Orthology Prediction Research Articles

NCBI RefSeq: reference sequence standards through 25years of curation and annotation.

Reference sequences and annotations serve as the foundation for many lines of research today, from organism and sequence identification to providing a core description of the genes, transcripts and proteins found in an organism's genome. Interpretation of data including transcriptomics, proteomics, sequence variation and comparative analyses based on reference gene annotations informs our understanding of gene function and possible disease mechanisms, leading to new biomedical discoveries. The Reference Sequence (RefSeq) resource created at the National Center for Biotechnology Information (NCBI) leverages both automatic processes and expert curation to create a robust set of reference sequences of genomic, transcript and protein data spanning the tree of life. RefSeq continues to refine its annotation and quality control processes and utilize better quality genomes resulting from advances in sequencing technologies as well as RNA-Seq data to produce high-quality annotated genomes, ortholog predictions across more organisms and other products that are easily accessible through multiple NCBI resources. This report summarizes the current status of the eukaryotic, prokaryotic and viral RefSeq resources, with a focus on eukaryotic annotation, the increase in taxonomic representation and the effect it will have on comparative genomics. The RefSeq resource is publicly accessible at https://www.ncbi.nlm.nih.gov/refseq.

Quest for Orthologs in the Era of Biodiversity Genomics.

The era of biodiversity genomics is characterised by large-scale genome sequencing efforts that aim to represent each living taxon with an assembled genome. Generating knowledge from this wealth of data has not kept up with this pace. We here discuss major challenges to integrating these novel genomes into a comprehensive functional and evolutionary network spanning the tree of life. In summary, the expanding datasets create a need for scalable gene annotation methods. To trace gene function across species, new methods must seek to increase the resolution of ortholog analyses, e.g., by extending analyses to the protein domain level and by accounting for alternative splicing. Additionally, the scope of orthology prediction should be pushed beyond well-investigated proteomes. This demands the development of specialised methods for the identification of orthologs to short proteins and non-coding RNAs, and for the functional characterization of novel gene families. Furthermore, protein structures predicted by machine learning are now readily available, but this new information is yet to be integrated with orthology-based analyses. Finally, an increasing focus should be placed on making orthology assignments adhere to the FAIR principles. This fosters green bioinformatics by avoiding redundant computations and helps integrating diverse scientific communities sharing the need for comparative genetics and genomics information. It should also help with communicating orthology-related concepts in a format that is accessible to the public, to counteract existing misinformation about evolution.

ZDB: bacterial comparative genomics made easy.

The analysis and comparison of genomes rely on different tools for tasks such as annotation, orthology prediction, and phylogenetic inference. Most tools are specialized for a single task, and additional efforts are necessary to integrate and visualize the results. To fill this gap, we developed zDB, an application integrating a Nextflow analysis pipeline and a Python visualization platform built on the Django framework. The application is available on GitHub (https://github.com/metagenlab/zDB) and from the bioconda channel. Starting from annotated Genbank files, zDB identifies orthologs and infers a phylogeny for each orthogroup. A species phylogeny is also constructed from shared single-copy orthologs. The results can be enriched with Pfam protein domain prediction, Cluster of Orthologs Genes and Kyoto Encyclopedia of Genes and Genomes annotations, and Swissprot homologs. The web application allows searching for specific genes or annotations, running Blast queries, and comparing genomic regions and whole genomes. The metabolic capacities of organisms can be compared at either the module or pathway levels. Finally, users can run queries to examine the conservation of specific genes or annotations across a chosen subset of genomes and display the results as a list of genes, Venn diagram, or heatmaps. Those features make zDB useful for both bioinformaticians and researchers more accustomed to laboratory research.IMPORTANCEGenome comparison and analysis rely on many independent tools, leaving to scientists the burden to integrate and visualize their results for interpretation. To alleviate this burden, we have built zDB, a comparative genomics tool that includes both an analysis pipeline and a visualization platform. The analysis pipeline automates gene annotation, orthology prediction, and phylogenetic inference, while the visualization platform allows scientists to easily explore the results in a web browser. Among other features, the interface allows users to visually compare whole genomes and targeted regions, assess the conservation of genes or metabolic pathways, perform Blast searches, or look for specific annotations. Altogether, this tool will be useful for a broad range of applications in comparative studies between two and hundred genomes. Furthermore, it is designed to allow sharing of data sets easily at a local or international scale, thereby supporting exploratory analyses for non-bioinformaticians on the genome of their favorite organisms.

