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Observational studies have shown that individual sleep traits habits are potential risk factors for major depression. However, it is not known whether there is a causal relationship between individual sleep traits habits such as continuous sleep duration, short sleep duration, short sleep duration, insomnia, nap during the day, snoring, and major depression. In this study, Mendelian randomization (MR) was used to predict major depressive disorder (MDD) in individuals sleep traits habits. Data were obtained from the genome-wide association study (GWAS). Nine MR analysis methods were used: Inverse Variance Weighted (IVW) [fixed effects/multiplicative random effects], simple mode, simple mode, weighted mode, simple median, weighted median, penalised weighted median, and MR-Egger, MR Egger (bootstrap). IVW was used as the main analysis method for the MR analysis of two samples, and the other methods were used as supplements. The results obtained through the IVW method supported a causal relationship between sleep duration and decreased risk of MDD (odds ratio, OR ivw : 0.998; 95 % CI: 0.996–0.999, P<0.001). Two-Sample MR, results showed that short sleep duration has a causal effect on the increased risk of MDD (odds ratio, OR ivw : 1.179; 95 % CI: 1.108–1.255, P<0.001). However, there were no sufficient evidence supported that long sleep duration has a causal effect on the decreased risk of MDD (odds ratio, OR ivw : 0.991; 95 % CI: 0.924–1.062, P = 0.793). A significant causal relationship between insomnia and increased risk of MDD was observed (OR: 1.233; 95 % CI: 1.214–1.253, P<0.001). Interestingly, our study also found that daytime napping has a causal effect on the increased risk of MDD (odds ratio, OR ivw : 1.519; 95 % CI: 1.376–1.678, P<0.001). The present results did not show a significant causal relationship between snoring and the risk of MDD (OR ivw : 1.000; 95 % CI: 0.998–1.002, P = 0.906). Obstructive sleep apnea (odds ratio, OR ivw : 1.021; 95 % CI: 0.972–1.072, P = 0.407) and morning person (odds ratio, OR ivw : 1.021; 95 % CI: 0.972–1.072, P = 0.407) have no causal effect on the increased risk of MDD. The study could not ascertain whether there were genetic differences among different ethnicities, nations, and regions, as it only included participants of European ancestry. In summary, our research provides genetic evidence for the relationship between individual sleep traits (short sleep duration, insomnia, daytime napping) and the increased risk of MDD. Interventions targeting lifestyle factors may reduce the risk of MDD. • This study explored the causality between lifestyle and major depressive disorder by Mendelian Randomization analysis. • This research provides genetic evidence supporting that sleep traits increase major depressive disorder risk. • The research primarily included European ancestry. The conclusions may not apply to other ethnic and regional groups. [ABSTRACT FROM AUTHOR]

Introduction: Colorectal cancer (CRC) is the third leading cancer, ranked after breast cancer and lung cancer worldwide. Previous studies reported that the mortality rate of CRC is high among patients due to late diagnosis and prognosis. Therefore, this study was conducted to identify miRNAs and the target genes that could potentially be used as a biomarker for CRC. Materials and methods: In this study, three datasets that consist of both miRNA and gene expression profile in normal and primary CRC tumour tissues were selected from the NCBI GEO database. The GEO2R tool analysis was performed followed by InteractiVenn analysis to find the intersection of differently expressed miRNAs (DEMs) and differently expressed genes (DEGs) between the datasets. A total of 91 DEMs were identified and further analysed using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, Gene Ontology (GO), miRWalk and DIANA-miRPath databases, while 321 DEGs were analysed using KEGG and GO analyses. Finally, the selected DEMs and DEGs were analysed using Cytoscape software to analyse the interaction networks. Results: Two overexpressed microRNAs (hsa-miR-30a-5p and hsa-miR-149-5p) and three underexpressed microRNAs (hsa-miR-520b, hsa-miR-409-3p, and hsa-miR-142-5p) along with their respective target genes were identified in this study. The down-regulated genes included HDAC9 and CALD1, while up-regulated genes comprised STOX2, PDLIM5, CPEB3, TLE4, FGD4, ZNF292, PRNP, EYA4, and ZBTB8A. Conclusion: In conclusion, this work has identified five miRNA-gene panels that holds potential for more accurate diagnosis, better prognostication, and the development of tailored therapeutic interventions for CRC patients. [ABSTRACT FROM AUTHOR]

Abstract Objectives Roscoea is a Sino-Himalayan alpine genus in pantropical family Zingiberaeae. As traditional Tibetan medicinal plants, many species of this genus are threatened by digging, logging, land clearance, grazing and climate change. Roscoea debilis is an endemic species in the Hengduan Mountains with a narrow distribution range. In this study, the assembled and annotated genome of Roscoea was presented in order to furnish significant resources for comparative and functional genomic investigations. The first complete reference genome of Roscoea is expected to shed light on research on conservation and evolutionary biology. Data description A chromosome-level genome of 1601.04 Mb was obtained for R. debilis by combining Illumina short reads (107.28 Gb) and PacBio Hi-Fi reads (64.08 Gb), achieving high-quality sequencing coverage of roughly 67 × and 40 ×. The assembly was additionally assisted by 271.65 Gb Hi-C data (169 ×), which resulted in a contig N50 of 136.17 Mb and a scaffold N50 of 90.48 Mb. Benchmarking Universal Single-Copy Orthologs (BUSCO) assessment results revealed that most of the core embryophyta genes (98.7%) in the BUSCO dataset (embryophyta_odb10) were successfully identified. Additionally, 96.44% of the genomic sequences were accurately mapped onto twelve pseudochromosomes.

Abstract Plant genomics plays a pivotal role in enhancing global food security and sustainability by offering innovative solutions for improving crop yield, disease resistance, and stress tolerance. As the number of sequenced genomes grows and the accuracy and contiguity of genome assemblies improve, structural annotation of plant genomes continues to be a significant challenge due to their large size, polyploidy, and rich repeat content. In this paper, we present an overview of the current landscape in crop genomics research, highlighting the diversity of genomic characteristics across various crop species. We also assessed the accuracy of popular gene prediction tools in identifying genes within crop genomes and examined the factors that impact their performance. Our findings highlight the strengths and limitations of BRAKER2 and Helixer as leading structural genome annotation tools and underscore the impact of genome complexity, fragmentation, and repeat content on their performance. Furthermore, we evaluated the suitability of the predicted proteins as a reliable search space in proteomics studies using mass spectrometry data. Our results provide valuable insights for future efforts to refine and advance the field of structural genome annotation.

