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Background: Protein kinases are a diverse superfamily of proteins common to organisms across the tree of life that are typically involved in signal transduction, allowing organisms to sense and respond to biotic or abiotic environmental factors. They have important roles in organismal physiology, including development, reproduction, acclimation to environmental stress, while their dysregulation can lead to disease, including several forms of cancer. Identifying the complement of protein kinases (the kinome) of any organism is useful for understanding its physiological capabilities, limitations and adaptations to environmental stress. The increasing availability of genomes makes it now possible to examine and compare the kinomes across a broad diversity of organisms. Here we present a pipeline respecting the FAIR principles (findable, accessible, interoperable and reusable) that facilitates the search and identification of protein kinases from a predicted proteome, and classifies them according to group of serine/threonine/tyrosine protein kinases present in eukaryotes. Results: KiNext is a Nextflow pipeline that regroups a number of existing bioinformatic tools to search for and classify the protein kinases of an organism in a reproducible manner, starting from a set of amino acid sequences. Conventional eukaryotic protein kinases (ePKs) and atypical protein kinases (aPKs) are identified by using Hidden Markov Models (HMMs) generated from the catalytic domains of kinases. Furthermore, KiNext categorizes ePKs into the eight kinase groups by employing dedicated Hidden Markov Models (HMMs) tailored for each group. The performance of the KiNext pipeline was validated against previously identified kinomes obtained with other tools that were already published for two marine species, the Pacific oyster Crassostrea gigas and the unicellular green alga Ostreoccocus tauri. KiNext outperformed previous results by finding previously unidentified kinases and by attributing a large proportion of previously unclassified kinases to a group in both species. These results demonstrate improvements in kinase identification and classification, all while providing traceability and reproducibility of results in a FAIR pipeline. The default HMM models provided with KiNext are most suitable for eukaryotes, but the pipeline can be easily modified to include HMM models for other taxa of interest. Conclusion: The KiNext pipeline enables efficient and reproducible identification of kinomes based on predicted amino acid sequences (i.e. proteomes). KiNext was designed to be easy to use, automated, portable and scalable. [ABSTRACT FROM AUTHOR]

Genome annotation is an important but challenging task. Accurate identification of short interspersed nuclear elements (SINEs) is particularly difficult due to their lack of highly conserved sequences. AnnoSINE is state-of-the-art software for annotating SINEs in plant genomes, but it is computationally inefficient for large genomes. Moreover, its applicability to animals is limited due to the absence of animal pHMMs in its HMM library. Therefore, we propose AnnoSINE_v2, which extends accurate SINE annotation for animal genomes with greatly optimized computational efficiency. Our results show that AnnoSINE_v2's annotation of SINEs has over 20% higher F1-score compared to the existing tools on animal genomes and enables the processing of complicated genomes, like human and zebrafish, which were beyond the capabilities of AnnoSINE_v1. AnnoSINE_v2 is freely available on Conda and GitHub: https://github.com/liaoherui/AnnoSINE_v2. [ABSTRACT FROM AUTHOR]

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]

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]

Background: Staphylococcus shinii appears as an umbrella species encompassing several strains of Staphylococcus pseudoxylosus and Staphylococcus xylosus. Given its phylogenetic closeness to S. xylosus, S. shinii can be found in similar ecological niches, including the microbiota of fermented meats where the species may contribute to colour and flavour development. In addition to these conventional functionalities, a biopreservation potential based on the production of antagonistic compounds may be available. Such potential, however, remains largely unexplored in contrast to the large body of research that is available on the biopreservative properties of lactic acid bacteria. The present study outlines the exploration of the genetic basis of competitiveness and antimicrobial activity of a fermented meat isolate, S. shinii IMDO-S216. To this end, its genome was sequenced, de novo assembled, and annotated. Results: The genome contained a single circular chromosome and eight plasmid replicons. Focus of the genomic exploration was on secondary metabolite biosynthetic gene clusters coding for ribosomally synthesized and posttranslationally modified peptides. One complete cluster was coding for a bacteriocin, namely lactococcin 972; the genes coding for the pre-bacteriocin, the ATP-binding cassette transporter, and the immunity protein were also identified. Five other complete clusters were identified, possibly functioning as competitiveness factors. These clusters were found to be involved in various responses such as membrane fluidity, iron intake from the medium, a quorum sensing system, and decreased sensitivity to antimicrobial peptides and competing microorganisms. The presence of these clusters was equally studied among a selection of multiple Staphylococcus species to assess their prevalence in closely-related organisms. Conclusions: Such factors possibly translate in an improved adaptation and competitiveness of S. shinii IMDO-S216 which are, in turn, likely to improve its fitness in a fermented meat matrix. [ABSTRACT FROM AUTHOR]

Objectives: The new data provide an important genomic resource for the Critically Endangered Cuban crocodile (Crocodylus rhombifer). Cuban crocodiles are restricted to the Zapata Swamp in southern Matanzas Province, Cuba, and readily hybridize with the widespread American crocodile (Crocodylus acutus) in areas of sympatry. The reported de novo assembly will contribute to studies of crocodylian evolutionary history and provide a resource for informing Cuban crocodile conservation. Data description: The final 2.2 Gb draft genome for C. rhombifer consists of 41,387 scaffolds (contigs: N50 = 104.67 Kb; scaffold: N50-518.55 Kb). Benchmarking Universal Single-Copy Orthologs (BUSCO) identified 92.3% of the 3,354 genes in the vertebrata_odb10 database. Approximately 42% of the genome (960Mbp) comprises repeat elements. We predicted 30,138 unique protein-coding sequences (17,737 unique genes) in the genome assembly. Functional annotation found the top Gene Ontology annotations for Biological Processes, Molecular Function, and Cellular Component were regulation, protein, and intracellular, respectively. This assembly will support future macroevolutionary, conservation, and molecular studies of the Cuban crocodile. [ABSTRACT FROM AUTHOR]

Background: Over the last decades, it was subject of many studies to investigate the genomic connection of milk production and health traits in dairy cattle. Thereby, incorporating functional information in genomic analyses has been shown to improve the understanding of biological and molecular mechanisms shaping complex traits and the accuracies of genomic prediction, especially in small populations and across-breed settings. Still, little is known about the contribution of different functional and evolutionary genome partitioning subsets to milk production and dairy health. Thus, we performed a uni- and a bivariate analysis of milk yield (MY) and eight health traits using a set of ~34,497 German Holstein cows with 50K chip genotypes and ~17 million imputed sequence variants divided into 27 subsets depending on their functional and evolutionary annotation. In the bivariate analysis, eight trait-combinations were observed that contrasted MY with each health trait. Two genomic relationship matrices (GRM) were included, one consisting of the 50K chip variants and one consisting of each set of subset variants, to obtain subset heritabilities and genetic correlations. In addition, 50K chip heritabilities and genetic correlations were estimated applying merely the 50K GRM. Results: In general, 50K chip heritabilities were larger than the subset heritabilities. The largest heritabilities were found for MY, which was 0.4358 for the 50K and 0.2757 for the subset heritabilities. Whereas all 50K genetic correlations were negative, subset genetic correlations were both, positive and negative (ranging from -0.9324 between MY and mastitis to 0.6662 between MY and digital dermatitis). The subsets containing variants which were annotated as noncoding related, splice sites, untranslated regions, metabolic quantitative trait loci, and young variants ranked highest in terms of their contribution to the traits' genetic variance. We were able to show that linkage disequilibrium between subset variants and adjacent variants did not cause these subsets' high effect. Conclusion: Our results confirm the connection of milk production and health traits in dairy cattle via the animals' metabolic state. In addition, they highlight the potential of including functional information in genomic analyses, which helps to dissect the extent and direction of the observed traits' connection in more detail. [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]

