Genome Annotation Tools Research Articles

Deciphering the tissue-specific functional effect of Alzheimer risk SNPs with deep genome annotation

Alzheimer’s disease (AD) is a highly heritable brain dementia, along with substantial failure of cognitive function. Large-scale genome-wide association studies (GWASs) have led to a set of SNPs significantly associated with AD and related traits. GWAS hits usually emerge as clusters where a lead SNP with the highest significance is surrounded by other less significant neighboring SNPs. Although functionality is not guaranteed even with the strongest associations in GWASs, lead SNPs have historically been the focus of the field, with the remaining associations inferred to be redundant. Recent deep genome annotation tools enable the prediction of function from a segment of a DNA sequence with significantly improved precision, which allows in-silico mutagenesis to interrogate the functional effect of SNP alleles. In this project, we explored the impact of top AD GWAS hits around APOE region on chromatin functions and whether it will be altered by the genetic context (i.e., alleles of neighboring SNPs). Our results showed that highly correlated SNPs in the same LD block could have distinct impacts on downstream functions. Although some GWAS lead SNPs showed dominant functional effects regardless of the neighborhood SNP alleles, several other SNPs did exhibit enhanced loss or gain of function under certain genetic contexts, suggesting potential additional information hidden in the LD blocks.

Bioinformatic Resources for Plant Genomic Research

Abstract: Genome assembly and annotation are crucial steps in plant genomics research as they provide valuable insights into plant genetic makeup, gene regulation, evolutionary history, and biological processes. In the emergence of high-throughput sequencing technologies, a plethora of genome assembly tools have been developed to meet the diverse needs of plant genome researchers. Choosing the most suitable tool to suit a specific research need can be daunting due to the complex and varied nature of plant genomes and reads from the sequencers. To assist informed decision-making in selecting the appropriate genome assembly and annotation tool(s), this review offers an extensive overview of the most widely used genome and transcriptome assembly tools. The review covers the specific information on each tool in tabular data, and the data types it can process. In addition, the review delves into transcriptome assembly tools, plant resource databases, and repositories (12 for Arabidopsis, 9 for Rice, 5 for Tomato, and 8 general use resources), which are vital for gene expression profiling and functional annotation and ontology tools that facilitate data integration and analysis.

Probiotic functional gene explorations in the genome of Limosilactobacillus fermentum GD5MG

Limosilactobacillus fermentum is an isolate obtained from oral gingival samples of healthy human individuals. The whole genome of Lb. fermentum GD5MG is composed of a circular DNA molecule containing 1,834,134 bp and exhibits a GC content of 52.80 %. The sequencing effort produced 38.6 million reads, each 150 bp in length, resulting in a sequencing depth of 2912.48x. Our examination unveiled a total of 1961 protein-coding genes, 27 rRNA genes, 24 tRNA genes, 3 non-coding RNA genes, and 63 pseudogenes with the use of gene annotations in NCBI Prokaryotic Genome Annotation tool. RAST revealed 1863 coding genes distributed across 209 subsystems, with a predominant involvement in amino acid, carbohydrate, and protein metabolism. Phylogenetic analysis infers that the Lb. fermentum GD5MG shares 281 gene clusters. Furthermore, the genome features showed a single CRISPR locus of 45 bp in length. Three genes associated with adhesion ability (strA, dltD, and dltA) and 26 genes related to acid tolerance, digestive enzyme secretion, and bile salt resistance were identified. Numerous genes associated with oral probiotic properties, comprising adhesion, acid and bile salt tolerance, oxidative stress tolerance, and sugar metabolism, were identified in the genome. Our findings shed light on the genomic characteristics of Lb. fermentum GD5MG, which are probable probiotics with functional benefits in humans.

BRAKER3: Fully automated genome annotation using RNA-seq and protein evidence with GeneMark-ETP, AUGUSTUS, and TSEBRA.