Objectives: Roscoea is a Sino-Himalayan alpine genus in pantropical family Zingiberaeae. As traditional Tibetan medicinal plants, many species of this genus are threatened by digging, logging, land clearance, grazing and climate change. Roscoea debilis is an endemic species in the Hengduan Mountains with a narrow distribution range. In this study, the assembled and annotated genome of Roscoea was presented in order to furnish significant resources for comparative and functional genomic investigations. The first complete reference genome of Roscoea is expected to shed light on research on conservation and evolutionary biology. Data description: A chromosome-level genome of 1601.04 Mb was obtained for R. debilis by combining Illumina short reads (107.28 Gb) and PacBio Hi-Fi reads (64.08 Gb), achieving high-quality sequencing coverage of roughly 67 × and 40 ×. The assembly was additionally assisted by 271.65 Gb Hi-C data (169 ×), which resulted in a contig N50 of 136.17 Mb and a scaffold N50 of 90.48 Mb. Benchmarking Universal Single-Copy Orthologs (BUSCO) assessment results revealed that most of the core embryophyta genes (98.7%) in the BUSCO dataset (embryophyta_odb10) were successfully identified. Additionally, 96.44% of the genomic sequences were accurately mapped onto twelve pseudochromosomes. [ABSTRACT FROM AUTHOR]

Since the advent of sequencing techniques and due to their continuous evolution, it has become easier and less expensive to obtain the complete genome sequence of any organism. Nevertheless, to elucidate all biological processes governing organism development, quality annotation is essential. In genome annotation, predicting gene structure is one of the most important and captivating challenges for computational biology. This aspect of annotation requires continual optimization, particularly for genomes as unusual as those of microsporidia. Indeed, this group of fungal‐related parasites exhibits specific features (highly reduced gene sizes, sequences with high rate of evolution) linked to their evolution as intracellular parasites, requiring the implementation of specific annotation approaches to consider all these features. This review aimed to outline these characteristics and to assess the increasingly efficient approaches and tools that have enhanced the accuracy of gene prediction for microsporidia, both in terms of sensitivity and specificity. Subsequently, a final part will be dedicated to postgenomic approaches aimed at reinforcing the annotation data generated by prediction software. These approaches include the characterization of other understudied genes, such as those encoding regulatory noncoding RNAs or very small proteins, which also play crucial roles in the life cycle of these microorganisms. [ABSTRACT FROM AUTHOR]

Plant genomics plays a pivotal role in enhancing global food security and sustainability by offering innovative solutions for improving crop yield, disease resistance, and stress tolerance. As the number of sequenced genomes grows and the accuracy and contiguity of genome assemblies improve, structural annotation of plant genomes continues to be a significant challenge due to their large size, polyploidy, and rich repeat content. In this paper, we present an overview of the current landscape in crop genomics research, highlighting the diversity of genomic characteristics across various crop species. We also assessed the accuracy of popular gene prediction tools in identifying genes within crop genomes and examined the factors that impact their performance. Our findings highlight the strengths and limitations of BRAKER2 and Helixer as leading structural genome annotation tools and underscore the impact of genome complexity, fragmentation, and repeat content on their performance. Furthermore, we evaluated the suitability of the predicted proteins as a reliable search space in proteomics studies using mass spectrometry data. Our results provide valuable insights for future efforts to refine and advance the field of structural genome annotation. [ABSTRACT FROM AUTHOR]

Abstract Objectives Brasenia is a monotypic genus in the family of Cabombaceae. The only species, B. schreberi, is a macrophyte distributed worldwide. Because it requires good water quality, it is endangered in China and other countries due to the deterioration of aquatic habitats. The young leaves and stems of B. schreberi are covered by thick mucilage, which has high medical value. As an allelopathic aquatic plant, it can also be used in the management of aquatic weeds. Here, we present its assembled and annotated genome to help shed light on medial and allelopathic substrates and facilitate their conservation. Data description Genomic DNA and RNA extracted from B. schreberi leaf tissues were used for whole genome and RNA sequencing using a Nanopore and/or MGI sequencer. The assembly was 1,055,148,839 bp in length, with 92 contigs and an N50 of 22,379,495 bp. The repetitive elements in the assembly were 555,442,205 bp. A completeness assessment of the assembly with BUSCO and compleasm indicated 88.4 and 90.9% completeness in the Eudicots database and 95.4 and 96.6% completeness in the Embryphyta database. Gene annotation revealed 67,747 genes that coded for 73,344 proteins.

Gene prediction is required to obtain optimal biologically meaningful information from genomic sequences, but automated gene prediction software is imperfect. In this study, we compare the original annotation of the Coccidioides immitis RS genome (the reference strain of C. immitis) to annotations using the Funannotate and Augustus genome prediction pipelines. A total of 25% of the originally predicted genes (denoted CIMG) were not found in either the Funannotate or Augustus predictions. A comparison of Funannotate and Augustus predictions also found overlapping but not identical sets of genes. The predicted genes found only in the original annotation (referred to as CIMG-unique) were less likely to have a meaningful functional annotation and a lower number of orthologs and homologs in other fungi than all CIMG genes predicted by the original annotation. The CIMG-unique genes were also more likely to be lineage-specific and poorly expressed. In addition, the CIMG-unique genes were found in clusters and tended to be more frequently associated with transposable elements than all CIMG-predicted genes. The CIMG-unique genes were more likely to have experimentally determined transcription start sites that were further away from the originally predicted transcription start sites, and experimentally determined initial transcription was less likely to result in stable CIMG-unique transcripts. A sample of CIMG-unique genes that were relatively well expressed and differentially expressed in mycelia and spherules was inspected in a genome browser, and the structure of only about half of them was found to be supported by RNA-seq data. These data suggest that some of the CIMG-unique genes are not authentic gene predictions. Genes that were predicted only by the Funannotate pipeline were also less likely to have a meaningful functional annotation, be shorter, and express less well than all the genes predicted by Funannotate. C. immitis genes predicted by more than one annotation are more likely to have predicted functions, many orthologs and homologs, and be well expressed. Lineage-specific genes are relatively uncommon in this group. These data emphasize the importance and limitations of gene prediction software and suggest that improvements to the annotation of the C. immitis genome should be considered.

Background: The X chromosome is often omitted in disease association studies despite containing thousands of genes that may provide insight into well-known sex differences in the risk of Alzheimer's disease (AD). Objective: To model the expression of X chromosome genes and evaluate their impact on AD risk in a sex-stratified manner. Methods: Using elastic net, we evaluated multiple modeling strategies in a set of 175 whole blood samples and 126 brain cortex samples, with whole genome sequencing and RNA-seq data. SNPs (MAF > 0.05) within the cis-regulatory window were used to train tissue-specific models of each gene. We apply the best models in both tissues to sex-stratified summary statistics from a meta-analysis of Alzheimer's Disease Genetics Consortium (ADGC) studies to identify AD-related genes on the X chromosome. Results: Across different model parameters, sample sex, and tissue types, we modeled the expression of 217 genes (95 genes in blood and 135 genes in brain cortex). The average model R2 was 0.12 (range from 0.03 to 0.34). We also compared sex-stratified and sex-combined models on the X chromosome. We further investigated genes that escaped X chromosome inactivation (XCI) to determine if their genetic regulation patterns were distinct. We found ten genes associated with AD at p < 0.05, with only ARMCX6 in female brain cortex (p = 0.008) nearing the significance threshold after adjusting for multiple testing (α = 0.002). Conclusions: We optimized the expression prediction of X chromosome genes, applied these models to sex-stratified AD GWAS summary statistics, and identified one putative AD risk gene, ARMCX6. [ABSTRACT FROM AUTHOR]

