GeneCAD: Plant Genome Annotation with a DNA Foundation Model

Green plants play a fundamental role in ecosystems, human health, and agriculture. As de novo genomes are being generated for all known eukaryotic species as advocated by the Earth BioGenome Project, increasing genomic information on green land plants is essential. However, setting standards for the generation and storage of the complex set of genomes that characterize the green lineage of life is a major challenge for plant scientists. Such standards will need to accommodate the immense variation in green plant genome size, transposable element content, and structural complexity while enabling research into the molecular and evolutionary processes that have resulted in this enormous genomic variation. Here we provide an overview and assessment of the current state of knowledge of green plant genomes. To date fewer than 300 complete chromosome-scale genome assemblies representing fewer than 900 species have been generated across the estimated 450,000 to 500,000 species in the green plant clade. These genomes range in size from 12 Mb to 27.6 Gb and are biased toward agricultural crops with large branches of the green tree of life untouched by genomic-scale sequencing. Locating suitable tissue samples of most species of plants, especially those taxa from extreme environments, remains one of the biggest hurdles to increasing our genomic inventory. Furthermore, the annotation of plant genomes is at present undergoing intensive improvement. It is our hope that this fresh overview will help in the development of genomic quality standards for a cohesive and meaningful synthesis of green plant genomes as we scale up for the future.

BackgroundGenome annotation is a crucial component of RNA-seq data analysis. Much effort has been devoted to producing an accurate and rational annotation of the human genome. An annotated genome provides a comprehensive catalogue of genomic functional elements. Currently, at least six human genome annotations are publicly available, including AceView Genes, Ensembl Genes, H-InvDB Genes, RefSeq Genes, UCSC Known Genes, and Vega Genes. Characteristics of these annotations differ because of variations in annotation strategies and information sources. When performing RNA-seq data analysis, researchers need to choose a genome annotation. However, the effect of genome annotation choice on downstream RNA-seq expression estimates is still unclear. This study (1) investigates the effect of different genome annotations on RNA-seq quantification and (2) provides guidelines for choosing a genome annotation based on research focus.ResultsWe define the complexity of human genome annotations in terms of the number of genes, isoforms, and exons. This definition facilitates an investigation of potential relationships between complexity and variations in RNA-seq quantification. We apply several evaluation metrics to demonstrate the impact of genome annotation choice on RNA-seq expression estimates. In the mapping stage, the least complex genome annotation, RefSeq Genes, appears to have the highest percentage of uniquely mapped short sequence reads. In the quantification stage, RefSeq Genes results in the most stable expression estimates in terms of the average coefficient of variation over all genes. Stable expression estimates in the quantification stage translate to accurate statistics for detecting differentially expressed genes. We observe that RefSeq Genes produces the most accurate fold-change measures with respect to a ground truth of RT-qPCR gene expression estimates.ConclusionsBased on the observed variations in the mapping, quantification, and differential expression calling stages, we demonstrate that the selection of human genome annotation results in different gene expression estimates. When conducting research that emphasizes reproducible and robust gene expression estimates, a less complex genome annotation may be preferred. However, simpler genome annotations may limit opportunities for identifying or characterizing novel transcriptional or regulatory mechanisms. When conducting research that aims to be more exploratory, a more complex genome annotation may be preferred.

Tineola bisselliella, the webbing clothes moth, is an economically important, globally distributed synanthropic pest species and member of the basal moth lineage Tineidae.Tineola bisselliellais facultatively keratinophagous. Therefore, their larvae can cause extensive damage, particularly to clothing, textiles, and museum specimens. Despite the economic and phylogenetic importance ofTineola bisselliella, there is a lack of quality genomic resources for this species, or for others within the Tineidae family. TheTineola bisselliellagenome presented here consists of 30 pseudochromosomes (29 autosomes and 1 Z chromosome) produced using synteny alignment to a closely related species,Tinea pellionella. The resulting final pseudochromosome-level assembly is 243.630 Mb and has an N50 length of 8.708 Mb. The assembly is highly contiguous and has similar or improved quality compared to other available Tineidae genomes, with 93.1% of lepidopteran orthologs complete and present. Annotation of the pseudochromosome-level genome assembly with the transcriptome we produced ultimately yielded 11,267 annotated genes. Synteny alignments between theTineola bisselliellagenome assembly and other Tineidae genomes found evidence for numerous small rearrangements with high synteny conservation. In contrast, synteny alignments performed betweenTineola bisselliellaand the more distantly relatedBombyx moriandMelitea cinxiarevealed more frequent small and large rearrangements as predicted by their evolutionary divergence. The reference quality annotated genome forTineola bisselliellapresented here will advance our understanding of the evolution of the lepidopteran karyotype by providing a chromosome-level genome for this basal moth lineage and provide future insights into the mechanisms underlying keratin digestion inTineola bisselliella.Significance StatementTineola bisselliella, the webbing clothes moth, is a globally distributed synanthropic pest species that feeds on both keratin-based materials and detritus. This dietary habit can cause substantial damage to clothing, textiles, rugs, taxidermy, and museum specimens. In fact, keratinophagous organisms cause an estimated $1 billion worth of damage annually in the United States. However, the lack of a high-quality annotated reference genome for this basal moth species has thus far limited our understanding of the mechanisms underlying keratin digestion relevant to pest control efforts and has hampered efforts to investigate the broader phylogenetic relationships the Tineidae family necessary to reveal patterns of lepidopteran chromosome evolution. Here, we present the first reference quality genome forTineola bisselliellaand the first annotated genome for any member of the Tineidae family, providing a critical resource for the lepidopteran genomics and pest management communities.

