Sequence Annotation Research Articles

Whole-Genome Sequencing and Genome Annotation of Pathogenic Elsinoë batatas Causing Stem and Foliage Scab Disease in Sweet Potato

A pathogen strain responsible for sweet potato stem and foliage scab disease was isolated from sweet potato stems. Through a phylogenetic analysis based on the rDNA internal transcribed spacer (ITS) region, combined with morphological methods, the isolated strain was identified as Elsinoë batatas. To comprehensively analyze the pathogenicity of the isolated strain from a genetic perspective, the whole-genome sequencing of E. batatas HD-1 was performed using both the PacBio and Illumina platforms. The genome of E. batatas HD-1 is about 26.31 Mb long in 167 scaffolds, with a GC content of 50.81%, and 7898 protein-coding genes, 131 non-coding RNAs, and 1954 interspersed repetitive sequences were predicted. Functional annotation revealed that 408 genes encode virulence factors involved in plant disease (DFVF—Plant). Notably, twenty-eight of these virulence genes encode secretory carbohydrate-active enzymes (CAZymes), including two endo-1,4-β-xylanase genes and seven cutinase genes, which suggested that endo-1,4-β-xylanase and cutinase play a vital role in the pathogenicity of E. batatas HD-1 within sweet potato. In total, twelve effectors were identified, including five LysM effectors and two CDIP effectors, suggesting that LysM and CDIP effectors play significant roles in the interaction between E. batatas HD-1 and sweet potato. Additionally, our analysis of biosynthetic gene clusters (BGCs) showed that two gene clusters are involved in melanin and choline metabolism. This study enriches the genomic resources of E. batatas and provides a theoretical foundation for future investigations into the pathogenic mechanisms of its infection in sweet potatoes, as well as potential targets for disease control.

MCHelper automatically curates transposable element libraries across eukaryotic species.

The number of species with high-quality genome sequences continues to increase, in part due to the scaling up of multiple large-scale biodiversity sequencing projects. While the need to annotate genic sequences in these genomes is widely acknowledged, the parallel need to annotate transposable element (TE) sequences that have been shown to alter genome architecture, rewire gene regulatory networks, and contribute to the evolution of host traits is becoming ever more evident. However, accurate genome-wide annotation of TE sequences is still technically challenging. Several de novo TE identification tools are now available, but manual curation of the libraries produced by these tools is needed to generate high-quality genome annotations. Manual curation is time-consuming, and thus impractical for large-scale genomic studies, and lacks reproducibility. In this work, we present the Manual Curator Helper tool MCHelper, which automates the TE library curation process. By leveraging MCHelper's fully automated mode with the outputs from three de novo TE identification tools, RepeatModeler2, EDTA, and REPET, in the fruit fly, rice, hooded crow, zebrafish, maize, and human, we show a substantial improvement in the quality of the TE libraries and genome annotations. MCHelper libraries are less redundant, with up to 65% reduction in the number of consensus sequences, have up to 11.4% fewer false positive sequences, and up to ∼48% fewer "unclassified/unknown" TE consensus sequences. Genome-wide TE annotations are also improved, including larger unfragmented insertions. Moreover, MCHelper is an easy-to-install and easy-to-use tool.

Gap-free telomere-to-telomere haplotype assembly of the tomato hind (Cephalopholis sonnerati)

The tomato hind (Cephalopholis sonnerati) is an emerging economically important grouper in recent years. With the increasing maturity of sequencing technologies and assembly methodologies, a higher quality reference genome has become both accessible and necessary. In this study, we present two telomere-to-telomere (T2T) gap-free haplotype assemblies of the tomato hind with lengths of 1039.53 Mb (YSFRI_Csonn_HA_1.0, N50 43.83 Mb) and 1039.91 Mb (YSFRI_Csonn_HB_1.0, N50 44.09 Mb). Reads from next-generation sequencing, ONT ultra-long sequencing, and PacBio HiFi sequencing exhibited mapping rates exceeding 99.8% when aligned to these two assemblies. Evaluation using Merqury indicated high accuracy for both assemblies, with average quality values of 51.80 and 51.83, respectively. Percentages of 97.9% and 97.8% of complete BUSCOs were achieved, and a total of 23,270 and 23,184 protein-code genes were inferred in each assembly. Moreover, telomere identification, centromere prediction, and repetitive sequence annotation were also successfully performed. These two assemblies provide robust foundation for the genetic analysis and development of molecular genetic breeding technologies in C. sonnerati.

