Structural Annotation Research Articles

DbAMP 3.0: updated resource of antimicrobial activity and structural annotation of peptides in the post-pandemic era.

Antimicrobial resistance is one of the most urgent global health threats, especially in the post-pandemic era. Antimicrobial peptides (AMPs) offer a promising alternative to traditional antibiotics, driving growing interest in recent years. dbAMP is a comprehensive database offering extensive annotations on AMPs, including sequence information, functional activity data, physicochemical properties and structural annotations. In this update, dbAMP has curated data from over 5200 publications, encompassing 33,065 AMPs and 2453 antimicrobial proteins from 3534 organisms. Additionally, dbAMP utilizes ESMFold to determine the three-dimensional structures of AMPs, providing over 30,000 structural annotations that facilitate structure-based functional insights for clinical drug development. Furthermore, dbAMP employs molecular docking techniques, providing over 100 docked complexes that contribute useful insights into the potential mechanisms of AMPs. The toxicity and stability of AMPs are critical factors in assessing their potential as clinical drugs. The updated dbAMP introduced an efficient tool for evaluating the hemolytic toxicity and half-life of AMPs, alongside an AMP optimization platform for designing AMPs with high antimicrobial activity, reduced toxicity and increased stability. The updated dbAMP is freely accessible at https://awi.cuhk.edu.cn/dbAMP/. Overall, dbAMP represents a comprehensive and essential resource for AMP analysis and design, poised to advance antimicrobial strategies in the post-pandemic era.

DNAproDB: an updated database for the automated and interactive analysis of protein-DNA complexes.

DNAproDB (https://dnaprodb.usc.edu/) is a database, visualization tool, and processing pipeline for analyzing structural features of protein-DNA interactions. Here, we present a substantially updated version of the database through additional structural annotations, search, and user interface functionalities. The update expands the number of pre-analyzed protein-DNA structures, which are automatically updated weekly. The analysis pipeline identifies water-mediated hydrogen bonds that are incorporated into the visualizations of protein-DNA complexes. Tertiary structure-aware nucleotide layouts are now available. New file formats and external database annotations are supported. The website has been redesigned, and interacting with graphs and data is more intuitive. We also present a statistical analysis on the updated collection of structures revealing salient patterns in protein-DNA interactions.

CMSSP: A Contrastive Mass Spectra-Structure Pretraining Model for Metabolite Identification.

A pivotal challenge in metabolite research is the structural annotation of metabolites from tandem mass spectrometry (MS/MS) data. The integration of artificial intelligence (AI) has revolutionized the interpretation of MS data, facilitating the identification of elusive metabolites within the metabolomics landscape. Innovative methodologies are primarily focusing on transforming MS/MS spectra or molecular structures into a unified modality to enable similarity-based comparison and interpretation. In this work, we present CMSSP, a novel Contrastive Mass Spectra-Structure Pretraining framework designed for metabolite annotation. The primary objective of CMSSP is to establish a representation space that facilitates a direct comparison between MS/MS spectra and molecular structures, transcending the limitations of distinct modalities. The evaluation on two benchmark test sets demonstrates the efficacy of the approach. CMSSP achieved a remarkable enhancement in annotation accuracy, outperforming the state-of-the-art methods by a significant margin. Specifically, it improved the top-1 accuracy by 30% on the CASMI 2017 data set and realized a 16% increase in top-10 accuracy on an independent test set. Moreover, the model displayed superior identification accuracy across all seven chemical categories, showcasing its robustness and versatility. Finally, the MS/MS data of 30 metabolites from Glycyrrhiza glabra were analyzed, achieving top-1 and top-3 accuracies of 86.7 and 100%, respectively. The CMSSP model serves as a potent tool for the dissection and interpretation of intricate MS/MS data, propelling the field toward more accurate and efficient metabolite annotation. This not only augments the analytical capabilities of metabolomics but also paves the way for future discoveries in understanding of complex biological systems.

