GAP Final Technical Report 12-14-04

Annotation of prokaryotic sequences can be separated into structural and functional annotation. Structural annotation is dependent on algorithmic interrogation of experimental evidence to discover the physical characteristics of a gene. This is done in an effort to construct accurate gene models, so understanding function or evolution of genes among organisms is not impeded. Functional annotation is dependent on sequence similarity to other known genes or proteins in an effort to assess the function of the gene. Combining structural and functional annotation across genomes in a comparative manner promotes higher levels of accurate annotation as well as an advanced understanding of genome evolution. As the availability of bacterial sequences increases and annotation methods improve, the value of comparative annotation will increase.

Motivation: Downstream analysis of genomic and transcriptomic sequence data is often executed by functional annotation that can be performed by various bioinformatics tools and biological databases. However, a full freely integrated tool is not available for such analysis. Besides, the current available software is not able to produce analytic lists of annotations or graphs to help users in evaluating the output results. Results: we present the Gene Ontology Functional Enrichment Annotation Tool (GO FEAT), a free web platform for functional annotation and enrichment of genomic and transcriptomic data based on sequence homology search. The analysis can be customized and visualized as per users’ needs and specifications. Availability: GO FEAT is freely available at http://computationalbiology.ufpa.br/gofeat/

Simple SummaryDue to the development of high-throughput sequencing technologies, computational genome annotation of sequences has become one of the principal research area in computational biology. First, we reviewed comparative annotation tools and pipelines for both annotations of structures and functions, which enable us to comprehend gene functions and their genome evolution. Second, we compared genome annotation tools that utilize homology-based and ab initio methods depending on the similarity of sequences or the lack of evidences. Third, we explored visualization tools that aid the annotation process and stressed the need for the quality control of annotations and re-annotations, because misannotations may happen due to experimental errors or missed genes by preceding technologies. Finally, we highlighted how emerging technologies can be used in future annotations.Next-Generation Sequencing (NGS) has made it easier to obtain genome-wide sequence data and it has shifted the research focus into genome annotation. The challenging tasks involved in annotation rely on the currently available tools and techniques to decode the information contained in nucleotide sequences. This information will improve our understanding of general aspects of life and evolution and improve our ability to diagnose genetic disorders. Here, we present a summary of both structural and functional annotations, as well as the associated comparative annotation tools and pipelines. We highlight visualization tools that immensely aid the annotation process and the contributions of the scientific community to the annotation. Further, we discuss quality-control practices and the need for re-annotation, and highlight the future of annotation.

BackgroundThe chicken genome was sequenced because of its phylogenetic position as a non-mammalian vertebrate, its use as a biomedical model especially to study embryology and development, its role as a source of human disease organisms and its importance as the major source of animal derived food protein. However, genomic sequence data is, in itself, of limited value; generally it is not equivalent to understanding biological function. The benefit of having a genome sequence is that it provides a basis for functional genomics. However, the sequence data currently available is poorly structurally and functionally annotated and many genes do not have standard nomenclature assigned.ResultsWe analysed eight chicken tissues and improved the chicken genome structural annotation by providing experimental support for the in vivo expression of 7,809 computationally predicted proteins, including 30 chicken proteins that were only electronically predicted or hypothetical translations in human. To improve functional annotation (based on Gene Ontology), we mapped these identified proteins to their human and mouse orthologs and used this orthology to transfer Gene Ontology (GO) functional annotations to the chicken proteins. The 8,213 orthology-based GO annotations that we produced represent an 8% increase in currently available chicken GO annotations. Orthologous chicken products were also assigned standardized nomenclature based on current chicken nomenclature guidelines.ConclusionWe demonstrate the utility of high-throughput expression proteomics for rapid experimental structural annotation of a newly sequenced eukaryote genome. These experimentally-supported predicted proteins were further annotated by assigning the proteins with standardized nomenclature and functional annotation. This method is widely applicable to a diverse range of species. Moreover, information from one genome can be used to improve the annotation of other genomes and inform gene prediction algorithms.