Gene prediction has remained an active area of bioinformatics research for a long time. Still, gene prediction in large eukaryotic genomes presents a challenge that must be addressed by new algorithms. The amount and significance of the evidence available from transcriptomes and proteomes vary across genomes, between genes, and even along a single gene. User-friendly and accurate annotation pipelines that can cope with such data heterogeneity are needed. The previously developed annotation pipelines BRAKER1 and BRAKER2 use RNA-seq or protein data, respectively, but not both. A further significant performance improvement integrating all three data types was made by the recently released GeneMark-ETP. We here present the BRAKER3 pipeline that builds on GeneMark-ETP and AUGUSTUS, and further improves accuracy using the TSEBRA combiner. BRAKER3 annotates protein-coding genes in eukaryotic genomes using both short-read RNA-seq and a large protein database, along with statistical models learned iteratively and specifically for the target genome. We benchmarked the new pipeline on genomes of 11 species under an assumed level of relatedness of the target species proteome to available proteomes. BRAKER3 outperforms BRAKER1 and BRAKER2. The average transcript-level F1-score is increased by about 20 percentage points on average, whereas the difference is most pronounced for species with large and complex genomes. BRAKER3 also outperforms other existing tools, MAKER2, Funannotate, and FINDER. The code of BRAKER3 is available on GitHub and as a ready-to-run Docker container for execution with Docker or Singularity. Overall, BRAKER3 is an accurate, easy-to-use tool for eukaryotic genome annotation.

Biodesulfurization of organosulfur compounds by a trehalose biosurfactant producing Gordonia sp. isolated from crude oil contaminated soil.

Certain factors hinder the commercialization of biodesulfurization process, including low substrate-specificity of the currently reported desulfurizing bacteria and restricted mass transfer of organic-sulfur compounds in biphasic systems. These obstacles must be addressed to clean organic-sulfur rich petro-fuels that pose serious environmental and health challenges. In current study, a dibenzothiophene desulfurizing strain, Gordonia rubripertincta W3S5 (source: oil contaminated soil) was systematically evaluated for its potential to remove sulfur from individual compounds and mixture of organic-sulfur compounds. Metabolic and genetic analyses confirmed that strain W3S5 desulfurized dibenzothiophene to 2-hydroxybiphenyl, suggesting that it follows the sulfur specific 4S pathway. Furthermore, this strain demonstrated the ability to produce trehalose biosurfactants (with an EI24 of 53%) in the presence of dibenzothiophene, as confirmed by TLC and FTIR analyses. Various genome annotation tools, such as ClassicRAST, BlastKOALA, BV-BRC, and NCBI-PGAP, predicted the presence of otsA, otsB, treY, treZ, treP, and Trehalose-monomycolate lipid synthesis genes in the genomic pool of strain W3S5, confirming the existence of the OtsAB, TreYZ, and TreP pathways. Overall, these results underscore the potential of strain W3S5 as a valuable candidate for enhancing desulfurization efficiency and addressing the mass transfer challenges essential for achieving a scaled-up scenario.

CoVEx: SARS-CoV-2 Mutation Explorer for genomic surveillance

Effective management of emerging diseases relies on timely pathogen identification and monitoring. The emergence of COVID-19 in December 2019, rapidly evolved into a global pandemic, with millions of cases and deaths reported worldwide. The accumulation of SARS-CoV-2 genomes provided unprecedented opportunities for studying the virus's evolutionary dynamics, understanding the impact of mutations, and identifying emerging Variants of Interest (VOIs) and Variants of Concern (VOCs). During the COVID-19 pandemic, health systems faced challenges in promptly detecting such variants and timely notifying. To facilitate the continuous monitoring of mutations, various initiatives and open-source pipelines have been established. However, these platforms often lack integration for conducting user sequence analysis and comparing it with publicly reported data on platforms like GISAID. Here, we present CoVEx, an easy-to-use tool for analyzing and visualizing SARS-CoV-2 variant sequences obtained using Illumina sequencing technology. CoVEx integrates quality control, alignment, genome annotation, lineage designation, and mutation analysis tools. Implemented in Python, CoVEx also has a mutation explorer feature that generates interactive graphs summarising identified mutations in an intuitive manner. Similarly, it leverages the Outbreak.info package to create heatmaps highlighting the mutations associated with designated Pangolin lineages. Furthermore, by comparing mutation profiles against GISAID data, CoVEx offers valuable insights into the prevalence and distribution of mutations worldwide. We validated CoVEx using raw sequence data (n = 108) and demonstrated its accuracy in assembling sequences and predicting Pangolin and Nextclade Pango lineages. Notably, the tool revealed the emergence of a previously unreported mutation, ORF1a:I2501T, within the Costa Rica GN.1 lineage. This finding highlights CoVEx's capability to identify novel mutations in the different lineages, providing valuable information to researchers and public health decision makers. CoVEx and documentation are freely available on GitLab: https://gitlab.com/CNCA_CeNAT/covex.