Chromosome-level genome assembly of the sacoglossan sea slug Elysia timida (Risso, 1818)

BackgroundSequencing and annotating genomes of non-model organisms helps to understand genome architecture, the genetic processes underlying species traits, and how these genes have evolved in closely-related taxa, among many other biological processes. However, many metazoan groups, such as the extremely diverse molluscs, are still underrepresented in the number of sequenced and annotated genomes. Although sequencing techniques have recently improved in quality and quantity, molluscs are still neglected due to difficulties in applying standardized protocols for obtaining genomic data.ResultsIn this study, we present the chromosome-level genome assembly and annotation of the sacoglossan sea slug species Elysia timida, known for its ability to store the chloroplasts of its food algae. In particular, by optimizing the long-read and chromosome conformation capture library preparations, the genome assembly was performed using PacBio HiFi and Arima HiC data. The scaffold and contig N50s, at 41.8 Mb and 1.92 Mb, respectively, are approximately 30-fold and fourfold higher compared to other published sacoglossan genome assemblies. Structural annotation resulted in 19,904 protein-coding genes, which are more contiguous and complete compared to publicly available annotations of Sacoglossa with respect to metazoan BUSCOs. We found no evidence for horizontal gene transfer (HGT), i.e. no photosynthetic genes encoded in the sacoglossan nucleus genome. However, we detected genes encoding polyketide synthases in E. timida, indicating that polypropionates are produced. HPLC–MS/MS analysis confirmed the presence of a large number of polypropionates, including known and yet uncharacterised compounds.ConclusionsWe can show that our methodological approach helps to obtain a high-quality genome assembly even for a "difficult-to-sequence" organism, which may facilitate genome sequencing in molluscs. This will enable a better understanding of complex biological processes in molluscs, such as functional kleptoplasty in Sacoglossa, by significantly improving the quality of genome assemblies and annotations.

A Chromosome-Scale and Annotated Reference Genome Assembly of Plecia longiforceps Duda, 1934 (Diptera: Bibionidae).

Urbanization is a leading factor effecting global biodiversity, driving rapid evolutionary processes in the local biota. Species that adapt and proliferate in city environments can become pests, with human activities facilitating their dispersal and excessive outbreaks. Here we present the first genome data of Plecia longiforceps, a lovebug pest in Eastern Asia with intensive aggregations recently occurring in the Seoul Metropolitan Area of Korea. PacBio HiFi and ONT Pore-C sequencing data were used to construct a highly continuous assembly with a total size of 707 Mb and 8 major pseudochromosomes, its integrity supported by the N50 length of 98.1 Mb and 96.8% BUSCO completeness. Structural and functional annotation using transcriptome data and ab initio predictions revealed a high proportion (69.3%) of repeat sequences, and synteny analysis with Bibio marci showed high levels of genomic collinearity. The genome will serve as an essential resource for both population genomics and molecular research on lovebug dispersal and outbreaks, and also implement studies on the eco-evolutionary processes of insects in urbanizing habitats.

Chemical Exposomics in Human Plasma by Lipid Removal and Large-Volume Injection Gas Chromatography-High-Resolution Mass Spectrometry.

For comprehensive chemical exposomics in blood, analytical workflows are evolving through advances in sample preparation and instrumental methods. We hypothesized that gas chromatography-high-resolution mass spectrometry (GC-HRMS) workflows could be enhanced by minimizing lipid coextractives, thereby enabling larger injection volumes and lower matrix interference for improved target sensitivity and nontarget molecular discovery. A simple protocol was developed for small plasma volumes (100-200 μL) by using isohexane (H) to extract supernatants of acetonitrile-plasma (A-P). The HA-P method was quantitative for a wide range of hydrophobic multiclass target analytes (i.e., log Kow > 3.0), and the extracts were free of major lipids, thereby enabling robust large-volume injections (LVIs; 25 μL) in long sequences (60-70 h, 70-80 injections) to a GC-Orbitrap HRMS. Without lipid removal, LVI was counterproductive because method sensitivity suffered from the abundant matrix signal, resulting in low ion injection times to the Orbitrap. The median method quantification limit was 0.09 ng/mL (range 0.005-4.83 ng/mL), and good accuracy was shown for a certified reference serum. Applying the method to plasma from a Swedish cohort (n = 32; 100 μL), 51 of 103 target analytes were detected. Simultaneous nontarget analysis resulted in 112 structural annotations (12.8% annotation rate), and Level 1 identification was achieved for 7 of 8 substances in follow-up confirmations. The HA-P method is potentially scalable for application in cohort studies and is also compatible with many liquid-chromatography-based exposomics workflows.

