1
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Cosentino S, Sriswasdi S, Iwasaki W. SonicParanoid2: fast, accurate, and comprehensive orthology inference with machine learning and language models. Genome Biol 2024; 25:195. [PMID: 39054525 PMCID: PMC11270883 DOI: 10.1186/s13059-024-03298-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 06/04/2024] [Indexed: 07/27/2024] Open
Abstract
Accurate inference of orthologous genes constitutes a prerequisite for comparative and evolutionary genomics. SonicParanoid is one of the fastest tools for orthology inference; however, its scalability and accuracy have been hampered by time-consuming all-versus-all alignments and the existence of proteins with complex domain architectures. Here, we present a substantial update of SonicParanoid, where a gradient boosting predictor halves the execution time and a language model doubles the recall. Application to empirical large-scale and standardized benchmark datasets shows that SonicParanoid2 is much faster than comparable methods and also the most accurate. SonicParanoid2 is available at https://gitlab.com/salvo981/sonicparanoid2 and https://zenodo.org/doi/10.5281/zenodo.11371108 .
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Affiliation(s)
- Salvatore Cosentino
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, the University of Tokyo, Kashiwa, Japan
| | - Sira Sriswasdi
- Center of Excellence in Computational Molecular Biology, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand
| | - Wataru Iwasaki
- Department of Integrated Biosciences, Graduate School of Frontier Sciences, the University of Tokyo, Kashiwa, Japan.
- Department of Biological Sciences, Graduate School of Science, the University of Tokyo, Bunkyo-ku, Japan.
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, the University of Tokyo, Kashiwa, Japan.
- Atmosphere and Ocean Research Institute, the University of Tokyo, Kashiwa, Japan.
- Institute for Quantitative Biosciences, the University of Tokyo, Bunkyo-ku, Japan.
- Collaborative Research Institute for Innovative Microbiology, the University of Tokyo, Bunkyo-ku, Japan.
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2
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Breunig K, Lei X, Montalbano M, Guardia GDA, Ostadrahimi S, Alers V, Kosti A, Chiou J, Klein N, Vinarov C, Wang L, Li M, Song W, Kraus WL, Libich DS, Tiziani S, Weintraub ST, Galante PAF, Penalva LOF. SERBP1 interacts with PARP1 and is present in PARylation-dependent protein complexes regulating splicing, cell division, and ribosome biogenesis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.22.586270. [PMID: 38585848 PMCID: PMC10996453 DOI: 10.1101/2024.03.22.586270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
RNA binding proteins (RBPs) containing intrinsically disordered regions (IDRs) are present in diverse molecular complexes where they function as dynamic regulators. Their characteristics promote liquid-liquid phase separation (LLPS) and the formation of membraneless organelles such as stress granules and nucleoli. IDR-RBPs are particularly relevant in the nervous system and their dysfunction is associated with neurodegenerative diseases and brain tumor development. SERBP1 is a unique member of this group, being mostly disordered and lacking canonical RNA-binding domains. Using a proteomics approach followed by functional analysis, we defined SERBP1's interactome. We uncovered novel SERBP1 roles in splicing, cell division, and ribosomal biogenesis and showed its participation in pathological stress granules and Tau aggregates in Alzheimer's disease brains. SERBP1 preferentially interacts with other G-quadruplex (G4) binders, implicated in different stages of gene expression, suggesting that G4 binding is a critical component of SERBP1 function in different settings. Similarly, we identified important associations between SERBP1 and PARP1/polyADP-ribosylation (PARylation). SERBP1 interacts with PARP1 and its associated factors and influences PARylation. Moreover, protein complexes in which SERBP1 participates contain mostly PARylated proteins and PAR binders. Based on these results, we propose a feedback regulatory model in which SERBP1 influences PARP1 function and PARylation, while PARylation modulates SERBP1 functions and participation in regulatory complexes.
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3
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Singh V, Singh V. Inferring Interaction Networks from Transcriptomic Data: Methods and Applications. Methods Mol Biol 2024; 2812:11-37. [PMID: 39068355 DOI: 10.1007/978-1-0716-3886-6_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Transcriptomic data is a treasure trove in modern molecular biology, as it offers a comprehensive viewpoint into the intricate nuances of gene expression dynamics underlying biological systems. This genetic information must be utilized to infer biomolecular interaction networks that can provide insights into the complex regulatory mechanisms underpinning the dynamic cellular processes. Gene regulatory networks and protein-protein interaction networks are two major classes of such networks. This chapter thoroughly investigates the wide range of methodologies used for distilling insightful revelations from transcriptomic data that include association-based methods (based on correlation among expression vectors), probabilistic models (using Bayesian and Gaussian models), and interologous methods. We reviewed different approaches for evaluating the significance of interactions based on the network topology and biological functions of the interacting molecules and discuss various strategies for the identification of functional modules. The chapter concludes with highlighting network-based techniques of prioritizing key genes, outlining the centrality-based, diffusion- based, and subgraph-based methods. The chapter provides a meticulous framework for investigating transcriptomic data to uncover assembly of complex molecular networks for their adaptable analyses across a broad spectrum of biological domains.
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Affiliation(s)
- Vikram Singh
- Centre for Computational Biology and Bioinformatics, Central University of Himachal Pradesh, Dharamshala, Himachal Pradesh, India
| | - Vikram Singh
- Centre for Computational Biology and Bioinformatics, Central University of Himachal Pradesh, Dharamshala, Himachal Pradesh, India.
