Improved ontology for eukaryotic single-exon coding sequences in biological databases

Indexación: Scopus.Efficient extraction of knowledge from biological data requires the development of structured vocabularies to unambiguously define biological terms. This paper proposes descriptions and definitions to disambiguate the term 'single-exon gene'. Eukaryotic Single-Exon Genes (SEGs) have been defined as genes that do not have introns in their protein coding sequences. They have been studied not only to determine their origin and evolution but also because their expression has been linked to several types of human cancer and neurological/developmental disorders and many exhibit tissue-specific transcription. Unfortunately, the term 'SEGs' is rife with ambiguity, leading to biological misinterpretations. In the classic definition, no distinction is made between SEGs that harbor introns in their untranslated regions (UTRs) versus those without. This distinction is important to make because the presence of introns in UTRs affects transcriptional regulation and post-transcriptional processing of the mRNA. In addition, recent whole-transcriptome shotgun sequencing has led to the discovery of many examples of single-exon mRNAs that arise from alternative splicing of multi-exon genes, these single-exon isoforms are being confused with SEGs despite their clearly different origin. The increasing expansion of RNA-seq datasets makes it imperative to distinguish the different SEG types before annotation errors become indelibly propagated in biological databases. This paper develops a structured vocabulary for their disambiguation, allowing a major reassessment of their evolutionary trajectories, regulation, RNA processing and transport, and provides the opportunity to improve the detection of gene associations with disorders including cancers, neurological and developmental diseases. © The Author(s) 2018. Published by Oxford University Press.https://academic.oup.com/database/article/doi/10.1093/database/bay089/509943

WormBase 2007

WormBase (www.wormbase.org) is the major publicly available database of information about Caenorhabditis elegans, an important system for basic biological and biomedical research. Derived from the initial ACeDB database of C. elegans genetic and sequence information, WormBase now includes the genomic, anatomical and functional information about C. elegans, other Caenorhabditis species and other nematodes. As such, it is a crucial resource not only for C. elegans biologists but the larger biomedical and bioinformatics communities. Coverage of core areas of C. elegans biology will allow the biomedical community to make full use of the results of intensive molecular genetic analysis and functional genomic studies of this organism. Improved search and display tools, wider cross-species comparisons and extended ontologies are some of the features that will help scientists extend their research and take advantage of other nematode species genome sequences

Meeting report: a workshop on Best Practices in Genome Annotation

Efforts to annotate the genomes of a wide variety of model organisms are currently carried out by sequencing centers, model organism databases and academic/institutional laboratories around the world. Different annotation methods and tools have been developed over time to meet the needs of biologists faced with the task of annotating biological data. While standardized methods are essential for consistent curation within each annotation group, methods and tools can differ between groups, especially when the groups are curating different organisms. Biocurators from several institutes met at the Third International Biocuration Conference in Berlin, Germany, April 2009 and hosted the ‘Best Practices in Genome Annotation: Inference from Evidence’ workshop to share their strategies, pipelines, standards and tools. This article documents the material presented in the workshop