Functional genomic regions associated with blast disease resistance in rice predicted syntenic orthologs and potential resistance gene candidates from diverse cereal genomes

Blast disease is a major production constraint in cereal crops worldwide. Genome regions associated with blast resistance in rice were used in homology-based prediction of resistance gene orthologs across cereals. Seventy-four Resistance Gene Analogs (RGAs) that were collocated with rice QTLs (Quantitative Trait Loci) associated with blast resistance, predicted 89 orthologs from cereals,including 61 from finger millet. The RGA orthologs found hits in cereal transcriptomes derived from blast-infected tissues. Over 92% of the putative RGAs encoded NBS-LRR (Nucleotide Binding Site -Leucine Rich Repeat) proteins. Despite conserved primary protein structures, NBS-LRRs were variable in the encoding genes. Primers designed on conserved NBS-LRRs motifs amplified multiple fragments in PCR indicating the presence of highly similar paralogs in genomes. Rice chromosomes 1, 2, 4, 6, and 11 dock clusters of RGAs and syntenic clusters of orthologous RGAs were predicted from other cereal genomes identifying genome hotspots for resistance gene cassettes. RGAs in such cassettes were tightly linked with 50 %–90 % within-cluster similarity. Local finger millet germplasm identified multi-modes of resistance, and lack of correlation between the resistance traits points to independent expression of resistance mechanisms and associated genes in different tissues and development stages. The significant diversity, genomic abundance, and potential functional redundancy complicate use of RGAs for resistance breeding in given host genetic backgrounds, pathogen pools and agro-climates; therefore, robust technology pipelines that enable system-level analysis of expression and effectiveness can increase the practical use of RGAs.

KEGG orthology prediction of bacterial proteins using natural language processing

BackgroundThe advent of high-throughput technologies has led to an exponential increase in uncharacterized bacterial protein sequences, surpassing the capacity of manual curation. A large number of bacterial protein sequences remain unannotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology, making it necessary to use auto annotation tools. These tools are now indispensable in the biological research landscape, bridging the gap between the vastness of unannotated sequences and meaningful biological insights.ResultsIn this work, we propose a novel pipeline for KEGG orthology annotation of bacterial protein sequences that uses natural language processing and deep learning. To assess the effectiveness of our pipeline, we conducted evaluations using the genomes of two randomly selected species from the KEGG database. In our evaluation, we obtain competitive results on precision, recall, and F1 score, with values of 0.948, 0.947, and 0.947, respectively.ConclusionsOur experimental results suggest that our pipeline demonstrates performance comparable to traditional methods and excels in identifying distant relatives with low sequence identity. This demonstrates the potential of our pipeline to significantly improve the accuracy and comprehensiveness of KEGG orthology annotation, thereby advancing our understanding of functional relationships within biological systems.

GTDrift: a resource for exploring the interplay between genetic drift, genomic and transcriptomic characteristics in eukaryotes.

We present GTDrift, a comprehensive data resource that enables explorations of genomic and transcriptomic characteristics alongside proxies of the intensity of genetic drift in individual species. This resource encompasses data for 1506 eukaryotic species, including 1413 animals and 93 green plants, and is organized in three components. The first two components contain approximations of the effective population size, which serve as indicators of the extent of random genetic drift within each species. In the first component, we meticulously investigated public databases to assemble data on life history traits such as longevity, adult body length and body mass for a set of 979 species. The second component includes estimations of the ratio between the rate of non-synonymous substitutions and the rate of synonymous substitutions (dN/dS) in protein-coding sequences for 1324 species. This ratio provides an estimate of the efficiency of natural selection in purging deleterious substitutions. Additionally, we present polymorphism-derived N e estimates for 66 species. The third component encompasses various genomic and transcriptomic characteristics. With this component, we aim to facilitate comparative transcriptomics analyses across species, by providing easy-to-use processed data for more than 16 000 RNA-seq samples across 491 species. These data include intron-centered alternative splicing frequencies, gene expression levels and sequencing depth statistics for each species, obtained with a homogeneous analysis protocol. To enable cross-species comparisons, we provide orthology predictions for conserved single-copy genes based on BUSCO gene sets. To illustrate the possible uses of this database, we identify the most frequently used introns for each gene and we assess how the sequencing depth available for each species affects our power to identify major and minor splice variants.

