains unknown.What do the results of this study add? Our study found an inverse relationship between GWG and GDM. Therefore, our study focuses on this group of GDM-negative pregnant women. Their excessive weight gain increases the risk of adverse pregnancy outcomes, even higher than GDM pregnant women.What are the implications of these findings for clinical practice and/or further research? GWG is associated with adverse pregnancy outcomes. Therefore, pregnant women without GDM also need increased attention and management of their weight before and during pregnancy. Prenatal care providers can utilise tools such as diet, exercise counselling, weight tracking, and setting weight gain goals to reduce inappropriate weight gain and mitigate its adverse effects on pregnancy outcomes.

High-resolution strain-level microbiome composition analysis from short reads.

BACKGROUND: Bacterial strains under the same species can exhibit different biological properties, making strain-level composition analysis an important step in understanding the dynamics of microbial communities. Metagenomic sequencing has become the major means for probing the microbial composition in host-associated or environmental samples. Although there are a plethora of composition analysis tools, they are not optimized to address the challenges in strain-level analysis: highly similar strain genomes and the presence of multiple strains under one species in a sample. Thus, this work aims to provide a high-resolution and more accurate strain-level analysis tool for short reads. RESULTS: In this work, we present a new strain-level composition analysis tool named StrainScan that employs a novel tree-based k-mers indexing structure to strike a balance between the strain identification accuracy and the computational complexity. We tested StrainScan extensively on a large number of simulated and real sequencing data and benchmarked StrainScan with popular strain-level analysis tools including Krakenuniq, StrainSeeker, Pathoscope2, Sigma, StrainGE, and StrainEst. The results show that StrainScan has higher accuracy and resolution than the state-of-the-art tools on strain-level composition analysis. It improves the F1 score by 20% in identifying multiple strains at the strain level. CONCLUSIONS: By using a novel k-mer indexing structure, StrainScan is able to provide strain-level analysis with higher resolution than existing tools, enabling it to return more informative strain composition analysis in one sample or across multiple samples. StrainScan takes short reads and a set of reference strains as input and its source codes are freely available at https://github.com/liaoherui/StrainScan . Video Abstract.

PhaBOX: a web server for identifying and characterizing phage contigs in metagenomic data.

Motivation: There is accumulating evidence showing the important roles of bacteriophages (phages) in regulating the structure and functions of the microbiome. However, lacking an easy-to-use and integrated phage analysis software hampers microbiome-related research from incorporating phages in the analysis. Results: In this work, we developed a web server, PhaBOX, which can comprehensively identify and analyze phage contigs in metagenomic data. It supports integrated phage analysis, including phage contig identification from the metagenomic assembly, lifestyle prediction, taxonomic classification, and host prediction. Instead of treating the algorithms as a black box, PhaBOX also supports visualization of the essential features for making predictions. The web server is designed with a user-friendly graphical interface that enables both informatics-trained and nonspecialist users to analyze phages in microbiome data with ease. Availability and implementation: The web server of PhaBOX is available via: https://phage.ee.cityu.edu.hk. The source code of PhaBOX is available at: https://github.com/KennthShang/PhaBOX.

PLASMe: a tool to identify PLASMid contigs from short-read assemblies using transformer.

Plasmids are mobile genetic elements that carry important accessory genes. Cataloging plasmids is a fundamental step to elucidate their roles in promoting horizontal gene transfer between bacteria. Next generation sequencing (NGS) is the main source for discovering new plasmids today. However, NGS assembly programs tend to return contigs, making plasmid detection difficult. This problem is particularly grave for metagenomic assemblies, which contain short contigs of heterogeneous origins. Available tools for plasmid contig detection still suffer from some limitations. In particular, alignment-based tools tend to miss diverged plasmids while learning-based tools often have lower precision. In this work, we develop a plasmid detection tool PLASMe that capitalizes on the strength of alignment and learning-based methods. Closely related plasmids can be easily identified using the alignment component in PLASMe while diverged plasmids can be predicted using order-specific Transformer models. By encoding plasmid sequences as a language defined on the protein cluster-based token set, Transformer can learn the importance of proteins and their correlation through positionally token embedding and the attention mechanism. We compared PLASMe and other tools on detecting complete plasmids, plasmid contigs, and contigs assembled from CAMI2 simulated data. PLASMe achieved the highest F1-score. After validating PLASMe on data with known labels, we also tested it on real metagenomic and plasmidome data. The examination of some commonly used marker genes shows that PLASMe exhibits more reliable performance than other tools.

HMMPolish: a coding region polishing tool for TGS-sequenced RNA viruses.