Cross-species network and transcript transfer

Metabolic processes, signal transduction, gene regulation, as well as gene and protein expression are largely controlled by biological networks. High-throughput experiments allow the measurement of a wide range of cellular states and interactions. However, networks are often not known in detail for specific biological systems and conditions. Gene and protein annotations are often transferred from model organisms to the species of interest. Therefore, the question arises whether biological networks can be transferred between species or whether they are specific for individual contexts. In this thesis, the following aspects are investigated: (i) the conservation and (ii) the cross-species transfer of eukaryotic protein-interaction and gene regulatory (transcription factor- target) networks, as well as (iii) the conservation of alternatively spliced variants. In the simplest case, interactions can be transferred between species, based solely on the sequence similarity of the orthologous genes. However, such a transfer often results either in the transfer of only a few interactions (medium/high sequence similarity threshold) or in the transfer of many speculative interactions (low sequence similarity threshold). Thus, advanced network transfer approaches also consider the annotations of orthologous genes involved in the interaction transfer, as well as features derived from the network structure, in order to enable a reliable interaction transfer, even between phylogenetically very distant species. In this work, such an approach for the transfer of protein interactions is presented (COIN). COIN uses a sophisticated machine-learning model in order to label transferred interactions as either correctly transferred (conserved) or as incorrectly transferred (not conserved). The comparison and the cross-species transfer of regulatory networks is more difficult than the transfer of protein interaction networks, as a huge fraction of the known regulations is only described in the (not machine-readable) scientific literature. In addition, compared to protein interactions, only a few conserved regulations are known, and regulatory elements appear to be strongly context-specific. In this work, the cross-species analysis of regulatory interaction networks is enabled with software tools and databases for global (ConReg) and thousands of context-specific (CroCo) regulatory interactions that are derived and integrated from the scientific literature, binding site predictions and experimental data. Genes and their protein products are the main players in biological networks. However, to date, the aspect is neglected that a gene can encode different proteins. These alternative proteins can differ strongly from each other with respect to their molecular structure, function and their role in networks. The identification of conserved and species-specific splice variants and the integration of variants in network models will allow a more complete cross-species transfer and comparison of biological networks. With ISAR we support the cross-species transfer and comparison of alternative variants by introducing a gene-structure aware (i.e. exon-intron structure aware) multiple sequence alignment approach for variants from orthologous and paralogous genes. The methods presented here and the appropriate databases allow the cross-species transfer of biological networks, the comparison of thousands of context-specific networks, and the cross-species comparison of alternatively spliced variants. Thus, they can be used as a starting point for the understanding of regulatory and signaling mechanisms in many biological systems.In biologischen Systemen werden Stoffwechselprozesse, Signalübertragungen sowie die Regulation von Gen- und Proteinexpression maßgeblich durch biologische Netzwerke gesteuert. Hochdurchsatz-Experimente ermöglichen die Messung einer Vielzahl von zellulären Zuständen und Wechselwirkungen. Allerdings sind für die meisten Systeme und Kontexte biologische Netzwerke nach wie vor unbekannt. Gen- und Proteinannotationen werden häufig von Modellorganismen übernommen. Demnach stellt sich die Frage, ob auch biologische Netzwerke und damit die systemischen Eigenschaften ähnlich sind und übertragen werden können. In dieser Arbeit wird: (i) Die Konservierung und (ii) die artenübergreifende Übertragung von eukaryotischen Protein-Interaktions- und regulatorischen (Transkriptionsfaktor-Zielgen) Netzwerken, sowie (iii) die Konservierung von Spleißvarianten untersucht. Interaktionen können im einfachsten Fall nur auf Basis der Sequenzähnlichkeit zwischen orthologen Genen übertragen werden. Allerdings führt eine solche Übertragung oft dazu, dass nur sehr wenige Interaktionen übertragen werden können (hoher bis mittlerer Sequenzschwellwert) oder dass ein Großteil der übertragenden Interaktionen sehr spekulativ ist (niedriger Sequenzschwellwert). Verbesserte Methoden berücksichtigen deswegen zusätzlich noch die Annotationen der Orthologen, Eigenschaften der Interaktionspartner sowie die Netzwerkstruktur und können somit auch Interaktionen auf phylogenetisch weit entfernte Arten (zuverlässig) übertragen. In dieser Arbeit wird ein solcher Ansatz für die Übertragung von Protein-Interaktionen vorgestellt (COIN). COIN verwendet Verfahren des maschinellen Lernens, um Interaktionen als richtig (konserviert) oder als falsch übertragend (nicht konserviert) zu klassifizieren. Der Vergleich und die artenübergreifende Übertragung von regulatorischen Interaktionen ist im Vergleich zu Protein-Interaktionen schwieriger, da ein Großteil der bekannten Regulationen nur in der (nicht maschinenlesbaren) wissenschaftlichen Literatur beschrieben ist. Zudem sind im Vergleich zu Protein-Interaktionen nur wenige konservierte Regulationen bekannt und regulatorische Elemente scheinen stark kontextabhängig zu sein. In dieser Arbeit wird die artenübergreifende Analyse von regulatorischen Netzwerken mit Softwarewerkzeugen und Datenbanken für globale (ConReg) und kontextspezifische (CroCo) regulatorische Interaktionen ermöglicht. Regulationen wurden dafür aus Vorhersagen, experimentellen Daten und aus der wissenschaftlichen Literatur abgeleitet und integriert. Grundbaustein für viele biologische Netzwerke sind Gene und deren Proteinprodukte. Bisherige Netzwerkmodelle vernachlässigen allerdings meist den Aspekt, dass ein Gen verschiedene Proteine kodieren kann, die sich von der Funktion, der Proteinstruktur und der Rolle in Netzwerken stark voneinander unterscheiden können. Die Identifizierung von konservierten und artspezifischen Proteinprodukten und deren Integration in Netzwerkmodelle würde einen vollständigeren Übertrag und Vergleich von Netzwerken ermöglichen. In dieser Arbeit wird der artenübergreifende Vergleich von Proteinprodukten mit einem multiplen Sequenzalignmentverfahren für alternative Varianten von paralogen und orthologen Genen unterstützt, unter Berücksichtigung der bekannten Exon-Intron-Grenzen (ISAR). Die in dieser Arbeit vorgestellten Verfahren, Datenbanken und Softwarewerkzeuge ermöglichen die Übertragung von biologischen Netzwerken, den Vergleich von tausenden kontextspezifischen Netzwerken und den artenübergreifenden Vergleich von alternativen Varianten. Sie können damit die Ausgangsbasis für ein Verständnis von Kommunikations- und Regulationsmechanismen in vielen biologischen Systemen bilden