First whole-genome sequence and assembly of the Ecuadorian brown-headed spider monkey (Ateles fusciceps fusciceps), a critically endangered species, using Oxford Nanopore Technologies.

The Ecuadorian brown-headed spider monkey (Ateles fusciceps fusciceps) is currently considered one of the most endangered primates in the world and is classified as critically endangered [International union for conservation of nature (IUCN)]. It faces multiple threats, the most significant one being habitat loss due to deforestation in western Ecuador. Genomic tools are keys for the management of endangered species, but this requires a reference genome, which until now was unavailable for A. f. fusciceps. The present study reports the first whole-genome sequence and assembly of A. f. fusciceps generated using Oxford Nanopore long reads. DNA was extracted from a subadult male, and libraries were prepared for sequencing following the Ligation Sequencing Kit SQK-LSK112 workflow. Sequencing was performed using a MinION Mk1C sequencer. The sequencing reads were processed to generate a genome assembly. Two different assemblers were used to obtain draft genomes using raw reads, of which the Flye assembly was found to be superior. The final assembly has a total length of 2.63 Gb and contains 3,861 contigs, with an N50 of 7,560,531 bp. The assembly was analyzed for annotation completeness based on primate ortholog prediction using a high-resolution database, and was found to be 84.3% complete, with a low number of duplicated genes indicating a precise assembly. The annotation of the assembly predicted 31,417 protein-coding genes, comparable with other mammal assemblies. A reference genome for this critically endangered species will allow researchers to gain insight into the genetics of its populations and thus aid conservation and management efforts of this vulnerable species.

Identification of Incomplete Annotations of Biosynthesis Pathways in Rhodophytes Using a Multi-Omics Approach.

Rhodophytes (red algae) are an important source of natural products and are, therefore, a current research focus in terms of metabolite production. The recent increase in publicly available Rhodophyte whole genome and transcriptome assemblies provides the resources needed for in silico metabolic pathway analysis. Thus, this study aimed to create a Rhodophyte multi-omics resource, utilising both genomes and transcriptome assemblies with functional annotations to explore Rhodophyte metabolism. The genomes and transcriptomes of 72 Rhodophytes were functionally annotated and integrated with metabolic reconstruction and phylogenetic inference, orthology prediction, and gene duplication analysis to analyse their metabolic pathways. This resource was utilised via two main investigations: the identification of bioactive sterol biosynthesis pathways and the evolutionary analysis of gene duplications for known enzymes. We report that sterol pathways, including campesterol, β-sitosterol, ergocalciferol and cholesterol biosynthesis pathways, all showed incomplete annotated pathways across all Rhodophytes despite prior in vivo studies showing otherwise. Gene duplication analysis revealed high rates of duplication of halide-associated haem peroxidases in Florideophyte algae, which are involved in the biosynthesis of drug-related halogenated secondary metabolites. In summary, this research revealed trends in Rhodophyte metabolic pathways that have been under-researched and require further functional analysis. Furthermore, the high duplication of haem peroxidases and other peroxidase enzymes offers insight into the potential drug development of Rhodophyte halogenated secondary metabolites.

VEuPathDB: the eukaryotic pathogen, vector and host bioinformatics resource center in 2023.

The Eukaryotic Pathogen, Vector and Host Informatics Resource (VEuPathDB, https://veupathdb.org) is a Bioinformatics Resource Center funded by the National Institutes of Health with additional funding from the Wellcome Trust. VEuPathDB supports>600 organisms that comprise invertebrate vectors, eukaryotic pathogens (protists and fungi) and relevant free-living or non-pathogenic species or hosts. Since 2004, VEuPathDB has analyzed omics data from the public domain using contemporary bioinformatic workflows, including orthology predictions via OrthoMCL, and integrated the analysis results with analysis tools, visualizations, and advanced search capabilities. The unique data mining platform coupled with>3000 pre-analyzed data sets facilitates the exploration of pertinent omics data in support of hypothesis driven research. Comparisons are easily made across data sets, data typesand organisms. A Galaxy workspace offers the opportunity for the analysis of private large-scale datasets and for porting to VEuPathDB for comparisons with integrated data. The MapVEu tool provides a platform for exploration of spatially resolved data such as vector surveillance and insecticide resistance monitoring. To address the growing body of omics data and advances in laboratory techniques, VEuPathDB has added several new data types, searches and features, improved the Galaxy workspace environment, redesigned the MapVEu interface and updated the infrastructure to accommodate these changes.