Access to accurate viral genomes is important to downstream data analysis. Third-generation sequencing (TGS) has recently become a popular platform for virus sequencing because of its long read length. However, its per-base error rate, which is higher than next-generation sequencing, can lead to genomes with errors. Polishing tools are thus needed to correct errors either before or after sequence assembly. Despite promising results of available polishing tools, there is still room to improve the error correction performance to perform more accurate genome assembly. The errors, particularly those in coding regions, can hamper analysis such as linage identification and variant monitoring. In this work, we developed a novel pipeline, HMMPolish, for correcting (polishing) errors in protein-coding regions of known RNA viruses. This tool can be applied to either raw TGS reads or the assembled sequences of the target virus. By utilizing profile Hidden Markov Models of protein families/domains in known viruses, HMMPolish can correct errors that are ignored by available polishers. We extensively validated HMMPolish on 34 datasets that covered four clinically important viruses, including HIV-1, influenza-A, norovirus, and severe acute respiratory syndrome coronavirus 2. These datasets contain reads with different properties, such as sequencing depth and platforms (PacBio or Nanopore). The benchmark results against popular/representative polishers show that HMMPolish competes favorably on error correction in coding regions of known RNA viruses.

PhaVIP: Phage VIrion Protein classification based on chaos game representation and Vision Transformer.

MOTIVATION: As viruses that mainly infect bacteria, phages are key players across a wide range of ecosystems. Analyzing phage proteins is indispensable for understanding phages' functions and roles in microbiomes. High-throughput sequencing enables us to obtain phages in different microbiomes with low cost. However, compared to the fast accumulation of newly identified phages, phage protein classification remains difficult. In particular, a fundamental need is to annotate virion proteins, the structural proteins, such as major tail, baseplate, etc. Although there are experimental methods for virion protein identification, they are too expensive or time-consuming, leaving a large number of proteins unclassified. Thus, there is a great demand to develop a computational method for fast and accurate phage virion protein (PVP) classification. RESULTS: In this work, we adapted the state-of-the-art image classification model, Vision Transformer, to conduct virion protein classification. By encoding protein sequences into unique images using chaos game representation, we can leverage Vision Transformer to learn both local and global features from sequence "images". Our method, PhaVIP, has two main functions: classifying PVP and non-PVP sequences and annotating the types of PVP, such as capsid and tail. We tested PhaVIP on several datasets with increasing difficulty and benchmarked it against alternative tools. The experimental results show that PhaVIP has superior performance. After validating the performance of PhaVIP, we investigated two applications that can use the output of PhaVIP: phage taxonomy classification and phage host prediction. The results showed the benefit of using classified proteins over all proteins. AVAILABILITY AND IMPLEMENTATION: The web server of PhaVIP is available via: https://phage.ee.cityu.edu.hk/phavip. The source code of PhaVIP is available via: https://github.com/KennthShang/PhaVIP.

Laboratory Experiment and Application Evaluation of a Bio-Nano-depressurization and Injection-Increasing Composite System in Medium-Low Permeability Offshore Reservoirs.

Given the high injection pressure and insufficient injection volume in the offshore oilfield, Bohai Oilfield has developed a bio-nano-depressurization and injection-increasing composite system solution (bio-nano-injection-increasing solution) composed of bio-surfactants, hydrophobic nano-polysilicon particles, and dispersant additives. In response to the current problems, a new type of bio-nano-depressurization and injection enhancement technology has been studied, which has multiple functions such as nano-scale inhibition and wetting reversal. The new technology has the technical advantages of efficient decompression, long-term injection, and wide adaptation. However, there is still a lack of optimization schemes and application effect prediction methods, which hinder the further popularization and application of the bio-nano-composite system solution. To solve this problem, this paper takes Well A1 in the Bohai Sea as an example to optimize the injection volume, concentration, and speed of the bio-nano-augmentation fluid and evaluates the application effect by using the proposed well testing, water absorption index, and numerical simulation methods. The research results show that the bio-nano-injection fluid can effectively improve the reservoir permeability and reduce the injection pressure. The application effect evaluation method proposed is reliable and can provide some reference for similar depressurization and injection-increasing technologies.

PRMT1 inhibition promotes ferroptosis sensitivity via ACSL1 upregulation in acute myeloid leukemia.

Acute myeloid leukemia (AML) is a hematological malignancy with an alarming mortality rate. The development of novel therapeutic targets or drugs for AML is urgently needed. Ferroptosis is a form of regulated cell death driven by iron-dependent lipid peroxidation. Recently, ferroptosis has emerged as a novel method for targeting cancer, including AML. Epigenetic dysregulation is a hallmark of AML, and a growing body of evidence suggests that ferroptosis is subject to epigenetic regulation. Here, we identified protein arginine methyltransferase 1 (PRMT1) as a ferroptosis regulator in AML. The type I PRMT inhibitor GSK3368715 promoted ferroptosis sensitivity in vitro and in vivo. Moreover, PRMT1-knockout cells exhibited significantly increased sensitivity to ferroptosis, suggesting that PRMT1 is the primary target of GSK3368715 in AML. Mechanistically, both GSK3368715 and PRMT1 knockout upregulated acyl-CoA synthetase long-chain family member 1 (ACSL1), which acts as a ferroptosis promoter by increasing lipid peroxidation. Knockout ACSL1 reduced the ferroptosis sensitivity of AML cells following GSK3368715 treatment. Additionally, the GSK3368715 treatment reduced the abundance of H4R3me2a, the main histone methylation modification mediated by PRMT1, in both genome-wide and ACSL1 promoter regions. Overall, our results demonstrated a previously unknown role of the PRMT1/ACSL1 axis in ferroptosis and suggested the potential value and applications of the combination of PRMT1 inhibitor and ferroptosis inducers in AML treatment.