Objectives: Brasenia is a monotypic genus in the family of Cabombaceae. The only species, B. schreberi, is a macrophyte distributed worldwide. Because it requires good water quality, it is endangered in China and other countries due to the deterioration of aquatic habitats. The young leaves and stems of B. schreberi are covered by thick mucilage, which has high medical value. As an allelopathic aquatic plant, it can also be used in the management of aquatic weeds. Here, we present its assembled and annotated genome to help shed light on medial and allelopathic substrates and facilitate their conservation. Data description: Genomic DNA and RNA extracted from B. schreberi leaf tissues were used for whole genome and RNA sequencing using a Nanopore and/or MGI sequencer. The assembly was 1,055,148,839 bp in length, with 92 contigs and an N50 of 22,379,495 bp. The repetitive elements in the assembly were 555,442,205 bp. A completeness assessment of the assembly with BUSCO and compleasm indicated 88.4 and 90.9% completeness in the Eudicots database and 95.4 and 96.6% completeness in the Embryphyta database. Gene annotation revealed 67,747 genes that coded for 73,344 proteins. [ABSTRACT FROM AUTHOR]

Abstract Purpose Autism spectrum disorder(ASD) is a disease associated with the neurodevelopment of the brain. The autism spectrum can be observed in early childhood, where the symptoms of the disease usually appear in children within the first year of their life. Currently, ASD can only be diagnosed based on the apparent symptoms due to the lack of information on genes related to the disease. Therefore, in this paper, we need to predict the largest number of disease-causing genes for a better diagnosis. Methods A hybrid stacking ensemble model with Synthetic Minority Oversampling TEchnique (Stack-SMOTE) is proposed to predict the genes associated with ASD. The proposed model uses the gene ontology database to measure the similarities between the genes using a hybrid gene similarity function(HGS). HGS is effective in measuring the similarity as it combines the features of information gain-based methods and graph-based methods. The proposed model solves the imbalanced ASD dataset problem using the Synthetic Minority Oversampling Technique (SMOTE), which generates synthetic data rather than duplicates the data to reduce the overfitting. Sequentially, a gradient boosting-based random forest classifier (GBBRF) is introduced as a new combination technique to enhance the prediction of ASD genes. Moreover, the GBBRF classifier combined with random forest(RF), k-nearest neighbor, support vector machine(SVM), and logistic regression(LR) to form the proposed Stacking-SMOTE model to optimize the prediction of ASD genes. Results The proposed Stacking-SMOTE model is evaluated using the Simons Foundation Autism Research Initiative (SFARI) gene database and a set of candidates ASD genes.The results of the proposed model-based SMOTE outperform other reported undersampling and oversampling techniques. Sequentially, the results of GBBRF achieve higher accuracy than using the basic classifiers. Moreover, the experimental results show that the proposed Stacking-SMOTE model outperforms the existing ASD prediction models with approximately 95.5% accuracy. Conclusion The proposed Stacking-SMOTE model demonstrates that SMOTE is effective in handling the autism imbalanced data. Sequentially, the integration between the gradient boosting and random forest classifier (GBBRF) support to build a robust stacking ensemble model(Stacking-SMOTE).

Abstract Background The Earth Biogenome Project has rapidly increased the number of available eukaryotic genomes, but most released genomes continue to lack annotation of protein-coding genes. In addition, no transcriptome data is available for some genomes. Results Various gene annotation tools have been developed but each has its limitations. Here, we introduce GALBA, a fully automated pipeline that utilizes miniprot, a rapid protein-to-genome aligner, in combination with AUGUSTUS to predict genes with high accuracy. Accuracy results indicate that GALBA is particularly strong in the annotation of large vertebrate genomes. We also present use cases in insects, vertebrates, and a land plant. GALBA is fully open source and available as a docker image for easy execution with Singularity in high-performance computing environments. Conclusions Our pipeline addresses the critical need for accurate gene annotation in newly sequenced genomes, and we believe that GALBA will greatly facilitate genome annotation for diverse organisms.

Nile tilapia is a freshwater fish of African origin, with productive and economic characteristics affecting global fish markets. The Oreochromis niloticus reference genome has a diploid set of 22 linkage groups (LGs) in addition to ungrouped sequences. A total of 42,622 genes have been identified, but 550 genomic gaps may include more. Our study focused on some of these genomic gaps, where appropriate primers were designed and then multiplied through the polymerase chain reaction (PCR) technique. From local samples, DNA was extracted and amplified with the new primers. Successful amplicons were sequenced and annotated using available bioinformatics tools. Five assessed sequences were annotated, of which three were newly predicted in O. niloticus, as mucin 1-like, and KLR genes, and SATB region. The other two sequences belonged to grid2 and trpm7 which were previously predicted. Although individual attempts to identify genomic gaps are not feasible in filling the large lack of information on the Nile tilapia genome, a good number and valuable new information has been reached. However, the following plan showed many technical problems, more time, effort and cost that could be avoided by suggesting the application of another technique, such as whole-genome sequencing, mapping, and assembly.

Metagenomics provides a tool to assess the functional potential of environmental and host‐associated microbiomes based on the analysis of environmental DNA: assembly, gene prediction and annotation. While gene prediction is straightforward for most bacterial and archaeal taxa, it has limited applicability in the majority of eukaryotic organisms, including fungi that contain introns in gene coding sequences. As a consequence, eukaryotic genes are underrepresented in metagenomics datasets and our understanding of the contribution of fungi and other eukaryotes to microbiome functioning is limited. Here, we developed a machine intelligence‐based algorithm that predicts fungal introns in environmental DNA with reasonable precision and used it to improve the annotation of environmental metagenomes. Intron removal increased the number of predicted genes by up to 9.1% and improved the annotation of several others. The proportion of newly predicted genes increased with the share of eukaryotic genes in the metagenome and—within fungal taxa—increased with the number of introns per gene. Our approach provides a tool named SVMmycointron for improved metagenome annotation, especially of microbiomes with a high proportion of eukaryotes. The scripts described in the paper are made publicly available and can be readily utilized by microbiome researchers analysing metagenomics data. [ABSTRACT FROM AUTHOR]