Objectives: A genome sequence of a threatened species can provide valuable genetic information that is important for improving the conservation strategies. The white-eared night heron (Gorsachius magnificus) is an endangered and poorly known ardeid bird. In order to support future studies on conservation genetics and evolutionary adaptation of this species, we have reported a de novo assembled and annotated whole-genome sequence of the G. magnificus. Data description: The final draft genome assembly of the G. magnificus was 1.19 Gb in size, with a contig N50 of 187.69 kb and a scaffold N50 of 7,338.28 kb. According to BUSCO analysis, the genome assembly contained 97.49% of the 8,338 genes in the Aves (odb10) dataset. Approximately 10.52% of the genome assembly was composed of repetitive sequences. A total of 14,613 protein-coding genes were predicted in the genome assembly, with functional annotations available for 14,611 genes. The genome assembly exhibited a heterozygosity rate of 0.49 heterozygosity per kilobase pair. This draft genome of G. magnificus provides valuable genomic resources for future studies on conservation and evolution. [ABSTRACT FROM AUTHOR]

Background: Evolutionarily conserved in plants, the enzyme D-myo-inositol-3-phosphate synthase (MIPS; EC 5.5.1.4) regulates the initial, rate-limiting reaction in the phytic acid biosynthetic pathway. They are reported to be transcriptional regulators involved in various physiological functions in the plants, growth, and biotic/abiotic stress responses. Even though the genomes of most legumes are fully sequenced and available, an all-inclusive study of the MIPS family members in legumes is still ongoing. Results: We found 24 MIPS genes in ten legumes: Arachis hypogea, Cicer arietinum, Cajanus cajan, Glycine max, Lablab purpureus, Medicago truncatula, Pisum sativum, Phaseolus vulgaris, Trifolium pratense and Vigna unguiculata. The total number of MIPS genes found in each species ranged from two to three. The MIPS genes were classified into five clades based on their evolutionary relationships with Arabidopsis genes. The structural patterns of intron/exon and the protein motifs that were conserved in each gene were highly group-specific. In legumes, MIPS genes were inconsistently distributed across their genomes. A comparison of genomes and gene sequences showed that this family was subjected to purifying selection and the gene expansion in MIPS family in legumes was mainly caused by segmental duplication. Through quantitative PCR, expression patterns of MIPS in response to various abiotic stresses, in the vegetative tissues of various legumes were studied. Expression pattern shows that MIPS genes control the development and differentiation of various organs, and have significant responses to salinity and drought stress. Conclusion: The MIPS genes in the genomes of legumes have been identified, characterized and their expression was analysed. The findings pave way for understanding their molecular functions and evolution, and lead to identify the putative MIPS genes associated with different cell and tissue development. [ABSTRACT FROM AUTHOR]

Objectives: Castanopsis is the third largest genus in the Fagaceae family and is essentially tropical or subtropical in origin. The species in this genus are mainly canopy-dominant trees, and the key components of evergreen broadleaved forests play a crucial role in the maintenance of local biodiversity. Castanopsis chinensis, distributed from South China to Vietnam, is a representative species. It currently suffers from a high disturbance of human activity and climate change. Here, we present its assembled genome to facilitate its preliminary conservation and breeding on the genome level. Data description: The C. chinensis genome was assembled and annotated by Nanopore and MGI whole-genome sequencing and RNA-seq reads using leaf tissues. The assembly was 888,699,661 bp in length, consisting of 133 contigs and a contig N50 of 23,395,510 bp. A completeness assessment of the assembly with Benchmarking Universal Single-Copy Orthologs (BUSCO) indicated a score of 98.3%. Repetitive elements comprised 471,006,885 bp, accounting for 55.9% of the assembled sequences. A total of 51,406 genes that coded for 54,310 proteins were predicted. Multiple databases were used to functionally annotate the protein sequences. [ABSTRACT FROM AUTHOR]

Background: Penicillium funiculosum NCIM1228 is a filamentous fungus that was identified in our laboratory to have high cellulolytic activity. Analysis of its secretome suggested that it responds to different carbon substrates by secreting specific enzymes capable of digesting those substrates. This phenomenon indicated the presence of a regulatory system guiding the expression of these hydrolyzing enzymes. Since transcription factors (TFs) are the key players in regulating the expression of enzymes, this study aimed first to identify the complete repertoire of Carbohydrate Active Enzymes (CAZymes) and TFs coded in its genome. The regulation of CAZymes was then analysed by studying the expression pattern of these CAZymes and TFs in different carbon substrates—Avicel (cellulosic substrate), wheat bran (WB; hemicellulosic substrate), Avicel + wheat bran, pre-treated wheat straw (a potential substrate for lignocellulosic ethanol), and glucose (control). Results: The P. funiculosum NCIM1228 genome was sequenced, and 10,739 genes were identified in its genome. These genes included a total of 298 CAZymes and 451 TF coding genes. A distinct expression pattern of the CAZymes was observed in different carbon substrates tested. Core cellulose hydrolyzing enzymes were highly expressed in the presence of Avicel, while pre-treated wheat straw and Avicel + wheat bran induced a mixture of CAZymes because of their heterogeneous nature. Wheat bran mainly induced hemicellulases, and the least number of CAZymes were expressed in glucose. TFs also exhibited distinct expression patterns in each of the carbon substrates. Though most of these TFs have not been functionally characterized before, homologs of NosA, Fcr1, and ATF21, which have been known to be involved in fruiting body development, protein secretion and stress response, were identified. Conclusions: Overall, the P. funiculosum NCIM1228 genome was sequenced, and the CAZymes and TFs present in its genome were annotated. The expression of the CAZymes and TFs in response to various polymeric sugars present in the lignocellulosic biomass was identified. This work thus provides a comprehensive mapping of transcription factors (TFs) involved in regulating the production of biomass hydrolyzing enzymes. [ABSTRACT FROM AUTHOR]

Background: Visualizing genome coverage is of vital importance to inspect and interpret various next-generation sequencing (NGS) data. Besides genome coverage, genome annotations are also crucial in the visualization. While different NGS data require different annotations, how to visualize genome coverage and add the annotations appropriately and conveniently is challenging. Many tools have been developed to address this issue. However, existing tools are often inflexible, complicated, lack necessary preprocessing steps and annotations, and the figures generated support limited customization. Results: Here, we introduce ggcoverage, an R package to visualize and annotate genome coverage of multi-groups and multi-omics. The input files for ggcoverage can be in BAM, BigWig, BedGraph and TSV formats. For better usability, ggcoverage provides reliable and efficient ways to perform read normalization, consensus peaks generation and track data loading with state-of-the-art tools. ggcoverage provides various available annotations to adapt to different NGS data (e.g. WGS/WES, RNA-seq, ChIP-seq) and all the available annotations can be easily superimposed with ' + '. ggcoverage can generate publication-quality plots and users can customize the plots with ggplot2. In addition, ggcoverage supports the visualization and annotation of protein coverage. Conclusions: ggcoverage provides a flexible, programmable, efficient and user-friendly way to visualize and annotate genome coverage of multi-groups and multi-omics. The ggcoverage package is available at https://github.com/showteeth/ggcoverage under the MIT license, and the vignettes are available at https://showteeth.github.io/ggcoverage/. [ABSTRACT FROM AUTHOR]