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4
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Mancuso CA, Johnson KA, Liu R, Krishnan A. Joint representation of molecular networks from multiple species improves gene classification. PLoS Comput Biol 2024; 20:e1011773. [PMID: 38198480 PMCID: PMC10805316 DOI: 10.1371/journal.pcbi.1011773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 01/23/2024] [Accepted: 12/20/2023] [Indexed: 01/12/2024] Open
Abstract
Network-based machine learning (ML) has the potential for predicting novel genes associated with nearly any health and disease context. However, this approach often uses network information from only the single species under consideration even though networks for most species are noisy and incomplete. While some recent methods have begun addressing this shortcoming by using networks from more than one species, they lack one or more key desirable properties: handling networks from more than two species simultaneously, incorporating many-to-many orthology information, or generating a network representation that is reusable across different types of and newly-defined prediction tasks. Here, we present GenePlexusZoo, a framework that casts molecular networks from multiple species into a single reusable feature space for network-based ML. We demonstrate that this multi-species network representation improves both gene classification within a single species and knowledge-transfer across species, even in cases where the inter-species correspondence is undetectable based on shared orthologous genes. Thus, GenePlexusZoo enables effectively leveraging the high evolutionary molecular, functional, and phenotypic conservation across species to discover novel genes associated with diverse biological contexts.
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Affiliation(s)
- Christopher A. Mancuso
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Kayla A. Johnson
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan, United States of America
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan, United States of America
| | - Renming Liu
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan, United States of America
| | - Arjun Krishnan
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, Michigan, United States of America
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5
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Dang H, Castro-Portuguez R, Espejo L, Backer G, Freitas S, Spence E, Meyers J, Shuck K, Gardea EA, Chang LM, Balsa J, Thorns N, Corban C, Liu T, Bean S, Sheehan S, Korstanje R, Sutphin GL. On the benefits of the tryptophan metabolite 3-hydroxyanthranilic acid in Caenorhabditis elegans and mouse aging. Nat Commun 2023; 14:8338. [PMID: 38097593 PMCID: PMC10721613 DOI: 10.1038/s41467-023-43527-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 11/13/2023] [Indexed: 12/17/2023] Open
Abstract
Tryptophan metabolism through the kynurenine pathway influences molecular processes critical to healthy aging including immune signaling, redox homeostasis, and energy production. Aberrant kynurenine metabolism occurs during normal aging and is implicated in many age-associated pathologies including chronic inflammation, atherosclerosis, neurodegeneration, and cancer. We and others previously identified three kynurenine pathway genes-tdo-2, kynu-1, and acsd-1-for which decreasing expression extends lifespan in invertebrates. Here we report that knockdown of haao-1, a fourth gene encoding the enzyme 3-hydroxyanthranilic acid (3HAA) dioxygenase (HAAO), extends lifespan by ~30% and delays age-associated health decline in Caenorhabditis elegans. Lifespan extension is mediated by increased physiological levels of the HAAO substrate 3HAA. 3HAA increases oxidative stress resistance and activates the Nrf2/SKN-1 oxidative stress response. In pilot studies, female Haao knockout mice or aging wild type male mice fed 3HAA supplemented diet were also long-lived. HAAO and 3HAA represent potential therapeutic targets for aging and age-associated disease.
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Affiliation(s)
- Hope Dang
- Molecular & Cellular Biology, University of Arizona, Tucson, AZ, USA
| | | | - Luis Espejo
- Molecular & Cellular Biology, University of Arizona, Tucson, AZ, USA
| | | | - Samuel Freitas
- Molecular & Cellular Biology, University of Arizona, Tucson, AZ, USA
| | - Erica Spence
- Molecular & Cellular Biology, University of Arizona, Tucson, AZ, USA
| | - Jeremy Meyers
- Molecular & Cellular Biology, University of Arizona, Tucson, AZ, USA
| | - Karissa Shuck
- Molecular & Cellular Biology, University of Arizona, Tucson, AZ, USA
| | - Emily A Gardea
- Molecular & Cellular Biology, University of Arizona, Tucson, AZ, USA
| | - Leah M Chang
- Molecular & Cellular Biology, University of Arizona, Tucson, AZ, USA
| | - Jonah Balsa
- Molecular & Cellular Biology, University of Arizona, Tucson, AZ, USA
| | - Niall Thorns
- Molecular & Cellular Biology, University of Arizona, Tucson, AZ, USA
| | | | - Teresa Liu
- The Jackson Laboratory, Bar Harbor, ME, USA
| | | | | | | | - George L Sutphin
- Molecular & Cellular Biology, University of Arizona, Tucson, AZ, USA.
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6
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Cabral‐Miranda F, Tamburini G, Martinez G, Ardiles AO, Medinas DB, Gerakis Y, Hung MD, Vidal R, Fuentealba M, Miedema T, Duran‐Aniotz C, Diaz J, Ibaceta‐Gonzalez C, Sabusap CM, Bermedo‐Garcia F, Mujica P, Adamson S, Vitangcol K, Huerta H, Zhang X, Nakamura T, Sardi SP, Lipton SA, Kennedy BK, Henriquez JP, Cárdenas JC, Plate L, Palacios AG, Hetz C. Unfolded protein response IRE1/XBP1 signaling is required for healthy mammalian brain aging. EMBO J 2022; 41:e111952. [PMID: 36314651 PMCID: PMC9670206 DOI: 10.15252/embj.2022111952] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 09/09/2022] [Accepted: 09/16/2022] [Indexed: 11/18/2022] Open
Abstract
Aging is a major risk factor to develop neurodegenerative diseases and is associated with decreased buffering capacity of the proteostasis network. We investigated the significance of the unfolded protein response (UPR), a major signaling pathway activated to cope with endoplasmic reticulum (ER) stress, in the functional deterioration of the mammalian brain during aging. We report that genetic disruption of the ER stress sensor IRE1 accelerated age-related cognitive decline. In mouse models, overexpressing an active form of the UPR transcription factor XBP1 restored synaptic and cognitive function, in addition to reducing cell senescence. Proteomic profiling of hippocampal tissue showed that XBP1 expression significantly restore changes associated with aging, including factors involved in synaptic function and pathways linked to neurodegenerative diseases. The genes modified by XBP1 in the aged hippocampus where also altered. Collectively, our results demonstrate that strategies to manipulate the UPR in mammals may help sustain healthy brain aging.
