Discovering Novel Ciliary Genes Through Machine Learning
Resumen:
The primary cilium is an evolutionarily conserved organelle present in most vertebrate cells and in specific neurons of invertebrates. It plays a central role in cellular signaling by sensing and transducing diverse stimuli—including mechanical, light, chemical, and thermal cues—and is involved in key biological processes such as development, chemotaxis, and certain forms of memory. Defects in primary cilium function lead to a group of severe disorders known as ciliopathies, many of which currently lack effective treatments. Genes encoding proteins required for the assembly and maintenance of the primary cilium are collectively referred to as ciliary genes. However, with few exceptions, ciliary proteins lack characteristic sequence motifs or structural domains, and the full complement of ciliary genes remains unknown. Here we apply a machine learning approach using publicly available single-cell RNA-seq data to identify novel ciliary genes. First, we compiled a reference set of known ciliary genes by selecting Caenorhabditis elegans genes annotated with Gene Ontology terms related to the primary cilium. This set was randomly divided, using 80% of the genes for model development and reserving the remaining 20% for independent evaluation. Single-cell RNA-seq data from C. elegans embryos were compiled and preprocessed. Genes with low variance across cell types were filtered out and expression values were normalized using MinMax scaling. We then focused on embryonic ciliated and non-ciliated neurons and performed dimensionality reduction using PCA followed by k-means clustering. Using the training set of ciliary genes, we identified clusters significantly enriched in these genes and selected genes located within enriched clusters in both neuronal populations. This strategy produced a list of eleven candidate genes, including five genes from the independent evaluation set. Notably, four of the remaining six genes lack functional annotation, making them strong candidates for previously unrecognized ciliary genes. By generating specific and testable biological hypotheses, these results provide a framework to guide ongoing experimental studies aimed at validating the involvement of these candidate genes in primary cilium assembly and maintenance.
| 2026 | |
| Agencia Nacional de Investigación e Innovación | |
|
cilia predicción de función Caenorhabditis elegans machine learning aprendizaje automático Ciencias Naturales y Exactas Ciencias de la Computación e Información Ciencias de la Información y Bioinformática |
|
| Inglés | |
| Institut Pasteur de Montevideo | |
| IPMON en REDI | |
| https://hdl.handle.net/20.500.12381/5685 | |
| Acceso abierto | |
| Reconocimiento 4.0 Internacional. (CC BY) |
| _version_ | 1877200627289817088 |
|---|---|
| author | Torriglia, Emilia |
| author2 | Irigoin, Florencia Romanelli-Cedrez, Laura Pazos Obregón, Flavio |
| author2_role | author author author |
| author_facet | Torriglia, Emilia Irigoin, Florencia Romanelli-Cedrez, Laura Pazos Obregón, Flavio |
| author_role | author |
| bitstream.checksum.fl_str_mv | 710ccfef5cb01d54b75d1d847d6b6b7b f743f397371fa314793f4c80189b55b4 |
| bitstream.checksumAlgorithm.fl_str_mv | MD5 MD5 |
| bitstream.url.fl_str_mv | https://redi.anii.org.uy/jspui/bitstream/20.500.12381/5685/2/license.txt https://redi.anii.org.uy/jspui/bitstream/20.500.12381/5685/1/Poster%20Frontiers%20in%20Bioscience%205%20v%20final.pdf |
| collection | IPMON en REDI |
| dc.creator.none.fl_str_mv | Torriglia, Emilia Irigoin, Florencia Romanelli-Cedrez, Laura Pazos Obregón, Flavio |
| dc.date.accessioned.none.fl_str_mv | 2026-09-21T18:38:36Z |
| dc.date.available.none.fl_str_mv | 2026-09-21T18:38:36Z |
| dc.date.issued.none.fl_str_mv | 2026-04 |
| dc.description.abstract.none.fl_txt_mv | The primary cilium is an evolutionarily conserved organelle present in most vertebrate cells and in specific neurons of invertebrates. It plays a central role in cellular signaling by sensing and transducing diverse stimuli—including mechanical, light, chemical, and thermal cues—and is involved in key biological processes such as development, chemotaxis, and certain forms of memory. Defects in primary cilium function lead to a group of severe disorders known as ciliopathies, many of which currently lack effective treatments. Genes encoding proteins required for the assembly and maintenance of the primary cilium are collectively referred to as ciliary genes. However, with few exceptions, ciliary proteins lack characteristic sequence motifs or structural domains, and the full complement of ciliary genes remains unknown. Here we apply a machine learning approach using publicly available single-cell RNA-seq data to identify novel ciliary genes. First, we compiled a reference set of known ciliary genes by selecting Caenorhabditis elegans genes annotated with Gene Ontology terms related to the primary cilium. This set was randomly divided, using 80% of the genes for model development and reserving the remaining 20% for independent evaluation. Single-cell RNA-seq data from C. elegans embryos were compiled and