AgAnimalGenomes: browsers for viewing and manually annotating farm animal genomes.

Current genome sequencing technologies have made it possible to generate highly contiguous genome assemblies for non-model animal species. Despite advances in genome assembly methods, there is still room for improvement in the delineation of specific gene features in the genomes. Here we present genome visualization and annotation tools to support seven livestock species (bovine, chicken, goat, horse, pig, sheep, and water buffalo), available in a new resource called AgAnimalGenomes. In addition to supporting the manual refinement of gene models, these browsers provide visualization tracks for hundreds of RNAseq experiments, as well as data generated by the Functional Annotation of Animal Genomes (FAANG) Consortium. For species with predicted gene sets from both Ensembl and RefSeq, the browsers provide special tracks showing the thousands of protein-coding genes that disagree across the two gene sources, serving as a valuable resource to alert researchers to gene model issues that may affect data interpretation. We describe the data and search methods available in the new genome browsers and how to use the provided tools to edit and create new gene models.

Mitochondrial genome annotation with MFannot: a critical analysis of gene identification and gene model prediction.

Compared to nuclear genomes, mitochondrial genomes (mitogenomes) are small and usually code for only a few dozen genes. Still, identifying genes and their structure can be challenging and time-consuming. Even automated tools for mitochondrial genome annotation often require manual analysis and curation by skilled experts. The most difficult steps are (i) the structural modelling of intron-containing genes; (ii) the identification and delineation of Group I and II introns; and (iii) the identification of moderately conserved, non-coding RNA (ncRNA) genes specifying 5S rRNAs, tmRNAs and RNase P RNAs. Additional challenges arise through genetic code evolution which can redefine the translational identity of both start and stop codons, thus obscuring protein-coding genes. Further, RNA editing can render gene identification difficult, if not impossible, without additional RNA sequence data. Current automated mito- and plastid-genome annotators are limited as they are typically tailored to specific eukaryotic groups. The MFannot annotator we developed is unique in its applicability to a broad taxonomic scope, its accuracy in gene model inference, and its capabilities in intron identification and classification. The pipeline leverages curated profile Hidden Markov Models (HMMs), covariance (CMs) and ERPIN models to better capture evolutionarily conserved signatures in the primary sequence (HMMs and CMs) as well as secondary structure (CMs and ERPIN). Here we formally describe MFannot, which has been available as a web-accessible service (https://megasun.bch.umontreal.ca/apps/mfannot/) to the research community for nearly 16 years. Further, we report its performance on particularly intron-rich mitogenomes and describe ongoing and future developments.

Endogenous Caulimovirids: Fossils, Zombies, and Living in Plant Genomes.

The Caulimoviridae is a family of double-stranded DNA viruses that infect plants. The genomes of most vascular plants contain endogenous caulimovirids (ECVs), a class of repetitive DNA elements that is abundant in some plant genomes, resulting from the integration of viral DNA in the chromosomes of germline cells during episodes of infection that have sometimes occurred millions of years ago. In this review, we reflect on 25 years of research on ECVs that has shown that members of the Caulimoviridae have occupied an unprecedented range of ecological niches over time and shed light on their diversity and macroevolution. We highlight gaps in knowledge and prospects of future research fueled by increased access to plant genome sequence data and new tools for genome annotation for addressing the extent, impact, and role of ECVs on plant biology and the origin and evolutionary trajectories of the Caulimoviridae.

Going through phages: a computational approach to revealing the role of prophage in Staphylococcus aureus.

Prophages have important roles in virulence, antibiotic resistance, and genome evolution in Staphylococcus aureus . Rapid growth in the number of sequenced S. aureus genomes allows for an investigation of prophage sequences at an unprecedented scale. We developed a novel computational pipeline for phage discovery and annotation. We combined PhiSpy, a phage discovery tool, with VGAS and PROKKA, genome annotation tools to detect and analyse prophage sequences in nearly 10 011 S . aureus genomes, discovering thousands of putative prophage sequences with genes encoding virulence factors and antibiotic resistance. To our knowledge, this is the first large-scale application of PhiSpy on a large-scale set of genomes (10 011 S . aureus ). Determining the presence of virulence and resistance encoding genes in prophage has implications for the potential transfer of these genes/functions to other bacteria via transduction and thus can provide insight into the evolution and spread of these genes/functions between bacterial strains. While the phage we have identified may be known, these phages were not necessarily known or characterized in S. aureus and the clustering and comparison we did for phage based on their gene content is novel. Moreover, the reporting of these genes with the S. aureus genomes is novel.