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Affiliation(s)
- Felipe Cabral‐Miranda
- Center for GeroscienceBrain Health and MetabolismSantiagoChile
- Biomedical Neuroscience Institute, Faculty of MedicineUniversity of ChileSantiagoChile
- Program of Cellular and Molecular Biology, Institute of Biomedical Sciences, Faculty of MedicineUniversity of ChileSantiagoChile
- Instituto de Ciências BiomédicasUniversidade Federal do Rio de JaneiroRio de JaneiroBrazil
| | - Giovanni Tamburini
- Center for GeroscienceBrain Health and MetabolismSantiagoChile
- Biomedical Neuroscience Institute, Faculty of MedicineUniversity of ChileSantiagoChile
- Program of Cellular and Molecular Biology, Institute of Biomedical Sciences, Faculty of MedicineUniversity of ChileSantiagoChile
| | - Gabriela Martinez
- Center for GeroscienceBrain Health and MetabolismSantiagoChile
- Biomedical Neuroscience Institute, Faculty of MedicineUniversity of ChileSantiagoChile
- Program of Cellular and Molecular Biology, Institute of Biomedical Sciences, Faculty of MedicineUniversity of ChileSantiagoChile
| | - Alvaro O Ardiles
- Centro Interdisciplinario de Neurociencia de ValparaísoUniversidad de ValparaisoValparaisoChile
- Centro de Neurología Traslacional, Escuela de MedicinaUniversidad de ValparaísoValparaisoChile
| | - Danilo B Medinas
- Center for GeroscienceBrain Health and MetabolismSantiagoChile
- Biomedical Neuroscience Institute, Faculty of MedicineUniversity of ChileSantiagoChile
- Program of Cellular and Molecular Biology, Institute of Biomedical Sciences, Faculty of MedicineUniversity of ChileSantiagoChile
| | - Yannis Gerakis
- Center for GeroscienceBrain Health and MetabolismSantiagoChile
- Biomedical Neuroscience Institute, Faculty of MedicineUniversity of ChileSantiagoChile
- Program of Cellular and Molecular Biology, Institute of Biomedical Sciences, Faculty of MedicineUniversity of ChileSantiagoChile
| | - Mei‐Li Diaz Hung
- Center for GeroscienceBrain Health and MetabolismSantiagoChile
- Biomedical Neuroscience Institute, Faculty of MedicineUniversity of ChileSantiagoChile
- Program of Cellular and Molecular Biology, Institute of Biomedical Sciences, Faculty of MedicineUniversity of ChileSantiagoChile
| | - René Vidal
- Center for GeroscienceBrain Health and MetabolismSantiagoChile
- Biomedical Neuroscience Institute, Faculty of MedicineUniversity of ChileSantiagoChile
- Center for Integrative BiologyUniversidad MayorSantiagoChile
| | - Matias Fuentealba
- Center for GeroscienceBrain Health and MetabolismSantiagoChile
- Biomedical Neuroscience Institute, Faculty of MedicineUniversity of ChileSantiagoChile
- Program of Cellular and Molecular Biology, Institute of Biomedical Sciences, Faculty of MedicineUniversity of ChileSantiagoChile
| | - Tim Miedema
- Center for GeroscienceBrain Health and MetabolismSantiagoChile
- Biomedical Neuroscience Institute, Faculty of MedicineUniversity of ChileSantiagoChile
- Program of Cellular and Molecular Biology, Institute of Biomedical Sciences, Faculty of MedicineUniversity of ChileSantiagoChile
| | - Claudia Duran‐Aniotz
- Center for GeroscienceBrain Health and MetabolismSantiagoChile
- Biomedical Neuroscience Institute, Faculty of MedicineUniversity of ChileSantiagoChile
- Program of Cellular and Molecular Biology, Institute of Biomedical Sciences, Faculty of MedicineUniversity of ChileSantiagoChile
| | - Javier Diaz
- Center for GeroscienceBrain Health and MetabolismSantiagoChile
- Biomedical Neuroscience Institute, Faculty of MedicineUniversity of ChileSantiagoChile
- Program of Cellular and Molecular Biology, Institute of Biomedical Sciences, Faculty of MedicineUniversity of ChileSantiagoChile
| | | | - Carleen M Sabusap
- Departments of Chemistry and Biological SciencesVanderbilt UniversityNashvilleTNUSA
| | - Francisca Bermedo‐Garcia
- Department of Cell Biology, Center for Advanced Microscopy (CMA BioBio)Universidad de ConcepciónConcepciónChile
| | - Paula Mujica
- Centro de Neurología Traslacional, Escuela de MedicinaUniversidad de ValparaísoValparaisoChile
| | | | | | - Hernan Huerta
- Center for Integrative BiologyUniversidad MayorSantiagoChile
| | - Xu Zhang
- Department of Molecular Medicine and Neurodegeneration New Medicines CenterThe Scripps Research InstituteLa JollaCAUSA
| | - Tomohiro Nakamura
- Department of Molecular Medicine and Neurodegeneration New Medicines CenterThe Scripps Research InstituteLa JollaCAUSA
| | | | - Stuart A Lipton
- Department of Molecular Medicine and Neurodegeneration New Medicines CenterThe Scripps Research InstituteLa JollaCAUSA
- Department of Neurosciences, School of MedicineUniversity of California, San DiegoLa JollaCAUSA
| | - Brian K Kennedy
- Buck Institute for Research on AgingNovatoCAUSA
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore; Centre for Healthy Longevity, National University Health System; Departments of Biochemistry and Physiology, Yong Loo Lin School of Medicine, National University of SingaporeSingaporeSingapore
| | - Juan Pablo Henriquez
- Department of Cell Biology, Center for Advanced Microscopy (CMA BioBio)Universidad de ConcepciónConcepciónChile
| | - J Cesar Cárdenas
- Center for GeroscienceBrain Health and MetabolismSantiagoChile