Algae-driven processes, such as direct CO2 fixation into glycerol, provide new routes for sustainable chemical production in synergy with greenhouse gas mitigation. The marine microalgae Dunaliella tertiolecta is reported to accumulate high amounts of intracellular glycerol upon exposure to high salt concentrations. We have conducted a comprehensive, time-resolved systems biology study to decipher the metabolic response of D. tertiolecta up to 24 h under continuous light conditions. Initially, due to a lack of reference sequences required for MS/MS-based protein identification, a high-quality draft genome of D. tertiolecta was generated. Subsequently, a database was designed by combining the genome with transcriptome data obtained before and after salt stress. This database allowed for detection of differentially expressed proteins and identification of phosphorylated proteins, which are involved in the short- and long-term adaptation to salt stress, respectively. Specifically, in the rapid salt adaptation response, proteins linked to the Ca2+ signaling pathway and ion channel proteins were significantly increased. While phosphorylation is key in maintaining ion homeostasis during the rapid adaptation to salt stress, phosphofructokinase is required for long-term adaption. Lacking β-carotene, synthesis under salt stress conditions might be substituted by the redox-sensitive protein CP12. Furthermore, salt stress induces upregulation of Calvin–Benson cycle-related proteins. [ABSTRACT FROM AUTHOR]

Purpose: Autism spectrum disorder(ASD) is a disease associated with the neurodevelopment of the brain. The autism spectrum can be observed in early childhood, where the symptoms of the disease usually appear in children within the first year of their life. Currently, ASD can only be diagnosed based on the apparent symptoms due to the lack of information on genes related to the disease. Therefore, in this paper, we need to predict the largest number of disease-causing genes for a better diagnosis. Methods: A hybrid stacking ensemble model with Synthetic Minority Oversampling TEchnique (Stack-SMOTE) is proposed to predict the genes associated with ASD. The proposed model uses the gene ontology database to measure the similarities between the genes using a hybrid gene similarity function(HGS). HGS is effective in measuring the similarity as it combines the features of information gain-based methods and graph-based methods. The proposed model solves the imbalanced ASD dataset problem using the Synthetic Minority Oversampling Technique (SMOTE), which generates synthetic data rather than duplicates the data to reduce the overfitting. Sequentially, a gradient boosting-based random forest classifier (GBBRF) is introduced as a new combination technique to enhance the prediction of ASD genes. Moreover, the GBBRF classifier combined with random forest(RF), k-nearest neighbor, support vector machine(SVM), and logistic regression(LR) to form the proposed Stacking-SMOTE model to optimize the prediction of ASD genes. Results: The proposed Stacking-SMOTE model is evaluated using the Simons Foundation Autism Research Initiative (SFARI) gene database and a set of candidates ASD genes.The results of the proposed model-based SMOTE outperform other reported undersampling and oversampling techniques. Sequentially, the results of GBBRF achieve higher accuracy than using the basic classifiers. Moreover, the experimental results show that the proposed Stacking-SMOTE model outperforms the existing ASD prediction models with approximately 95.5% accuracy. Conclusion: The proposed Stacking-SMOTE model demonstrates that SMOTE is effective in handling the autism imbalanced data. Sequentially, the integration between the gradient boosting and random forest classifier (GBBRF) support to build a robust stacking ensemble model(Stacking-SMOTE). [ABSTRACT FROM AUTHOR]

The imbalanced distribution of classes is a common issue in almost classification problems. Therefore, we must be familiar with class-imbalanced techniques to handle this problem. Autism spectrum disorder(ASD) disease affects the development of the brain. Therefore, patients with autism have some limitations to interact with others on the social level. So, it is necessary to predict the genes related to ASD for early diagnosis and treatment. Recent studies utilize different machine learning techniques to predict ASD genes that suffer from the imbalanced ASD dataset problem. In this paper, recent ASD gene prediction models are utilized to compare different techniques influence using undersampling and oversampling algorithms on the model performance. Moreover, a new combined technique (SMOTE-RUS) is proposed using Synthetic Oversampling Technique (SMOTE) and random undersampling (RUS) technique to solve the imbalanced dataset problem. SMOTE-RUS is used to build an effective model to predict ASD genes. The proposed technique results prove that it is effective to get a more robust gene prediction model. Moreover, it outperforms other models using a single resampling technique. [ABSTRACT FROM AUTHOR]

BackgroundGene annotation in eukaryotes is a non-trivial task that requires meticulous analysis of accumulated transcript data. Challenges include transcriptionally active regions of the genome that contain overlapping genes, genes that produce numerous transcripts, transposable elements and numerous diverse sequence repeats. Currently available gene annotation software applications depend on pre-constructed full-length gene sequence assemblies which are not guaranteed to be error-free. The origins of these sequences are often uncertain, making it difficult to identify and rectify errors in them. This hinders the creation of an accurate and holistic representation of the transcriptomic landscape across multiple tissue types and experimental conditions. Therefore, to gauge the extent of diversity in gene structures, a comprehensive analysis of genome-wide expression data is imperative.ResultsWe present FINDER, a fully automated computational tool that optimizes the entire process of annotating genes and transcript structures. Unlike current state-of-the-art pipelines, FINDER automates the RNA-Seq pre-processing step by working directly with raw sequence reads and optimizes gene prediction from BRAKER2 by supplementing these reads with associated proteins. The FINDER pipeline (1) reports transcripts and recognizes genes that are expressed under specific conditions, (2) generates all possible alternatively spliced transcripts from expressed RNA-Seq data, (3) analyzes read coverage patterns to modify existing transcript models and create new ones, and (4) scores genes as high- or low-confidence based on the available evidence across multiple datasets. We demonstrate the ability of FINDER to automatically annotate a diverse pool of genomes from eight species.ConclusionsFINDER takes a completely automated approach to annotate genes directly from raw expression data. It is capable of processing eukaryotic genomes of all sizes and requires no manual supervision-ideal for bench researchers with limited experience in handling computational tools.

BACKGROUND:The red flour beetle Tribolium castaneum has emerged as an important model organism for the study of gene function in development and physiology, for ecological and evolutionary genomics, for pest control and a plethora of other topics. RNA interference (RNAi), transgenesis and genome editing are well established and the resources for genome-wide RNAi screening have become available in this model. All these techniques depend on a high quality genome assembly and precise gene models. However, the first version of the genome assembly was generated by Sanger sequencing, and with a small set of RNA sequence data limiting annotation quality. RESULTS:Here, we present an improved genome assembly (Tcas5.2) and an enhanced genome annotation resulting in a new official gene set (OGS3) for Tribolium castaneum, which significantly increase the quality of the genomic resources. By adding large-distance jumping library DNA sequencing to join scaffolds and fill small gaps, the gaps in the genome assembly were reduced and the N50 increased to 4753kbp. The precision of the gene models was enhanced by the use of a large body of RNA-Seq reads of different life history stages and tissue types, leading to the discovery of 1452 novel gene sequences. We also added new features such as alternative splicing, well defined UTRs and microRNA target predictions. For quality control, 399 gene models were evaluated by manual inspection. The current gene set was submitted to Genbank and accepted as a RefSeq genome by NCBI. CONCLUSIONS:The new genome assembly (Tcas5.2) and the official gene set (OGS3) provide enhanced genomic resources for genetic work in Tribolium castaneum. The much improved information on transcription start sites supports transgenic and gene editing approaches. Further, novel types of information such as splice variants and microRNA target genes open additional possibilities for analysis.