BackgroundDespite the improvements of tools for automated annotation of genome sequences, manual curation at the structural and functional level can provide an increased level of refinement to genome annotation. The Institute for Genomic Research Rice Genome Annotation (hereafter named the Osa1 Genome Annotation) is the product of an automated pipeline and, for this reason, will benefit from the input of biologists with expertise in rice and/or particular gene families. Leveraging knowledge from a dispersed community of scientists is a demonstrated way of improving a genome annotation. This requires tools that facilitate 1) the submission of gene annotation to an annotation project, 2) the review of the submitted models by project annotators, and 3) the incorporation of the submitted models in the ongoing annotation effort.ResultsWe have developed the Eukaryotic Community Annotation Package (EuCAP), an annotation tool, and have applied it to the rice genome. The primary level of curation by community annotators (CA) has been the annotation of gene families. Annotation can be submitted by email or through the EuCAP Web Tool. The CA models are aligned to the rice pseudomolecules and the coordinates of these alignments, along with functional annotation, are stored in the MySQL EuCAP Gene Model database. Web pages displaying the alignments of the CA models to the Osa1 Genome models are automatically generated from the EuCAP Gene Model database. The alignments are reviewed by the project annotators (PAs) in the context of experimental evidence. Upon approval by the PAs, the CA models, along with the corresponding functional annotations, are integrated into the Osa1 Genome Annotation. The CA annotations, grouped by family, are displayed on the Community Annotation pages of the project website , as well as in the Community Annotation track of the Genome Browser.ConclusionWe have applied EuCAP to rice. As of July 2007, the structural and/or functional annotation of 1,094 genes representing 57 families have been deposited and integrated into the current gene set. All of the EuCAP components are open-source, thereby allowing the implementation of EuCAP for the annotation of other genomes. EuCAP is available at .

BackgroundGenome annotation is one way of summarizing the existing knowledge about genomic characteristics of an organism. There has been an increased interest during the last several decades in computer-based structural and functional genome annotation. Many methods for this purpose have been developed for eukaryotes and prokaryotes. Our study focuses on comparison of functional annotations of prokaryotic genomes. To the best of our knowledge there is no fully automated system for detailed comparison of functional genome annotations generated by different annotation methods (AMs).ResultsThe presence of many AMs and development of new ones introduce needs to: a/ compare different annotations for a single genome, and b/ generate annotation by combining individual ones. To address these issues we developed an Automated Tool for Bacterial GEnome Annotation ComparisON (BEACON) that benefits both AM developers and annotation analysers. BEACON provides detailed comparison of gene function annotations of prokaryotic genomes obtained by different AMs and generates extended annotations through combination of individual ones. For the illustration of BEACON’s utility, we provide a comparison analysis of multiple different annotations generated for four genomes and show on these examples that the extended annotation can increase the number of genes annotated by putative functions up to 27 %, while the number of genes without any function assignment is reduced.ConclusionsWe developed BEACON, a fast tool for an automated and a systematic comparison of different annotations of single genomes. The extended annotation assigns putative functions to many genes with unknown functions. BEACON is available under GNU General Public License version 3.0 and is accessible at: http://www.cbrc.kaust.edu.sa/BEACON/.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1826-4) contains supplementary material, which is available to authorized users.