Etiological characteristics and whole genome sequence analysis of Clostridium perfringens causing a food poisoning outbreak

This study investigated the etiological characteristics and whole genome sequencing of clostridium perfringens (CP) from a food poisoning outbreak. Multiplex real-time PCR was employed to screen pathogens in collected samples. Based on the preliminary screening results, the isolation, cultivation, and identification of suspected pathogenic CP bacteria were performed. The nucleic acid tests were conducted for CP-related virulence genes on CP isolates, anal swab specimens and food samples. The molecular typing of CP isolates was analyzed by using pulsed-field gel electrophoresis (PFGE). A phylogenetic tree was established to analyze nucleotide and amino acid homology after sequencing the PLC gene. The whole genome sequencing and gene annotation on the representative strain QD2022FB4-CP4 were performed to explore its drug resistance, toxin genes and biological characteristics. The analysis revealed that the food poisoning was triggered by F-type Clostridium perfringens, which infected the consumers by contaminating'roujiamo' sandwiches. The whole genome sequencing of the strain QD2022FB4-CP found that it had active metabolic processes, multiple virulence genes and multidrug resistance characteristics.

Research on Medical Text Parsing Method Based on BiGRU-BiLSTM Multi-Task Learning

With the development of technology, the popularity of online medical treatment is becoming more and more widespread. However, the accuracy and credibility of online medical treatment are affected by model design and semantic understanding. In particular, there are still some problems in the accurate understanding of complex structured texts, which affects the accuracy of judging users’ intentions and needs. Therefore, this paper proposes a new method for medical text parsing, which realizes core tasks such as named entity recognition, intention recognition, and slot filling through a multi-task learning framework; uses BERT to obtain contextual semantic information; and combines BiGRU and BiLSTM neural networks, and uses CRF to realize sequence annotation and DPCNN to realize classification prediction. Thus, the task of entity recognition and intention recognition can be accomplished. On this basis, this paper builds a multi-task learning model based on BiGRU-BiLSTM, and uses CBLUE and CMID databases to verify the method. The verification results show that the accuracy of named entity recognition and intention recognition reaches 86% and 89%, respectively, which improves the performance of various tasks. The ability of the model to process complex text is enhanced. This method can improve the text generalization ability and improve the accuracy of online medical intelligent dialogue when it is used to analyze medical texts.

Temporal dynamics of the bacterial community structure and functions associated with marine sponges collected off Karah Island, Terengganu, Malaysia