Background: The Earth Biogenome Project has rapidly increased the number of available eukaryotic genomes, but most released genomes continue to lack annotation of protein-coding genes. In addition, no transcriptome data is available for some genomes. Results: Various gene annotation tools have been developed but each has its limitations. Here, we introduce GALBA, a fully automated pipeline that utilizes miniprot, a rapid protein-to-genome aligner, in combination with AUGUSTUS to predict genes with high accuracy. Accuracy results indicate that GALBA is particularly strong in the annotation of large vertebrate genomes. We also present use cases in insects, vertebrates, and a land plant. GALBA is fully open source and available as a docker image for easy execution with Singularity in high-performance computing environments. Conclusions: Our pipeline addresses the critical need for accurate gene annotation in newly sequenced genomes, and we believe that GALBA will greatly facilitate genome annotation for diverse organisms. [ABSTRACT FROM AUTHOR]

A wide range of scientific fields, such as forensics, anthropology, medicine, and molecular evolution, benefits from the analysis of mitogenomic data. With the development of new sequencing technologies, the amount of mitochondrial sequence data to be analyzed has increased exponentially over the last few years. The accurate annotation of mitochondrial DNA is a prerequisite for any mitogenomic comparative analysis. To sustain with the growth of the available mitochondrial sequence data, highly efficient automatic computational methods are, hence, needed. Automatic annotation methods are typically based on databases that contain information about already annotated (and often precurated) mitogenomes of different species. However, the existing approaches have several shortcomings: 1) they do not scale well with the size of the database; 2) they do not allow for a fast (and easy) update of the database; and 3) they can only be applied to a relatively small taxonomic subset of all species. Here, we present a novel approach that does not have any of these aforementioned shortcomings, (1), (2), and (3). The reference database of mitogenomes is represented as a richly annotated de Bruijn graph. To generate gene predictions for a new user-supplied mitogenome, the method utilizes a clustering routine that uses the mapping information of the provided sequence to this graph. The method is implemented in a software package called DeGeCI (De Bruijn graph Gene Cluster Identification). For a large set of mitogenomes, for which expert-curated annotations are available, DeGeCI generates gene predictions of high conformity. In a comparative evaluation with MITOS2, a state-of-the-art annotation tool for mitochondrial genomes, DeGeCI shows better database scalability while still matching MITOS2 in terms of result quality and providing a fully automated means to update the underlying database. Moreover, unlike MITOS2, DeGeCI can be run in parallel on several processors to make use of modern multi-processor system [ABSTRACT FROM AUTHOR]

The prediction of gene structure within the genome sequence is the starting point of genome analysis, and its accuracy has a significant impact on the quality of subsequent analyses. Gene structure prediction is roughly divided into RNA-Seq-based methods, ab initio -based methods, homology-based methods, and the integration of individual prediction methods. Integrated methods are mainstream in recent genome projects because they improve prediction accuracy by combining or taking the best individual prediction findings; however, adequate prediction accuracy for eukaryotic species has not yet been achieved. Therefore, we developed an integrated tool, GINGER, that solves various issues related to gene structure prediction in higher eukaryotes. By handling artefacts in alignments of RNA and protein sequences, reconstructing gene structures via dynamic programming with appropriately weighted and scored exon/intron/intergenic regions, and applying different prediction processes and filtering criteria to multi-exon and single-exon genes, we achieved a significant improvement in accuracy compared to the existing integration methods. The feature of GINGER is its high prediction accuracy at the gene and exon levels, which is pronounced for species with more complex gene architectures. GINGER is implemented using Nextflow, which allows for the efficient and effective use of computing resources. [ABSTRACT FROM AUTHOR]

Ormosia is a genus of the Fabaceae family that shows a distinct evolutionary history due to its typical Asian-American tropical disjunction distribution pattern. However, both its phylogeny and biogeographic mechanisms have not been fully resolved. In addition, Ormosia species have great economic and ecological potential in the wood and handicraft (using their attractive seeds) industries, reforestation, and folk medicine (due to their flavonoids, alkaloids, and terpenoids), making them highly valuable in research, especially from a genomic perspective. We report the genome assemblies of two common Ormosia species, Ormosia emarginata and Ormosia semicastrata, in South China, using both long and short sequencing reads. The genome assemblies of O. emarginata and O. semicastrata comprised 90 contigs with a total length of 1,420,917,605 bp and 63 contigs with a total length of 1,511,766,959 bp, respectively. Benchmarking Universal Single-Copy Orthologs (BUSCO) assessment revealed 97.0% and 98.3% completeness of the O. emarginata and O. semicastrata assemblies, respectively. The assemblies contain 48,599 and 52,067 protein-coding genes, respectively. Phylogenetic analyses using 1032 single-copy genes with 19 species indicated that they are closely related to Lupinus albus. We investigated genes related to plant hormones, signaling, the circadian rhythm, transcription factors, and secondary metabolites derived from the whole genome and tandem and proximal duplications, indicating that these duplications should play important roles in the growth, development, and defense of Ormosia species. To our knowledge, our study is the first report on Ormosia genome assemblies. This information will facilitate phylogenetic and biogeographic analyses and species breeding in the future. [ABSTRACT FROM AUTHOR]

Nile tilapia is a freshwater fish of African origin, with productive and economical characteristics affecting global fish markets. The Oreochromis niloticus reference genome has a diploid set of 22 linkage groups (LGs) in addition to ungrouped sequences. A total of 42,622 genes have been identified, but 550 genomic gaps may include more. Our study focused on some of these genomic gaps, where appropriate primers were designed and then multiplied through the polymerase chain reaction (PCR) technique. From local samples, DNA was extracted and amplified with the new primers. Successful amplicons were sequenced and annotated using available bioinformatics tools. Five assessed sequences were annotated, of which three were newly predicted in O. niloticus, as mucin 1-like, and KLR genes, and SATB region. The other two sequences belonged to grid2 and trpm7 which were previously predicted. Although individual attempts to identify genomic gaps are not feasible in filling the large lack of information on the Nile tilapia genome, a good number and valuable new information has been reached. However, the following plan showed many technical problems, more time, effort, and cost that could be avoided by suggesting the application of another technique, such as whole-genome sequencing, mapping, and assembly. [ABSTRACT FROM AUTHOR]

In present genomic era, rapid genetic gains can be achieved by exploitation of novel genes associated with the trait of interest employing molecular breeding and genetic engineering. In the present study genes responsible for drought stress in rice 10746 expressed sequence tags (ESTs), expressed under drought stress condition were retrieved from the NCBI. The downloaded ESTs were clustered and assembled into 1120 contigs and 5559 singletones using CAP3 programme. The contigs were further subjected to identification of transcription factor, a total of 62 putative transcription factors were identified and sorted into 17 putative TF families. The contigs were subjected to BLASTX in NCBI to identify unique sequence which were further aligned to Oryza sativa Indica Group (ASM465v1) in gramene database using BLAT to retrieve the upstream and downstream sequences for putative gene identification. The retrieved sequences were analysed for transcription start site, PolyA tails and coding sequences which are essential features of gene using online tool fsgene. The present study found that, 46 contigs out of 1120 contigs has key gene structure and was considered as putative novel genes which may contribute to the drought tolerance in indica rice. These genes may be useful in development of drought tolerant varieties through smart breeding. [ABSTRACT FROM AUTHOR]