The Echinococcus canadensis (G7) genome: A key knowledge of parasitic platyhelminth human diseases

Background: The parasite Echinococcus canadensis (G7) (phylum Platyhelminthes, class Cestoda) is one of the causative agents of echinococcosis. Echinococcosis is a worldwide chronic zoonosis affecting humans as well as domestic and wild mammals, which has been reported as a prioritized neglected disease by the World Health Organisation. No genomic data, comparative genomic analyses or efficient therapeutic and diagnostic tools are available for this severe disease. The information presented in this study will help to understand the peculiar biological characters and to design species-specific control tools. Results: We sequenced, assembled and annotated the 115-Mb genome of E. canadensis (G7). Comparative genomic analyses using whole genome data of three Echinococcus species not only confirmed the status of E. canadensis (G7) as a separate species but also demonstrated a high nucleotide sequences divergence in relation to E. granulosus (G1). The E. canadensis (G7) genome contains 11,449 genes with a core set of 881 orthologs shared among five cestode species. Comparative genomics revealed that there are more single nucleotide polymorphisms (SNPs) between E. canadensis (G7) and E. granulosus (G1) than between E. canadensis (G7) and E. multilocularis. This result was unexpected since E. canadensis (G7) and E. granulosus (G1) were considered to belong to the species complex E. granulosus sensu lato. We described SNPs in known drug targets and metabolism genes in the E. canadensis (G7) genome. Regarding gene regulation, we analysed three particular features: CpG island distribution along the three Echinococcus genomes, DNA methylation system and small RNA pathway. The results suggest the occurrence of yet unknown gene regulation mechanisms in Echinococcus. Conclusions: This is the first work that addresses Echinococcus comparative genomics. The resources presented here will promote the study of mechanisms of parasite development as well as new tools for drug discovery. The availability of a high-quality genome assembly is critical for fully exploring the biology of a pathogenic organism. The E. canadensis (G7) genome presented in this study provides a unique opportunity to address the genetic diversity among the genus Echinococcus and its particular developmental features. At present, there is no unequivocal taxonomic classification of Echinococcus species; however, the genome-wide SNPs analysis performed here revealed the phylogenetic distance among these three Echinococcus species. Additional cestode genomes need to be sequenced to be able to resolve their phylogeny.Fil: Maldonado, Lucas Luciano. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones en Microbiología y Parasitología Médica. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones en Microbiología y Parasitología Médica; ArgentinaFil: Assis, Juliana. Fundación Oswaldo Cruz; BrasilFil: Gomes Araújo, Flávio M.. Fundación Oswaldo Cruz; BrasilFil: Salim, Anna C. M.. Fundación Oswaldo Cruz; BrasilFil: Macchiaroli, Natalia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones en Microbiología y Parasitología Médica. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones en Microbiología y Parasitología Médica; ArgentinaFil: Cucher, Marcela Alejandra. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones en Microbiología y Parasitología Médica. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones en Microbiología y Parasitología Médica; ArgentinaFil: Camicia, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones en Microbiología y Parasitología Médica. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones en Microbiología y Parasitología Médica; ArgentinaFil: Fox, Adolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones en Microbiología y Parasitología Médica. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones en Microbiología y Parasitología Médica; ArgentinaFil: Rosenzvit, Mara Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones en Microbiología y Parasitología Médica. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones en Microbiología y Parasitología Médica; ArgentinaFil: Oliveira, Guilherme. Instituto Tecnológico Vale; Brasil. Fundación Oswaldo Cruz; BrasilFil: Kamenetzky, Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Investigaciones en Microbiología y Parasitología Médica. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones en Microbiología y Parasitología Médica; Argentin

