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Results for “Molecular Sequence Annotation”

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PubMed2024

Haplotype-resolved genome assembly and implementation of VitExpress, an open interactive transcriptomic platform for grapevine.

Haplotype-resolved genome assemblies were produced for Chasselas and Ugni Blanc, two heterozygous Vitis vinifera cultivars by combining high-fidelity long-read sequencing and high-throughput chromosome conformation capture (Hi-C). The telomere-to-telomere full coverage of the chromosomes allowed us to assemble separately the two haplo-genomes of both cultivars and revealed structural variations between the two haplotypes of a given cultivar. The deletions/insertions, inversions, translocations, and duplications provide insight into the evolutionary history and parental relationship among grape varieties. Integration of de novo single long-read sequencing of full-length transcript isoforms (Iso-Seq) yielded a highly improved genome annotation. Given its higher contiguity, and the robustness of the IsoSeq-based annotation, the Chasselas assembly meets the standard to become the annotated reference genome for V. vinifera. Building on these resources, we developed VitExpress, an open interactive transcriptomic platform, that provides a genome browser and integrated web tools for expression profiling, and a set of statistical tools (StatTools) for the identification of highly correlated genes. Implementation of the correlation finder tool for MybA1, a major regulator of the anthocyanin pathway, identified candidate genes associated with anthocyanin metabolism, whose expression patterns were experimentally validated as discriminating between black and white grapes. These resources and innovative tools for mining genome-related data are anticipated to foster advances in several areas of grapevine research.

Vitis↗

PubMed2025

Annotation matters: the effect of structural gene annotation on orthology inference.

MOTIVATION: In silico gene annotation, the process of identifying the genes present in a genome, remains a challenging task. As genome assemblies rapidly increase, the corresponding gene models and repertoires often fall short in quality. Despite advances in annotation methods, a lack of community standards means that most published gene annotations result from ad hoc pipelines. As a result, only a few species have nearly complete and accurate gene models. This annotation quality is thought to affect downstream analyses, including orthology inference, often the first step of comparative genomics studies. RESULTS: We show that different annotation methods yield markedly distinct orthology inferences. We compared orthology assignments of gene models obtained by four prominent protein-coding gene model sources: the NCBI Eukaryotic Genome Annotation Pipeline, the Ensembl Gene Annotation System, the UniProt Reference Proteomes, and Augustus 3.4 (an ab initio pipeline). We observe significant discrepancies between sources, namely in the proportion of orthologous genes per genome, the completeness of Hierarchical Orthologous Groups, and the accuracy and recall of the predicted orthologs on a standard orthology benchmark.

Molecular Sequence Annotation↗

PubMed2026

Annotated genome assemblies of two temperate North American dung beetles, Canthon chalcites and Phanaeus vindex.

Dung beetles serve as cultivators of their natural habitats, improving soil health and functions in both natural and anthropogenic environments. Despite their ecological importance, whole genome sequences for Scarabaeinae are limited. Here, we present the draft annotated genome assemblies for 2 temperate species of North American dung beetles collected from eastern Tennessee: Canthon chalcites and Phanaeus vindex. Both genome assemblies were generated from PacBio long reads and have high completeness, with BUSCO scores of 98.1% and 98.6% for C. chalcites and P. vindex, respectively. For C. chalcites, the BRAKER3 pipeline predicted 12,799 genes, and the gene set was 93.7% complete. For P. vindex, the BRAKER3 predicted 12,252 genes, and the gene set was 94.9% complete. From the annotated gene sets, orthologous protein sequence analyses among C. chalcites, P. vindex, the dung beetle species Onthophagus taurus, and the more evolutionarily distant beetle Tribolium castaneum indicated that there are 260 unique protein clusters for C. chalcites and 210 unique protein clusters for P. vindex. These 2 draft genomes provide valuable data for comparative genomics, evolution, and phylogenic studies for dung beetle species.

Animals↗

PubMed2025

DescribePROT Database of Residue-Level Protein Structure and Function Annotations.

DescribePROT is a freely available online database of structural and functional descriptors of proteins at the amino acid level. It provides access to 13 diverse descriptors that include sequence conservation, putative secondary structure, solvent accessibility, intrinsic disorder, and signal peptides, and putative annotations of residues that interact with proteins, peptides and nucleic acids. These data can be used to elucidate protein functions, to support efforts to develop therapeutics, and to develop and evaluate future predictors of protein structure and function. DescribePROT includes 7.8 billion predictions for 1.4 million proteins from 83 complete proteomes of popular model organisms. This information can be downloaded at multiple levels of scope (entire database, specific organisms, and individual proteins) and can be interacted with using a graphical interface that simultaneously displays data on multiple descriptors. We describe the contents of this resource, provide directions on how to use its interface, and offer instructions on how to obtain and interact with the underlying data. Moreover, we briefly discuss plans for a future expansion of this database. DescribePROT is available at http://biomine.cs.vcu.edu/servers/DESCRIBEPROT/ .