Background: The rapid expansion of Whole-Genome Sequencing has revolutionized the fields of clinical and food microbiology. However, its implementation as a routine laboratory technique remains challenging due to the growth of data at a faster rate than can be effectively analyzed and critical gaps in bioinformatics knowledge. Results: To address both issues, CamPype was developed as a new bioinformatics workflow for the genomics analysis of sequencing data of bacteria, especially Campylobacter, which is the main cause of gastroenteritis worldwide making a negative impact on the economy of the public health systems. CamPype allows fully customization of stages to run and tools to use, including read quality control filtering, read contamination, reads extension and assembly, bacterial typing, genome annotation, searching for antibiotic resistance genes, virulence genes and plasmids, pangenome construction and identification of nucleotide variants. All results are processed and resumed in an interactive HTML report for best data visualization and interpretation. Conclusions: The minimal user intervention of CamPype makes of this workflow an attractive resource for microbiology laboratories with no expertise in bioinformatics as a first line method for bacterial typing and epidemiological analyses, that would help to reduce the costs of disease outbreaks, or for comparative genomic analyses. CamPype is publicly available at https://github.com/JoseBarbero/CamPype. [ABSTRACT FROM AUTHOR]

Background: White clover (Trifolium repens L.), an excellent perennial legume forage, is an allotetraploid native to southeastern Europe and southern Asia. It has high nutritional, ecological, genetic breeding, and medicinal values and exhibits excellent resistance to cold, drought, trample, and weed infestation. Thus, white clover is widely planted in Europe, America, and China; however, the lack of reference genome limits its breeding and cultivation. This study generated a white clover de novo genome assembly at the chromosomal level and annotated its components. Results: The PacBio third-generation Hi-Fi assembly and sequencing methods generated a 1096 Mb genome size of T. repens, with contigs of N50 = 14 Mb, scaffolds of N50 = 65 Mb, and BUSCO value of 98.5%. The newly assembled genome has better continuity and integrity than the previously reported white clover reference genome; thus provides important resources for the molecular breeding and evolution of white clover and other forage. Additionally, we annotated 90,128 high-confidence gene models from the genome. White clover was closely related to Trifolium pratense and Trifolium medium but distantly related to Glycine max, Vigna radiata, Medicago truncatula, and Cicer arietinum. The expansion, contraction, and GO functional enrichment analysis of the gene families showed that T. repens gene families were associated with biological processes, molecular function, cellular components, and environmental resistance, which explained its excellent agronomic traits. Conclusions: This study reports a high-quality de novo assembly of white clover genome obtained at the chromosomal level using PacBio Hi-Fi sequencing, a third-generation sequencing. The generated high-quality genome assembly of white clover provides a key basis for accelerating the research and molecular breeding of this important forage crop. The genome is also valuable for future studies on legume forage biology, evolution, and genome-wide mapping of quantitative trait loci associated with the relevant agronomic traits. [ABSTRACT FROM AUTHOR]

Background: Viral genomics and epidemiology have been increasingly important tools for analysing the spread of key pathogens affecting daily lives of individuals worldwide. With the rapidly expanding scale of pathogen genome sequencing efforts for epidemics and outbreaks efficient workflows in extracting genomic information are becoming increasingly important for answering key research questions. Results: Here we present Genofunc, a toolkit offering a range of command line orientated functions for processing of raw virus genome sequences into aligned and annotated data ready for analysis. The tool contains functions such as genome annotation, feature extraction etc. for processing of large genomic datasets both manual or as part of pipeline such as Snakemake or Nextflow ready for down-stream phylogenetic analysis. Originally designed for a large-scale HIV sequencing project, Genofunc has been benchmarked against annotated sequence gene coordinates from the Los Alamos HIV database as validation with downstream phylogenetic analysis result comparable to past literature as case study. Conclusion: Genofunc is implemented fully in Python and licensed under the MIT license. Source code and documentation is available at: https://github.com/xiaoyu518/genofunc. [ABSTRACT FROM AUTHOR]

Objectives: Nepenthes belongs to the monotypic family Nepenthaceae, one of the largest carnivorous plant families. Nepenthes species show impressive adaptive radiation and suffer from being overexploited in nature. Nepenthes mirabilis is the most widely distributed species and the only Nepenthes species that is naturally distributed within China. Herein, we reported the genome and transcriptome assemblies of N. mirabilis. The assemblies will be useful resources for comparative genomics, to understand the adaptation and conservation of carnivorous species. Data description: This work produced ~ 139.5 Gb N. mirabilis whole genome sequencing reads using leaf tissues, and ~ 21.7 Gb and ~ 27.9 Gb of raw RNA-seq reads for its leaves and flowers, respectively. Transcriptome assembly obtained 339,802 transcripts, in which 79,758 open reading frames (ORFs) were identified. Function analysis indicated that these ORFs were mainly associated with proteolysis and DNA integration. The assembled genome was 691,409,685 bp with 159,555 contigs/scaffolds and an N50 of 10,307 bp. The BUSCO assessment of the assembled genome and transcriptome indicated 91.1% and 93.7% completeness, respectively. A total of 42,961 genes were predicted in the genome identified, coding for 45,461 proteins. The predicted genes were annotated using multiple databases, facilitating future functional analyses of them. This is the first genome report on the Nepenthaceae family. [ABSTRACT FROM AUTHOR]

Objectives: Citrus yellow vein clearing virus (CYVCV) is an emerging disease that poses a significant threat to the citrus industry in California. In this study, the viral genomic RNA was isolated from Eureka lemon plants in the greenhouse exhibiting CYVCV symptoms. Subsequently, the corresponding DNA genome amplicon was sequenced and annotated. These efforts expand the genotype database of CYVCV, which aims to enhance detection assays, promote understanding of the virus's genetics and evolution, and support the management of this disease. Data description: In this report, we present the complete genome sequence of the CYVCV California isolate (CA1). The genome was found to be 7,530 bp in length, with a G + C content of 51.7%. The 5′ and 3′ termini were determined using 5′ and 3′ termini rapid amplification of cDNA ends (RACE) systems. Furthermore, our analysis revealed the presence of six open reading frames (ORFs) potentially encoding proteins. All sequence data and annotation have been deposited in GenBank under the accession number OR037276.1. [ABSTRACT FROM AUTHOR]

Background: Massive parallel sequencing technologies have enabled the elucidation of plant phylogenetic relationships from chloroplast genomes at a high pace. These include members of the family Rhamnaceae. The current Rhamnaceae phylogenetic tree is from 13 out of 24 Rhamnaceae chloroplast genomes, and only one chloroplast genome of the genus Ventilago is available. Hence, the phylogenetic relationships in Rhamnaceae remain incomplete, and more representative species are needed. Results: The complete chloroplast genome of Ventilago harmandiana Pierre was outlined using a hybrid assembly of long- and short-read technologies. The accuracy and validity of the final genome were confirmed with PCR amplifications and investigation of coverage depth. Sanger sequencing was used to correct for differences in lengths and nucleotide bases between inverted repeats because of the homopolymers. The phylogenetic trees reconstructed using prevalent methods for phylogenetic inference were topologically similar. The clustering based on codon usage was congruent with the molecular phylogenetic tree. The groups of genera in each tribe were in accordance with tribal classification based on molecular markers. We resolved the phylogenetic relationships among six Hovenia species, three Rhamnus species, and two Ventilago species. Our reconstructed tree provides the most complete and reliable low-level taxonomy to date for the family Rhamnaceae. Similar to other higher plants, the RNA editing mostly resulted in converting serine to leucine. Besides, most genes were subjected to purifying selection. Annotation anomalies, including indel calling errors, unaligned open reading frames of the same gene, inconsistent prediction of intergenic regions, and misannotated genes, were identified in the published chloroplast genomes used in this study. These could be a result of the usual imperfections in computational tools, and/or existing errors in reference genomes. Importantly, these are points of concern with regards to utilizing published chloroplast genomes for comparative genomic analysis. Conclusions: In summary, we successfully demonstrated the use of comprehensive genomic data, including DNA and amino acid sequences, to build a reliable and high-resolution phylogenetic tree for the family Rhamnaceae. Additionally, our study indicates that the revision of genome annotation before comparative genomic analyses is necessary to prevent the propagation of errors and complications in downstream analysis and interpretation. [ABSTRACT FROM AUTHOR]