The release of the first reference genome of walnut (Juglans regia L.) enabled many achievements in the characterization of walnut genetic and functional variation. However, it is highly fragmented, preventing the integration of genetic, transcriptomic, and proteomic information to fully elucidate walnut biological processes. Here, we report the new chromosome-scale assembly of the walnut reference genome (Chandler v2.0) obtained by combining Oxford Nanopore long-read sequencing with chromosome conformation capture (Hi-C) technology. Relative to the previous reference genome, the new assembly features an 84.4-fold increase in N50 size, with the 16 chromosomal pseudomolecules assembled and representing 95% of its total length. Using full-length transcripts from single-molecule real-time sequencing, we predicted 37,554 gene models, with a mean gene length higher than the previous gene annotations. Most of the new protein-coding genes (90%) present both start and stop codons, which represents a significant improvement compared with Chandler v1.0 (only 48%). We then tested the potential impact of the new chromosome-level genome on different areas of walnut research. By studying the proteome changes occurring during male flower development, we observed that the virtual proteome obtained from Chandler v2.0 presents fewer artifacts than the previous reference genome, enabling the identification of a new potential pollen allergen in walnut. Also, the new chromosome-scale genome facilitates in-depth studies of intraspecies genetic diversity by revealing previously undetected autozygous regions in Chandler, likely resulting from inbreeding, and 195 genomic regions highly differentiated between Western and Eastern walnut cultivars. Overall, Chandler v2.0 will serve as a valuable resource to better understand and explore walnut biology.

Abstract Purpose Autism spectrum disorder (ASD) is the most prevalent disease today. The causes of its infection may be attributed to genetic causes by 80% and environmental causes by 20%. In spite of this, the majority of the current research is concerned with environmental causes, and the least proportion with the genetic causes of the disease. Autism is a complex disease, which makes it difficult to identify the genes that cause the disease. Methods Hybrid ensemble-based classification (HEC-ASD) model for predicting ASD genes using gradient boosting machines is proposed. The proposed model utilizes gene ontology (GO) to construct a gene functional similarity matrix using hybrid gene similarity (HGS) method. HGS measures the semantic similarity between genes effectively. It combines the graph-based method, such as Wang method with the number of directed children’s nodes of gene term from GO. Moreover, an ensemble gradient boosting classifier is adapted to enhance the prediction of genes forming a robust classification model. Results The proposed model is evaluated using the Simons Foundation Autism Research Initiative (SFARI) gene database. The experimental results are promising as they improve the classification performance for predicting ASD genes. The results are compared with other approaches that used gene regulatory network (GRN), protein to protein interaction network (PPI), or GO. The HEC-ASD model reaches the highest prediction accuracy of 0.88% using ensemble learning classifiers. Conclusion The proposed model demonstrates that ensemble learning technique using gradient boosting is effective in predicting autism spectrum disorder genes. Moreover, the HEC-ASD model utilized GO rather than using PPI network and GRN.

A wide range of scientific fields, such as forensics, anthropology, medicine, and molecular evolution, benefits from the analysis of mitogenomic data. With the development of new sequencing technologies, the amount of mitochondrial sequence data to be analyzed has increased exponentially over the last few years. The accurate annotation of mitochondrial DNA is a prerequisite for any mitogenomic comparative analysis. To sustain with the growth of the available mitochondrial sequence data, highly efficient automatic computational methods are, hence, needed. Automatic annotation methods are typically based on databases that contain information about already annotated (and often pre-curated) mitogenomes of different species. However, the existing approaches have several shortcomings: 1) they do not scale well with the size of the database; 2) they do not allow for a fast (and easy) update of the database; and 3) they can only be applied to a relatively small taxonomic subset of all species. Here, we present a novel approach that does not have any of these aforementioned shortcomings, (1), (2), and (3). The reference database of mitogenomes is represented as a richly annotated de Bruijn graph. To generate gene predictions for a new user-supplied mitogenome, the method utilizes a clustering routine that uses the mapping information of the provided sequence to this graph. The method is implemented in a software package called DeGeCI (De Bruijn graph Gene Cluster Identification). For a large set of mitogenomes, for which expert-curated annotations are available, DeGeCI generates gene predictions of high conformity. In a comparative evaluation with MITOS2, a state-of-the-art annotation tool for mitochondrial genomes, DeGeCI shows better database scalability while still matching MITOS2 in terms of result quality and providing a fully automated means to update the underlying database. Moreover, unlike MITOS2, DeGeCI can be run in parallel on several processors to make use of modern multi-processor systems.

Heterosigma akashiwo is a eukaryotic, cosmopolitan, and unicellular alga (class: Raphidophyceae), and produces fish-killing blooms. There is a substantial scientific and practical interest in its ecophysiological characteristics that determine bloom dynamics and its adaptation to broad climate zones. A well-annotated genomic/genetic sequence information enables researchers to characterize organisms using modern molecular technology. In the present study, we conducted H. akashiwo RNA sequencing, a de novo transcriptome assembly of 84,693,530 high-quality deduplicated short-read sequences.Obtained RNA reads were assembled by Trinity assembler and 144,777 contigs were identified with N50 values of 1085. Total 60,877 open reading frames with the length of 150 bp or greater were predicted. For further analyses, top Gene Ontology terms, pfam hits, and blast hits were annotated for all the predicted genes. The raw data were deposited in the NCBI SRA database (BioProject PRJDB6241 and PRJDB15108), and the assemblies are available in NCBI TSA database (ICRV01). The annotation information can be obtained in Dryad and can be accessed via doi: 10.5061/dryad.m0cfxpp56.

Transfer RNAs are the largest, most complex non-coding RNA family, universal to all living organisms. tRNAscan-SE has been the de facto tool for predicting tRNA genes in whole genomes. The newly developed version 2.0 has incorporated advanced methodologies with improved probabilistic search software and a suite of new gene models, enabling better functional classification of predicted genes. This chapter describes the use of the UNIX command-driven and online web versions, illustrating different search modes and options.