preprocessed. Genes with low variance across cell types were filtered out and expression values were normalized using MinMax scaling. We then focused on embryonic ciliated and non-ciliated neurons and performed dimensionality reduction using PCA followed by k-means clustering. Using the training set of ciliary genes, we identified clusters significantly enriched in these genes and selected genes located within enriched clusters in both neuronal populations. This strategy produced a list of eleven candidate genes, including five genes from the independent evaluation set. Notably, four of the remaining six genes lack functional annotation, making them strong candidates for previously unrecognized ciliary genes. By generating specific and testable biological hypotheses, these results provide a framework to guide ongoing experimental studies aimed at validating the involvement of these candidate genes in primary cilium assembly and maintenance. |
| dc.description.sponsorship.none.fl_txt_mv | Agencia Nacional de Investigación e Innovación |
| dc.identifier.anii.es.fl_str_mv | POS_NAC_2025_1_187756 |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12381/5685 |
| dc.language.iso.none.fl_str_mv | eng |
| dc.rights.*.fl_str_mv | Acceso abierto |
| dc.rights.license.none.fl_str_mv | Reconocimiento 4.0 Internacional. (CC BY) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.es.fl_str_mv | Frontiers in Bioscience 5. Buenos Aires, Argentina. 22-24/04/2026 |
| dc.source.none.fl_str_mv | reponame:IPMON en REDI instname:Institut Pasteur de Montevideo instacron:Institut Pasteur de Montevideo |
| dc.subject.anii.none.fl_str_mv | Ciencias Naturales y Exactas Ciencias de la Computación e Información Ciencias de la Información y Bioinformática |
| dc.subject.es.fl_str_mv | cilia predicción de función Caenorhabditis elegans machine learning aprendizaje automático |
| dc.title.none.fl_str_mv | Discovering Novel Ciliary Genes Through Machine Learning |
| dc.type.es.fl_str_mv | Documento de conferencia |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/conferenceObject |
| dc.type.version.es.fl_str_mv | Publicado |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | The primary cilium is an evolutionarily conserved organelle present in most vertebrate cells and in specific neurons of invertebrates. It plays a central role in cellular signaling by sensing and transducing diverse stimuli—including mechanical, light, chemical, and thermal cues—and is involved in key biological processes such as development, chemotaxis, and certain forms of memory. Defects in primary cilium function lead to a group of severe disorders known as ciliopathies, many of which currently lack effective treatments. Genes encoding proteins required for the assembly and maintenance of the primary cilium are collectively referred to as ciliary genes. However, with few exceptions, ciliary proteins lack characteristic sequence motifs or structural domains, and the full complement of ciliary genes remains unknown. Here we apply a machine learning approach using publicly available single-cell RNA-seq data to identify novel ciliary genes. First, we compiled a reference set of known ciliary genes by selecting Caenorhabditis elegans genes annotated with Gene Ontology terms related to the primary cilium. This set was randomly divided, using 80% of the genes for model development and reserving the remaining 20% for independent evaluation. Single-cell RNA-seq data from C. elegans embryos were compiled and preprocessed. Genes with low variance across cell types were filtered out and expression values were normalized using MinMax scaling. We then focused on embryonic ciliated and non-ciliated neurons and performed dimensionality reduction using PCA followed by k-means clustering. Using the training set of ciliary genes, we identified clusters significantly enriched in these genes and selected genes located within enriched clusters in both neuronal populations. This strategy produced a list of eleven candidate genes, including five genes from the independent evaluation set. Notably, four of the remaining six genes lack functional annotation, making them strong candidates for previously unrecognized ciliary genes. By generating specific and testable biological hypotheses, these results provide a framework to guide ongoing experimental studies aimed at validating the involvement of these candidate genes in primary cilium assembly and maintenance. |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | IPMON_e21bcb0bb1e36de523c9166c474db673 |
| identifier_str_mv | POS_NAC_2025_1_187756 |
| instacron_str | Institut Pasteur de Montevideo |
| institution | Institut Pasteur de Montevideo |
| instname_str | Institut Pasteur de Montevideo |
| language | eng |
| network_acronym_str | IPMON |
| network_name_str | IPMON en REDI |
| oai_identifier_str | oai:redi.anii.org.uy:20.500.12381/5685 |
| publishDate | 2026 |
| reponame_str | IPMON en REDI |
| repository.mail.fl_str_mv | msarroca@pasteur.edu.uy |
| repository.name.fl_str_mv | IPMON en REDI - Institut Pasteur de Montevideo |
| repository_id_str | 9421_2 |
| rights_invalid_str_mv | Reconocimiento 4.0 Internacional. (CC BY) Acceso abierto |