Doctor of Philosophy

dissertationWhole genome sequencing projects have expanded our understanding of evolution, organism development, and human disease. Now advances in secondgeneration technologies are making whole genome sequencing routine even for small laboratories. However, advances in annotation technology have not kept pace with genome sequencing, and annotation has become the major bottleneck for many genome projects (especially those with limited bioinformatics expertise). At the same time, challenges associated with genomics research extend beyond merely annotating genomes, as annotations must be subjected to diverse downstream analyses, the complexities of which can confound smaller research groups. Additionally, with improvements in genome assembly and the wide availability of next generation transcriptome data (mRNA-seq), researchers have the opportunity to re-annotate previously published genomes, which creates new difficulties for data integration and management that are not well addressed by existing tools. In response to the challenges facing second-generation genome projects, I have developed the annotation pipeline MAKER2 together with accessory software for downstream analysis and data management. The MAKER2 annotation pipeline finds repeats within a genome, aligns ESTs and cDNAs, identifies sites of protein homology, and produces database-ready gene annotations in association with supporting evidence. However MAKER2 can go beyond structural annotation to identify and integrate functional annotations. MAKER2 also provides researchers iv with the capability to re-annotate legacy genome datasets and to incorporate mRNAseq. Additionally, MAKER2 supports distributed parallelization on computer clusters, thus providing a scalable solution for datasets of any size. Annotations produced by MAKER2 can be directly loaded into many popular downstream annotation analysis and management tools from the Generic Model Organism Database Project. By using MAKER2 with these tools, research groups can quickly build genome annotations, perform analyses, and distribute their data to the wider scientific community. Here I describe the internal architecture of MAKER2, and document its computational capabilities. I also describe my work to annotate and analyze eight emerging model organism genomes in collaboration with their associated genome projects. Thus, in the course of my thesis work, I have addressed a specific need within the scientific community for easy-to-use annotation and analysis tools while also expanding our understanding of evolution and biology

Gene structure in the sea urchin Strongylocentrotus purpuratus based on transcriptome analysis

A comprehensive transcriptome analysis has been performed on protein-coding RNAs of Strongylocentrotus purpuratus, including 10 different embryonic stages, six feeding larval and metamorphosed juvenile stages, and six adult tissues. In this study, we pooled the transcriptomes from all of these sources and focused on the insights they provide for gene structure in the genome of this recently sequenced model system. The genome had initially been annotated by use of computational gene model prediction algorithms. A large fraction of these predicted genes were recovered in the transcriptome when the reads were mapped to the genome and appropriately filtered and analyzed. However, in a manually curated subset, we discovered that more than half the computational gene model predictions were imperfect, containing errors such as missing exons, prediction of nonexistent exons, erroneous intron/exon boundaries, fusion of adjacent genes, and prediction of multiple genes from single genes. The transcriptome data have been used to provide a systematic upgrade of the gene model predictions throughout the genome, very greatly improving the research usability of the genomic sequence. We have constructed new public databases that incorporate information from the transcriptome analyses. The transcript-based gene model data were used to define average structural parameters for S. purpuratus protein-coding genes. In addition, we constructed a custom sea urchin gene ontology, and assigned about 7000 different annotated transcripts to 24 functional classes. Strong correlations became evident between given functional ontology classes and structural properties, including gene size, exon number, and exon and intron size

