Discovering Novel Ciliary Genes Through Machine Learning

Torriglia, Emilia - Irigoin, Florencia - Romanelli-Cedrez, Laura - Pazos Obregón, Flavio

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.

Detalles Bibliográficos
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