Accurate gene annotations are fundamental for interpreting genetic variation, cellular function, and disease mechanisms. However, current human gene annotations are largely derived from transcriptomic data of individuals with European ancestry, leaving gaps of annotation that remain uncharacterized. Here, we generate over 800 million full-length reads with long-read RNA-seq in 43 lymphoblastoid cell line samples from eight genetically-diverse human populations and build a cross-ancestry gene annotation. We demonstrate that transcripts from non-European samples are underrepresented in reference gene annotations, leading to incomplete characterization in allele-specific transcript usage. Furthermore, we show that personal genome assemblies enhance transcript discovery compared to the generic GRCh38 reference assembly, even though genomic regions unique to each individual are heavily depleted of genes. These findings underscore the urgent need for a more inclusive gene annotation framework that accurately represents global transcriptome diversity.

Crustacean aquaculture is expected to be a major source of fishery commodities in the near future. Hemocytes are key players of the immune system in shrimps; however, their classification, maturation, and differentiation are still under debate. To date, only discrete and inconsistent information on the classification of shrimp hemocytes has been reported, showing that the morphological characteristics are not sufficient to resolve their actual roles. Our present study using single-cell RNA sequencing revealed six types of hemocytes of Marsupenaeus japonicus based on their transcriptional profiles. We identified markers of each subpopulation and predicted the differentiation pathways involved in their maturation. We also predicted cell growth factors that might play crucial roles in hemocyte differentiation. Different immune roles among these subpopulations were suggested from the analysis of differentially expressed immune-related genes. These results provide a unified classification of shrimp hemocytes, which improves the understanding of its immune system.

Northern (Glaucomys sabrinus) and southern (Glaucomys volans) flying squirrels are widespread species distributed across North America. Northern flying squirrels are common inhabitants of the boreal forest, also occurring in coniferous forest remnants farther south, whereas the southern flying squirrel range is centered in eastern temperate woodlands. These two flying squirrel species exhibit a hybrid zone across a latitudinal gradient in an area of recent secondary contact. Glaucomys hybrid offspring are viable and can successfully backcross with either parental species, however, the fitness implications of such events are currently unknown. Some populations of G. sabrinus are endangered, and thus, interspecific hybridization is a key conservation concern in flying squirrels. To provide a resource for future studies to evaluate hybridization and possible introgression, we sequenced and assembled a de novo long-read genome from a G. volans individual sampled in southern Ontario, Canada, while four short-read genomes (two G. sabrinus and two G. volans, all from Ontario) were resequenced on Illumina platforms. The final genome assembly consisted of approximately 2.40 Gb with a scaffold N50 of 455.26 Kb. Benchmarking Universal Single-Copy Orthologs reconstructed 3,742 (91.2%) complete mammalian genes and genome annotation using RNA-Seq identified the locations of 19,124 protein-coding genes. The four short-read individuals were aligned to our reference genome to investigate the demographic history of the two species. A principal component analysis clearly separated resequenced individuals, while inferring population size history using the Pairwise Sequentially Markovian Coalescent model noted an approximate species split 1 million years ago, and a single, possibly recently introgressed individual.