Differential evolution of non-coding DNA across eukaryotes and its close relationship with complex multicellularity on Earth

Here, I elaborate on the hypothesis that complex multicellularity (CM, sensu Knoll) is a major evolutionary transition (sensu Szathmary), which has convergently evolved a few times in Eukarya only: within red and brown algae, plants, animals, and fungi. Paradoxically, CM seems to correlate with the expansion of non-coding DNA (ncDNA) in the genome rather than with genome size or the total number of genes. Thus, I investigated the correlation between genome and organismal complexities across 461 eukaryotes under a phylogenetically controlled framework. To that end, I introduce the first formal definitions and criteria to distinguish ‘unicellularity’, ‘simple’ (SM) and ‘complex’ multicellularity. Rather than using the limited available estimations of unique cell types, the 461 species were classified according to our criteria by reviewing their life cycle and body plan development from literature. Then, I investigated the evolutionary association between genome size and 35 genome-wide features (introns and exons from protein-coding genes, repeats and intergenic regions) describing the coding and ncDNA complexities of the 461 genomes. To that end, I developed ‘GenomeContent’, a program that systematically retrieves massive multidimensional datasets from gene annotations and calculates over 100 genome-wide statistics. R-scripts coupled to parallel computing were created to calculate >260,000 phylogenetic controlled pairwise correlations. As previously reported, both repetitive and non-repetitive DNA are found to be scaling strongly and positively with genome size across most eukaryotic lineages. Contrasting previous studies, I demonstrate that changes in the length and repeat composition of introns are only weakly or moderately associated with changes in genome size at the global phylogenetic scale, while changes in intron abundance (within and across genes) are either not or only very weakly associated with changes in genome size. Our evolutionary correlations are robust to: different phylogenetic regression methods, uncertainties in the tree of eukaryotes, variations in genome size estimates, and randomly reduced datasets. Then, I investigated the correlation between the 35 genome-wide features and the cellular complexity of the 461 eukaryotes with phylogenetic Principal Component Analyses. Our results endorse a genetic distinction between SM and CM in Archaeplastida and Metazoa, but not so clearly in Fungi. Remarkably, complex multicellular organisms and their closest ancestral relatives are characterized by high intron-richness, regardless of genome size. Finally, I argue why and how a vast expansion of non-coding RNA (ncRNA) regulators rather than of novel protein regulators can promote the emergence of CM in Eukarya. As a proof of concept, I co-developed a novel ‘ceRNA-motif pipeline’ for the prediction of “competing endogenous” ncRNAs (ceRNAs) that regulate microRNAs in plants. We identified three candidate ceRNAs motifs: MIM166, MIM171 and MIM159/319, which were found to be conserved across land plants and be potentially involved in diverse developmental processes and stress responses. Collectively, the findings of this dissertation support our hypothesis that CM on Earth is a major evolutionary transition promoted by the expansion of two major ncDNA classes, introns and regulatory ncRNAs, which might have boosted the irreversible commitment of cell types in certain lineages by canalizing the timing and kinetics of the eukaryotic transcriptome.:Cover page Abstract Acknowledgements Index 1. The structure of this thesis 1.1. Structure of this PhD dissertation 1.2. Publications of this PhD dissertation 1.3. Computational infrastructure and resources 1.4. Disclosure of financial support and information use 1.5. Acknowledgements 1.6. Author contributions and use of impersonal and personal pronouns 2. Biological background 2.1. The complexity of the eukaryotic genome 2.2. The problem of counting and defining “genes” in eukaryotes 2.3. The “function” concept for genes and “dark matter” 2.4. Increases of organismal complexity on Earth through multicellularity 2.5. Multicellularity is a “fitness transition” in individuality 2.6. The complexity of cell differentiation in multicellularity 3. Technical background 3.1. The Phylogenetic Comparative Method (PCM) 3.2. RNA secondary structure prediction 3.3. Some standards for genome and gene annotation 4. What is in a eukaryotic genome? GenomeContent provides a good answer 4.1. Background 4.2. Motivation: an interoperable tool for data retrieval of gene annotations 4.3. Methods 4.4. Results 4.5. Discussion 5. The evolutionary correlation between genome size and ncDNA 5.1. Background 5.2. Motivation: estimating the relationship between genome size and ncDNA 5.3. Methods 5.4. Results 5.5. Discussion 6. The relationship between non-coding DNA and Complex Multicellularity 6.1. Background 6.2. Motivation: How to define and measure complex multicellularity across eukaryotes? 6.3. Methods 6.4. Results 6.5. Discussion 7. The ceRNA motif pipeline: regulation of microRNAs by target mimics 7.1. Background 7.2. A revisited protocol for the computational analysis of Target Mimics 7.3. Motivation: a novel pipeline for ceRNA motif discovery 7.4. Methods 7.5. Results 7.6. Discussion 8. Conclusions and outlook 8.1. Contributions and lessons for the bioinformatics of large-scale comparative analyses 8.2. Intron features are evolutionarily decoupled among themselves and from genome size throughout Eukarya 8.3. “Complex multicellularity” is a major evolutionary transition 8.4. Role of RNA throughout the evolution of life and complex multicellularity on Earth 9. Supplementary Data Bibliography Curriculum Scientiae Selbständigkeitserklärung (declaration of authorship