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At UCL we have developed several automated protocols for generating protein family resources (CATH; Gene3D). These resources can be used to perform comparative genome analyses in order to understand the evolution of protein families. Also to identify biologically and/or medically interesting families for which no structural data currently exists and which may therefore be important targets for structure genomics initiatives.The CATH domain structure database, established by Orengo and Thornton in 1993, now contains a significant proportion of protein structures from the PDB clustered into 1400 evolutionary families. Relationships have been identified using robust structure comparison methods (SSAP, CATHEDRAL). We have also benchmarked and optimised various 1D-profiles and HMM based protocols for assigning genome sequences to families within the resource (e.g. SAM-T99, SAMOSA, CATH-ISL).In this way we can assign structural data to a large proportion (up to 60%) of whole or partial sequences in completed genomes and >80% of genes coding for enzymes and other proteins in biochemical pathways. However, in order to include all families regardless of whether their structure is known or not, a new protein family resource has been developed (Gene3D). In Gene3D, complete genes have been clustered according to sequence similarity alone, using a robust clustering method (Pfscape). 120 completed genomes from all kingdoms have been clustered into 220,000 gene families, 70,000 of which contain 2 or more sequences. Subsequently, we have labelled those gene families for which CATH structural or Pfam functional domain annotations can be provided for all or part of the gene.Preliminary analysis of the genome annotations reveals that a significant proportion (up to 70%) of CATH annotated genes or gene regions in genomes are assigned to domain families that are common to all three kingdoms of life. However, only 20% of the genome sequences are assigned to gene families common to all kingdoms. Since a large proportion of these genes are multidomain proteins this supports the view that a great deal of functional diversity within the genomes has been achieved by combining domain modules in different ways.In collaboration with Professor Janet Thornton, we have analysed a subset of 56 bacterial genomes to determine the recurrence of specific domain structure families within the genomes. This revealed a small but essential group of universal, and in some cases, highly recurring domain families. For some size-dependent families, domain recurrence is highly correlated with increase in genome size, whilst in other size-independent families no correlation is observed. Statistical analysis allowed us to distinguish three groups. Within the size-dependent families we differentiated two groups: linearly-distributed and non-linearly-distributed. Functional annotation using the COGs revealed that these domains were predominantly involved in metabolism and regulation, respectively. Whilst a third group of Evenly-distributed size independent domains are primarily involved in protein translation and biosynthesis.By mapping CATH and Pfam domains families onto all the genome sequences in Gene3D we observe that a few hundred highly recurrent families are dominating at least 50% of whole or partial genome sequences. Many of these families are common to both prokaryotes and eukaryotes and are performing essential generic functions. In many of the largest families, significant divergence in sequence has been accompanied by modifications in structure and function. Targetting representatives in these families for structure determination will allow the structure genomics initiatives to map both fold and function space and reveal the mechanisms by which divergence in protein families promotes evolution of new functions.

Ensuring high-quality functional annotations in newly sequenced genomes has become a fundamental problem in next-generation sequencing genomics. This problem takes additional relevance in bacteriophage genomics, where well-annotated reference genomes are scarce and often highly diverged from newly sequenced genomes. This makes automated annotation pipelines especially prone to introduce and propagate functional annotation errors, devaluing the contribution of sequencing projects to the understanding of bacteriophage biology and decreasing the likelihood that novel genetic mechanisms with industrial and clinical application can be mined from the extremely diverse genetic repertoire of bacteriophage genomes. Genome annotation by expert biocurators ensures high-quality annotations, but such approaches are hard to scale and raise financial sustainability issues. Here we show how undergraduate biology courses devoted to genome annotation can be leveraged to generate high-quality functional annotations of bacteriophage genomes by integrating targeted training within the framework of an intercampus annotation competition. Students receive advanced training on biological ontologies, orthology assessment methods, biocuration standards and critical reading of scientific manuscripts. Working in small teams, students then participate in the Community Assessment of Community Annotation with Ontologies (CACAO) intercampus competition, generating Gene Ontology (GO) annotations of bacteriophage and bacterial gene products and challenging the accuracy of annotations made by other teams in a pre-established set of innings for annotation, challenge and revision. Revised annotations are reviewed by an expert panel before submission to the Gene Ontology Consortium. This approach yields tangible benefits on many fronts. Students develop critical reading skills, get exposed to key concepts in genome biology and obtain essential hands-on experience on the use of ontologies and bioinformatics methods to assess orthology, synteny and other genetic features. Team-work in a competitive framework and intercampus rivalry motivate students to improve the accuracy of their annotations, leading to high-quality functional annotations on both reference and newly sequenced bacteriophage genomes.