The marine sponge microbiome has been investigated for decades, yet its temporal fluctuation/stability is poorly understood. This study aimed to describe the seasonal dynamics of the bacterial composition and predicted functions associated with two tropical marine sponge species (Aaptos suberitoides and Xestospongia testudinaria) collected off Karah Island, Malaysia, in the three seasons (dry season, pre- and post-monsoon season) using 16S rRNA gene amplicon sequencing and Functional Annotation of Prokaryotic Taxa (FAPROTAX). The bacterial taxonomic structures demonstrated divergences among A. suberitoides, X. testudinaria, and seawater, regardless of seasons. Chloroflexi, Proteobacteria, Cyanobacteria, Actinobacteriota, and Acidobacteriota comprised the core bacterial communities in two marine sponge species throughout the seasons, however, there were differences in bacterial composition between the two marine sponges. In addition, A. suberitoides was enriched with Dadabacteria, Spirochaetota, and Bdellovibrionota, whereas X. testudinaria abundantly harbored Poribacteria, PAUC34f, and Anck6. At the genus level, SAR202 clade and Candidatus Synechococcus spongiarum group dominated marine sponge samples throughout the year. Several genera, such as SAR202 clade, Candidatus Synechococcus spongiarum group, and Entotheonellaceae, were discovered only in marine sponge samples. Spirochaeta and Dadabacteriales displayed higher relative abundances in A. suberitoides, while Poribacteria and PAUC34f were more abundant in X. testudinaria. SAR202 clade was positively correlated with dissolved oxygen (DO), pH, phosphate, and ammonia in A. suberitoides. Likewise, Candidatus Synechococcus spongiarum group was negatively correlated with salinity and nitrate in X. testudinaria. Predicted functions by FAPROTAX annotated the phototrophic-related functions in both marine sponges, which might be attributed to a sponge-specific taxon Candidatus Synechococcus spongiarum group. The functions “predatory or exoparasitic” and “fermentation” were highly detected in A. suberitoides, which might be related to the bacterial genera Bdellovibrio and Spirochaeta, respectively. This study guides a deep understanding of the bacterial community structures associated with tropical marine sponges and their temporal behavior.

How Trustworthy Are the Genomic Sequences of SARS-CoV-2 in GenBank?

Well-annotated gene and genomic sequences serve as a foundation for making inferences in molecular biology and evolution and can directly impact public health. The first SARS-CoV-2 genome was submitted to the GenBank database hosted by the U.S. National Center for Biotechnology Information and used to develop the two successful vaccines. Conserved protein domains are often chosen as targets for developing antiviral medicines or vaccines. Mutation and substitution patterns provide crucial information not only on functional motifs and genome/protein interactions but also for characterizing phylogenetic relationships among viral strains. These patterns, together with the collection time of viral samples, serve as the basis for addressing the question of when and where the host-switching event occurred. Unfortunately, viral genomic sequences submitted to GenBank undergo little quality control, and critical information in the annotation is frequently changed without being recorded. Researchers often have no choice but to hold blind faith in the authenticity of the sequences. There have been reports of incorrect genome annotation but no report that casts doubt on the genomic sequences themselves because it seems theoretically impossible to identify genomic sequences that may not be authentic. This paper takes an innovative approach to show that some SARS-CoV-2 genomes submitted to GenBank cannot possibly be authentic. Specifically, some SARS-CoV-2 genomic sequences deposited in GenBank with collection times in 2023 and 2024, isolated from saliva, nasopharyngeal, sewage, and stool, are identical to the reference genome of SARS-CoV-2 (NC_045512). The probability of such occurrence is effectively 0. I also compile SARS-CoV-2 genomes with changed sample collection times. One may be led astray in bioinformatic analysis without being aware of errors in sequences and sequence annotation.

BakRep - a searchable large-scale web repository for bacterial genomes, characterizations and metadata.

Bacteria are fascinating research objects in many disciplines for countless reasons, and whole-genome sequencing (WGS) has become the paramount methodology to advance our microbiological understanding. Meanwhile, access to cost-effective sequencing platforms has accelerated bacterial WGS to unprecedented levels, introducing new challenges in terms of data accessibility, computational demands, heterogeneity of analysis workflows and, thus, ultimately its scientific usability. To this end, a previous study released a uniformly processed set of 661 405 bacterial genome assemblies obtained from the European Nucleotide Archive as of November 2018. Building on these accomplishments, we conducted further genome-based analyses like taxonomic classification, multilocus sequence typing and annotation of all genomes. Here, we present BakRep, a searchable large-scale web repository of these genomes enriched with consistent genome characterizations and original metadata. The platform provides a flexible search engine combining taxonomic, genomic and metadata information, as well as interactive elements to visualize genomic features. Furthermore, all results can be downloaded for offline analyses via an accompanying command line tool. The web repository is accessible via https://bakrep.computational.bio.