Plant, animal and protist genomes often contain endogenous viral elements (EVEs), which correspond to partial and sometimes entire viral genomes that have been captured in the genome of their host organism through a variety of integration mechanisms. While the number of sequenced eukaryotic genomes is rapidly increasing, the annotation and characterization of EVEs remains largely overlooked. EVEs that derive from members of the family Caulimoviridae are widespread across tracheophyte plants, and sometimes they occur in very high copy numbers. However, existing programs for annotating repetitive DNA elements in plant genomes are poor at identifying and then classifying these EVEs. Other than accurately annotating plant genomes, there is intrinsic value in a tool that could identify caulimovirid EVEs as they testify to recent or ancient host-virus interactions and provide valuable insights into virus evolution. In response to this research need, we have developed CAULIFINDER, an automated and sensitive annotation software package. CAULIFINDER consists of two complementary workflows, one to reconstruct, annotate and group caulimovirid EVEs in a given plant genome and the second to classify these genetic elements into officially recognized or tentative genera in the Caulimoviridae. We have benchmarked the CAULIFINDER package using the Vitis vinifera reference genome, which contains a rich assortment of caulimovirid EVEs that have previously been characterized using manual methods. The CAULIFINDER package is distributed in the form of a Docker image. [ABSTRACT FROM AUTHOR]

Background: Chrysanthemum seticuspe has emerged as a model plant species of cultivated chrysanthemums, especially for studies involving diploid and self-compatible pure lines (Gojo-0). Its genome was sequenced and assembled into chromosomes. However, the genome annotation of C. seticuspe still needs to be improved to elucidate the complex regulatory networks in this species. Results: In addition to the 74,259 mRNAs annotated in the C. seticuspe genome, we identified 18,265 novel mRNAs, 51,425 novel lncRNAs, 501 novel miRNAs and 22,065 novel siRNAs. Two C-class genes and YABBY family genes were highly expressed in disc florets, while B-class genes were highly expressed in ray florets. A WGCNA was performed to identify the hub lncRNAs and mRNAs in ray floret- and disc floret-specific modules, and CDM19, BBX22, HTH, HSP70 and several lncRNAs were identified. ceRNA and lncNAT networks related to flower development were also constructed, and we found a latent functional lncNAT–mRNA combination, LXLOC_026470 and MIF2. Conclusions: The annotations of mRNAs, lncRNAs and small RNAs in the C. seticuspe genome have been improved. The expression profiles of flower development-related genes, ceRNA networks and lncNAT networks were identified, laying a foundation for elucidating the regulatory mechanisms underlying disc floret and ray floret formation. [ABSTRACT FROM AUTHOR]

Background: Identification of deleterious genetic variants using DNA sequencing data relies on increasingly detailed filtering strategies to isolate the small subset of variants that are more likely to underlie a disease phenotype. Datasets reflecting population allele frequencies of different types of variants serve as powerful filtering tools, especially in the context of rare disease analysis. While such population-scale allele frequency datasets now exist for structural variants (SVs), it remains a challenge to match SV calls between multiple datasets, thereby complicating estimates of a putative SV's population allele frequency. Results: We introduce SVAFotate, a software tool that enables the annotation of SVs with variant allele frequency and related information from existing SV datasets. As a result, VCF files annotated by SVAFotate offer a variety of metrics to aid in the stratification of SVs as common or rare in the broader human population. Conclusions: Here we demonstrate the use of SVAFotate in the classification of SVs with regards to their population frequency and illustrate how SVAFotate's annotations can be used to filter and prioritize SVs. Lastly, we detail how best to utilize these SV annotations in the analysis of genetic variation in studies of rare disease. [ABSTRACT FROM AUTHOR]

Background: Red clover (Trifolium pratense L.) is a diploid perennial temperate legume with 14 chromosomes (2n = 14) native to Europe and West Asia, with high nutritional and economic value. It is a very important forage grass and is widely grown in marine climates, such as the United States and Sweden. Genetic research and molecular breeding are limited by the lack of high-quality reference genomes. In this study, we used Illumina, PacBio HiFi, and Hi-C to obtain a high-quality chromosome-scale red clover genome and used genome annotation results to analyze evolutionary relationships among related species. Results: The red clover genome obtained by PacBio HiFi assembly sequencing was 423 M. The assembly quality was the highest among legume genome assemblies published to date. The contig N50 was 13 Mb, scaffold N50 was 55 Mb, and BUSCO completeness was 97.9%, accounting for 92.8% of the predicted genome. Genome annotation revealed 44,588 gene models with high confidence and 52.81% repetitive elements in red clover genome. Based on a comparison of genome annotation results, red clover was closely related to Trifolium medium and distantly related to Glycine max, Vigna radiata, Medicago truncatula, and Cicer arietinum among legumes. Analyses of gene family expansions and contractions and forward gene selection revealed gene families and genes related to environmental stress resistance and energy metabolism. Conclusions: We report a high-quality de novo genome assembly for the red clover at the chromosome level, with a substantial improvement in assembly quality over those of previously published red clover genomes. These annotated gene models can provide an important resource for molecular genetic breeding and legume evolution studies. Furthermore, we analyzed the evolutionary relationships among red clover and closely related species, providing a basis for evolutionary studies of clover leaf and legumes, genomics analyses of forage grass, the improvement of agronomic traits. [ABSTRACT FROM AUTHOR]

Background: An optimal starting point for relating genome function to organismal biology is a high-quality nuclear genome assembly, and long-read sequencing is revolutionizing the production of this genomic resource in insects. Despite this, nuclear genome assemblies have been under-represented for agricultural insect pests, particularly from the order Coleoptera. Here we present a de novo genome assembly and structural annotation for the coconut rhinoceros beetle, Oryctes rhinoceros (Coleoptera: Scarabaeidae), based on Oxford Nanopore Technologies (ONT) long-read data generated from a wild-caught female, as well as the assembly process that also led to the recovery of the complete circular genome assemblies of the beetle's mitochondrial genome and that of the biocontrol agent, Oryctes rhinoceros nudivirus (OrNV). As an invasive pest of palm trees, O. rhinoceros is undergoing an expansion in its range across the Pacific Islands, requiring new approaches to management that may include strategies facilitated by genome assembly and annotation. Results: High-quality DNA isolated from an adult female was used to create four ONT libraries that were sequenced using four MinION flow cells, producing a total of 27.2 Gb of high-quality long-read sequences. We employed an iterative assembly process and polishing with one lane of high-accuracy Illumina reads, obtaining a final size of the assembly of 377.36 Mb that had high contiguity (fragment N50 length = 12 Mb) and accuracy, as evidenced by the exceptionally high completeness of the benchmarked set of conserved single-copy orthologous genes (BUSCO completeness = 99.1%). These quality metrics place our assembly ahead of the published Coleopteran genomes, including that of an insect model, the red flour beetle (Tribolium castaneum). The structural annotation of the nuclear genome assembly contained a highly-accurate set of 16,371 protein-coding genes, with only 2.8% missing BUSCOs, and the expected number of non-coding RNAs. The number and structure of paralogous genes in a gene family like Sigma GST is lower than in another scarab beetle (Onthophagus taurus), but higher than in the red flour beetle (Tribolium castaneum), which suggests expansion of this GST class in Scarabaeidae. The quality of our gene models was also confirmed with the correct placement of O. rhinoceros among other members of the rhinoceros beetles (subfamily Dynastinae) in a phylogeny based on the sequences of 95 protein-coding genes in 373 beetle species from all major lineages of Coleoptera. Finally, we provide a list of 30 candidate dsRNA targets whose orthologs have been experimentally validated as highly effective targets for RNAi-based control of several beetles. Conclusions: The genomic resources produced in this study form a foundation for further functional genetic research and management programs that may inform the control and surveillance of O. rhinoceros populations, and we demonstrate the efficacy of de novo genome assembly using long-read ONT data from a single field-caught insect. [ABSTRACT FROM AUTHOR]