Deep proteogenomics; high throughput gene validation by multidimensional liquid chromatography and mass spectrometry of proteins from the fungal wheat pathogen Stagonospora nodorum

BACKGROUND: Stagonospora nodorum, a fungal ascomycete in the class dothideomycetes, is a damaging pathogen of wheat. It is a model for necrotrophic fungi that cause necrotic symptoms via the interaction of multiple effector proteins with cultivar-specific receptors. A draft genome sequence and annotation was published in 2007. A second-pass gene prediction using a training set of 795 fully EST-supported genes predicted a total of 10762 version 2 nuclear-encoded genes, with an additional 5354 less reliable version 1 genes also retained. RESULTS: In this study, we subjected soluble mycelial proteins to proteolysis followed by 2D LC MALDI-MS/MS. Comparison of the detected peptides with the gene models validated 2134 genes. 62% of these genes (1324) were not supported by prior EST evidence. Of the 2134 validated genes, all but 188 were version 2 annotations. Statistical analysis of the validated gene models revealed a preponderance of cytoplasmic and nuclear localised proteins, and proteins with intracellularassociated GO terms. These statistical associations are consistent with the source of the peptides used in the study. Comparison with a 6-frame translation of the S. nodorum genome assembly confirmed 905 existing gene annotations (including 119 not previously confirmed) and provided evidence supporting 144 genes with coding exon frameshift modifications, 604 genes with extensions of coding exons into annotated introns or untranslated regions (UTRs), 3 new gene annotations which were supported by tblastn to NR, and 44 potential new genes residing within un-assembled regions of the genome. CONCLUSION: We conclude that 2D LC MALDI-MS/MS is a powerful, rapid and economical tool to aid in the annotation of fungal genomic assemblies

Complete reannotation of the Arabidopsis genome: methods, tools, protocols and the final release

BACKGROUND: Since the initial publication of its complete genome sequence, Arabidopsis thaliana has become more important than ever as a model for plant research. However, the initial genome annotation was submitted by multiple centers using inconsistent methods, making the data difficult to use for many applications. RESULTS: Over the course of three years, TIGR has completed its effort to standardize the structural and functional annotation of the Arabidopsis genome. Using both manual and automated methods, Arabidopsis gene structures were refined and gene products were renamed and assigned to Gene Ontology categories. We present an overview of the methods employed, tools developed, and protocols followed, summarizing the contents of each data release with special emphasis on our final annotation release (version 5). CONCLUSION: Over the entire period, several thousand new genes and pseudogenes were added to the annotation. Approximately one third of the originally annotated gene models were significantly refined yielding improved gene structure annotations, and every protein-coding gene was manually inspected and classified using Gene Ontology terms