The recent advancement of high-throughput genome sequencing technologies has resulted in a considerable increase in demands for large-scale genome annotation. While annotation is a crucial step for downstream data analyses and experimental studies, this process requires substantial expertise and knowledge of bioinformatics. Here we present MEGANTE, a web-based annotation system that makes plant genome annotation easy for researchers unfamiliar with bioinformatics. Without any complicated configuration, users can perform genomic sequence annotations simply by uploading a sequence and selecting the species to query. MEGANTE automatically runs several analysis programs and integrates the results to select the appropriate consensus exon–intron structures and to predict open reading frames (ORFs) at each locus. Functional annotation, including a similarity search against known proteins and a functional domain search, are also performed for the predicted ORFs. The resultant annotation information is visualized with a widely used genome browser, GBrowse. For ease of analysis, the results can be downloaded in Microsoft Excel format. All of the query sequences and annotation results are stored on the server side so that users can access their own data from virtually anywhere on the web. The current release of MEGANTE targets 24 plant species from the Brassicaceae, Fabaceae, Musaceae, Poaceae, Salicaceae, Solanaceae, Rosaceae and Vitaceae families, and it allows users to submit a sequence up to 10 Mb in length and to save up to 100 sequences with the annotation information on the server. The MEGANTE web service is available at https://megante.dna.affrc.go.jp/.

ژن‌های کاذب نسخه‌هایی از ژن اجدادی می‌باشند که به مرور زمان فعالیت آنها نسبت به ژن اولیه تغییر کرده است و در ژنوم بر اثر فرآیندهایی مانند مضاعف شدگی ژنی و همچنین رونویسی واژگون ایجاد شده‌اند. ژن‌های کاذب تا مدت‌ها به‌عنوان توالی‌های غیر عملکردی ژنوم در نظر گرفته می‌شدند. با این وجود پژوهش‌های اخیر گزارشاتی مبنی بر فعالیت زیستی این ژن‌ها ارائه داده‌اند، در نتیجه عملکردی بودن این ژن‌ها موجب افزایش حاشیه نویسی صحیح‌تر این ژن‌ها در ژنوم موجودات شده است. در پژوهش حاضر به منظور بهبود حاشیه نویسی ژنوم گوسفند، برای نخستین بار با استفاده از روش‌های محاسباتی بر پایه بررسی تشابه با استفاده از نرم‌افزار PseudoPipe، ژن‌های کاذب مرتبط با ژن‌های کدکننده پروتئین در سطح ژنوم شناسایی شدند. همچنین گروه‌های کارکردی ژن‌های والدی که ژن‌های کاذب از آنها مشتق شده‌اند با استفاده از پایگاه اینترنتی DAVID بررسی شدند. در نهایت ویژگی‌های مختلف ژن‌های کاذب کاندید جدید شناسایی شده با ژن‌های کاذب شناخته‌شده در گونه‌های انسان، موش و گاو مقایسه شدند. به طور کلی 4098 ژن کاذب با سطح اطمینان بالا شامل 1102 ژن کاذب از نوع مضاعف شده و 2996 از نوع پردازش شده شناسایی شدند. نتایج نشان داد که ژن‌های کاذب شناسایی شده در فرآیندهای زیستی گوناگونی مانند splicing mRNA، پیدایش ریبوزوم، اتصال rRNA، انتقال الکترون میتوکندریایی، ترجمه و غیره نقش دارند. مقایسه ویژگی‌های مختلف ژن‌های شناسایی شده با دیگر گونه‌ها نشان داد که نتایج حاصل از این پژوهش در تطابق با پژوهش‌های گذشته می‌باشد. نتایج حاصل از این پژوهش به بهبود حاشیه نویسی ژنوم گوسفند کمک خواهد کرد.

The first complete plant genome sequence, Arabidopsis thaliana, was reported and published in 2000, marking the beginning of the plant genome era. Over the past 20 years, draft or reference genomes of approximately 800 plant species have been sequenced and the number continues to grow exponentially. The release of these plant genomes has dramatically advanced studies in various disciplines of plant biology. With tremendous advances in sequencing technology and the initiation of many genome sequencing and updating projects, improving the accuracy of the genome annotation stage remains challenging. This is because only a few genomes are available as annotation standards achieved through enormous investments in human curation efforts. Therefore, high-quality genome annotation, as one way of summarizing the existing knowledge of the genomic characteristics of an organism, will be one of the principal research focuses for the plant genome. In addition, genome annotation will play an essential role in some challenging fields in the future, i.e., dissecting information from plant pan-genomes. Here, we summarize the progress of plant genome annotation and the challenges of annotating more complex plant genomes and present state-of-the-art tools and databases for this process.