Evaluation of Nontargeted Mass Spectral Data Acquisition Strategies for Water Analysis and Toxicity-Based Feature Prioritization by MS2Tox.

The machine-learning tool MS2Tox can prioritize hazardous nontargeted molecular features in environmental waters, by predicting acute fish lethality of unknown molecules based on their MS2 spectra, prior to structural annotation. It has yet to be investigated how the extent of molecular coverage, MS2 spectra quality, and toxicity prediction confidence depend on sample complexity and MS2 data acquisition strategies. We compared two common nontargeted MS2 acquisition strategies with liquid chromatography high-resolution mass spectrometry for structural annotation accuracy by SIRIUS+CSI:FingerID and MS2Tox toxicity prediction of 191 reference chemicals spiked to LC-MS water, groundwater, surface water, and wastewater. Data-dependent acquisition (DDA) resulted in higher rates (19-62%) of correct structural annotations among reference chemicals in all matrices except wastewaters, compared to data-independent acquisition (DIA, 19-50%). However, DIA resulted in higher MS2 detection rates (59-84% DIA, 37-82% DDA), leading to higher true positive rates for spectral library matching, 40-73% compared to 34-72%. DDA resulted in higher MS2Tox toxicity prediction accuracy than DIA, with root-mean-square errors of 0.62 and 0.71 log-mM, respectively. Given the importance of MS2 spectral quality, we introduce a "CombinedConfidence" score to convey relative confidence in MS2Tox predictions and apply this approach to prioritize potentially ecotoxic nontargeted features in environmental waters.

An approach of 2D convolutional neural network–based seismic data fault interpretation with linear annotation and pixel thinking

AbstractThis article introduces an improved method for geological fault interpretation utilizing a 2D convolutional neural network approach with a focus on coalbed horizons, dealing with the problem as an image classification task. At the beginning, linear annotations, reflecting geological fault features, are applied to multiple seismic sections. By considering texture differences between fault and non‐fault areas, we construct samples that represent these distinct zones for training deep neural networks. Initially, fault annotations are transformed into single dots to facilitate pixel‐based processing. To depict a specific dot's geological structure, we employ a matrix clipped around the point, determined by a combination of range and step parameters. Convolutional layers generate filters equivalent to seismic data transformation, streamlining the need for analysis and selection of seismic attributes. The article discusses enhancing the efficiency of 2D convolutional neural network–based fault interpretation by optimizing sample selection, data extraction and model construction procedures. Through the incorporation of data from two mining areas (totalling 27.09 km2) in sample creation, the overall accuracy exceeds 0.99. Recognition extends seamlessly to unlabelled sections, showcasing the innovative technical route and methodology of fault interpretation with linear annotation and pixel‐based thinking. This study presents a method that integrates planar and raster thinking, transitioning from vision‐oriented geological structure annotation to algorithm‐oriented pixel location. The proposed 2D convolutional neural network–based matrix‐oriented fault/non‐fault binary classification demonstrates feasibility and reproducibility, offering a new automated approach for fault detection in coalbeds through convolutional neural network algorithms.

Structural and Functional Annotations of Hypothetical Proteins of Candida auris for Novel Drug Target Identification: An insilico Approach