Maternal consumption of yoghurt activating the aryl hydrocarbon receptor increases group 3 innate lymphoid cells in murine offspring.

Key progresses in the sequencing and functional annotation of microbial organisms have revolutionized research in the fields of human metabolism and food biotechnology. In particular, the gut microbiome is now recognized as an important mediator of the impact of nutrition on human metabolism. Annotated genomes of a large number of bacteria are now available worldwide, which selectively transform food through fermentation to produce specific bioactive compounds with the potential to modulate human health. A previous research has demonstrated that the maternal microbiota shapes the neonatal immune system. Similarly, this report shows that lactic acid bacteria can be selected to produce fermented food that can also modulate postnatal intestinal immunity.

Anti-quorum sensing and anti-biofilm activities of Pasteurella multocida strains

A total of 52 Pasteurella multocida strains of capsular serogroups (A, B and D) were screened for anti-quorum sensing activity against Chromobacterium violaceum. Of which, 12 strains of serogroups A were found to possess anti-quorum sensing activity. Inhibition activity was highest for strain NIVEDIPm9 and lowest for strain NIVEDIPm30 based on zone of pigment inhibition. Further, cell free extract of NIVEDIPm9 strain showed highest anti-biofilm activity in reference E. coli strain and concentration dependent degradation activity of C6-AHL molecule. In whole genome sequence annotation of NIVEDIPm9 strain predicted the presence of four metallo-β-lactamases (MBL) fold metallo-hydrolase proteins. In docking studies, MBL1 and MBL3 proteins showed high binding affinity with autoinduce signalling molecules AHL compound of OH-C10, binding energy value were −6.3 and −6.2 kcal/mol. Interaction study of VAF and quorum sensing molecules showed that OmpA and HgbA proteins were stimulated by all the ten molecules (C4-AHLs, C6-AHLs, C10-AHLs, C14-AHLs, 3-oxo-C10-AHLs, 3OH-C10-HSL, C8-HSL, C10-HSL, C12-HSL, C14-HSL), while toxA gene was stimulated by OH-C10-AHL molecule, sodC gene was stimulated by none. In conclusion, we described the anti-quorum sensing activities of diverse P. multocida strains causing Pasteurellosis in livestock.

Alternative transcripts recode human genes to express overlapping, frameshifted microproteins.

Overlapping genes were thought to be essentially absent from the human genome until the discovery of abundant, frameshifted internal open reading frames (iORFs) nested within annotated protein coding sequences. However, it is currently unclear how many functional human iORFs exist and how they are expressed. We demonstrate that, in hundreds of cases, alternative transcript variants that bypass the start codon of annotated coding sequences (CDSs) can recode a human gene to express the iORF-encoded microprotein. While many human genes generate such non- coding alternative transcripts, they are poorly annotated. Here we develope a new analysis pipeline enabling the assignment of translated human iORFs to alternative transcripts, and provide long- read sequencing and molecular validation of their expression in dozens of cases. Finally, we demonstrate that a conserved DEDD2 iORF switches the function of this gene from pro- to anti- apoptotic. This work thus demonstrates that alternative transcript variants can broadly reprogram human genes to express frameshifted iORFs, revealing new levels of complexity in the human transcriptome and proteome.

Improving viral annotation with artificial intelligence.

Viruses of bacteria, "phages," are fundamental, poorly understood components of microbial community structure and function. Additionally, their dependence on hosts for replication positions phages as unique sensors of ecosystem features and environmental pressures. High-throughput sequencing approaches have begun to give us access to the diversity and range of phage populations in complex microbial community samples, and metagenomics is currently the primary tool with which we study phage populations. The study of phages by metagenomic sequencing, however, is fundamentally limited by viral diversity, which results in the vast majority of viral genomes and metagenome-annotated genomes lacking annotation. To harness bacteriophages for applications in human and environmental health and disease, we need new methods to organize and annotate viral sequence diversity. We recently demonstrated that methods that leverage self-supervised representation learning can supplement statistical sequence representations for remote viral protein homology detection in the ocean virome and propose that consideration of the functional content of viral sequences allows for the identification of similarity in otherwise sequence-diverse viruses and viral-like elements for biological discovery. In this review, we describe the potential and pitfalls of large language models for viral annotation. We describe the need for new approaches to annotate viral sequences in metagenomes, the fundamentals of what protein language models are and how one can use them for sequence annotation, the strengths and weaknesses of these models, and future directions toward developing better models for viral annotation more broadly.