Background: Paspalum notatum 'Flugge' is a diploid with 20 chromosomes (2n = 20) multi-purpose subtropical herb native to South America and has a high ecological significance. It is currently widely planted in tropical and subtropical regions. Despite the gene pool of P. notatum 'Flugge' being unearthed to a large extent in the past decade, no details about the genomic information of relevant species in Paspalum have been reported. In this study, the complete genome information of P. notatum was established and annotated through sequencing and de novo assembly of its genome. Results: The latest PacBio third-generation HiFi assembly and sequencing revealed that the genome size of P. notatum 'Flugge' is 541 M. The assembly result is the higher index among the genomes of the gramineous family published so far, with a contig N50 = 52Mbp, scaffold N50 = 49Mbp, and BUSCOs = 98.1%, accounting for 98.5% of the estimated genome. Genome annotation revealed 36,511 high-confidence gene models, thus providing an important resource for future molecular breeding and evolutionary research. A comparison of the genome annotation results of P. notatum 'Flugge' with other closely related species revealed that it had a close relationship with Zea mays but not close compared to Brachypodium distachyon, Setaria viridis, Oryza sativa, Puccinellia tenuiflora, Echinochloa crusgalli. An analysis of the expansion and contraction of gene families suggested that P. notatum 'Flugge' contains gene families associated with environmental resistance, increased reproductive ability, and molecular evolution, which explained its excellent agronomic traits. Conclusion: This study is the first to report the high-quality chromosome-scale-based genome of P. notatum 'Flugge' assembled using the latest PacBio third-generation HiFi sequencing reads. The study provides an excellent genetic resource bank for gramineous crops and invaluable perspectives regarding the evolution of gramineous plants. [ABSTRACT FROM AUTHOR]

Objective: Elizabethkingia meningoseptica is a multidrug resistance strain which primarily causes meningitis in neonates and immunocompromised patients. Being a nosocomial infection causing agent, less information is available in literature, specifically, about its genomic makeup and associated features. An attempt is made to study them through bioinformatics tools with respect to compositions, embedded periodicities, open reading frames, origin of replication, phylogeny, orthologous gene clusters analysis and pathways. Results: Complete DNA and protein sequence pertaining to E. meningoseptica were thoroughly analyzed as part of the study. E. meningoseptica G4076 genome showed 7593 ORFs it is GC rich. Fourier based analysis showed the presence of typical three base periodicity at the genome level. Putative origin of replication has been identified. Phylogenetically, E. meningoseptica is relatively closer to E. anophelis compared to other Elizabethkingia species. A total of 2606 COGs were shared by all five Elizabethkingia species. Out of 3391 annotated proteins, we could identify 18 unique ones involved in metabolic pathway of E. meningoseptica and this can be an initiation point for drug designing and development. Our study is novel in the aspect in characterizing and analyzing the whole genome data of E. meningoseptica. [ABSTRACT FROM AUTHOR]

Background: BRAKER is a suite of automatic pipelines, BRAKER1 and BRAKER2, for the accurate annotation of protein-coding genes in eukaryotic genomes. Each pipeline trains statistical models of protein-coding genes based on provided evidence and, then predicts protein-coding genes in genomic sequences using both the extrinsic evidence and statistical models. For training and prediction, BRAKER1 and BRAKER2 incorporate complementary extrinsic evidence: BRAKER1 uses only RNA-seq data while BRAKER2 uses only a database of cross-species proteins. The BRAKER suite has so far not been able to reliably exceed the accuracy of BRAKER1 and BRAKER2 when incorporating both types of evidence simultaneously. Currently, for a novel genome project where both RNA-seq and protein data are available, the best option is to run both pipelines independently, and to pick one, likely better output. Therefore, one or another type of the extrinsic evidence would remain unexploited. Results: We present TSEBRA, a software that selects gene predictions (transcripts) from the sets generated by BRAKER1 and BRAKER2. TSEBRA uses a set of rules to compare scores of overlapping transcripts based on their support by RNA-seq and homologous protein evidence. We show in computational experiments on genomes of 11 species that TSEBRA achieves higher accuracy than either BRAKER1 or BRAKER2 running alone and that TSEBRA compares favorably with the combiner tool EVidenceModeler. Conclusion: TSEBRA is an easy-to-use and fast software tool. It can be used in concert with the BRAKER pipeline to generate a gene prediction set supported by both RNA-seq and homologous protein evidence. [ABSTRACT FROM AUTHOR]

Background: Functional genomic analyses rely on high-quality genome assemblies and annotations. Highly contiguous genome assemblies have become available for a variety of species, but accurate and complete annotation of gene models, inclusive of alternative splice isoforms and transcription start and termination sites, remains difficult with traditional approaches. Results: Here, we utilized full-length isoform sequencing (Iso-Seq), a long-read RNA sequencing technology, to obtain a comprehensive annotation of the transcriptome of the ant Harpegnathos saltator. The improved genome annotations include additional splice isoforms and extended 3′ untranslated regions for more than 4000 genes. Reanalysis of RNA-seq experiments using these annotations revealed several genes with caste-specific differential expression and tissue- or caste-specific splicing patterns that were missed in previous analyses. The extended 3′ untranslated regions afforded great improvements in the analysis of existing single-cell RNA-seq data, resulting in the recovery of the transcriptomes of 18% more cells. The deeper single-cell transcriptomes obtained with these new annotations allowed us to identify additional markers for several cell types in the ant brain, as well as genes differentially expressed across castes in specific cell types. Conclusions: Our results demonstrate that Iso-Seq is an efficient and effective approach to improve genome annotations and maximize the amount of information that can be obtained from existing and future genomic datasets in Harpegnathos and other organisms. [ABSTRACT FROM AUTHOR]

Background: Ab initio prediction of splice sites is an essential step in eukaryotic genome annotation. Recent predictors have exploited Deep Learning algorithms and reliable gene structures from model organisms. However, Deep Learning methods for non-model organisms are lacking. Results: We developed Spliceator to predict splice sites in a wide range of species, including model and non-model organisms. Spliceator uses a convolutional neural network and is trained on carefully validated data from over 100 organisms. We show that Spliceator achieves consistently high accuracy (89–92%) compared to existing methods on independent benchmarks from human, fish, fly, worm, plant and protist organisms. Conclusions: Spliceator is a new Deep Learning method trained on high-quality data, which can be used to predict splice sites in diverse organisms, ranging from human to protists, with consistently high accuracy. [ABSTRACT FROM AUTHOR]

Background: Functional genome annotation is the process of labelling functional genomic regions with descriptive information. Manual curation can produce higher quality genome annotations than fully automated methods. Manual annotation efforts are time-consuming and complex; however, software can help reduce these drawbacks. Results: We created Manual Annotation Studio (MAS) to improve the efficiency of the process of manual functional annotation prokaryotic and viral genomes. MAS allows users to upload unannotated genomes, provides an interface to edit and upload annotations, tracks annotation history and progress, and saves data to a relational database. MAS provides users with pertinent information through a simple point and click interface to execute and visualize results for multiple homology search tools (blastp, rpsblast, and HHsearch) against multiple databases (Swiss-Prot, nr, CDD, PDB, and an internally generated database). MAS was designed to accept connections over the local area network (LAN) of a lab or organization so multiple users can access it simultaneously. MAS can take advantage of high-performance computing (HPC) clusters by interfacing with SGE or SLURM and data can be exported from MAS in a variety of formats (FASTA, GenBank, GFF, and excel). Conclusions: MAS streamlines and provides structure to manual functional annotation projects. MAS enhances the ability of users to generate, interpret, and compare results from multiple tools. The structure that MAS provides can improve project organization and reduce annotation errors. MAS is ideal for team-based annotation projects because it facilitates collaboration. [ABSTRACT FROM AUTHOR]