Small proteins play essential roles in bacterial physiology and virulence, however, automated algorithms for genome annotation are often not yet able to accurately predict the corresponding genes. The accuracy and reliability of genome annotations, particularly for small open reading frames (sORFs), can be significantly improved by integrating protein evidence from experimental approaches. Here we present a highly optimized and flexible bioinformatics workflow for bacterial proteogenomics covering all steps from (i) generation of protein databases, (ii) database searches and (iii) peptide-to-genome mapping to (iv) visualization of results. We used the workflow to identify high quality peptide spectrum matches (PSMs) for small proteins (≤ 100 aa, SP100) in Staphylococcus aureus Newman. Protein extracts from S. aureus were subjected to different experimental workflows for protein digestion and prefractionation and measured with highly sensitive mass spectrometers. In total, 175 proteins with up to 100 aa (SP100) were identified. Out of these 24 (ranging from 9 to 99 aa) were novel and not contained in the used genome annotation.144 SP100 are highly conserved and were found in at least 50% of the publicly available S. aureus genomes, while 127 are additionally conserved in other staphylococci. Almost half of the identified SP100 were basic, suggesting a role in binding to more acidic molecules such as nucleic acids or phospholipids.

BackgroundAn accurate genome annotation is essential in many contexts, including RNA sequencing studies. Annotations include known genes and isoforms, detailing their location (chromosome, start, and end) and coding sequence, among other important metadata.ResultsWe characterized changes in human Ensembl annotations from 2014 to 2023 and the important gains in our biological understanding in recent years. While generally gene and isoform annotations increased (2014: 58,812 genes ; 2023: 62,710), some years dropped (e.g., 2016). A similar pattern exists for the gene and isoform biotypes; both 2015 (19,825) and 2017 (19,828) have fewer genes annotated as protein-coding than 2014 (19,953) and 2016 (19,961)— 2023 has the most (20,048). PCBP1-AS1 had the most annotated isoforms (296). We quantified expression for isoforms that were new between 2019 and 2023 across nine GTEx tissues (58 samples) to demonstrate our significant gains in understanding recently. We saw 2,054 of these ‘new’ isoforms expressed in cerebellar hemisphere (594 in liver). For many genes, we saw that the relative expression of the ‘new’ isoforms was much greater than the previously known isoforms.ConclusionsThis study demonstrates the importance of an accurate genome annotation to truly understand the underlying complexity of biology that is often oversimplified by ignoring transcriptional complexity.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12864-025-12167-8.

In eukaryotes, alternative splicing (AS) greatly expands the diversity of transcripts. However, it is challenging to accurately determine full-length splicing isoforms. Recently, more studies have taken advantage of Pacific Bioscience (PacBio) long-read sequencing to identify full-length transcripts. Nevertheless, the high error rate of PacBio reads seriously offsets the advantages of long reads, especially for accurately identifying splicing junctions. To best capitalize on the features of long reads, we used Illumina RNA-seq reads to improve PacBio circular consensus sequence (CCS) quality and to validate splicing patterns in the rice transcriptome. We evaluated the impact of CCS accuracy on the number and the validation rate of splicing isoforms, and integrated a comprehensive pipeline of splicing transcripts analysis by Iso-Seq and RNA-seq (STAIR) to identify the full-length multi-exon isoforms in rice seedling transcriptome (Oryza sativaL. ssp. japonica). STAIR discovered 11733 full-length multi-exon isoforms, 6599 more than the SMRT Portal RS_IsoSeq pipeline did. Of these splicing isoforms identified, 4453 (37.9%) were missed in assembled transcripts from RNA-seq reads, and 5204 (44.4%), including 268 multi-exon long non-coding RNAs (lncRNAs), were not reported in the MSU_osa1r7 annotation. Some randomly selected unreported splicing junctions were verified by polymerase chain reaction (PCR) amplification. In addition, we investigated alternative polyadenylation (APA) events in transcripts and identified 829 major polyadenylation [poly(A)] site clusters (PACs). Theanalysis of splicing isoforms and APA events will facilitate the annotation of the rice genome and studies on the expression and polyadenylation of AS genes in different developmental stages or growth conditions ofrice.