Global prevalence of SARS-CoV-2 3CL protease mutations associated with nirmatrelvir or ensitrelvir resistance.

BACKGROUND: Nirmatrelvir-ritonavir (Paxlovid) and ensitrelvir are 3-chymotrypsin-like cysteine protease (3CLpro) inhibitors which have been approved for the treatment of COVID-19 in 2021 and 2022, respectively. Previous studies have identified 3CLpro mutations that are associated with reduced susceptibility to these antivirals. The aim of the current study was to estimate the global prevalence of 3CLpro inhibitor-resistant SARS-CoV-2 strains. METHODS: We compiled a list of 3CLpro mutations which have been associated with nirmatrelvir or ensitrelvir resistance based on either viral replication or 3CLpro activity assays, and determined their prevalence among 13.4 million sequences deposited in GISAID as of December 14, 2022, about 1 year after the approval of nirmatrelvir-ritonavir. We analyzed the prevalence for different time periods, SARS-CoV-2 lineages and geographical locations. FINDINGS: Overall, 0.5% (67,095/13,446,588) of the sequences contained at least one mutation that was shown to affect the inhibitory activity of nirmatrelvir or ensitrelvir on viral replication or 3CLpro activity. We did not observe any increasing trend of resistance after the widespread clinical use of nirmatrelvir-ritonavir. G15S (2070 per million) and T21I (1386 per million) were the most prevalent mutations, and these mutations were dominant in some SARS-CoV-2 lineages. E166V and S144E, previously shown to affect the inhibitory activity of nirmatrelvir on viral replication or protease activity by > 100-folds, were found in <1 per million sequences. INTERPRETATION: Our data suggest that 3CLpro inhibitor resistance is currently rare. However, continuous global genotypic and phenotypic surveillance would be crucial in the early detection of resistant mutants. FUNDING: Richard and Carol Yu, May Tam Mak Mei Yin, The Shaw Foundation Hong Kong, Michael Tong, Marina Lee, Government Consultancy Service, the Emergency Key Program of Guangzhou Laboratory (See acknowledgements for full list).

HOTSPOT: hierarchical host prediction for assembled plasmid contigs with transformer.

MOTIVATION: As prevalent extrachromosomal replicons in many bacteria, plasmids play an essential role in their hosts' evolution and adaptation. The host range of a plasmid refers to the taxonomic range of bacteria in which it can replicate and thrive. Understanding host ranges of plasmids sheds light on studying the roles of plasmids in bacterial evolution and adaptation. Metagenomic sequencing has become a major means to obtain new plasmids and derive their hosts. However, host prediction for assembled plasmid contigs still needs to tackle several challenges: different sequence compositions and copy numbers between plasmids and the hosts, high diversity in plasmids, and limited plasmid annotations. Existing tools have not yet achieved an ideal tradeoff between sensitivity and precision on metagenomic assembled contigs. RESULTS: In this work, we construct a hierarchical classification tool named HOTSPOT, whose backbone is a phylogenetic tree of the bacterial hosts from phylum to species. By incorporating the state-of-the-art language model, Transformer, in each node's taxon classifier, the top-down tree search achieves an accurate host taxonomy prediction for the input plasmid contigs. We rigorously tested HOTSPOT on multiple datasets, including RefSeq complete plasmids, artificial contigs, simulated metagenomic data, mock metagenomic data, the Hi-C dataset, and the CAMI2 marine dataset. All experiments show that HOTSPOT outperforms other popular methods. AVAILABILITY AND IMPLEMENTATION: The source code of HOTSPOT is available via: https://github.com/Orin-beep/HOTSPOT.

VirBot: an RNA viral contig detector for metagenomic data.

SUMMARY: Without relying on cultivation, metagenomic sequencing greatly accelerated the novel RNA virus detection. However, it is not trivial to accurately identify RNA viral contigs from a mixture of species. The low content of RNA viruses in metagenomic data requires a highly specific detector, while new RNA viruses can exhibit high genetic diversity, posing a challenge for alignment-based tools. In this work, we developed VirBot, a simple yet effective RNA virus identification tool based on the protein families and the corresponding adaptive score cutoffs. We benchmarked it with seven popular tools for virus identification on both simulated and real sequencing data. VirBot shows its high specificity in metagenomic datasets and superior sensitivity in detecting novel RNA viruses. AVAILABILITY AND IMPLEMENTATION: https://github.com/GreyGuoweiChen/RNA_virus_detector. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.