Background: The insertion sequence elements (IS elements) represent the smallest and the most abundant mobile elements in prokaryotic genomes. It has been shown that they play a significant role in genome organization and evolution. To better understand their function in the host genome, it is desirable to have an effective detection and annotation tool. This need becomes even more crucial when considering rapid-growing genomic and metagenomic data. The existing tools for IS elements detection and annotation are usually based on comparing sequence similarity with a database of known IS families. Thus, they have limited ability to discover distant and putative novel IS elements. Results: In this paper, we present digIS, a software tool based on profile hidden Markov models assembled from catalytic domains of transposases. It shows a very good performance in detecting known IS elements when tested on datasets with manually curated annotation. The main contribution of digIS is in its ability to detect distant and putative novel IS elements while maintaining a moderate level of false positives. In this category it outperforms existing tools, especially when tested on large datasets of archaeal and bacterial genomes. Conclusion: We provide digIS, a software tool using a novel approach based on manually curated profile hidden Markov models, which is able to detect distant and putative novel IS elements. Although digIS can find known IS elements as well, we expect it to be used primarily by scientists interested in finding novel IS elements. The tool is available at https://github.com/janka2012/digIS. [ABSTRACT FROM AUTHOR]

Background: Spliced leader (SL) trans-splicing replaces the 5′ end of pre-mRNAs with the spliced leader, an exon derived from a specialised non-coding RNA originating from elsewhere in the genome. This process is essential for resolving polycistronic pre-mRNAs produced by eukaryotic operons into monocistronic transcripts. SL trans-splicing and operons may have independently evolved multiple times throughout Eukarya, yet our understanding of these phenomena is limited to only a few well-characterised organisms, most notably C. elegans and trypanosomes. The primary barrier to systematic discovery and characterisation of SL trans-splicing and operons is the lack of computational tools for exploiting the surge of transcriptomic and genomic resources for a wide range of eukaryotes. Results: Here we present two novel pipelines that automate the discovery of SLs and the prediction of operons in eukaryotic genomes from RNA-Seq data. SLIDR assembles putative SLs from 5′ read tails present after read alignment to a reference genome or transcriptome, which are then verified by interrogating corresponding SL RNA genes for sequence motifs expected in bona fide SL RNA molecules. SLOPPR identifies RNA-Seq reads that contain a given 5′ SL sequence, quantifies genome-wide SL trans-splicing events and predicts operons via distinct patterns of SL trans-splicing events across adjacent genes. We tested both pipelines with organisms known to carry out SL trans-splicing and organise their genes into operons, and demonstrate that (1) SLIDR correctly detects expected SLs and often discovers novel SL variants; (2) SLOPPR correctly identifies functionally specialised SLs, correctly predicts known operons and detects plausible novel operons. Conclusions: SLIDR and SLOPPR are flexible tools that will accelerate research into the evolutionary dynamics of SL trans-splicing and operons throughout Eukarya and improve gene discovery and annotation for a wide range of eukaryotic genomes. Both pipelines are implemented in Bash and R and are built upon readily available software commonly installed on most bioinformatics servers. Biological insight can be gleaned even from sparse, low-coverage datasets, implying that an untapped wealth of information can be retrieved from existing RNA-Seq datasets as well as from novel full-isoform sequencing protocols as they become more widely available. [ABSTRACT FROM AUTHOR]

Objectives: Kazakhstan is a Central Asian crossroad of European and Asian populations situated along the way of the Great Silk Way. The territory of Kazakhstan has historically been inhabited by nomadic tribes and today is the multi-ethnic country with the dominant Kazakh ethnic group. We sequenced and analyzed the whole-genomes of five ethnic healthy Kazakh individuals with high coverage using next-generation sequencing platform. This whole-genome sequence data of healthy Kazakh individuals can be a valuable reference for biomedical studies investigating disease associations and population-wide genomic studies of ethnically diverse Central Asian region. Data description: Blood samples have been collected from five ethnic healthy Kazakh individuals living in Kazakhstan. The genomic DNA was extracted from blood and sequenced. Sequencing was performed on Illumina HiSeq2000 next-generation sequencing platform. We sequenced and analyzed the whole-genomes of ethnic Kazakh individuals with the coverage ranging from 26 to 32X. Ranging from 98.85 to 99.58% base pairs were totally mapped and aligned on the human reference genome GRCh37 hg19. Het/Hom and Ts/Tv ratios for each whole genome ranged from 1.35 to 1.49 and from 2.07 to 2.08, respectively. Sequencing data are available in the National Center for Biotechnology Information SRA database under the accession number PRJNA374772. [ABSTRACT FROM AUTHOR]

Background: High-throughput sequencing has increased the number of available microbial genomes recovered from isolates, single cells, and metagenomes. Accordingly, fast and comprehensive functional gene annotation pipelines are needed to analyze and compare these genomes. Although several approaches exist for genome annotation, these are typically not designed for easy incorporation into analysis pipelines, do not combine results from different annotation databases or offer easy-to-use summaries of metabolic reconstructions, and typically require large amounts of computing power for high-throughput analysis not available to the average user. Results: Here, we introduce MicrobeAnnotator, a fully automated, easy-to-use pipeline for the comprehensive functional annotation of microbial genomes that combines results from several reference protein databases and returns the matching annotations together with key metadata such as the interlinked identifiers of matching reference proteins from multiple databases [KEGG Orthology (KO), Enzyme Commission (E.C.), Gene Ontology (GO), Pfam, and InterPro]. Further, the functional annotations are summarized into Kyoto Encyclopedia of Genes and Genomes (KEGG) modules as part of a graphical output (heatmap) that allows the user to quickly detect differences among (multiple) query genomes and cluster the genomes based on their metabolic similarity. MicrobeAnnotator is implemented in Python 3 and is freely available under an open-source Artistic License 2.0 from https://github.com/cruizperez/MicrobeAnnotator. Conclusions: We demonstrated the capabilities of MicrobeAnnotator by annotating 100 Escherichia coli and 78 environmental Candidate Phyla Radiation (CPR) bacterial genomes and comparing the results to those of other popular tools. We showed that the use of multiple annotation databases allows MicrobeAnnotator to recover more annotations per genome compared to faster tools that use reduced databases and is computationally efficient for use in personal computers. The output of MicrobeAnnotator can be easily incorporated into other analysis pipelines while the results of other annotation tools can be seemingly incorporated into MicrobeAnnotator to generate summary plots. [ABSTRACT FROM AUTHOR]

Background: Recent advances in sequencing technologies have led to an explosion in the number of genomes available, but accurate genome annotation remains a major challenge. The prediction of protein-coding genes in eukaryotic genomes is especially problematic, due to their complex exon–intron structures. Even the best eukaryotic gene prediction algorithms can make serious errors that will significantly affect subsequent analyses. Results: We first investigated the prevalence of gene prediction errors in a large set of 176,478 proteins from ten primate proteomes available in public databases. Using the well-studied human proteins as a reference, a total of 82,305 potential errors were detected, including 44,001 deletions, 27,289 insertions and 11,015 mismatched segments where part of the correct protein sequence is replaced with an alternative erroneous sequence. We then focused on the mismatched sequence errors that cause particular problems for downstream applications. A detailed characterization allowed us to identify the potential causes for the gene misprediction in approximately half (5446) of these cases. As a proof-of-concept, we also developed a simple method which allowed us to propose improved sequences for 603 primate proteins. Conclusions: Gene prediction errors in primate proteomes affect up to 50% of the sequences. Major causes of errors include undetermined genome regions, genome sequencing or assembly issues, and limitations in the models used to represent gene exon–intron structures. Nevertheless, existing genome sequences can still be exploited to improve protein sequence quality. Perspectives of the work include the characterization of other types of gene prediction errors, as well as the development of a more comprehensive algorithm for protein sequence error correction. [ABSTRACT FROM AUTHOR]