- Center for Integrative BiologyUniversidad MayorSantiagoChile
- Buck Institute for Research on AgingNovatoCAUSA
| | - Lars Plate
- Departments of Chemistry and Biological SciencesVanderbilt UniversityNashvilleTNUSA
| | - Adrian G Palacios
- Centro Interdisciplinario de Neurociencia de ValparaísoUniversidad de ValparaisoValparaisoChile
| | - Claudio Hetz
- Center for GeroscienceBrain Health and MetabolismSantiagoChile
- Biomedical Neuroscience Institute, Faculty of MedicineUniversity of ChileSantiagoChile
- Program of Cellular and Molecular Biology, Institute of Biomedical Sciences, Faculty of MedicineUniversity of ChileSantiagoChile
- Buck Institute for Research on AgingNovatoCAUSA
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7
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Zhang T, Zhang J, Xue T, Rashid MH. A Brain Tumor Image Segmentation Method Based on Quantum Entanglement and Wormhole Behaved Particle Swarm Optimization. Front Med (Lausanne) 2022; 9:794126. [PMID: 35620714 PMCID: PMC9127532 DOI: 10.3389/fmed.2022.794126] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 03/17/2022] [Indexed: 12/26/2022] Open
Abstract
Purpose Although classical techniques for image segmentation may work well for some images, they may perform poorly or not work at all for others. It often depends on the properties of the particular image segmentation task under study. The reliable segmentation of brain tumors in medical images represents a particularly challenging and essential task. For example, some brain tumors may exhibit complex so-called “bottle-neck” shapes which are essentially circles with long indistinct tapering tails, known as a “dual tail.” Such challenging conditions may not be readily segmented, particularly in the extended tail region or around the so-called “bottle-neck” area. In those cases, existing image segmentation techniques often fail to work well. Methods Existing research on image segmentation using wormhole and entangle theory is first analyzed. Next, a random positioning search method that uses a quantum-behaved particle swarm optimization (QPSO) approach is improved by using a hyperbolic wormhole path measure for seeding and linking particles. Finally, our novel quantum and wormhole-behaved particle swarm optimization (QWPSO) is proposed. Results Experimental results show that our QWPSO algorithm can better cluster complex “dual tail” regions into groupings with greater adaptability than conventional QPSO. Experimental work also improves operational efficiency and segmentation accuracy compared with current competing reference methods. Conclusion Our QWPSO method appears extremely promising for isolating smeared/indistinct regions of complex shape typical of medical image segmentation tasks. The technique is especially advantageous for segmentation in the so-called “bottle-neck” and “dual tail”-shaped regions appearing in brain tumor images.
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Affiliation(s)
- Tianchi Zhang
- School of Information Science and Engineering, Chongqing Jiaotong University, Chongqing, China
| | - Jing Zhang
- School of Information Science and Engineering, University of Jinan, Jinan, China.,Shandong Provincial Key Laboratory of Network-Based Intelligent Computing, Jinan, China
| | - Teng Xue
- School of Information Science and Engineering, University of Jinan, Jinan, China.,Shandong Provincial Key Laboratory of Network-Based Intelligent Computing, Jinan, China
| | - Mohammad Hasanur Rashid
- School of Information Science and Engineering, University of Jinan, Jinan, China.,Shandong Provincial Key Laboratory of Network-Based Intelligent Computing, Jinan, China
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8
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Nevers Y, Jones TEM, Jyothi D, Yates B, Ferret M, Portell-Silva L, Codo L, Cosentino S, Marcet-Houben M, Vlasova A, Poidevin L, Kress A, Hickman M, Persson E, Piližota I, Guijarro-Clarke C, Iwasaki W, Lecompte O, Sonnhammer E, Roos DS, Gabaldón T, Thybert D, Thomas PD, Hu Y, Emms DM, Bruford E, Capella-Gutierrez S, Martin MJ, Dessimoz C, Altenhoff A. The Quest for Orthologs orthology benchmark service in 2022. Nucleic Acids Res 2022; 50:W623-W632. [PMID: 35552456 PMCID: PMC9252809 DOI: 10.1093/nar/gkac330] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 04/07/2022] [Accepted: 04/30/2022] [Indexed: 11/15/2022] Open
Abstract
The Orthology Benchmark Service (https://orthology.benchmarkservice.org) is the gold standard for orthology inference evaluation, supported and maintained by the Quest for Orthologs consortium. It is an essential resource to compare existing and new methods of orthology inference (the bedrock for many comparative genomics and phylogenetic analysis) over a standard dataset and through common procedures. The Quest for Orthologs Consortium is dedicated to maintaining the resource up to date, through regular updates of the Reference Proteomes and increasingly accessible data through the OpenEBench platform. For this update, we have added a new benchmark based on curated orthology assertion from the Vertebrate Gene Nomenclature Committee, and provided an example meta-analysis of the public predictions present on the platform.