Candida auris is an emerging fungal pathogen that causes severe invasive infections in healthcare facilities which are difficult to control and treat due to its resistance to major antifungal drugs. A large fraction of C. auris proteins are uncharacterized and hypothetical, and structural and functional characterization of these proteins can aid in the selection of novel drug targets. The present study involves a computational approach for the structural and functional characterization of hypothetical proteins from Candida auris. After the sequenceretrieval, hypothetical proteins were predicted for physiochemical properties and subcellular localization, structurally modeled using I-TASSER, quality assessed through Verify3D, ERRAT, and PROCHECK, and functionally annotated to reveal conserved domains and roles in pathogen pathways. Finally, the immunogenic assessments, non-human homologous analysis, and druggability analysis reveal three hypothetical proteins of C. auris (KND95408.2, KND95415.2, and KND95429.2) as novel drug targets. Furthermore, the stable conformations of theseselected drug targets with the minimum root mean square deviations (RMSD) and fluctuation (RMSF) were analyzed by MD simulations. Conclusively, the functional annotations of these hypothetical proteins can help in understanding the disease mechanisms at the molecular level, as well as provide new targets for drug development against Candida auris.

Identification and functional characterization of bidirectional gene pairs and their intergenic regions in cotton

BackgroundIn research to improve the quality of transgenic crops, it is often necessary to introduce multiple functionally related genes into recipient plants simultaneously to improve crop genetic traits effectively. Compared with unidirectional promoters, bidirectional promoters simultaneously regulate the expression of multiple genes and improve the efficiency of biotechnology. Therefore, in this study, bidirectional gene pairs were systematically analyzed in Gossypium hirsutum TM-1, and the structure, function and evolutionary relationships of the bidirectional genes were analyzed. The endogenous bidirectional promoters of cotton were mined, and their specific regulatory elements and biological functions were explored to provide useful promoter resources and a theoretical basis for cultivating new cotton germplasms with excellent fiber quality.ResultsUsing an improved search model, a total of 1,383 bidirectional transcript pairs were identified in the Gossypium hirsutum TM-1 genome, and their gene structure and functional annotations were systematically analyzed. Thirty bidirectional intergenic sequences were randomly screened for promoter activity analysis via a transient expression system, and 25 intergenic sequences were found to have bidirectional promoter activity. Comparative analysis of the bidirectional gene profiles of the four cotton subspecies revealed that these subspecies presented abundant bidirectional gene pairs with high homology and that the bidirectional genes in the cotton subspecies were more similar in terms of their molecular functions, cellular components and biological processes. In addition, parallel analysis of bidirectional genes in dicotyledons and monocotyledons revealed that abundant bidirectional gene pairs exist in different species. Although the total number of orthologous bidirectional genes was similar, there was a significant difference in the number of orthologous bidirectional gene pairs between dicotyledons and monocotyledons. This evolutionary analysis of the function and structure of homologous bidirectional gene pairs in different varieties and different subspecies of the same species revealed potential pathways by which these gene pairs originated, which may be necessary for the evolution of a new species.ConclusionIn this study, many bidirectional gene pairs in Gossypium hirsutum TM-1 were identified using computer programming, and systematic analysis was conducted to explore their functions and evolutionary relationships. In addition, the promoter activity of the bidirectional intergenic sequences was verified. The combination of computer programming screening, experimental validation and other methods is expected to provide preferred bidirectional promoters for transgenic breeding work via multigene cotransformation methods, and this information is valuable for genetic engineering research and applications.

An algorithm-driven intelligent mining and identification strategy for natural product mass spectrometry

Efficiently mining and identification of new compounds from the extensive MS/MS datasets of plant extracts poses a significant challenge due to the structural diversity and compositional complexity inherent in natural products (NPs). Various data post-processing techniques have been developed to simplify the interpretation of MS/MS data; however, they often suffer from limited specificity and precision. Meanwhile, structure annotation following data post-processing is particularly time-consuming. In this study, we introduced an innovative strategy named MS-SMART, which integrates three intelligent algorithms: automatic mining of diagnostic ions, rapid filtration of alkaloids from untargeted MS/MS data, and structural recommendations for filtered components. The feasibility of this approach for rapidly discovering novel compounds was demonstrated using berberine-type alkaloids as an example. Firstly, diagnostic ions were automatically extracted and validated using available reference data. Subsequently, berberine-type compounds were filtered from raw MS/MS data. Finally, the structures of the target components were recommended using building blocks derived from berberines reported in various plants. A total of 103, 198, 60, 80 and 51 berberines were efficiently identified in diverse families and genera, including Stephaniae Epigaeae Radix, Coptidis Rhizoma, Phellodendri Chinensis Cortex, Phellodendri Amurensis Cortex and Corydalis Decumbentis Rhizoma, with 99, 169, 50, 64 and 40 new compounds identified, respectively. Among these, 8, 14, 8, 7 and 12 berberines were confirmed by reference compounds. This strategy provides a new research paradigm for the rapid discovery and identification of different types of new compounds in complex samples.