Wheat genome sequencing has passed through major steps in a decade, starting from the sequencing of large contiguous sequences obtained from chromosome-specific BAC libraries, to reach high-quality genome assemblies of a dozen of bread wheat varieties and wild relatives. While access to an assembled genome sequence is crucial for research, the resource that is mainly used by the community is not the sequence itself, but rather the annotated features, i.e., genes and transposable elements. In this chapter, we describe the work performed to predict the repertoire of 107 k high-confidence genes and 4 million TE copies in the hexaploid wheat genome (cultivar CHINESE SPRING; IWGSC RefSeq) and the procedures established to transfer the annotation through the different releases of genome assembly. Limitations and implications for building a wheat pangenome are discussed, as well as the possibilities for future improvements of structural annotation, and opportunities offered by novel approaches for functional annotation.

: Evolutionary trace report_maker offers a new type of service for researchers investigating the function of novel proteins. It pools, from different sources, information about protein sequence, structure and elementary annotation, and to that background superimposes inference about the evolutionary behavior of individual residues, using real-valued evolutionary trace method. As its only input it takes a Protein Data Bank identifier or UniProt accession number, and returns a human-readable document in PDF format, supplemented by the original data needed to reproduce the results quoted in the report.

The EOSC-Life project aims to create an open collaborative digital space for life science in the European Open Science Cloud (EOSC). The ABiMS bioinformatics platform, member of the European Marine Biological Resource Centre (EMBRC) research infrastructure, is involved in the work package 2 (WP2) dedicated to make computational tools, workflows and registries findable, accessible, interoperable and reusable (FAIR). The Galaxy Genome Annotation [1](GGA) ( https://galaxy-genome-annotation.github.io ) project consists of several projects and tool suites that are working closely together to deliver a comprehensive, scalable and easy to use Genome Annotation experience. The Galaxy Genome annotation environment not only offers a wide array of high-profile tools in Galaxy [2] for structural and functional annotation, but also a highly integrated set of “dockerized” GMOD tools [3], a collection of open-source applications for visualizing, annotating, and managing genomic data (JBrowse, Apollo, Tripal, Chado). Galaxy is used as a data loading orchestrator for administrators, with dedicated Galaxy tools and workflows to interact with GMOD tools, and Python libraries to make all tools work together. As part of the EOSC-Life WP2, we planned to provide the GGA environment available in the cloud. So we are currently developing Ansible recipes ( https://github.com/abims-sbr/GGA_Cloud ) to deploy the GGA environment in an Openstack cloud infrastructure. These Ansible recipes allow the deployment of a Virtual Machine in a cloud via the Terraform software, the installation of the GGA environment and its dependencies, as well as loading data into the Galaxy library.

BackgroundProtein function is often dependent on subsets of solvent-exposed residues that may exist in a similar three-dimensional configuration in non homologous proteins thus having different order and/or spacing in the sequence. Hence, functional annotation by means of sequence or fold similarity is not adequate for such cases.ResultsWe describe a method for the function-related annotation of protein structures by means of the detection of local structural similarity with a library of annotated functional sites. An automatic procedure was used to annotate the function of local surface regions. Next, we employed a sequence-independent algorithm to compare exhaustively these functional patches with a larger collection of protein surface cavities. After tuning and validating the algorithm on a dataset of well annotated structures, we applied it to a list of protein structures that are classified as being of unknown function in the Protein Data Bank. By this strategy, we were able to provide functional clues to proteins that do not show any significant sequence or global structural similarity with proteins in the current databases.ConclusionThis method is able to spot structural similarities associated to function-related similarities, independently on sequence or fold resemblance, therefore is a valuable tool for the functional analysis of uncharacterized proteins. Results are available at