Databases, Protein↗

PubMed2026

Whole-genome sequencing and analysis of the endophytic fungus Alternaria alternata Y-2 from Leymus chinensis.

To explore the genetic basis and functional potential of beneficial symbiosis between the endophytic fungus Alternaria alternata Y-2 and its host Leymus chinensis, we performed Illumina-based draft whole-genome sequencing and systematic bioinformatic analysis. Although this assembly does not reach telomere-to-telomere completeness, it provides high-quality gene-level information for gene prediction, functional annotation, carbohydrate-active enzyme (CAZyme) identification, and secondary metabolite biosynthetic gene cluster analysis. The final genome size of A. alternata Y-2 was 34,383,676 bp with a GC content of 51.0%, containing 12,724 predicted protein-coding genes, 90 tRNAs, and 12 rRNAs. BUSCO assessment showed 98.9% completeness, supporting the high quality of this draft genome. A total of 12,627 genes were successfully annotated in the NCBI NR database, and 17,183 genes were functionally categorized using GO terms. In total, 448 CAZyme genes and 21 secondary metabolite biosynthetic gene clusters were identified, which are potentially involved in lignocellulose degradation, cellular redox homeostasis and biosynthesis of bioactive metabolites. Based on ITS sequence alignment, NR annotation, and phylogenetic analysis of single-copy orthologous genes, the strain was confidently identified as A. alternata. This study firstly reports the draft genome of an endophytic A. alternata strain derived from L. chinensis and provides valuable genetic resources for exploring the endophytic lifestyle, stress tolerance, and bioactive metabolite potential of this fungus.

Alternaria↗

PubMed2026

scATAnno: Automated Cell Type Annotation for Single-cell ATAC-seq Data.

Recent advances in single-cell epigenomic techniques have increased the demand for single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) analysis. One key analytical task is to determine cell type identity based on epigenetic data. Here, we introduce scATAnno, a Python package designed to automatically annotate scATAC-seq data using large-scale scATAC-seq reference atlases. This workflow generates reference atlases from publicly available datasets, enabling accurate cell type annotation by integrating query data with reference atlases without the use of single-cell RNA sequencing (scRNA-seq) data. To enhance annotation accuracy, we incorporated k-nearest neighbors (KNN)-based and weighted distance-based uncertainty scores to effectively detect cell populations within the query data that are distinct from all cell types in the reference data. We compared and benchmarked scATAnno against five other published cell annotation approaches, demonstrating its superior performance across multiple datasets and metrics. We further showcased the utility of scATAnno across multiple datasets, including peripheral blood mononuclear cells (PBMCs), triple-negative breast cancer (TNBC), and basal cell carcinoma (BCC), and demonstrated that scATAnno accurately annotates cell types across diverse biological conditions. Overall, scATAnno is a useful tool for scATAC-seq reference atlas construction and cell type annotation and can facilitate the interpretation of new scATAC-seq datasets in complex biological systems. scATAnno is publicly available at https://scatanno-main.readthedocs.io/.

Single-Cell Analysis↗

PubMed2025

De novo assembly of transcriptomes of six Hua species (Semisulcospiridae, Cerithioidea, Gastropoda).

Species in Semisulcospiridae are important in freshwater ecology and have great research value, yet their genomic resources remain very limited. Here, we present de novo assembled transcriptomes from six species of Hua in Semisulcospiridae, including Hua textrix (Heude, 1888), H. yangi L.-N. Du, J.-X. Yang & Chen, 2023, H. wujiangensis L.-N. Du, J.-X. Yang & Chen, 2023, and three undescribed species. Assembly was performed using Trinity, resulting in average contig lengths ranging from 716.6 to 883.3 bp and transcript numbers ranging from 147,147 to 268,741. Benchmarking Universal Single-Copy Ortholog (BUSCO) analysis was used to assess the transcriptome completeness. The functional annotation of transcripts for each species had over 18,000 BLAST hits, 17,000 GO terms, 15,000 KEGG pathways, 8,000 Pfam accessions, and 140 COG functional categories. This study provides valuable transcriptomic resources for the six Hua species, which can be used for various research of Semisulcospiridae, including biodiversity, phylogeny, and comparative genomics.