AlexandrusPS: A User-Friendly Pipeline for the Automated Detection of Orthologous Gene Clusters and Subsequent Positive Selection Analysis.

The detection of adaptive selection in a system approach considering all protein-coding genes allows for the identification of mechanisms and pathways that enabled adaptation to different environments. Currently, available programs for the estimation of positive selection signals can be divided into two groups. They are either easy to apply but can analyze only one gene family at a time, restricting system analysis; or they can handle larger cohorts of gene families, but require considerable prerequisite data such as orthology associations, codon alignments, phylogenetic trees, and proper configuration files. All these steps require extensive computational expertise, restricting this endeavor to specialists. Here, we introduce AlexandrusPS, a high-throughput pipeline that overcomes technical challenges when conducting transcriptome-wide positive selection analyses on large sets of nucleotide and protein sequences. The pipeline streamlines 1) the execution of an accurate orthology prediction as a precondition for positive selection analysis, 2) preparing and organizing configuration files for CodeML, 3) performing positive selection analysis using CodeML, and 4) generating an output that is easy to interpret, including all maximum likelihood and log-likelihood test results. The only input needed from the user is the CDS and peptide FASTA files of proteins of interest. The pipeline is provided in a Docker image, requiring no program or module installation, enabling the application of the pipeline in any computing environment. AlexandrusPS and its documentation are available via GitHub (https://github.com/alejocn5/AlexandrusPS).

HiMAP2: Identifying phylogenetically informative genetic markers from diverse genomic resources.

Multiplexed amplicon sequencing offers a cost-effective and rapid solution for phylogenomic studies that include a large number of individuals. Selecting informative genetic markers is a critical initial step in designing such multiplexed amplicon panels, but screening various genomic data and selecting markers that are informative for the question at hand can be laborious. Here, we present a flexible and user-friendly tool, HiMAP2, for identifying, visualizing and filtering phylogenetically informative loci from diverse genomic and transcriptomic resources. This bioinformatics pipeline includes orthology prediction, exon extraction and filtering of aligned exon sequences according to user-defined specifications. Additionally, HiMAP2 facilitates exploration of the final filtered exons by incorporating phylogenetic inference of individual exon trees with raxml-ng as well as the estimation of a species tree using astral. Finally, results of the marker selection can be visualized and refined with an interactive Bokeh application that can be used to generate publication-quality figures. Source code and user instructions for HiMAP2 are available at https://github.com/popphylotools/HiMAP_v2.

InParanoiDB 9: Ortholog Groups for Protein Domains and Full-Length Proteins

Prediction of orthologs is an important bioinformatics pursuit that is frequently used for inferring protein function and evolutionary analyses. The InParanoid database is a well known resource of ortholog predictions between a wide variety of organisms. Although orthologs have historically been inferred at the level of full-length protein sequences, many proteins consist of several independent protein domains that may be orthologous to domains in other proteins in a way that differs from the full-length protein case. To be able to capture all types of orthologous relations, conventional full-length protein orthologs can be complemented with orthologs inferred at the domain level. We here present InParanoiDB 9, covering 640 species and providing orthologs for both protein domains and full-length proteins. InParanoiDB 9 was built using the faster InParanoid-DIAMOND algorithm for orthology analysis, as well as Domainoid and Pfam to infer orthologous domains. InParanoiDB 9 is based on proteomes from 447 eukaryotes, 158 bacteria and 35 archaea, and includes over one billion predicted ortholog groups. A new website has been built for the database, providing multiple search options as well as visualization of groups of orthologs and orthologous domains. This release constitutes a major upgrade of the InParanoid database in terms of the number of species as well as the new capability to operate on the domain level. InParanoiDB 9 is available at https://inparanoidb.sbc.su.se/.

Coexpression reveals conserved gene programs that co-vary with cell type across kingdoms.