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Affiliation(s)
- Yannis Nevers
- To whom correspondence should be addressed. Tel: +41 21 692 5449;
| | - Tamsin E M Jones
- HUGO Gene Nomenclature Committee (HGNC), European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Dushyanth Jyothi
- Protein Function development, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Bethan Yates
- HUGO Gene Nomenclature Committee (HGNC), European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Meritxell Ferret
- Barcelona Supercomputing Centre (BSC-CNS). Plaça Eusebi Güell, 1-3 08034 Barcelona, Spain
| | - Laura Portell-Silva
- Barcelona Supercomputing Centre (BSC-CNS). Plaça Eusebi Güell, 1-3 08034 Barcelona, Spain
| | - Laia Codo
- Barcelona Supercomputing Centre (BSC-CNS). Plaça Eusebi Güell, 1-3 08034 Barcelona, Spain
| | - Salvatore Cosentino
- Department of Biological Sciences, Graduate School of Science, the University of Tokyo, Tokyo, Japan
| | - Marina Marcet-Houben
- Barcelona Supercomputing Centre (BSC-CNS). Plaça Eusebi Güell, 1-3 08034 Barcelona, Spain,Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028 Barcelona, Spain
| | - Anna Vlasova
- Barcelona Supercomputing Centre (BSC-CNS). Plaça Eusebi Güell, 1-3 08034 Barcelona, Spain,Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028 Barcelona, Spain
| | - Laetitia Poidevin
- Department of Computer Science, ICube, UMR 7357, Centre de Recherche en Biomédecine de Strasbourg, University of Strasbourg, CNRS, Strasbourg, France,BiGEst-ICube Platform, ICube, UMR 7357, Centre de Recherche en Biomédecine de Strasbourg, University of Strasbourg, CNRS, Strasbourg, France
| | - Arnaud Kress
- Department of Computer Science, ICube, UMR 7357, Centre de Recherche en Biomédecine de Strasbourg, University of Strasbourg, CNRS, Strasbourg, France,BiGEst-ICube Platform, ICube, UMR 7357, Centre de Recherche en Biomédecine de Strasbourg, University of Strasbourg, CNRS, Strasbourg, France
| | - Mark Hickman
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Emma Persson
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
| | - Ivana Piližota
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Cristina Guijarro-Clarke
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | | | - Wataru Iwasaki
- Department of Biological Sciences, Graduate School of Science, the University of Tokyo, Tokyo, Japan,Department of Integrated Biosciences, Graduate School of Frontier Sciences, the University of Tokyo, Kashiwa, Japan
| | - Odile Lecompte
- Department of Computer Science, ICube, UMR 7357, Centre de Recherche en Biomédecine de Strasbourg, University of Strasbourg, CNRS, Strasbourg, France
| | - Erik Sonnhammer
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
| | - David S Roos
- Department of Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Toni Gabaldón
- Barcelona Supercomputing Centre (BSC-CNS). Plaça Eusebi Güell, 1-3 08034 Barcelona, Spain,Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac, 10, 08028 Barcelona, Spain,Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain,Centro de Investigaciones Biomédicas en Red de Enfermedades Infecciosas, Barcelona, Spain
| | - David Thybert
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Paul D Thomas
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90032, USA
| | - Yanhui Hu
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Harvard University, Boston, MA 02115, USA
| | - David M Emms
- Department of Plant Sciences, University of Oxford, Oxford OX1 3RB, UK
| | - Elspeth Bruford
- HUGO Gene Nomenclature Committee (HGNC), European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK,Department of Haematology, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | | | - Maria J Martin
- Protein Function development, European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Christophe Dessimoz
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland,Swiss Institute for Bioinformatics, University of Lausanne, Lausanne, Switzerland,Department of Computer Science, University College London, London, UK,Centre for Life's Origins and Evolution, Department of Genetics, Evolution and Environment, University College London, London, UK
| | - Adrian Altenhoff
- Swiss Institute for Bioinformatics, University of Lausanne, Lausanne, Switzerland,Computer Science Department, ETH Zurich, Zurich, Switzerland
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9
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Linard B, Ebersberger I, McGlynn SE, Glover N, Mochizuki T, Patricio M, Lecompte O, Nevers Y, Thomas PD, Gabaldón T, Sonnhammer E, Dessimoz C, Uchiyama I. Ten Years of Collaborative Progress in the Quest for Orthologs. Mol Biol Evol 2021; 38:3033-3045. [PMID: 33822172 PMCID: PMC8321534 DOI: 10.1093/molbev/msab098] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 02/07/2021] [Accepted: 04/01/2021] [Indexed: 12/19/2022] Open
Abstract
Accurate determination of the evolutionary relationships between genes is a foundational challenge in biology. Homology-evolutionary relatedness-is in many cases readily determined based on sequence similarity analysis. By contrast, whether or not two genes directly descended from a common ancestor by a speciation event (orthologs) or duplication event (paralogs) is more challenging, yet provides critical information on the history of a gene. Since 2009, this task has been the focus of the Quest for Orthologs (QFO) Consortium. The sixth QFO meeting took place in Okazaki, Japan in conjunction with the 67th National Institute for Basic Biology conference. Here, we report recent advances, applications, and oncoming challenges that were discussed during the conference. Steady progress has been made toward standardization and scalability of new and existing tools. A feature of the conference was the presentation of a panel of accessible tools for phylogenetic profiling and several developments to bring orthology beyond the gene unit-from domains to networks. This meeting brought into light several challenges to come: leveraging orthology computations to get the most of the incoming avalanche of genomic data, integrating orthology from domain to biological network levels, building better gene models, and adapting orthology approaches to the broad evolutionary and genomic diversity recognized in different forms of life and viruses.