Transcriptome↗

PubMed2026

Whole-genome sequences of the dwarf honey bee subgenus Micrapis: Apis andreniformis and Apis florea.

The Micrapis subgenus, which includes the black dwarf honey bee (Apis andreniformis) and the red dwarf honey bee (Apis florea), remains underrepresented in genomic studies despite its ecological significance. Here, we present high-quality de novo genome assemblies for both species, generated using a hybrid sequencing approach combining Oxford Nanopore Technologies long reads with Illumina short reads. The final assemblies are highly contiguous, with contig N50 values of 5.0 Mb (A. andreniformis) and 4.3 Mb (A. florea), representing a major improvement over the previously published A. florea genome. Genome completeness assessments indicate high quality, with BUSCO scores exceeding 98.5% using the Hymenoptera database and k-mer analyses supporting base-level accuracy. Repeat annotation revealed a relatively low repetitive sequence content (∼6%), consistent with other Apis species. Using RNA sequencing data, we annotated 12,189 genes for A. andreniformis and 12,207 genes for A. florea, with ∼98% completeness in predicted proteomes. These genome assemblies provide a valuable resource for comparative and functional genomic studies, with the potential to offer new insights into the genetic basis of dwarf honey bee adaptations.

Male↗

PubMed2026

Enhanced identification of key bacterial motility genes via a cross-species genomic hybrid feature machine learning approach.

Efficient and accurate identification of functional genes is critical to biological research, yet traditional single-species approaches are often limited by low efficiency. Previously, we established a novel method for identifying key genes using cross-species protein domain features and machine learning. However, the high multiplicity of gene members associated with specific domains creates a substantial workload for subsequent experimental validation. To address this, this study proposes an enhanced approach that integrates EggNOG-based protein sequence annotation with domain analysis. Unannotated sequences are subsequently analyzed for protein domains, generating a comprehensive "direct gene annotation plus domain" hybrid feature matrix. While the hybrid matrix model yielded comparable predictive accuracy, it significantly enhanced feature resolution: the top 50 predicted features were all known motility-related genes or domains. Furthermore, among the top 100 ranked features, 58 are confirmed to be directly related to motility based on experimental evidence. Although strict genus-level control still yielded 51 confirmed features, excessive taxonomic restriction drastically reduces the number of training genomes, which may paradoxically impair identification efficiency. These results demonstrate that the new method effectively reduces the subsequent experimental workload and enables high-throughput identification of functional genes in a single analysis. With accuracy and efficiency far exceeding those of existing single-species identification methods, it provides a highly efficient solution for mining key genes underlying other complex bacterial phenotypes.

Machine Learning↗

PubMed2026

GeneExt: a gene model extension tool for enhanced single-cell RNA-seq analysis.

MOTIVATION: Incomplete gene models negatively impact single-cell gene expression quantification. This is particularly true in non-model species where often gene 3' ends are inaccurately annotated, while most scRNA-seq methods only capture the 3' transcript region. This results in many genes being incorrectly quantified or not detected. RESULTS: GeneExt leverages scRNA-seq data to refine gene annotations. We exemplify GeneExt usage and its impact on the gene expression quantification of eight non-model organism single-cell atlases. By extending and homogenizing gene annotations, our tool will help improve biological interpretation and cross-species comparisons of cell type expression atlases. AVAILABILITY: GeneExt is available at https://github.com/sebepedroslab/GeneExt (DOI: https://doi.org/10.5281/zenodo.18712940) under a GNU General Public license, together with test data and usage instructions.

Software↗

PubMed2025

Fishing for a reelGene: evaluating gene models with evolution and machine learning.

Assembled genomes and their associated annotations have transformed our study of gene function. However, each new annotated assembly generates new gene models. Inconsistencies between annotations likely arise from biological and technical causes, including pseudogene misclassification, transposon activity, and intron retention from sequencing of unspliced transcripts. To evaluate gene model predictions, we developed reelGene, a pipeline of machine learning models focused on (1) transcription boundaries, (2) mRNA integrity, and (3) protein structure. The first two models leverage sequence characteristics and evolutionary conservation across related taxa to learn the grammar of conserved transcription boundaries and mRNA sequences, while the third uses the conserved evolutionary grammar of protein sequences to predict whether a gene can produce a protein. Evaluating 1.8 million transcript models in Zea mays ssp. mays (maize), reelGene classified 28% as incorrectly annotated or non-functional. We find that reelGene classifies 92.2% of genes in the maize proteome and 99.2% of genes within the maize classical gene list as functional. reelGene also provides a way to further investigate genome biology- for instance, reelGene indicates that 10.3% of dispensable genes in B73 are functional, and within retained duplicate genes, reelGene identifies a 30% bias toward the retention of the M1 subgenome when one copy is functional and the other is non-functional. As an annotation-evaluating tool, reelGene is directly applicable to species of the Andropogoneae tribe, including other important crops like sorghum and miscanthus. As a community resource, reelGene has been integrated onto MaizeGDB both as a browser track and as an individual Shiny App, allowing researchers to evaluate gene model accuracy and further investigate genome biology.