What makes a mouse a mouse, and not a hamster? Differences in gene regulation between the two organisms play a critical role. Comparative analysis of gene coexpression networks provides a general framework for investigating the evolution of gene regulation across species. Here, we compare coexpression networks from 37 species and quantify the conservation of gene activity 1) as a function of evolutionary time, 2) across orthology prediction algorithms, and 3) with reference to cell- and tissue-specificity. We find that ancient genes are expressed in multiple cell types and have well conserved coexpression patterns, however they are expressed at different levels across cell types. Thus, differential regulation of ancient gene programs contributes to transcriptional cell identity. We propose that this differential regulation may play a role in cell diversification in both the animal and plant kingdoms.

InParanoid-DIAMOND: faster orthology analysis with the InParanoid algorithm.

SummaryPredicting orthologs, genes in different species having shared ancestry, is an important task in bioinformatics. Orthology prediction tools are required to make accurate and fast predictions, in order to analyze large amounts of data within a feasible time frame. InParanoid is a well-known algorithm for orthology analysis, shown to perform well in benchmarks, but having the major limitation of long runtimes on large datasets. Here, we present an update to the InParanoid algorithm that can use the faster tool DIAMOND instead of BLAST for the homolog search step. We show that it reduces the runtime by 94%, while still obtaining similar performance in the Quest for Orthologs benchmark.Availability and implementationThe source code is available at (https://bitbucket.org/sonnhammergroup/inparanoid).Supplementary information Supplementary data are available at Bioinformatics online.

Drosophila functional screening of de novo variants in autism uncovers damaging variants and facilitates discovery of rare neurodevelopmental diseases.

SUMMARYIndividuals with autism spectrum disorder (ASD) exhibit an increased burden of de novo mutations (DNMs) in a broadening range of genes. While these studies have implicated hundreds of genes in ASD pathogenesis, which DNMs cause functional consequences in vivo remains unclear. We functionally test the effects of ASD missense DNMs using Drosophila through “humanization” rescue and overexpression-based strategies. We examine 79 ASD variants in 74 genes identified in the Simons Simplex Collection and find 38% of them to cause functional alterations. Moreover, we identify GLRA2 as the cause of a spectrum of neurodevelopmental phenotypes beyond ASD in 13 previously undiagnosed subjects. Functional characterization of variants in ASD candidate genes points to conserved neurobiological mechanisms and facilitates gene discovery for rare neurodevelopmental diseases.

Analysis of carbohydrate-active enzymes and sugar transporters in Penicillium echinulatum: A genome-wide comparative study of the fungal lignocellulolytic system

Penicillium echinulatum 2HH is an ascomycete well known for its production of cellulolytic enzymes. Understanding lignocellulolytic and sugar uptake systems is essential to obtain efficient fungi strains for the production of bioethanol. In this study we performed a genome-wide functional annotation of carbohydrate-active enzymes and sugar transporters involved in the lignocellulolytic system of P. echinulatum 2HH and S1M29 strains (wildtype and mutant, respectively) and eleven related fungi. Additionally, signal peptide and orthology prediction were carried out. We encountered a diverse assortment of cellulolytic enzymes in P. echinulatum, especially in terms of β-glucosidases and endoglucanases. Other enzymes required for the breakdown of cellulosic biomass were also found, including cellobiohydrolases, lytic cellulose monooxygenases and cellobiose dehydrogenases. The S1M29 mutant, which is known to produce an increased cellulase activity, and the 2HH wild type strain of P. echinulatum did not show significant differences between their enzymatic repertoire. Nevertheless, we unveiled an amino acid substitution for a predicted intracellular β-glucosidase of the mutant, which might contribute to hyperexpression of cellulases through a cellodextrin induction pathway. Most of the P. echinulatum enzymes presented orthologs in P. oxalicum 114–2, supporting the presence of highly similar cellulolytic mechanisms and a close phylogenetic relationship between these fungi. A phylogenetic analysis of intracellular β-glucosidases and sugar transporters allowed us to identify several proteins potentially involved in the accumulation of intracellular cellodextrins. These may prove valuable targets in the genetic engineering of P. echinulatum focused on industrial cellulases production. Our study marks an important step in characterizing and understanding the molecular mechanisms employed by P. echinulatum in the enzymatic hydrolysis of lignocellulosic biomass.