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Affiliation(s)
- Benjamin Linard
- LIRMM, University of Montpellier, CNRS, Montpellier, France.,SPYGEN, Le Bourget-du-Lac, France
| | - Ingo Ebersberger
- Institute of Cell Biology and Neuroscience, Goethe University Frankfurt, Frankfurt, Germany.,Senckenberg Biodiversity and Climate Research Centre (S-BIKF), Frankfurt, Germany.,LOEWE Center for Translational Biodiversity Genomics (TBG), Frankfurt, Germany
| | - Shawn E McGlynn
- Earth-Life Science Institute, Tokyo Institute of Technology, Meguro, Tokyo, Japan.,Blue Marble Space Institute of Science, Seattle, WA, USA
| | - Natasha Glover
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.,Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Tomohiro Mochizuki
- Earth-Life Science Institute, Tokyo Institute of Technology, Meguro, Tokyo, Japan
| | - Mateus Patricio
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Odile Lecompte
- Department of Computer Science, ICube, UMR 7357, University of Strasbourg, CNRS, Fédération de Médecine Translationnelle de Strasbourg, Strasbourg, France
| | - Yannis Nevers
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.,Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Paul D Thomas
- Division of Bioinformatics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Toni Gabaldón
- Barcelona Supercomputing Centre (BCS-CNS), Jordi Girona, Barcelona, Spain.,Institute for Research in Biomedicine (IRB), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.,Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Erik Sonnhammer
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
| | - Christophe Dessimoz
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.,Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.,Department of Computer Science, University College London, London, United Kingdom.,Department of Genetics, Evolution and Environment, University College London, London, United Kingdom
| | - Ikuo Uchiyama
- Department of Theoretical Biology, National Institute for Basic Biology, National Institutes of Natural Sciences, Okazaki, Aichi, Japan
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10
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Composition of Caenorhabditis elegans extracellular vesicles suggests roles in metabolism, immunity, and aging. GeroScience 2020; 42:1133-1145. [PMID: 32578074 DOI: 10.1007/s11357-020-00204-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 05/11/2020] [Indexed: 12/15/2022] Open
Abstract
The nematode Caenorhabditis elegans has been instrumental in the identification of evolutionarily conserved mechanisms of aging. C. elegans also has recently been found to have evolutionarily conserved extracellular vesicle (EV) signaling pathways. We have been developing tools that allow for the detailed study of EV biology in C. elegans. Here we apply our recently published method for high specificity purification of EVs from C. elegans to carry out target-independent proteomic and RNA analysis of nematode EVs. We identify diverse coding and non-coding RNA and protein cargo types commonly found in human EVs. The EV cargo spectrum is distinct from whole worms, suggesting that protein and RNA cargos are actively recruited to EVs. Gene ontology analysis revealed C. elegans EVs are enriched for extracellular-associated and signaling proteins, and network analysis indicates enrichment for metabolic, immune, and basement membrane associated proteins. Tissue enrichment and gene expression analysis suggests the secreted EV proteins are likely to be derived from intestine, muscle, and excretory tissue. An unbiased comparison of the EV proteins with a large database of C. elegans genome-wide microarray data showed significant overlap with gene sets that are associated with aging and immunity. Taken together our data suggest C. elegans could be a promising in vivo model for studying the genetics and physiology of EVs in a variety of contexts including aging, metabolism, and immune response.
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11
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Glover N, Dessimoz C, Ebersberger I, Forslund SK, Gabaldón T, Huerta-Cepas J, Martin MJ, Muffato M, Patricio M, Pereira C, da Silva AS, Wang Y, Sonnhammer E, Thomas PD. Advances and Applications in the Quest for Orthologs. Mol Biol Evol 2020; 36:2157-2164. [PMID: 31241141 PMCID: PMC6759064 DOI: 10.1093/molbev/msz150] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Gene families evolve by the processes of speciation (creating orthologs), gene duplication (paralogs), and horizontal gene transfer (xenologs), in addition to sequence divergence and gene loss. Orthologs in particular play an essential role in comparative genomics and phylogenomic analyses. With the continued sequencing of organisms across the tree of life, the data are available to reconstruct the unique evolutionary histories of tens of thousands of gene families. Accurate reconstruction of these histories, however, is a challenging computational problem, and the focus of the Quest for Orthologs Consortium. We review the recent advances and outstanding challenges in this field, as revealed at a symposium and meeting held at the University of Southern California in 2017. Key advances have been made both at the level of orthology algorithm development and with respect to coordination across the community of algorithm developers and orthology end-users. Applications spanned a broad range, including gene function prediction, phylostratigraphy, genome evolution, and phylogenomics. The meetings highlighted the increasing use of meta-analyses integrating results from multiple different algorithms, and discussed ongoing challenges in orthology inference as well as the next steps toward improvement and integration of orthology resources.