Spatial Transcriptomics Identifies Cellular and Molecular Characteristics of Scleroderma Skin Lesions: Pilot Study in Juvenile Scleroderma.

Juvenile localized and systemic scleroderma are rare autoimmune diseases which cause significant disability and morbidity in children. The mechanisms driving juvenile scleroderma remain unclear, necessitating further cellular and molecular level studies. The Visium CytAssist spatial transcriptomics (ST) platform, which preserves the spatial location of cells and simultaneously sequences the whole transcriptome, was employed to profile the histopathological slides from skin lesions of juvenile scleroderma patients. (1) Spatial domains were identified from ST data and exhibited strong concordance with the pathologist's annotations of anatomical structures. (2) The integration of paired ST data and single-cell RNA sequencing (scRNA-seq) from the same patients validated the comparable accuracy of the two platforms and facilitated the estimation of cell type composition in ST data. (3) The pathologist-annotated immune infiltrates, such as perivascular immune infiltrates, were clearly delineated by the ST analysis, underscoring the biological relevance of the findings. This is the first study utilizing spatial transcriptomics to investigate skin lesions in juvenile scleroderma patients. The validity of the ST data was corroborated by gene expression analyses and the pathologist's assessments. Integration with scRNA-seq data facilitated the cell type-level analysis and validation. Analyses of immune infiltrates through combined ST data and pathological review enhances our understanding of the pathogenesis of juvenile scleroderma.

High-quality genome assemblies for nine non-model North American insect species representing six orders (Insecta: Coleoptera, Diptera, Hemiptera, Hymenoptera, Lepidoptera, Neuroptera).

Field-collected specimens were used to obtain nine high-quality genome assemblies from a total of 10 insect species native to prairies and savannas of central Illinois (USA): Mellilla xanthometata (Lepidoptera: Geometridae), Stenolophus ochropezus (Coleoptera: Carabidae), Forcipata loca (Hemiptera: Cicadellidae), Coelinius sp. (Hymenoptera: Braconidae), Thaumatomyia glabra (Diptera: Chloropidae), Brachynemurus abdominalus (Neuroptera: Myrmeleontidae), Catonia carolina (Hemiptera: Achilidae), Oncometopia orbona (Hemiptera: Cicadellidae), Flexamia atlantica (Hemiptera: Cicadellidae) and Stictocephala bisonia (Hemiptera: Membracidae). Sequencing library preparation from single specimens was successful despite extremely small DNA yields (<0.1 μg) for some samples. Additional sequencing and assembly workflows were adapted to each sample depending on the initial DNA yield. PacBio circular consensus (CCS/HiFi) or continuous long reads (CLR) libraries were used to sequence DNA fragments up to 50 kb in length, with Illumina sequenced linked-reads (TellSeq libraries) and Omni-C libraries used for scaffolding and gap-filling. Assembled genome sizes ranged from 135 MB to 3.2 GB. The number of assembled scaffolds ranged from 47 to >13,000, with the longest scaffold per assembly ranging from ~23 to 439 Mb. Genome completeness was high, with BUSCO scores ranging from 85.5% completeness for the largest genome (Stictocephala bisonia) to 98.8% completeness for the smallest genome (Coelinius sp.). The unique content was estimated using RepeatMasker and GenomeScope2, which ranged from 50.7% to 75.8% and roughly decreased with increasing genome size. Structural annotation predicted a range of 19,281-72,469 protein models for sequenced species. Sequencing costs per genome at the time ranged from US$3-5k, averaged ~1600 CPU-hours on a high-performance cluster and required approximately 14 h of bioinformatics analyses with samples using PacBio HiFi data. Most assemblies would benefit from further manual curation to correct possible scaffold misjoins and translocations suggested by off-diagonal or depleted signals in Omni-C contact maps.