Regulatory genome annotation of 33 insect species

Annotation of newly sequenced genomes frequently includes genes, but rarely covers important non-coding genomic features such as the cis-regulatory modules—e.g., enhancers and silencers—that regulate gene expression. Here, we begin to remedy this situation by developing a workflow for rapid initial annotation of insect regulatory sequences, and provide a searchable database resource with enhancer predictions for 33 genomes. Using our previously developed SCRMshaw computational enhancer prediction method, we predict over 2.8 million regulatory sequences along with the tissues where they are expected to be active, in a set of insect species ranging over 360 million years of evolution. Extensive analysis and validation of the data provides several lines of evidence suggesting that we achieve a high true-positive rate for enhancer prediction. One, we show that our predictions target specific loci, rather than random genomic locations. Two, we predict enhancers in orthologous loci across a diverged set of species to a significantly higher degree than random expectation would allow. Three, we demonstrate that our predictions are highly enriched for regions of accessible chromatin. Four, we achieve a validation rate in excess of 70% using in vivo reporter gene assays. As we continue to annotate both new tissues and new species, our regulatory annotation resource will provide a rich source of data for the research community and will have utility for both small-scale (single gene, single species) and large-scale (many genes, many species) studies of gene regulation. In particular, the ability to search for functionally related regulatory elements in orthologous loci should greatly facilitate studies of enhancer evolution even among distantly related species.

Regulatory genome annotation of 33 insect species.

Annotation of newly sequenced genomes frequently includes genes, but rarely covers important non-coding genomic features such as the cis-regulatory modules-e.g., enhancers and silencers-that regulate gene expression. Here, we begin to remedy this situation by developing a workflow for rapid initial annotation of insect regulatory sequences, and provide a searchable database resource with enhancer predictions for 33 genomes. Using our previously developed SCRMshaw computational enhancer prediction method, we predict over 2.8 million regulatory sequences along with the tissues where they are expected to be active, in a set of insect species ranging over 360 million years of evolution. Extensive analysis and validation of the data provides several lines of evidence suggesting that we achieve a high true-positive rate for enhancer prediction. One, we show that our predictions target specific loci, rather than random genomic locations. Two, we predict enhancers in orthologous loci across a diverged set of species to a significantly higher degree than random expectation would allow. Three, we demonstrate that our predictions are highly enriched for regions of accessible chromatin. Four, we achieve a validation rate in excess of 70% using in vivo reporter gene assays. As we continue to annotate both new tissues and new species, our regulatory annotation resource will provide a rich source of data for the research community and will have utility for both small-scale (single gene, single species) and large-scale (many genes, many species) studies of gene regulation. In particular, the ability to search for functionally related regulatory elements in orthologous loci should greatly facilitate studies of enhancer evolution even among distantly related species.

Integrated Transcriptomics and Metabolomics Studies Reveal Steroid Biosynthesis Pathway and BCL2 Inhibitory Diazo-Progesterone of Drimia indica for Conservation and Sustainable Utilization.