The human gastrointestinal tract contains diverse microbial communities, including archaea. Among them, Methanobrevibacter smithii represents a highly active and clinically relevant methanogenic archaeon, being involved in gastrointestinal disorders, such as inflammatory bowel disease and obesity. Herein, we present an integrated approach using sequence and structure information to improve the annotation of M. smithii proteins using advanced protein structure prediction and annotation tools, such as AlphaFold2, trRosetta, ProFunc, and DeepFri. Of an initial set of 873 481 archaeal proteins, we found 707 754 proteins exclusively present in the human gut. Having analysed archaeal proteins together with 87 282 994 bacterial proteins, we identified unique archaeal proteins and archaeal–bacterial homologs. We then predicted and characterized functional domains and structures of 73 unique and homologous archaeal protein clusters linked the human gut and M. smithii. We refined annotations based on the predicted structures, extending existing sequence similarity-based annotations. We identified gut-specific archaeal proteins that may be involved in defense mechanisms, virulence, adhesion, and the degradation of toxic substances. Interestingly, we identified potential glycosyltransferases that could be associated with N-linked and O-glycosylation. Additionally, we found preliminary evidence for interdomain horizontal gene transfer between Clostridia species and M. smithii, which includes sporulation Stage V proteins AE and AD. Our study broadens the understanding of archaeal biology, particularly M. smithii, and highlights the importance of considering both sequence and structure for the prediction of protein function.

Comprehensive protein function annotation is essential for understanding microbiome-related disease mechanisms in the host organisms. However, a large portion of human gut microbial proteins lack functional annotation. Here, we have developed a new metagenome analysis workflow integrating de novo genome reconstruction, taxonomic profiling, and deep learning-based functional annotations from DeepFRI. This is the first approach to apply deep learning-based functional annotations in metagenomics. We validate DeepFRI functional annotations by comparing them to orthology-based annotations from eggNOG on a set of 1,070 infant metagenomes from the DIABIMMUNE cohort. Using this workflow, we generated a sequence catalogue of 1.9 million nonredundant microbial genes. The functional annotations revealed 70% concordance between Gene Ontology annotations predicted by DeepFRI and eggNOG. DeepFRI improved the annotation coverage, with 99% of the gene catalogue obtaining Gene Ontology molecular function annotations, although they are less specific than those from eggNOG. Additionally, we constructed pangenomes in a reference-free manner using high-quality metagenome-assembled genomes (MAGs) and analyzed the associated annotations. eggNOG annotated more genes on well-studied organisms, such as Escherichia coli, while DeepFRI was less sensitive to taxa. Further, we show that DeepFRI provides additional annotations in comparison to the previous DIABIMMUNE studies. This workflow will contribute to novel understanding of the functional signature of the human gut microbiome in health and disease as well as guiding future metagenomics studies. IMPORTANCE The past decade has seen advancement in high-throughput sequencing technologies resulting in rapid accumulation of genomic data from microbial communities. While this growth in sequence data and gene discovery is impressive, the majority of microbial gene functions remain uncharacterized. The coverage of functional information coming from either experimental sources or inferences is low. To solve these challenges, we have developed a new workflow to computationally assemble microbial genomes and annotate the genes using a deep learning-based model DeepFRI. This improved microbial gene annotation coverage to 1.9 million metagenome-assembled genes, representing 99% of the assembled genes, which is a significant improvement compared to 12% Gene Ontology term annotation coverage by commonly used orthology-based approaches. Importantly, the workflow supports pangenome reconstruction in a reference-free manner, allowing us to analyze the functional potential of individual bacterial species. We therefore propose this alternative approach combining deep-learning functional predictions with the commonly used orthology-based annotations as one that could help us uncover novel functions observed in metagenomic microbiome studies.