SynGAP: a synteny-based toolkit for gene structure annotation polishing

Genome sequencing has become a routine task for biologists, but the challenge of gene structure annotation persists, impeding accurate genomic and genetic research. Here, we present a bioinformatics toolkit, SynGAP (Synteny-based Gene structure Annotation Polisher), which uses gene synteny information to accomplish precise and automated polishing of gene structure annotation of genomes. SynGAP offers exceptional capabilities in the improvement of gene structure annotation quality and the profiling of integrative gene synteny between species. Furthermore, an expression variation index is designed for comparative transcriptomics analysis to explore candidate genes responsible for the development of distinct traits observed in phylogenetically related species.

The pineapple reference genome: Telomere-to-telomere assembly, manually curated annotation, and comparative analysis.

Pineapple is the third most crucial tropical fruit worldwide and available in five varieties. Genomes of different pineapple varieties have been released to date; however, none of them are complete, with all exhibiting substantial gaps and representing only two of the five pineapple varieties. This significantly hinders the advancement of pineapple breeding efforts. In this study, we sequenced the genomes of three varieties: a wild pineapple variety, a fiber pineapple variety, and a globally cultivated edible pineapple variety. We constructed the first gap-free reference genome (Ref) for pineapple. By consolidating multiple sources of evidence and manually revising each gene structure annotation, we identified 26,656 protein-coding genes. The BUSCO evaluation indicated a completeness of 99.2%, demonstrating the high quality of the gene structure annotations in this genome. Utilizing these resources, we identified 7,209 structural variations across the three varieties. Approximately 30.8% of pineapple genes were located within ±5 kb of structural variations, including 30 genes associated with anthocyanin synthesis. Further analysis and functional experiments demonstrated that the high expression of AcMYB528 aligns with the accumulation of anthocyanins in the leaves, both of which may be affected by a 1.9-kb insertion fragment. In addition, we developed the Ananas Genome Database, which offers data browsing, retrieval, analysis, and download functions. The construction of this database addresses the lack of pineapple genome resource databases. In summary, we acquired a seamless pineapple reference genome with high-quality gene structure annotations, providing a solid foundation for pineapple genomics and a valuable reference for pineapple breeding.

Intelligent skin-removal photoacoustic computed tomography for human based on deep learning.

Photoacoustic computed tomography (PACT) has centimeter-level imaging ability and can be used to detect the human body. However, strong photoacoustic signals from skin cover deep tissue information, hindering the frontal display and analysis of photoacoustic images of deep regions of interest. Therefore, we propose a 2.5 D deep learning model based on feature pyramid structure and single-type skin annotation to extract the skin region, and design a mask generation algorithm to remove skin automatically. PACT imaging experiments on the human periphery blood vessel verified the correctness our proposed skin-removal method. Compared with previous studies, our method exhibits high robustness to the uneven illumination, irregular skin boundary, and reconstruction artifacts in the images, and the reconstruction errors of PACT images decreased by 20% ~ 90% with a 1.65 dB improvement in the signal-to-noise ratio at the same time. This study may provide a promising way for high-definition PACT imaging of deep tissues.

Discovery and generalization of tissue structures from spatial omics data

Tissues are organized into anatomical and functional units at different scales. New technologies for high-dimensional molecular profiling in situ have enabled the characterization of structure-function relationships in increasing molecular detail. However, it remains a challenge to consistently identify key functional units across experiments, tissues, and disease contexts, a task that demands extensive manual annotation. Here, we present spatial cellular graph partitioning (SCGP), a flexible method for the unsupervised annotation of tissue structures. We further present a reference-query extension pipeline, SCGP-Extension, that generalizes reference tissue structure labels to previously unseen samples, performing data integration and tissue structure discovery. Our experiments demonstrate reliable, robust partitioning of spatial data in a wide variety of contexts and best-in-class accuracy in identifying expertly annotated structures. Downstream analysis on SCGP-identified tissue structures reveals disease-relevant insights regarding diabetic kidney disease, skin disorder, and neoplastic diseases, underscoring its potential to drive biological insight and discovery from spatial datasets.