Orthology Prediction and Phylogenetic Analysis Methods in Plants.

In this chapter, we outline a pipeline for ortholog prediction and phylogenetic analysis in plants. This computational pipeline uses algorithms from different software to enable bioinformatic-beginner biologists to predict orthologs that can be shared with many distinct plant nonmodel and model species and identify gene loss events. Prediction of orthologs allows (1) investigation of the evolutionary relationships of plant genomes, (2) discovery of their origin, function, and (3) the impact of their adaptability to the environment.We developed a pipeline to fit, not only eukaryote but also prokaryote organisms, with small or large genomes. All results acquired from the orthologs predication will enable phylogenetic tree construction, using gene and species (phylogenomic) phylogeny approaches.

Paralog Explorer: A resource for mining information about paralogs in common research organisms

Paralogs are genes which arose via gene duplication, and when such paralogs retain overlapping or redundant function, this poses a challenge to functional genetics research. Recent technological advancements have made it possible to systematically probe gene function for redundant genes using dual or multiplex gene perturbation, and there is a need for a simple bioinformatic tool to identify putative paralogs of a gene(s) of interest. We have developed Paralog Explorer (https://www.flyrnai.org/tools/paralogs/), an online resource that allows researchers to quickly and accurately identify candidate paralogous genes in the genomes of the model organisms D. melanogaster, C. elegans, D. rerio, M. musculus, and H. sapiens. Paralog Explorer deploys an effective between-species ortholog prediction software, DIOPT, to analyze within-species paralogs. Paralog Explorer allows users to identify candidate paralogs, and to navigate relevant databases regarding gene co-expression, protein–protein and genetic interaction, as well as gene ontology and phenotype annotations. Altogether, this tool extends the value of current ortholog prediction resources by providing sophisticated features useful for identification and study of paralogous genes.

Prediction and enrichment analyses of the Homo sapiens-Drosophila melanogaster COPD-related orthologs: potential for modeling of human COPD genomic responses with the fruit fly.

The significant similarities in airway epithelial cells between mammals and the fruit fly Drosophila melanogaster have rendered the latter an important model organism for studies of chronic inflammatory lung diseases. Focusing on the chronic obstructive pulmonary disease (COPD), we here mapped human gene orthologs associated with this disease in D. melanogaster to identify functionally equivalent genes for immediate, further screening with the fruit fly model. The DIOPT-DIST tool was accessed for the prediction of the COPD-associated orthologs between humans and Drosophila. Enrichment analyses with respect to pathways of the retrieved functional homologs were performed using the ToppFun and FlyMine tools, identifying 73 unique human genes as well as 438 fruit fly genes. The ToppFun analysis verified that the human gene list is associated with COPD phenotypes. Furthermore, the FlyMine investigation highlighted that the Drosophila genes are functionally connected mainly with the "ABC-family proteins mediated transport" and the "β-catenin-independent WNT signaling pathway." These results suggest an evolutionarily conserved role toward responses to inhaled toxicants and CO2 in both species. We reason that the predicted orthologous genes should be further studied in the Drosophila models of cigarette smoke-induced COPD.

Echinobase: leveraging an extant model organism database to build a knowledgebase supporting research on the genomics and biology of echinoderms.

Echinobase (www.echinobase.org) is a third generation web resource supporting genomic research on echinoderms. The new version was built by cloning the mature Xenopus model organism knowledgebase, Xenbase, refactoring data ingestion pipelines and modifying the user interface to adapt to multispecies echinoderm content. This approach leveraged over 15 years of previous database and web application development to generate a new fully featured informatics resource in a single year. In addition to the software stack, Echinobase uses the private cloud and physical hosts that support Xenbase. Echinobase currently supports six echinoderm species, focused on those used for genomics, developmental biology and gene regulatory network analyses. Over 38 000 gene pages, 18 000 publications, new improved genome assemblies, JBrowse genome browser and BLAST + services are available and supported by the development of a new echinoderm anatomical ontology, uniformly applied formal gene nomenclature, and consistent orthology predictions. A novel feature of Echinobase is integrating support for multiple, disparate species. New genomes from the diverse echinoderm phylum will be added and supported as data becomes available. The common code development design of the integrated knowledgebases ensures parallel improvements as each resource evolves. This approach is widely applicable for developing new model organism informatics resources.