Progress, challenge and prospect of plant plastome annotation.

The plastome (plastid genome) represents an indispensable molecular data source for studying phylogeny and evolution in plants. Although the plastome size is much smaller than that of nuclear genome, and multiple plastome annotation tools have been specifically developed, accurate annotation of plastomes is still a challenging task. Different plastome annotation tools apply different principles and workflows, and annotation errors frequently occur in published plastomes and those issued in GenBank. It is therefore timely to compare available annotation tools and establish standards for plastome annotation. In this review, we review the basic characteristics of plastomes, trends in the publication of new plastomes, the annotation principles and application of major plastome annotation tools, and common errors in plastome annotation. We propose possible methods to judge pseudogenes and RNA-editing genes, jointly consider sequence similarity, customed algorithms, conserved domain or protein structure. We also propose the necessity of establishing a database of reference plastomes with standardized annotations, and put forward a set of quantitative standards for evaluating plastome annotation quality for the scientific community. In addition, we discuss how to generate standardized GenBank annotation flatfiles for submission and downstream analysis. Finally, we prospect future technologies for plastome annotation integrating plastome annotation approaches with diverse evidences and algorithms of nuclear genome annotation tools. This review will help researchers more efficiently use available tools to achieve high-quality plastome annotation, and promote the process of standardized annotation of the plastome.

PHASTEST: faster than PHASTER, better than PHAST.

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.

PhaGAA: an integrated web server platform for phage genome annotation and analysis.

Phage genome annotation plays a key role in the design of phage therapy. To date, there have been various genome annotation tools for phages, but most of these tools focus on mono-functional annotation and have complex operational processes. Accordingly, comprehensive and user-friendly platforms for phage genome annotation are needed. Here, we propose PhaGAA, an online integrated platform for phage genome annotation and analysis. By incorporating several annotation tools, PhaGAA is constructed to annotate prophage genome at DNA- and protein-levels and provide the analytical results. Furthermore, PhaGAA could mine and annotate phage genomes from bacterial genome or metagenome. In summary, PhaGAA will be a useful resource for experimental biologists, and help advance the phage synthetic biology in basic and application research. PhaGAA is freely available at http://phage.xialab.info/. Supplementary data are available at Bioinformatics online.

StandEnA: a customizable workflow for standardized annotation and generating a presence-absence matrix of proteins.

Several genome annotation tools standardize annotation outputs for comparability. During standardization, these tools do not allow user-friendly customization of annotation databases; limiting their flexibility and applicability in downstream analysis. StandEnA is a user-friendly command-line tool for Linux that facilitates the generation of custom databases by retrieving protein sequences from multiple databases. Directed by a user-defined list of standard names, StandEnA retrieves synonyms to search for corresponding sequences in a set of public databases. Custom databases are used in prokaryotic genome annotation to generate standardized presence-absence matrices and reference files containing standard database identifiers. To showcase StandEnA, we applied it to six metagenome-assembled genomes to analyze three different pathways. StandEnA is an open-source software available at https://github.com/mdsufz/StandEnA. Supplementary data are available at Bioinformatics Advances online.

Structure-guided isoform identification for the human transcriptome.

Recently developed methods to predict three-dimensional protein structure with high accuracy have opened new avenues for genome and proteome research. We explore a new hypothesis in genome annotation, namely whether computationally predicted structures can help to identify which of multiple possible gene isoforms represents a functional protein product. Guided by protein structure predictions, we evaluated over 230,000 isoforms of human protein-coding genes assembled from over 10,000 RNA sequencing experiments across many human tissues. From this set of assembled transcripts, we identified hundreds of isoforms with more confidently predicted structure and potentially superior function in comparison to canonical isoforms in the latest human gene database. We illustrate our new method with examples where structure provides a guide to function in combination with expression and evolutionary evidence. Additionally, we provide the complete set of structures as a resource to better understand the function of human genes and their isoforms. These results demonstrate the promise of protein structure prediction as a genome annotation tool, allowing us to refine even the most highly curated catalog of human proteins. More generally we demonstrate a practical, structure-guided approach that can be used to enhance the annotation of any genome.