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Affiliation(s)
- Natasha Glover
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.,Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
| | - Christophe Dessimoz
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.,Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.,Department of Genetics, Evolution & Environment, University College London, London, United Kingdom.,Department of Computer Science, University College London, London, United Kingdom
| | - Ingo Ebersberger
- Applied Bioinformatics Group, Institute of Cell Biology and Neuroscience, Goethe University Frankfurt, Frankfurt, Germany.,Senckenberg Biodiversity and Climate Research Centre (BIK-F), Frankfurt, Germany.,LOEWE Centre for Translational Biodiversity Genomics (LOEWE-TBG), Frankfurt, Germany
| | - Sofia K Forslund
- Experimental and Clinical Research Center, A Cooperation of Charité-Universitätsmedizin Berlin and Max Delbruck Center for Molecular Medicine, Berlin, Germany.,Max Delbruck Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany.,Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität u Berlin, Berlin, Germany.,Berlin Institute of Health (BIH), Berlin, Germany.,Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Toni Gabaldón
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain.,ICREA, Barcelona, Spain
| | - Jaime Huerta-Cepas
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.,Centro de Biotecnología y Genómica de Plantas, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - Maria-Jesus Martin
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Matthieu Muffato
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Mateus Patricio
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Cécile Pereira
- Eura Nova, Marseille, France.,Department of Microbiology and Cell Science, Institute for Food and Agricultural Sciences, University of Florida, Gainesville, FL
| | - Alan Sousa da Silva
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Yan Wang
- Department of Microbiology and Plant Pathology, Institute for Integrative Genome Biology, University of California-Riverside, Riverside, CA
| | - Erik Sonnhammer
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden
| | - Paul D Thomas
- Division of Bioinformatics, Department of Preventive Medicine, University of Southern California, Los Angeles, CA
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12
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Kruempel JC, Howington MB, Leiser SF. Computational tools for geroscience. TRANSLATIONAL MEDICINE OF AGING 2019; 3:132-143. [PMID: 33241167 PMCID: PMC7685266 DOI: 10.1016/j.tma.2019.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
The rapid progress of the past three decades has led the geroscience field near a point where human interventions in aging are plausible. Advances across scientific areas, such as high throughput "-omics" approaches, have led to an exponentially increasing quantity of data available for biogerontologists. To best translate the lifespan and healthspan extending interventions discovered by basic scientists into preventative medicine, it is imperative that the current data are comprehensively utilized to generate testable hypotheses about translational interventions. Building a translational pipeline for geroscience will require both systematic efforts to identify interventions that extend healthspan across taxa and diagnostics that can identify patients who may benefit from interventions prior to the onset of an age-related morbidity. Databases and computational tools that organize and analyze both the wealth of information available on basic biogerontology research and clinical data on aging populations will be critical in developing such a pipeline. Here, we review the current landscape of databases and computational resources available for translational aging research. We discuss key platforms and tools available for aging research, with a focus on how each tool can be used in concert with hypothesis driven experiments to move closer to human interventions in aging.
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Affiliation(s)
- Joseph C.P. Kruempel
- Molecular & Integrative Physiology Department, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Marshall B. Howington
- Cellular and Molecular Biology Program, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Scott F. Leiser
- Molecular & Integrative Physiology Department, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, 48109, USA
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13
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Torres Manno MA, Pizarro MD, Prunello M, Magni C, Daurelio LD, Espariz M. GeM-Pro: a tool for genome functional mining and microbial profiling. Appl Microbiol Biotechnol 2019; 103:3123-3134. [DOI: 10.1007/s00253-019-09648-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 01/11/2019] [Accepted: 01/14/2019] [Indexed: 11/30/2022]
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14
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Glover NM, Altenhoff A, Dessimoz C. Assigning confidence scores to homoeologs using fuzzy logic. PeerJ 2019; 6:e6231. [PMID: 30648004 PMCID: PMC6330999 DOI: 10.7717/peerj.6231] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2018] [Accepted: 12/07/2018] [Indexed: 01/01/2023] Open
Abstract
In polyploid genomes, homoeologs are a specific subtype of homologs, and can be thought of as orthologs between subgenomes. In Orthologous MAtrix, we infer homoeologs in three polyploid plant species: upland cotton (Gossypium hirsutum), rapeseed (Brassica napus), and bread wheat (Triticum aestivum). While we can typically recognize the features of a "good" homoeolog prediction (a consistent evolutionary distance, high synteny, and a one-to-one relationship), none of them is a hard-fast criterion. We devised a novel fuzzy logic-based method to assign confidence scores to each pair of predicted homoeologs. We inferred homoeolog pairs and used the new and improved method to assign confidence scores, which ranged from 0 to 100. Most confidence scores were between 70 and 100, but the distribution varied between genomes. The new confidence scores show an improvement over our previous method and were manually evaluated using a subset from various confidence ranges.
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Affiliation(s)
- Natasha M Glover
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.,Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Adrian Altenhoff
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.,Department of Computer Science, ETH Zurich, Zurich, Switzerland
| | - Christophe Dessimoz
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.,Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland.,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.,Department of Genetics, Evolution, and Environment, University College London, London, UK.,Department of Computer Science, University College London, London, UK
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15
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Abstract
The distinction between orthologs and paralogs, genes that started diverging by speciation versus duplication, is relevant in a wide range of contexts, most notably phylogenetic tree inference and protein function annotation. In this chapter, we provide an overview of the methods used to infer orthology and paralogy. We survey both graph-based approaches (and their various grouping strategies) and tree-based approaches, which solve the more general problem of gene/species tree reconciliation. We discuss conceptual differences among the various orthology inference methods and databases and examine the difficult issue of verifying and benchmarking orthology predictions. Finally, we review typical applications of orthologous genes, groups, and reconciled trees and conclude with thoughts on future methodological developments.