Background: The rnpB gene encodes for an essential catalytic RNA (RNase P). Like other essential RNAs, RNase P's sequence is highly variable. However, unlike other essential RNAs (i.e. tRNA, 16 S, 6 S,...) its structure is also variable with at least 5 distinct structure types observed in prokaryotes. This structural variability makes it labor intensive and challenging to create and maintain covariance models for the detection of RNase P RNA in genomic and metagenomic sequences. The lack of a facile and rapid annotation algorithm has led to the rnpB gene being the most grossly under annotated essential gene in completed prokaryotic genomes with only a 24% annotation rate. Here we describe the coupling of the largest RNase P RNA database with the local alignment scoring algorithm to create the most sensitive and rapid prokaryote rnpB gene identification and annotation algorithm to date. Results: Of the 2772 completed microbial genomes downloaded from GenBank only 665 genomes had an annotated rnpB gene. We applied P Finder to these genomes and were able to identify 2733 or nearly 99% of the 2772 microbial genomes examined. From these results four new rnpB genes that encode the minimal T-type P RNase P RNAs were identified computationally for the first time. In addition, only the second C-type RNase P RNA was identified in Sphaerobacter thermophilus. Of special note, no RNase P RNAs were detected in several obligate endosymbionts of sap sucking insects suggesting a novel evolutionary adaptation. Conclusions: The coupling of the largest RNase P RNA database and associated structure class identification with the P Finder algorithm is both sensitive and rapid, yielding high quality results to aid researchers annotating either genomic or metagenomic data. It is the only algorithm to date that can identify challenging RNAse P classes such as C-type and the minimal T-type RNase P RNAs. P Finder is written in C# and has a user-friendly GUI that can run on multiple 64-bit windows platforms (Windows Vista/7/8/10). P Finder is free available for download at https://github.com/JChristopherEllis/P-Finder as well as a small sample RNase P RNA file for testing. [ABSTRACT FROM AUTHOR]

Background: The draft genome assemblies produced by new sequencing technologies present important challenges for automatic gene prediction pipelines, leading to less accurate gene models. New benchmark methods are needed to evaluate the accuracy of gene prediction methods in the face of incomplete genome assemblies, low genome coverage and quality, complex gene structures, or a lack of suitable sequences for evidence-based annotations. Results: We describe the construction of a new benchmark, called G3PO (benchmark for Gene and Protein Prediction PrOgrams), designed to represent many of the typical challenges faced by current genome annotation projects. The benchmark is based on a carefully validated and curated set of real eukaryotic genes from 147 phylogenetically disperse organisms, and a number of test sets are defined to evaluate the effects of different features, including genome sequence quality, gene structure complexity, protein length, etc. We used the benchmark to perform an independent comparative analysis of the most widely used ab initio gene prediction programs and identified the main strengths and weaknesses of the programs. More importantly, we highlight a number of features that could be exploited in order to improve the accuracy of current prediction tools. Conclusions: The experiments showed that ab initio gene structure prediction is a very challenging task, which should be further investigated. We believe that the baseline results associated with the complex gene test sets in G3PO provide useful guidelines for future studies. [ABSTRACT FROM AUTHOR]

Draft genomes of the fungal species Fusarium xylarioides, Teratosphaeria gauchensis and T. zuluensis are presented. In addition an annotation of the genome of Ceratocystis fimbriata is presented. Overall these genomes provide a valuable resource for understanding the molecular processes underlying pathogenicity and potential management strategies of these economically important fungi. [ABSTRACT FROM AUTHOR]

Background: The advent of high-throughput experimental techniques paved the way to genome-wide computational analysis and predictive annotation studies. When considering the joint annotation of a large set of related entities, like all proteins of a certain genome, many candidate annotations could be inconsistent, or very unlikely, given the existing knowledge. A sound predictive framework capable of accounting for this type of constraints in making predictions could substantially contribute to the quality of machine-generated annotations at a genomic scale. Results: We present Ocelot, a predictive pipeline which simultaneously addresses functional and interaction annotation of all proteins of a given genome. The system combines sequence-based predictors for functional and protein-protein interaction (PPI) prediction with a consistency layer enforcing (soft) constraints as fuzzy logic rules. The enforced rules represent the available prior knowledge about the classification task, including taxonomic constraints over each GO hierarchy (e.g. a protein labeled with a GO term should also be labeled with all ancestor terms) as well as rules combining interaction and function prediction. An extensive experimental evaluation on the Yeast genome shows that the integration of prior knowledge via rules substantially improves the quality of the predictions. The system largely outperforms GoFDR, the only high-ranking system at the last CAFA challenge with a readily available implementation, when GoFDR is given access to intra-genome information only (as Ocelot), and has comparable or better results (depending on the hierarchy and performance measure) when GoFDR is allowed to use information from other genomes. Our system also compares favorably to recent methods based on deep learning. [ABSTRACT FROM AUTHOR]

Background: Methicillin-resistant Staphylococcus aureus (MRSA) is known as a leading cause of morbidity and mortality. Investigation of the MRSA's virulence and resistance mechanisms is a continuing concern toward controlling such burdens through using high throughput whole Genome Sequencing (WGS) and molecular diagnostic assays. The objective of the present study is to perform whole-genome sequencing of MRSA isolated from Sudan using Illumina Next Generation Sequencing (NGS) platform. Results: The genome of MRSA strain SO-1977 consists of 2,827,644 bp with 32.8% G + C, 59 RNAs and 2629 predicted coding sequences (CDSs). The genome has 26 systems, one of which is the major class in the disease virulence and defence. A total of 83 genes were annotated to virulence disease and defence category some of these genes coding as functional proteins. Based on genome analysis, it is speculated that the SO-1977 strain has resistant genes to Teicoplanin, Fluoroquinolones, Quinolone, Cephamycins, Tetracycline, Acriflavin and Carbapenems. The results revealed that the SO-1977, strain isolated from Sudan has a wide range of antibiotic resistance compared to related strains. Conclusion: The study reports for the first time the whole genome sequence of Sudan MRSA isolates. The release of the genome sequence of the strain SO-1977 will avail MRSA in public databases for further investigations on the evolution of resistant mechanism and dissemination of the -resistant genes of MRSA. [ABSTRACT FROM AUTHOR]

Background: Accurate structural annotation of genomes is still a challenge, despite the progress made over the past decade. The prediction of gene structure remains difficult, especially for eukaryotic species, and is often erroneous and incomplete. We used a proteogenomics strategy, taking advantage of the combination of proteomics datasets and bioinformatics tools, to identify novel protein coding-genes and splice isoforms, assign correct start sites, and validate predicted exons and genes. Results: Our proteogenomics workflow, Peptimapper, was applied to the genome annotation of Ectocarpus sp., a key reference genome for both the brown algal lineage and stramenopiles. We generated proteomics data from various life cycle stages of Ectocarpus sp. strains and sub-cellular fractions using a shotgun approach. First, we directly generated peptide sequence tags (PSTs) from the proteomics data. Second, we mapped PSTs onto the translated genomic sequence. Closely located hits (i.e., PSTs locations on the genome) were then clustered to detect potential coding regions based on parameters optimized for the organism. Third, we evaluated each cluster and compared it to gene predictions from existing conventional genome annotation approaches. Finally, we integrated cluster locations into GFF files to use a genome viewer. We identified two potential novel genes, a ribosomal protein L22 and an aryl sulfotransferase and corrected the gene structure of a dihydrolipoamide acetyltransferase. We experimentally validated the results by RT-PCR and using transcriptomics data. Conclusions: Peptimapper is a complementary tool for the expert annotation of genomes. It is suitable for any organism and is distributed through a Docker image available on two public bioinformatics docker repositories: Docker Hub and BioShaDock. This workflow is also accessible through the Galaxy framework and for use by non-computer scientists at https://galaxy.protim.eu. Data are available via ProteomeXchange under identifier PXD010618. [ABSTRACT FROM AUTHOR]