Machine Learning↗

PubMed2025

argNorm: normalization of antibiotic resistance gene annotations to the Antibiotic Resistance Ontology (ARO).

SUMMARY: Currently available and frequently used tools for annotating antibiotic resistance genes (ARGs) in genomes and metagenomes provide results using inconsistent nomenclature. This makes the comparison of different ARG annotation outputs challenging. The comparability of ARG annotation outputs can be improved by mapping gene names and their categories to a common controlled vocabulary such as the Antibiotic Resistance Ontology (ARO). We developed argNorm, a command line tool and Python library, to normalize all detected genes across six ARG annotation tools (eight databases) to the ARO. argNorm also adds information to the outputs using the same ARG categorization so that they are comparable across tools. AVAILABILITY AND IMPLEMENTATION: argNorm is available as an open-source tool at: https://github.com/BigDataBiology/argNorm. It can also be downloaded as a PyPI package and is available on Bioconda and as an nf-core module.

Molecular Sequence Annotation↗

PubMed2024

Chromosome-scale assembly with improved annotation provides insights into breed-wide genomic structure and diversity in domestic cats.

INTRODUCTION: Comprehensive genomic resources offer insights into biological features, including traits/disease-related genetic loci. The current reference genome assembly for the domestic cat (Felis catus), Felis_Catus_9.0 (felCat9), derived from sequences of the Abyssinian cat, may inadequately represent the general cat population, limiting the extent of deducible genetic variations. OBJECTIVES: The goal was to develop Anicom American Shorthair 1.0 (AnAms1.0), a reference-grade chromosome-scale cat genome assembly. METHODS: In contrast to prior assemblies relying on Abyssinian cat sequences, AnAms1.0 was constructed from the sequences of more popular American Shorthair breed, which is related to more breeds than the Abyssinian cat. By combining advanced genomics technologies, including PacBio long-read sequencing and Hi-C- and optical mapping data-based sequence scaffolding, we compared AnAms1.0 to existing Felidae genome assemblies (20 scaffolds, scaffolds N50 > 150 Mbp). Homology-based and ab initio gene annotation through Iso-Seq and RNA-Seq was used to identify new coding genes and splice variants. RESULTS: AnAms1.0 demonstrated superior contiguity and accuracy than existing Felidae genome assemblies. Using AnAms1.0, we identified over 1.5 thousand structural variants and 29 million repetitions compared to felCat9. Additionally, we identified > 1,600 novel protein-coding genes. Notably, olfactory receptor structural variants and cardiomyopathy-related variants were identified. CONCLUSION: AnAms1.0 facilitates the discovery of novel genes related to normal and disease phenotypes in domestic cats. The analyzed data are publicly accessible on Cats-I (https://cat.annotation.jp/), which we established as a platform for accumulating and sharing genomic resources to discover novel genetic traits and advance veterinary medicine.

Animals↗

PubMed2017

Functional mapping and annotation of genetic associations with FUMA.

A main challenge in genome-wide association studies (GWAS) is to pinpoint possible causal variants. Results from GWAS typically do not directly translate into causal variants because the majority of hits are in non-coding or intergenic regions, and the presence of linkage disequilibrium leads to effects being statistically spread out across multiple variants. Post-GWAS annotation facilitates the selection of most likely causal variant(s). Multiple resources are available for post-GWAS annotation, yet these can be time consuming and do not provide integrated visual aids for data interpretation. We, therefore, develop FUMA: an integrative web-based platform using information from multiple biological resources to facilitate functional annotation of GWAS results, gene prioritization and interactive visualization. FUMA accommodates positional, expression quantitative trait loci (eQTL) and chromatin interaction mappings, and provides gene-based, pathway and tissue enrichment results. FUMA results directly aid in generating hypotheses that are testable in functional experiments aimed at proving causal relations.

Chromatin↗

PubMed2025

Annotated genome of the Atlantic dog whelk, Nucella lapillus.