Approximately 70% of breast cancers express estrogen receptor alpha (ERα), a hormone-regulated transcription factor, making it an excellent target for endocrine therapy. When endogenous and exogenous ligands engage ERα, the receptor binds to regulatory regions in the genome known as enhancers, where it associates with coregulatory proteins, increases histone modifications associated with active enhancers, promotes chromatin looping, stimulates enhancer transcription and the production of enhancer RNAs (eRNAs), and ultimately regulates target gene transcription. We have previously used global run-on sequencing (GRO-seq), a high-throughput method that identifies the location and orientation of all transcriptionally active RNA polymerases across the genome, to identify active enhancers based on enhancer transcription and enrichment of the aforementioned genomic features. Current studies on the roles of eRNAs have suggested that eRNAs may 1) recruit regulatory proteins to enhancers; 2) promote RNA polymerase II transition; 3) regulate histone modification; and 4) increase chromosome looping. One challenge facing the field is the lack of accurate eRNA annotations and cloned full-length eRNAs; eRNAs have typically been studied as signals from genomic assays on ‘browser tracks,’ not as physical entities (e.g., cDNAs). We have employed precision nuclear run-on of capped RNA (PRO-cap) to determine the transcription start sites of all expressed eRNAs. In addition, we have also used ultra-deep RNA-sequencing of polyA-depleted and polyA-enriched RNA fractions to detect eRNAs and examine for the potential splicing of eRNAs. Combining these genomic data, our annotations provide critical information for the large-scale cloning of eRNAs. We are now testing whether eRNAs originating from ERα enhancers may regulate target gene expression, either in cis or in trans. We cloning specific eRNAs based on our genomic annotations and testing them either by (1) tethering them to their cognate ERα enhancers by using CRISPR/dCas9 technology (i.e., cis) or (2) ectopically expressing them (i.e., trans) in MCF-7 breast cancer cells. Ultimately, our genomic annotation and biochemical tools will allow us to determine the mechanisms by which eRNA contribute to ERα enhancer assembly and function. This work is supported by grants from the NIH/NIDDK (DK058110) and CPRIT (RP160319) to W.L.K.

PHASTEST (PHAge Search Tool with Enhanced Sequence Translation) is the successor to the PHAST and PHASTER prophage finding web servers. PHASTEST is designed to support the rapid identification, annotation and visualization of prophage sequences within bacterial genomes and plasmids. PHASTEST also supports rapid annotation and interactive visualization of all other genes (protein coding regions, tRNA/tmRNA/rRNA sequences) in bacterial genomes. Given that bacterial genome sequencing has become so routine, the need for fast tools to comprehensively annotate bacterial genomes has become progressively more important. PHASTEST not only offers faster and more accurate prophage annotations than its predecessors, it also provides more complete whole genome annotations and much improved genome visualization capabilities. In standardized tests, we found that PHASTEST is 31% faster and 2-3% more accurate in prophage identification than PHASTER. Specifically, PHASTEST can process a typical bacterial genome in 3.2 min(raw sequence) or in 1.3 minwhen given a pre-annotated GenBank file. Improvements in PHASTEST's ability to annotate bacterial genomes now make it a particularly powerful tool for whole genome annotation. In addition, PHASTEST now offers a much more modern and responsive visualization interface that allows users to generate, edit, annotate and interactively visualize (via zooming, rotating, dragging, panning, resetting), colourful, publication quality genome maps. PHASTEST continues to offer popular options such as an API for programmatic queries, a Docker image for local installations, support for multiple (metagenomic) queries and the ability to perform automated look-ups against thousands of previously PHAST-annotated bacterial genomes. PHASTEST is available online at https://phastest.ca.

Phenotypes and diseases are emergent properties of whole organisms. At Mouse Genome Informatics (MGI, www.informatics.jax.org), we curate models of human disease to the mice used in experiments, specifically using the key allele pairs and strain backgrounds that define the full genetic makeup of the mice. In order to derive gene-to-phenotype and gene-to-human disease model relationships from annotated mouse models, we need to identify models that contain mutations in only a single causative gene. These derived gene annotations can then be used to provide users with a high-level summary of gene function and be used in candidate disease gene analysis. Filtering the various kinds of genotypes to determine which phenotypes are caused by a mutation in a particular gene can be a laborious and time-consuming process. At MGI we have developed a gene annotation derivation algorithm that computes gene-to-phenotype and gene-to-disease annotations from our existing corpus of annotations to genotypes (allele pairs and strain background). This algorithm differentiates between simple genotypes with causative mutations in a single gene and more complex genotypes where mutations in multiple genes may contribute to the phenotype. The process identifies alleles functioning as tools (e.g., reporters, recombinases) and filters these out. Several improvements in allele descriptions in MGI have been used to refine the accuracy of this algorithm. These include 1) creation of allele attributes that are used to identify tools, 2) introduction of relationships between alleles and the genes expressed by the allele, 3) introduction of relationships between multi-genic alleles and all genes in the mutation region. Using this algorithm, derived gene-to-phenotype and gene-to-disease annotations were created for 16,000 and 2,100 mouse markers, respectively, starting from over 57,900 and 4,800 genotypes with at least one phenotype and disease annotation, respectively. Implementation of this algorithm provides consistent and accurate gene annotations across MGI and provides a vital time-savings relative to manual annotation by curators. Supported by NIH grant HG000330.