IProm-phage: A two-layer model to identify phage promoters and their types using a convolutional neural network

The increased interest in phages as antibacterial agents has resulted in a rise in the number of sequenced phage genomes, necessitating the development of user-friendly bioinformatics tools for genome annotation. A promoter is a DNA sequence that is used in the annotation of phage genomes. In this study we proposed a two layer model called "iProm-phage" for the prediction and classification of phage promoters. Model first layer identify query sequence as promoter or non-promoter and if the query sequence is predicted as promoter then model second layer classify it as phage or host promoter. Furthermore, rather than using non-coding regions of the genome as a negative set, we created a more challenging negative dataset using promoter sequences. The presented approach improves discrimination while decreasing the frequency of erroneous positive predictions. For feature selection, we investigated 10 distinct feature encoding approaches and utilized them with several machine-learning algorithms and a 1-D convolutional neural network model. We discovered that the one-hot encoding approach and the CNN model outperformed based on performance metrics. Based on the results of the 5-fold cross validation, the proposed predictor has a high potential. Furthermore, to make it easier for other experimental scientists to obtain the results they require, we set up a freely accessible and user-friendly web server at http://nsclbio.jbnu.ac.kr/tools/iProm-phage/.

Genomic and Bioinformatic Resources for Perennial Fruit Species.

In the post-genomic era, data management and development of bioinformatic tools are critical for the adequate exploitation of genomics data. In this review, we address the actual situation for the subset of crops represented by the perennial fruit species. The agronomical singularity of these species compared to plant and crop model species provides significant challenges on the implementation of good practices generally not addressed in other species. Studies are usually performed over several years in non-controlled environments, usage of rootstock is common, and breeders heavily rely on vegetative propagation. A reference genome is now available for all the major species as well as many members of the economically important genera for breeding purposes. Development of pangenome for these species is beginning to gain momentum which will require a substantial effort in term of bioinformatic tool development. The available tools for genome annotation and functional analysis will also be presented.

Prokaryotic Genome Annotation.

In the last decade, the high-throughput and relatively low cost of short-read sequencing technologies have revolutionized prokaryotic genomics. This has led to an exponential increase in the number of bacterial and archaeal genome sequences available, as well as corresponding increase of genome assembly and annotation tools developed. Together, these hardware and software technologies have given scientists unprecedented options to study their chosen microbial systems without the need for large teams of bioinformaticists or supercomputing facilities. While these analysis tools largely fall into only a few categories, each may have different requirements, caveats and file formats, and some may be rarely updated or even abandoned. And so, despite the apparent ease in sequencing and analyzing a prokaryotic genome, it is no wonder that the budding genomicist may quickly find oneself overwhelmed. Here, we aim to provide the reader with an overview of genome annotation and its most important considerations, as well as an easy-to-follow protocol to get started with annotating a prokaryotic genome.

Annotation depth confounds direct comparison of gene expression across species

BackgroundComparisons of the molecular framework among organisms can be done on both structural and functional levels. One of the most common top-down approaches for functional comparisons is RNA sequencing. This estimation of organismal transcriptional responses is of interest for understanding evolution of molecular activity, which is used for answering a diversity of questions ranging from basic biology to pre-clinical species selection and translation. However, direct comparison between species is often hindered by evolutionary divergence in structure of molecular framework, as well as large difference in the depth of our understanding of the genetic background between humans and other species. Here, we focus on the latter. We attempt to understand how differences in transcriptome annotation affect direct gene abundance comparisons between species.ResultsWe examine and suggest some straightforward approaches for direct comparison given the current available tools and using a sample dataset from human, cynomolgus monkey, dog, rat and mouse with a common quantitation and normalization approach. In addition, we examine how variation in genome annotation depth and quality across species may affect these direct comparisons.ConclusionsOur findings suggest that further efforts for better genome annotation or computational normalization tools may be of strong interest.

Balrog: A universal protein model for prokaryotic gene prediction.

Low-cost, high-throughput sequencing has led to an enormous increase in the number of sequenced microbial genomes, with well over 100,000 genomes in public archives today. Automatic genome annotation tools are integral to understanding these organisms, yet older gene finding methods must be retrained on each new genome. We have developed a universal model of prokaryotic genes by fitting a temporal convolutional network to amino-acid sequences from a large, diverse set of microbial genomes. We incorporated the new model into a gene finding system, Balrog (Bacterial Annotation by Learned Representation Of Genes), which does not require genome-specific training and which matches or outperforms other state-of-the-art gene finding tools. Balrog is freely available under the MIT license at https://github.com/salzberg-lab/Balrog.