This study is the first report on the sequence of the transcriptome of Drimia indica, a non-model plant with medicinal properties found in a forest tribal belt, using the Illumina NovaSeq platform. The primary objectives of this study were to elucidate the gene expression profiles in different tissues, identify key regulatory genes and pathways involved in secondary metabolite biosynthesis, and explore the plant's potential pharmacological properties. The study generated 670087 unigenes from both leaves and roots and identified putative homologs of annotated sequences against UniProt/Swiss-Prot and KEGG databases. The functional annotation of the identified unigenes revealed the secondary metabolite biosynthetic process as the most prominent pathway, with gene enrichment analysis predominantly accounting for secondary metabolite pathways, such as terpenoid, steroid, flavonoid, alkaloid, selenocompound, and cortisol synthesis. The study also identified regulatory genes NAC, Bhlh, WRKY, and C2H2 on the transcriptome dataset. The functionally annotated unigenes suggested phytocompounds in Drimia indica to have multi-potent properties, such as anti-cancer, anti-inflammatory, and anti-diabetic activities, which has been further validated by GC-MS-based metabolite profiling. Notably, we have identified two novel molecules, di-azo progesterone and 4H-pyran-4-one 2,3-dihydro-3,5-dihydroxy- 6-methyl, with potential BCL2 inhibitory anticancer properties, supported by stable binding interactions observed in molecular docking and dynamics simulations. Additionally, an abundance of mono-nucleotide SSR markers has been identified, useful for genetic diversity studies. This study provides a foundational understanding of the molecular mechanisms in Drimia indica, highlighting its potential as a source for novel therapeutic agents and contributing valuable insights for future pharmacological and agricultural applications. However, further in vivo studies are warranted to confirm these findings and validate their pharmacological efficacy and therapeutic potential. The SSR markers identified also offer valuable tools for molecular genetics, plant breeding, and sustainable drug development.

Chromosome-level genome assembly of American sweetgum (Liquidambar styraciflua, Altingiaceae)

The deciduous American sweetgum (Liquidambar styraciflua, Altingiaceae) is a popular ornamental and economically valuable tree renowned for its sweet-smelling bark resin, abundant volatile substances, and spectacular fall leaf color. However, the absence of a reference genome hinders thorough investigations into the mechanisms underlying phenotypic variation, secondary metabolite synthesis and adaptation, both in this species and other Liquidambar members. In this study, we sequenced and constructed a chromosome-level assembly of the L. styraciflua genome, covering 662.48 Mb with a scaffold N50 of 39.54 Mb, by integrating PacBio, Illumina and chromosome conformation capture data. We identified 58.83% of the genome sequences as repetitive elements and 25,713 protein-coding genes, 97.28% of which were functionally annotated. The genome sequencing reads, assembly and annotation data have been deposited in publicly available repositories. This high-quality genome assembly provides valuable resources for further evolutionary and functional genomic studies in American sweetgum and other Liquidambar species.

Influenza sequence validation and annotation using VADR

Abstract Tens of thousands of influenza sequences are deposited into the GenBank database each year. The software tool FLu ANnotation tool (FLAN) has been used by GenBank since 2007 to validate and annotate incoming influenza sequence submissions and has been publicly available as a webserver but not as a standalone tool. Viral Annotation DefineR (VADR) is a general sequence validation and annotation software package used by GenBank for norovirus, dengue virus and SARS-CoV-2 virus sequence processing that is available as a standalone tool. We have created VADR influenza models based on the FLAN reference sequences and adapted VADR to accurately annotate influenza sequences. VADR and FLAN show consistent results on the vast majority of influenza sequences, and when they disagree, VADR is usually correct. VADR can also accurately process influenza D sequences as well as influenza A H17, H18, H19, N10 and N11 subtype sequences, which FLAN cannot. VADR 1.6.3 and the associated influenza models are now freely available for users to download and use. Database URL: https://bitbucket.org/nawrockie/vadr-models-flu.

Genome-Wide Identification and Expression Analysis of MYB Transcription Factor Family in Response to Various Abiotic Stresses in Coconut (Cocos nucifera L.).