Seed sugar composition, mainly including fructose, glucose, sucrose, raffinose, and stachyose, is an important indicator of soybean [Glycine max (L.) Merr.] seed quality. However, research on soybean sugar composition is limited. To better understand the genetic architecture underlying the sugar composition in soybean seeds, we conducted a genome-wide association study (GWAS) using a population of 323 soybean germplasm accessions which were grown and evaluated under three different environments. A total of 31,245 single-nucleotide polymorphisms (SNPs) with minor allele frequencies (MAFs) ≥ 5% and missing data ≤ 10% were selected and used in the GWAS. The analysis identified 72 quantitative trait loci (QTLs) associated with individual sugars and 14 with total sugar. Ten candidate genes within the 100 Kb flanking regions of the lead SNPs across six chromosomes were significantly associated with sugar contents. According to GO and KEGG classification, eight genes were involved in the sugar metabolism in soybean and showed similar functions in Arabidopsis. The other two, located in known QTL regions associated with sugar composition, may play a role in sugar metabolism in soybean. This study advances our understanding of the genetic basis of soybean sugar composition and facilitates the identification of genes controlling this trait. The identified candidate genes will help improve seed sugar composition in soybean. [ABSTRACT FROM AUTHOR]

Eustoma grandiflorum (Raf.) Shinn. is an annual herbaceous plant native to the southern United States, Mexico, and the Greater Antilles. It has a large flower with a variety of colors and is an important flower crop. In this study, we established a chromosome-scale de novo assembly of E. grandiflorum genome sequences by integrating four genomic and genetic approaches: (1) Pacific Biosciences (PacBio) Sequel deep sequencing, (2) error correction of the assembly by Illumina short reads, (3) scaffolding by chromatin conformation capture sequencing (Hi-C), and (4) genetic linkage maps derived from an F2 mapping population. Thirty-six pseudomolecules and 64 unplaced scaffolds were created, with a total length of 1,324.8 Mb. A total of 36,619 genes were predicted on the genome as high-confidence genes. A comparison of genome structure between E. grandiflorum and C. canephora or O. pumila suggested whole-genome duplication after the divergence between the families Gentianaceae and Rubiaceae. Phylogenetic analysis with single-copy genes suggested that the divergence time between Gentianaceae and Rubiaceae was 74.94 MYA. Genetic diversity analysis was performed for nine commercial E. grandiflorum varieties bred in Japan, from which 254,205 variants were identified. This first report on the construction of a reference genome sequence in the genus Eustoma is expected to contribute to genetic and genomic studies in this genus and in the family Gentianaceae. [ABSTRACT FROM AUTHOR]

Purpose: Autism spectrum disorder (ASD) is the most prevalent disease today. The causes of its infection may be attributed to genetic causes by 80% and environmental causes by 20%. In spite of this, the majority of the current research is concerned with environmental causes, and the least proportion with the genetic causes of the disease. Autism is a complex disease, which makes it difficult to identify the genes that cause the disease. Methods: Hybrid ensemble-based classification (HEC-ASD) model for predicting ASD genes using gradient boosting machines is proposed. The proposed model utilizes gene ontology (GO) to construct a gene functional similarity matrix using hybrid gene similarity (HGS) method. HGS measures the semantic similarity between genes effectively. It combines the graph-based method, such as Wang method with the number of directed children's nodes of gene term from GO. Moreover, an ensemble gradient boosting classifier is adapted to enhance the prediction of genes forming a robust classification model. Results: The proposed model is evaluated using the Simons Foundation Autism Research Initiative (SFARI) gene database. The experimental results are promising as they improve the classification performance for predicting ASD genes. The results are compared with other approaches that used gene regulatory network (GRN), protein to protein interaction network (PPI), or GO. The HEC-ASD model reaches the highest prediction accuracy of 0.88% using ensemble learning classifiers. Conclusion: The proposed model demonstrates that ensemble learning technique using gradient boosting is effective in predicting autism spectrum disorder genes. Moreover, the HEC-ASD model utilized GO rather than using PPI network and GRN. [ABSTRACT FROM AUTHOR]

Background: The annotation of genomes is a crucial step regarding the analysis of new genomic data and resulting insights, and this especially for emerging organisms which allow researchers to access unexplored lineages, so as to expand our knowledge of poorly represented taxonomic groups. Complete pipelines for eukaryotic genome annotation have been proposed for more than a decade, but the issue is still challenging. One of the most widely used tools in the field is MAKER2, an annotation pipeline using experimental evidence (mRNA-seq and proteins) and combining different gene prediction tools. MAKER2 enables individual laboratories and small-scale projects to annotate non-model organisms for which pre-existing gene models are not available. The optimal use of MAKER2 requires gathering evidence data (by searching and assembling transcripts, and/or collecting homologous proteins from related organisms), elaborating the best annotation strategy (training of gene models) and efficiently orchestrating the different steps of the software in a grid computing environment, which is tedious, time-consuming and requires a great deal of bioinformatic skills. Methods: To address these issues, we present AMAW (Automated MAKER2 Annotation Wrapper), a wrapper pipeline for MAKER2 that automates the above-mentioned tasks. Importantly, AMAW also exists as a Singularity container recipe easy to deploy on a grid computer, thereby overcoming the tricky installation of MAKER2. Use case: The performance of AMAW is illustrated through the annotation of a selection of 32 protist genomes, for which we compared its annotations with those produced with gene models directly available in AUGUSTUS. Conclusions: Importantly, AMAW also exists as a Singularity container recipe easy to deploy on a grid computer, thereby overcoming the tricky installation of MAKER2

Abstract Background FragGeneScan is currently the most accurate and popular tool for gene prediction in short and error-prone reads, but its execution speed is insufficient for use on larger data sets. The parallelization which should have addressed this is inefficient. Its alternative implementation FragGeneScan+ is faster, but introduced a number of bugs related to memory management, race conditions and even output accuracy. Results This paper introduces FragGeneScanRs, a faster Rust implementation of the FragGeneScan gene prediction model. Its command line interface is backward compatible and adds extra features for more flexible usage. Its output is equivalent to the original FragGeneScan implementation. Conclusions Compared to the current C implementation, shotgun metagenomic reads are processed up to 22 times faster using a single thread, with better scaling for multithreaded execution. The Rust code of FragGeneScanRs is freely available from GitHub under the GPL-3.0 license with instructions for installation, usage and other documentation ( https://github.com/unipept/FragGeneScanRs ).

Gene prediction is required to obtain optimal biologically meaningful information from genomic sequences, but automated gene prediction software is imperfect. In this study, we compare the original annotation of the Coccidioides immitis RS genome (the reference strain of C. immitis) to annotations using the Funannotate and Augustus genome prediction pipelines. A total of 25% of the originally predicted genes (denoted CIMG) were not found in either the Funannotate or Augustus predictions. A comparison of Funannotate and Augustus predictions also found overlapping but not identical sets of genes. The predicted genes found only in the original annotation (referred to as CIMG-unique) were less likely to have a meaningful functional annotation and a lower number of orthologs and homologs in other fungi than all CIMG genes predicted by the original annotation. The CIMG-unique genes were also more likely to be lineage-specific and poorly expressed. In addition, the CIMG-unique genes were found in clusters and tended to be more frequently associated with transposable elements than all CIMG-predicted genes. The CIMG-unique genes were more likely to have experimentally determined transcription start sites that were further away from the originally predicted transcription start sites, and experimentally determined initial transcription was less likely to result in stable CIMG-unique transcripts. A sample of CIMG-unique genes that were relatively well expressed and differentially expressed in mycelia and spherules was inspected in a genome browser, and the structure of only about half of them was found to be supported by RNA-seq data. These data suggest that some of the CIMG-unique genes are not authentic gene predictions. Genes that were predicted only by the Funannotate pipeline were also less likely to have a meaningful functional annotation, be shorter, and express less well than all the genes predicted by Funannotate. C. immitis genes predicted by more than one annotation are more likely to have predicted functions, many orthologs and homologs, and be well expressed. Lineage-specific genes are relatively uncommon in this group. These data emphasize the importance and limitations of gene prediction software and suggest that improvements to the annotation of the C. immitis genome should be considered.