Reproducible MS/MS library cleaning pipeline in matchms

Mass spectral libraries have proven to be essential for mass spectrum annotation, both for library matching and training new machine learning algorithms. A key step in training machine learning models is the availability of high-quality training data. Public libraries of mass spectrometry data that are open to user submission often suffer from limited metadata curation and harmonization. The resulting variability in data quality makes training of machine learning models challenging. Here we present a library cleaning pipeline designed for cleaning tandem mass spectrometry library data. The pipeline is designed with ease of use, flexibility, and reproducibility as leading principles.Scientific contributionThis pipeline will result in cleaner public mass spectral libraries that will improve library searching and the quality of machine-learning training datasets in mass spectrometry. This pipeline builds on previous work by adding new functionality for curating and correcting annotated libraries, by validating structure annotations. Due to the high quality of our software, the reproducibility, and improved logging, we think our new pipeline has the potential to become the standard in the field for cleaning tandem mass spectrometry libraries.Graphical

Analysis of Runs of Homozygosity in Aberdeen Angus Cattle.

A large number of cattle breeds have marked phenotypic differences. They are valuable models for studying genome evolution. ROH analysis can facilitate the discovery of genomic regions that may explain phenotypic differences between breeds affecting traits of economic importance. This paper investigates genome-wide ROH of 189 Aberdeen Angus bulls using the Illumina Bovine GGP HD Beadchip150K to structurally and functionally annotate genes located within or in close ROH of the Aberdeen Angus cattle genome. The method of sequential SNP detection was used to determine the ROH. Based on this parameter, two ROH classes were allocated. The total length of all ROH islands was 11,493 Mb. As a result of studying the genomic architecture of the experimental population of Aberdeen Angus bulls, nine ROH islands and 255 SNPs were identified. Thirteen of these overlapped with regions bearing 'selection imprints' previously identified in other breeds of cattle, and five of these regions were identified in other Aberdeen Angus populations. The total length of the ROH islands was 11,493 Mb. The size of individual islands ranged from 0.038 to 1.812 Mb. Structural annotation showed the presence of 87 genes within the identified ROH islets.

Comparative genomics between Trichomonas tenax and Trichomonas vaginalis: CAZymes and candidate virulence factors.

The oral trichomonad Trichomonas tenax is increasingly appreciated as a likely contributor to periodontitis, a chronic inflammatory disease induced by dysbiotic microbiota, in humans and domestic animals and is strongly associated with its worst prognosis. Our current understanding of the molecular basis of T. tenax interactions with host cells and the microbiota of the oral cavity are still rather limited. One laboratory strain of T. tenax (Hs-4:NIH/ATCC 30207) can be grown axenically and two draft genome assemblies have been published for that strain, although the structural and functional annotation of these genomes is not available. GenSAS and Galaxy were used to annotate two publicly available draft genomes for T. tenax, with a focus on protein-coding genes. A custom pipeline was used to annotate the CAZymes for T. tenax and the human sexually transmitted parasite Trichomonas vaginalis, the most well-characterized trichomonad. A combination of bioinformatics analyses was used to screen for homologs of T. vaginalis virulence and colonization factors within the T. tenax annotated proteins. Our annotation of the two T. tenax draft genome sequences and their comparison with T. vaginalis proteins provide evidence for several candidate virulence factors. These include candidate surface proteins, secreted proteins and enzymes mediating potential interactions with host cells and/or members of the oral microbiota. The CAZymes annotation identified a broad range of glycoside hydrolase (GH) families, with the majority of these being shared between the two Trichomonas species. The presence of candidate T. tenax virulence genes supports the hypothesis that this species is associated with periodontitis through direct and indirect mechanisms. Notably, several GH proteins could represent potential new virulence factors for both Trichomonas species. These data support a model where T. tenax interactions with host cells and members of the oral microbiota could synergistically contribute to the damaging inflammation characteristic of periodontitis, supporting a causal link between T. tenax and periodontitis.