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16
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OrthoList 2: A New Comparative Genomic Analysis of Human and Caenorhabditis elegans Genes. Genetics 2018; 210:445-461. [PMID: 30120140 DOI: 10.1534/genetics.118.301307] [Citation(s) in RCA: 179] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Accepted: 08/15/2018] [Indexed: 11/18/2022] Open
Abstract
OrthoList, a compendium of Caenorhabditis elegans genes with human orthologs compiled in 2011 by a meta-analysis of four orthology-prediction methods, has been a popular tool for identifying conserved genes for research into biological and disease mechanisms. However, the efficacy of orthology prediction depends on the accuracy of gene-model predictions, an ongoing process, and orthology-prediction algorithms have also been updated over time. Here we present OrthoList 2 (OL2), a new comparative genomic analysis between C. elegans and humans, and the first assessment of how changes over time affect the landscape of predicted orthologs between two species. Although we find that updates to the orthology-prediction methods significantly changed the landscape of C. elegans-human orthologs predicted by individual programs and-unexpectedly-reduced agreement among them, we also show that our meta-analysis approach "buffered" against changes in gene content. We show that adding results from more programs did not lead to many additions to the list and discuss reasons to avoid assigning "scores" based on support by individual orthology-prediction programs; the treatment of "legacy" genes no longer predicted by these programs; and the practical difficulties of updating due to encountering deprecated, changed, or retired gene identifiers. In addition, we consider what other criteria may support claims of orthology and alternative approaches to find potential orthologs that elude identification by these programs. Finally, we created a new web-based tool that allows for rapid searches of OL2 by gene identifiers, protein domains [InterPro and SMART (Simple Modular Architecture Research Tool], or human disease associations ([OMIM (Online Mendelian Inheritence in Man], and also includes available RNA-interference resources to facilitate potential translational cross-species studies.
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17
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Sutphin GL, Backer G, Sheehan S, Bean S, Corban C, Liu T, Peters MJ, van Meurs JBJ, Murabito JM, Johnson AD, Korstanje R. Caenorhabditis elegans orthologs of human genes differentially expressed with age are enriched for determinants of longevity. Aging Cell 2017; 16:672-682. [PMID: 28401650 PMCID: PMC5506438 DOI: 10.1111/acel.12595] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/05/2017] [Indexed: 12/21/2022] Open
Abstract
We report a systematic RNAi longevity screen of 82 Caenorhabditis elegans genes selected based on orthology to human genes differentially expressed with age. We find substantial enrichment in genes for which knockdown increased lifespan. This enrichment is markedly higher than published genomewide longevity screens in C. elegans and similar to screens that preselected candidates based on longevity‐correlated metrics (e.g., stress resistance). Of the 50 genes that affected lifespan, 46 were previously unreported. The five genes with the greatest impact on lifespan (>20% extension) encode the enzyme kynureninase (kynu‐1), a neuronal leucine‐rich repeat protein (iglr‐1), a tetraspanin (tsp‐3), a regulator of calcineurin (rcan‐1), and a voltage‐gated calcium channel subunit (unc‐36). Knockdown of each gene extended healthspan without impairing reproduction. kynu‐1(RNAi) alone delayed pathology in C. elegans models of Alzheimer's disease and Huntington's disease. Each gene displayed a distinct pattern of interaction with known aging pathways. In the context of published work, kynu‐1, tsp‐3, and rcan‐1 are of particular interest for immediate follow‐up. kynu‐1 is an understudied member of the kynurenine metabolic pathway with a mechanistically distinct impact on lifespan. Our data suggest that tsp‐3 is a novel modulator of hypoxic signaling and rcan‐1 is a context‐specific calcineurin regulator. Our results validate C. elegans as a comparative tool for prioritizing human candidate aging genes, confirm age‐associated gene expression data as valuable source of novel longevity determinants, and prioritize select genes for mechanistic follow‐up.
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Affiliation(s)
| | - Grant Backer
- The Jackson Laboratory; 600 Main Street Bar Harbor ME 04609 USA
| | - Susan Sheehan
- The Jackson Laboratory; 600 Main Street Bar Harbor ME 04609 USA
| | - Shannon Bean
- The Jackson Laboratory; 600 Main Street Bar Harbor ME 04609 USA
| | - Caroline Corban
- The Jackson Laboratory; 600 Main Street Bar Harbor ME 04609 USA
| | - Teresa Liu
- The Jackson Laboratory; 600 Main Street Bar Harbor ME 04609 USA
| | - Marjolein J. Peters
- Department of Internal Medicine; Erasmus Medical Center; Postbus 2040 3000 CA Rotterdam The Netherlands
| | - Joyce B. J. van Meurs
- Department of Internal Medicine; Erasmus Medical Center; Postbus 2040 3000 CA Rotterdam The Netherlands
| | - Joanne M. Murabito
- Section of General Internal Medicine; Boston University School of Medicine; 801 Massachusetts Ave, Crosstown Center Boston MA 02118 USA
- The National Heart, Lung, and Blood Institute's Framingham Heart Study; 73 Mt. Wayte Ave, Suite 2 Framingham MA 01702-5827 USA
| | - Andrew D. Johnson
- The National Heart, Lung, and Blood Institute's Framingham Heart Study; 73 Mt. Wayte Ave, Suite 2 Framingham MA 01702-5827 USA
- Population Sciences Branch; National Heart, Lung, and Blood Institute; Building 31, Room 5A52, 31 Center Drive MSC 2486 Bethesda MD 20892 USA
| | - Ron Korstanje
- The Jackson Laboratory; 600 Main Street Bar Harbor ME 04609 USA
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