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The study applies phenomics and machine-learning approaches in Schizosaccharomyces pombe to infer protein functions from colony-growth phenotypes. Fitness effects of deletion mutants were measured for 3509 non-essential genes across 131 conditions spanning nutrients, drugs, and stresses, revealing phenotypes for 3492 mutants. Machine learning then predicted Gene Ontology terms, validating concordance with existing PomBase annotations and uncovering novel aging-related proteins.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/broad-functional-profiling-of-fission-yeast-proteins-using-phenomics-and-machine-learning/290964/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/broad-functional-profiling-of-fission-yeast-proteins-using-phenomics-and-machine-learning/290964.png","ImageObject",300,407,{"name":92,"@type":93},"Finn","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-02","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What biological system and experimental approach were used to profile protein functions?","Question",{"text":113,"@type":114},"The study used the fission yeast Schizosaccharomyces pombe and phenomics-based colony-growth assays with genome-wide deletion mutants. Phenotype measurements were taken across many controlled conditions to capture functional cues.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How many deletion mutants and test conditions were analyzed?",{"text":118,"@type":114},"Fitness phenotypes were measured for 3509 non-essential gene deletion mutants across 131 conditions. These conditions included variations in nutrients, drugs, and stresses.",{"name":120,"@type":111,"acceptedAnswer":121},"How were functional predictions generated beyond phenotyping?",{"text":122,"@type":114},"Machine-learning methods predicted Gene Ontology (GO) terms using protein-network and protein-homology information via NETFF. The resulting predictions were compared for concordance with existing PomBase GO annotations and integrated with phenotype-correlation networks.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290964,1789643052,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},34359740700684,"https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487","RESEARCH ARTICLE  \n*For correspondence:  \n[c.orengo@ucl.ac.uk](c.orengo@ucl.ac.uk) (CO); [j.bahler@ucl.ac.uk](j.bahler@ucl.ac.uk) (JB)  \n†These authors contributed equally to this work  \nCompeting interest: The authors declare that no competing interests exist.  \nFunding: See page 20  \nSent for Review  \n05 April 2023  \nPreprint posted  \n08 April 2023  \nReviewed preprint posted  \n31 May 2023  \nReviewed preprint revised  \n06 September 2023  \nVersion of Record published  \n03 October 2023  \nReviewing Editor: Adèle L Marston, University of Edinburgh, United Kingdom  \n Copyright Rodríguez-López, Bordin et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use andredistribution provided that the original author and source are credited.  \nBroad functional profiling of fission yeast proteins using phenomics and machine learning  \nMaría Rodríguez-López1†, Nicola Bordin2†, Jon Lees2,3, Harry Scholes2, Shaimaa Hassan1,4, Quentin Saintain1, Stephan Kamrad1, Christine Orengo2*, Jürg Bähler1*  \n1 University College London, Institute of Healthy Ageing and Department of Genetics, Evolution & Environment, London, United Kingdom; 2 University College London, Institute of Structural and Molecular Biology, London, United Kingdom; 3 University of Bristol, Bristol, United Kingdom; 4 Helwan University, Faculty of Pharmacy, Cairo, Egypt  \n\n| Abstract Many proteins remain poorly characterized even in well-studied organisms, presenting a bottleneck for research. We applied phenomics and machine-learning approaches with Schizosaccharomyces pombe for broad cues on protein functions. We assayed colony-growth phenotypes to measure the fitness of deletion mutants for 3509 non-essential genes in 131 conditions with different nutrients, drugs, and stresses. These analyses exposed phenotypes for 3492 mutants, including 124 mutants of ‘priority unstudied’ proteins conserved in humans, providing varied functional clues. For example, over 900 proteins were newly implicated in the resistance to oxidative stress. Phenotypecorrelation networks suggested roles for poorly characterized proteins through ‘guilt by association’with known proteins. For complementary functional insights, we predicted Gene Ontology (GO) terms using machine learning methods exploiting protein-network and protein-homology data (NETFF) . We obtained 56,594 high-scoring GO predictions, of which 22,060 also featured high information content. Our phenotype-correlation data and NET-FF predictions showed a strong concordance with existing PomBase GO annotations and protein networks, with integrated analyses revealing 1675 novel GO predictions for 783 genes, including 47 predictions for 23 priority unstudied proteins. Experimental validation identified new proteins involved in cellular aging, showing that these predictions and phenomics data provide a rich resource to uncover new protein functions. |\n| --- |\n| eLife assessment\u003Cbr>This important study combines extensive phenotyping of genome-wide deletion mutants and machine learning-based prediction to generate a large scale resource for understanding the functions of thousands of fission yeast protein-coding genes. This resource is supported by convincing phenotyping data and state-of-the-art bioinformatic analyses and will be of interest to many geneticists. |\n\nIntroduction  \nMost biomedical research focuses on genes that are already well-studied, a situation which has changed little in the past 40 years of biochemical, cell biological, and genetic investigations (Kustatscher et al., 2022; Edwards et al., 2011; Stoeger et al., 2018; Pfeiffer and Hoffmann, 2007; Su and Hogenesch, 2007; Haynes et al., 2018; Oprea et al., 2018; Wood et al., 2019; Sinha et al., 2018) .  \nRodríguez-López, Bordin et al. eLife 2023;12:RP88229. DOI: [https://doi.org/10.7554/eLife.88229](https://doi.org/10.7554/eLife.88229) 1 of 25  \n Research article Computational and Systems Biology | Genetics and Genomi","cbCaisoeGAVpw7j5","https://ap.wps.com/l/cbCaisoeGAVpw7j5","pdf",3464853,25,"English","# Abstract\n# Introduction\n## Research gap in protein functional characterization\n## Schizosaccharomyces pombe as a model system","[{\"question\":\"What biological system and experimental approach were used to profile protein functions?\",\"answer\":\"The study used the fission yeast Schizosaccharomyces pombe and phenomics-based colony-growth assays with genome-wide deletion mutants. Phenotype measurements were taken across many controlled conditions to capture functional cues.\"},{\"question\":\"How many deletion mutants and test conditions were analyzed?\",\"answer\":\"Fitness phenotypes were measured for 3509 non-essential gene deletion mutants across 131 conditions. These conditions included variations in nutrients, drugs, and stresses.\"},{\"question\":\"How were functional predictions generated beyond phenotyping?\",\"answer\":\"Machine-learning methods predicted Gene Ontology (GO) terms using protein-network and protein-homology information via NETFF. The resulting predictions were compared for concordance with existing PomBase GO annotations and integrated with phenotype-correlation networks.\"}]","Broad functional profiling of fission yeast proteins using phenomics and machine learning | PDF",63]