Abiotic stresses such as nitrogen deficiency, drought, and salinity significantly impact coconut production, yet the molecular mechanisms underlying coconut's response to these stresses are poorly understood. MYB proteins, a large and diverse family of transcription factors (TF), play crucial roles in plant responses to various abiotic stresses, but their genome-wide characterization and functional roles in coconut have not been comprehensively explored. This study identified 214 CnMYB genes (39 1R-MYB, 171 R2R3-MYB, 2 3R-MYB, and 2 4R-MYB) in the coconut genome. Phylogenetic analysis revealed that these genes are unevenly distributed across the 16 chromosomes, with conserved consensus sequences, motifs, and gene structures within the same subgroups. Synteny analysis indicated that segmental duplication primarily drove CnMYB evolution in coconut, with low nonsynonymous/synonymous ratios suggesting strong purifying selection. The gene ontology (GO) annotation of protein sequences provided insights into the biological functions of the CnMYB gene family. CnMYB47/70/83/119/186 and CnMYB2/45/85/158/195 were identified as homologous genes linked to nitrogen deficiency, drought, and salinity stress through BLAST, highlighting the key role of CnMYB genes in abiotic stress tolerance. Quantitative analysis of PCR showed 10 CnMYB genes in leaves and petioles and found that the expression of CnMYB45/47/70/83/85/119/186 was higher in 3-month-old than one-year-old coconut, whereas CnMYB2/158/195 was higher in one-year-old coconut. Moreover, the expression of CnMYB70, CnMYB2, and CnMYB2/158 was high under nitrogen deficiency, drought, and salinity stress, respectively. The predicted secondary and tertiary structures of three key CnMYB proteins involved in abiotic stress revealed distinct inter-proteomic features. The predicted interaction between CnMYB2/158 and Hsp70 supports its role in coconut's drought and salinity stress responses. These results expand our understanding of the relationships between the evolution and function of MYB genes, and provide valuable insights into the MYB gene family's role in abiotic stress in coconut.

Real-Time Extraction of News Events Based on BERT Model

Abstract For the large number of news reports generated every day, in order to obtain effective information from these unstructured news text data more efficiently. In this paper, we study the real-time crawling of news data from news websites through crawling techniques and propose a BERT model-based approach to extract events from news long text. In this study, NetEase news website is selected as an example for real-time extraction to crawl the news data of this website. BERT model as a pre-trained model based on two-way encoded representation of transformer performs well on natural language understanding and natural language generation tasks. In this study, we will fine-tune the training based on BERT model on news corpus related dataset and perform sequence annotation through CRF layer to finally complete the event extraction task. In this paper, the DUEE dataset is chosen to train the model, and the experiments show that the overall performance of the BERT model is better than other network models. Finally, the model of this paper is further optimised, using the ALBERT and RoBERTa models improved on the basis of the BERT model, experiments were conducted, the results show that both models are improved compared to the BERT model, the ALBERT model algorithm performs the best, the model algorithm's F1 value is 1% higher than that of BERT. The results show that the performance is optimised.

Whole-genome sequence of Sclerotium delphinii, a pathogenic fungus of Dendrobium officinale southern blight

Dendrobium officinale is a rare and precious medicinal plant. Southern blight is a destructive disease in the artificial cultivation of D. officinale, and one of its pathogens is Sclerotium delphinii. S. delphinii is a phytopathogenic fungus with a wide host range with extremely strong pathogenicity. In this study, S. delphinii was isolated from D. officinale with southern blight. Subsequently, this specific strain underwent thorough whole-genome sequencing using the PacBio Sequel II platform, which employed single-molecule real-time (SMRT) technology. Comprehensive annotations were obtained through functional annotation of protein sequences using various publicly available databases. The genome of S. delphinii measures 73.66 Mb, with an N90 contig size of 2,707,110 bp, and it contains 18,506 putative predictive genes. This study represents the first report on the genome size assembly and annotation of S. delphinii, making it the initial species within the Sclerotium genus to undergo whole-genome sequencing, which can provide solid data and a theoretical basis for further research on the pathogenesis, omics of S. delphinii.