| spelling | Reconocimiento 4.0 Internacional. (CC BY)Acceso abiertoinfo:eu-repo/semantics/openAccess2026-09-21T18:38:36Z2026-09-21T18:38:36Z2026-04https://hdl.handle.net/20.500.12381/5685POS_NAC_2025_1_187756The primary cilium is an evolutionarily conserved organelle present in most vertebrate cells and in specific neurons of invertebrates. It plays a central role in cellular signaling by sensing and transducing diverse stimuli—including mechanical, light, chemical, and thermal cues—and is involved in key biological processes such as development, chemotaxis, and certain forms of memory. Defects in primary cilium function lead to a group of severe disorders known as ciliopathies, many of which currently lack effective treatments. Genes encoding proteins required for the assembly and maintenance of the primary cilium are collectively referred to as ciliary genes. However, with few exceptions, ciliary proteins lack characteristic sequence motifs or structural domains, and the full complement of ciliary genes remains unknown. Here we apply a machine learning approach using publicly available single-cell RNA-seq data to identify novel ciliary genes. First, we compiled a reference set of known ciliary genes by selecting Caenorhabditis elegans genes annotated with Gene Ontology terms related to the primary cilium. This set was randomly divided, using 80% of the genes for model development and reserving the remaining 20% for independent evaluation. Single-cell RNA-seq data from C. elegans embryos were compiled and preprocessed. Genes with low variance across cell types were filtered out and expression values were normalized using MinMax scaling. We then focused on embryonic ciliated and non-ciliated neurons and performed dimensionality reduction using PCA followed by k-means clustering. Using the training set of ciliary genes, we identified clusters significantly enriched in these genes and selected genes located within enriched clusters in both neuronal populations. This strategy produced a list of eleven candidate genes, including five genes from the independent evaluation set. Notably, four of the remaining six genes lack functional annotation, making them strong candidates for previously unrecognized ciliary genes. By generating specific and testable biological hypotheses, these results provide a framework to guide ongoing experimental studies aimed at validating the involvement of these candidate genes in primary cilium assembly and maintenance.Agencia Nacional de Investigación e InnovaciónengFrontiers in Bioscience 5. Buenos Aires, Argentina. 22-24/04/2026reponame:IPMON en REDIinstname:Institut Pasteur de Montevideoinstacron:Institut Pasteur de Montevideociliapredicción de funciónCaenorhabditis elegansmachine learningaprendizaje automáticoCiencias Naturales y ExactasCiencias de la Computación e InformaciónCiencias de la Información y BioinformáticaDiscovering Novel Ciliary Genes Through Machine LearningDocumento de conferenciaPublicadoinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjectInstitut Pasteur de MontevideoInstituto de Investigaciones Biológicas Clemente EstableFacultad de Medicina, Universidad de la República//Ciencias Naturales y Exactas/Ciencias de la Computación e Información/Ciencias de la Información y BioinformáticaTorriglia, EmiliaIrigoin, FlorenciaRomanelli-Cedrez, LauraPazos Obregón, FlavioLICENSElicense.txtlicense.txttext/plain; charset=utf-85124https://redi.anii.org.uy/jspui/bitstream/20.500.12381/5685/2/license.txt710ccfef5cb01d54b75d1d847d6b6b7bMD52ORIGINALPoster Frontiers in Bioscience 5 v final.pdfPoster Frontiers in Bioscience 5 v final.pdfapplication/pdf1552169https://redi.anii.org.uy/jspui/bitstream/20.500.12381/5685/1/Poster%20Frontiers%20in%20Bioscience%205%20v%20final.pdff743f397371fa314793f4c80189b55b4MD5120.500.12381/56852026-09-21 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científico-tecnológicohttps://pasteur.uy/https://redi.anii.org.uy/oai/requestmsarroca@pasteur.edu.uyUruguayopendoar:9421_22026-09-21T18:38:37IPMON en REDI - Institut Pasteur de Montevideofalse |
| spellingShingle | Discovering Novel Ciliary Genes Through Machine Learning Torriglia, Emilia cilia predicción de función Caenorhabditis elegans machine learning aprendizaje automático Ciencias Naturales y Exactas Ciencias de la Computación e Información Ciencias de la Información y Bioinformática |
| status_str | publishedVersion |
| title | Discovering Novel Ciliary Genes Through Machine Learning |
| title_full | Discovering Novel Ciliary Genes Through Machine Learning |
| title_fullStr | Discovering Novel Ciliary Genes Through Machine Learning |
| title_full_unstemmed | Discovering Novel Ciliary Genes Through Machine Learning |
| title_short | Discovering Novel Ciliary Genes Through Machine Learning |
| title_sort | Discovering Novel Ciliary Genes Through Machine Learning |
| topic | cilia predicción de función Caenorhabditis elegans machine learning aprendizaje automático Ciencias Naturales y Exactas Ciencias de la Computación e Información Ciencias de la Información y Bioinformática |
| url | https://hdl.handle.net/20.500.12381/5685 |