Background: The blat is a widely used sequence alignment tool. It is especially useful for aligning long sequences and gapped mapping, which cannot be performed properly by other fast sequence mappers designed for short reads. However, the blat tool is single threaded and when used to map whole genome or whole transcriptome sequences to reference genomes this program can take days to finish, making it unsuitable for large scale sequencing projects and iterative analysis. Here, we present pblat (parallel blat), a parallelized blat algorithm with multithread and cluster computing support, which functions to rapidly fine map large scale DNA/RNA sequences against genomes. Results: The pblat algorithm takes advantage of modern multicore processors and significantly reduces the run time with the number of threads used. pblat utilizes almost equal amount of memory as when running blat. The results generated by pblat are identical with those generated by blat. The pblat tool is easy to install and can run on Linux and Mac OS systems. In addition, we provide a cluster version of pblat (pblat-cluster) running on computing clusters with MPI support. Conclusion: pblat is open source and free available for non-commercial users. It is easy to install and easy to use. pblat and pblat-cluster would facilitate the high-throughput mapping of large scale genomic and transcript sequences to reference genomes with both high speed and high precision. [ABSTRACT FROM AUTHOR]

Background: Genome-scale metabolic modeling is a cornerstone of systems biology analysis of microbial organisms and communities, yet these genome-scale modeling efforts are invariably based on incomplete functional annotations. Annotated genomes typically contain 30–50% of genes without functional annotation, severely limiting our knowledge of the "parts lists" that the organisms have at their disposal. These incomplete annotations may be sufficient to derive a model of a core set of well-studied metabolic pathways that support growth in pure culture. However, pathways important for growth on unusual metabolites exchanged in complex microbial communities are often less understood, resulting in missing functional annotations in newly sequenced genomes. Results: Here, we present results on a comprehensive reannotation of 27 bacterial reference genomes, focusing on enzymes with EC numbers annotated by KEGG, RAST, EFICAz, and the BRENDA enzyme database, and on membrane transport annotations by TransportDB, KEGG and RAST. Our analysis shows that annotation using multiple tools can result in a drastically larger metabolic network reconstruction, adding on average 40% more EC numbers, 3–8 times more substrate-specific transporters, and 37% more metabolic genes. These results are even more pronounced for bacterial species that are phylogenetically distant from well-studied model organisms such as E. coli. Conclusions: Metabolic annotations are often incomplete and inconsistent. Combining multiple functional annotation tools can greatly improve genome coverage and metabolic network size, especially for non-model organisms and non-core pathways. [ABSTRACT FROM AUTHOR]

Annotation of plant genomes is still a challenging task due to the abundance of repetitive sequences, especially long terminal repeat (LTR) retrotransposons. LTR_FINDER is a widely used program for the identification of LTR retrotransposons but its application on large genomes is hindered by its single-threaded processes. Here we report an accessory program that allows parallel operation of LTR_FINDER, resulting in up to 8500X faster identification of LTR elements. It takes only 72 min to process the 14.5 Gb bread wheat (Triticum aestivum) genome in comparison to 1.16 years required by the original sequential version. LTR_FINDER_parallel is freely available at https://github.com/oushujun/LTR_FINDER_parallel. [ABSTRACT FROM AUTHOR]

Background: Predicting the deleteriousness of observed genomic variants has taken a step forward with the introduction of the Combined Annotation Dependent Depletion (CADD) approach, which trains a classifier on the wealth of available human genomic information. This raises the question whether it can be done with less data for non-human species. Here, we investigate the prerequisites to construct a CADD-based model for a non-human species. Results: Performance of the mouse model is competitive with that of the human CADD model and better than established methods like PhastCons conservation scores and SIFT. Like in the human case, performance varies for different genomic regions and is best for coding regions. We also show the benefits of generating a species-specific model over lifting variants to a different species or applying a generic model. With fewer genomic annotations, performance on the test set as well as on the three validation sets is still good. Conclusions: It is feasible to construct species-specific CADD models even when annotations such as epigenetic markers are not available. The minimal requirement for these models is the availability of a set of genomes of closely related species that can be used to infer an ancestor genome and substitution rates for the data generation. [ABSTRACT FROM AUTHOR]

Background: Genome annotation is of key importance in many research questions. The identification of protein-coding genes is often based on transcriptome sequencing data, ab-initio or homology-based prediction. Recently, it was demonstrated that intron position conservation improves homology-based gene prediction, and that experimental data improves ab-initio gene prediction. Results: Here, we present an extension of the gene prediction program GeMoMa that utilizes amino acid sequence conservation, intron position conservation and optionally RNA-seq data for homology-based gene prediction. We show on published benchmark data for plants, animals and fungi that GeMoMa performs better than the gene prediction programs BRAKER1, MAKER2, and CodingQuarry, and purely RNA-seq-based pipelines for transcript identification. In addition, we demonstrate that using multiple reference organisms may help to further improve the performance of GeMoMa. Finally, we apply GeMoMa to four nematode species and to the recently published barley reference genome indicating that current annotations of protein-coding genes may be refined using GeMoMa predictions. Conclusions: GeMoMa might be of great utility for annotating newly sequenced genomes but also for finding homologs of a specific gene or gene family. GeMoMa has been published under GNU GPL3 and is freely available at http://www.jstacs.de/index.php/GeMoMa. [ABSTRACT FROM AUTHOR]

Background: The accurate determination of the genomic coordinates for a given gene – its gene model – is of vital importance to the utility of its annotation, and the accuracy of bioinformatic analyses derived from it. Currently-available methods of computational gene prediction, while on the whole successful, frequently disagree on the model for a given predicted gene, with some or all of the variant gene models often failing to match the biologically observed structure. Many prediction methods can be bolstered by using experimental data such as RNA-seq. However, these resources are not always available, and rarely give a comprehensive portrait of an organism’s transcriptome due to temporal and tissue-specific expression profiles. Results: Orthology between genes provides evolutionary evidence to guide the construction of gene models. OMGene (Optimise My Gene) aims to improve gene model accuracy in the absence of experimental data by optimising the consistency of multiple sequence alignments of orthologous genes from multiple species. Using RNA-seq data sets from plants, mammals, and fungi, considering intron/exon junction representation and exon coverage, and assessing the intra-orthogroup consistency of subcellular localisation predictions, we demonstrate the utility of OMGene for improving gene models in annotated genomes. Conclusions: We show that significant improvements in the accuracy of gene model annotations can be made, both in established and in de novo annotated genomes, by leveraging information from multiple species. [ABSTRACT FROM AUTHOR]

Background: High quality functional annotation is essential for understanding the phenotypic consequences encoded in a genome. Despite improvements in bioinformatics methods, millions of sequences in databanks are not assigned reliable functions. The curation of protein functions in the context of biological processes is a way to evaluate and improve their annotation. Results: We developed an expert system using paraconsistent logic, named GROOLS (Genomic Rule Object-Oriented Logic System), that evaluates the completeness and the consistency of predicted functions through biological processes like metabolic pathways. Using a generic and hierarchical representation of knowledge, biological processes are modeled in a graph from which observations (i.e. predictions and expectations) are propagated by rules. At the end of the reasoning, conclusions are assigned to biological process components and highlight uncertainties and inconsistencies. Results on 14 microbial organisms are presented. Conclusions: GROOLS software is designed to evaluate the overall accuracy of functional unit and pathway predictions according to organism experimental data like growth phenotypes. It assists biocurators in the functional annotation of proteins by focusing on missing or contradictory observations. [ABSTRACT FROM AUTHOR]