Nucella lapillus is an important player in rocky shore food chains and has been a focal organism of ecological and evolutionary studies for decades. Despite poor dispersal, they have a broad geographic range, which makes them an ideal species to examine isolation by distance and selection across environmental gradients. Here we present the fully annotated genome of N. lapillus generated with Oxford Nanopore Techonology sequencing at ∼37× coverage. The genome assembly is 2.32 Gbp and consists of 2,525 contigs, with an N50 length of 2 Mbp. Repeat annotation identified 2,491 families that cover 67.56% of the genome, which is similar to other gastropods. Despite its large size and high proportion of repeats, the genome is of high quality. Benchmarking Universal Single-Copy Ortholog (BUSCO) analysis revealed a score of 96.8%. Functional annotation of the genome produced 45,848 protein-coding genes with a 96.6% BUSCO score. Genomic resources for mollusks lag behind that of other phyla, perhaps because many of their innate characteristics complicate DNA extraction, sequencing, and assembly. This new N. lapillus genome will increase our genomic understanding of the second largest phylum (and the most diverse class within said phylum) and serve as a key resource to advance studies on the organismal biology and population genetics of this iconic species as well as the connection between genomic variation and community-level processes.

Animals↗

PubMed2025

A Comprehensive Bioinformatics Approach to Analysis of Variants: Variant Calling, Annotation, and Prioritization.

Next-Generation Sequencing (NGS), also known as high-throughput sequencing technologies, has enabled rapid and efficient sequencing of large amounts of DNA and RNA. These technologies have revolutionized the field of genomics, transcriptomics, and proteomics and have been widely used in cancer research, leading to advances in clinical diagnosis and treatment. Improvements in the NGS technologies enabled millions of fragments to be sequenced simultaneously in a time- and cost-effective manner and resulted in large amount of genomic data which require efficient analysis methods. Analysis of the genomic data requires both efficient computer resources and bioinformatics approaches. This chapter details a comprehensive computational approach and analysis steps for genomic data analysis.

Computational Biology↗

PubMed2025

DeNoFo: a file format and toolkit for standardized, comparable de novo gene annotation.

MOTIVATION: De novo genes emerge from previously non-coding regions of the genome, challenging the traditional view that new genes primarily arise through duplication and adaptation of existing ones. Characterized by their rapid evolution and their novel structural properties or functional roles, de novo genes represent a young area of research. Therefore, the field currently lacks established standards and methodologies, leading to inconsistent terminology and challenges in comparing and reproducing results. RESULTS: This work presents a standardized annotation format to document the methodology of de novo gene datasets in a reproducible way. We developed DeNoFo, a toolkit to provide easy access to this format that simplifies annotation of datasets and facilitates comparison across studies. Unifying the different protocols and methods in one standardized format, while providing integration into established file formats, such as fasta or gff, ensures comparability of studies and advances new insights in this rapidly evolving field. AVAILABILITY AND IMPLEMENTATION: DeNoFo is available through the official Python Package Index (PyPI) and at https://github.com/EDohmen/denofo. All tools have a graphical user interface and a command line interface. The toolkit is implemented in Python3, available for all major platforms and installable with pip and uv.

Software↗

PubMed2026

Whole-Genome Sequence Dataset of Rhodococcus qingshengii IEGM 267-Terpenoid Biotransformer Toward Genetic Functional Annotation.

Background/Objectives: Microbial biotransformation of monoterpenoids is a promising approach for obtaining bioactive compounds. Rhodococcus species are attractive biocatalysts due to their metabolic versatility and ability to transform hydrophobic substrates. In this study, we investigated the catalytic potential of Rhodococcus qingshengii IEGM 267 toward carveol isomers and explored genomic features that may underlie this activity. Methods: The strain was cultivated in mineral medium supplemented with (-)-trans-carveol. Biotransformation products were analyzed by TLC and GC-MS. The draft genome was sequenced, assembled, taxonomically assigned, and annotated using standard bioinformatics tools. Results: Rhodococcus qingshengii IEGM 267 efficiently converted (-)-trans-carveol to carvone. Genome analysis confirmed the taxonomic assignment of the strain and revealed a large repertoire of oxidoreductases, including monooxygenases, hydroxylases, and dehydrogenases. Seven genes encoding cytochrome P450-dependent oxygenases were identified as candidate enzymes potentially involved in carveol oxidation. Conclusions: R. qingshengii IEGM 267 is an efficient and stereoselective biocatalyst for (-)-trans-carveol oxidation. The results of bioinformatics analysis suggest an alternative enzymatic basis for this transformation and provide a foundation for future functional characterization.

Rhodococcus↗