Algae-driven processes, such as direct CO2 fixation into glycerol, provide new routes for sustainable chemical production in synergy with greenhouse gas mitigation. The marine microalgae Dunaliella tertiolecta is reported to accumulate high amounts of intracellular glycerol upon exposure to high salt concentrations. We have conducted a comprehensive, time-resolved systems biology study to decipher the metabolic response of D. tertiolecta up to 24 h under continuous light conditions. Initially, due to a lack of reference sequences required for MS/MS-based protein identification, a high-quality draft genome of D. tertiolecta was generated. Subsequently, a database was designed by combining the genome with transcriptome data obtained before and after salt stress. This database allowed for detection of differentially expressed proteins and identification of phosphorylated proteins, which are involved in the short- and long-term adaptation to salt stress, respectively. Specifically, in the rapid salt adaptation response, proteins linked to the Ca2+ signaling pathway and ion channel proteins were significantly increased. While phosphorylation is key in maintaining ion homeostasis during the rapid adaptation to salt stress, phosphofructokinase is required for long-term adaption. Lacking β-carotene, synthesis under salt stress conditions might be substituted by the redox-sensitive protein CP12. Furthermore, salt stress induces upregulation of Calvin–Benson cycle-related proteins.

Dollar spot is one of the most damaging diseases in turfgrass, reducing its quality and playability. Two species, Clarireedia monteithiana and C. jacksonii (formerly Sclerotinia homoeocarpa) have been reported so far in the United States To study the Clarireedia genome, two isolates H2 and H3, sampled from seashore paspalum in Hawaii in 2019 were sequenced via Illumina paired-end sequencing by synthesis technology and PacBio SMRT sequencing. Both isolates were identified as C. aff. paspali, a novel species in the United States Using short and long reads, C. aff. paspali H3 contained 193 contigs with 48.6 Mbp and presented the most completed assembly and annotation among Clarireedia species. Out of the 13,428 protein models from AUGUSTUS, 349 cytoplasmic effectors and 13 apoplastic effectors were identified by EffectorP. To further decipher Clarireedia pathogenicity, C. aff. paspali genomes (H2 and H3), as well as available C. jacksonii (LWC-10 and HRI11), C. monteithiana (DRR09 and RB-19) genomes were screened for fifty-four pathogenesis determinants, previously identified in S. sclerotiorum. Seventeen orthologs of pathogenicity genes have been identified in Clarireedia species involved in oxalic acid production (pac1, nox1), mitogen-activated protein kinase cascade (pka1, smk3, ste12), appressorium formation (caf1, pks13, ams2, rgb1, rhs1) and glycolytic pathway (gpd). Within these genes, 366 species-specific SNPs were recorded between Clarireedia species; twenty-eight were non-synonymous and non-conservative. The predicted protein structure of six of these genes showed superimposition of the models among Clarireedia spp. The genomic variations revealed here could potentially lead to differences in pathogenesis and other physiological functions among Clarireedia species.

Abstract Background At present, the bioinformatics research on the relationship between aging-related diseases and genes is mainly through the establishment of a machine learning multi-label model to classify each gene. Most of the existing methods for predicting pathogenic genes mainly rely on specific types of gene features, or directly encode multiple features with different dimensions, use the same encoder to concatenate and predict the final results, which will be subject to many limitations in the applicability of the algorithm. Possible shortcomings of the above include: incomplete coverage of gene features by a single type of biomics data, overfitting of small dimensional datasets by a single encoder, or underfitting of larger dimensional datasets. Methods We use the known gene disease association data and gene descriptors, such as gene ontology terms (GO), protein interaction data (PPI), PathDIP, Kyoto Encyclopedia of genes and genomes Genes (KEGG), etc, as input for deep learning to predict the association between genes and diseases. Our innovation is to use Mashup algorithm to reduce the dimensionality of PPI, GO and other large biological networks, and add new pathway data in KEGG database, and then combine a variety of biological information sources through modular Deep Neural Network (DNN) to predict the genes related to aging diseases. Result and conclusion The results show that our algorithm is more effective than the standard neural network algorithm (the Area Under the ROC curve from 0.8795 to 0.9153), gradient enhanced tree classifier and logistic regression classifier. In this paper, we firstly use DNN to learn the similar genes associated with the known diseases from the complex multi-dimensional feature space, and then provide the evidence that the assumed genes are associated with a certain disease.

In recent decades, antimicrobial resistance has been augmented as a global concern to public health owing to the global spread of multidrug-resistant strains from different ESKAPE pathogens. This alarming trend and the lack of new antibiotics with novel modes of action in the pipeline necessitate the development of non-antibiotic ways to treat illnesses caused by these isolates. In molecular biology, computational approaches have become crucial tools, particularly in one of the most challenging areas of multidrug resistance. The rapid advancements in bioinformatics have led to a plethora of computational approaches involving genomics, systems biology, and structural biology currently gaining momentum among molecular biologists since they can be useful and provide valuable information on the complex mechanisms of AMR research in ESKAPE pathogens. These computational approaches would be helpful in elucidating the AMR mechanisms, identifying important hub genes/proteins, and their promising targets together with their interactions with important drug targets, which is a crucial step in drug discovery. Therefore, the present review aims to provide holistic information on currently employed bioinformatic tools and their application in the discovery of multifunctional novel therapeutic drugs to combat the current problem of AMR in ESKAPE pathogens. The review also summarizes the recent advancement in the AMR research in ESKAPE pathogens utilizing the in silico approaches. [ABSTRACT FROM AUTHOR]

The hagfishes (Myxiniformes) arose from agnathan (jawless vertebrate) lineages and they are one of only two extant cyclostome taxa, together with lampreys (Petromyzontiformes). Even though whole genome sequencing has been achieved for diverse vertebrate taxa, genome-wide sequence information has been highly limited for cyclostomes. Here we sequenced the genome of the inshore hagfish Eptatretus burgeri using DNA extracted from the testis, with a short-read sequencing platform, aiming to reconstruct a high-coverage protein-coding gene catalogue. The obtained genome assembly, scaffolded with mate-pair reads and paired RNA-seq reads, exhibited an N50 scaffold length of 293 Kbp, which allowed the genome-wide prediction of coding genes. This computation resulted in the gene models whose completeness was estimated at the complete coverage of more than 83 % and the partial coverage of more than 93 % by referring to evolutionarily conserved single-copy orthologs. The high contiguity of the assembly and completeness of the gene models promise a high utility in various comparative analyses including phylogenomics and phylome exploration.