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DIGNiFI: Discovering causative genes for orphan diseases using protein-protein interaction networks
BACKGROUND: An orphan disease is any disease that affects a small percentage of the population. Orphan diseases are a great burden to patients and society, and most of them are genetic in origin. Unfortunately, our current understanding of the genes responsible for inherited orphan diseases is still...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5374555/ https://www.ncbi.nlm.nih.gov/pubmed/28361678 http://dx.doi.org/10.1186/s12918-017-0402-8 |
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author | Liu, Xiaoxia Yang, Zhihao Lin, Hongfei Simmons, Michael Lu, Zhiyong |
author_facet | Liu, Xiaoxia Yang, Zhihao Lin, Hongfei Simmons, Michael Lu, Zhiyong |
author_sort | Liu, Xiaoxia |
collection | PubMed |
description | BACKGROUND: An orphan disease is any disease that affects a small percentage of the population. Orphan diseases are a great burden to patients and society, and most of them are genetic in origin. Unfortunately, our current understanding of the genes responsible for inherited orphan diseases is still quite limited. Developing effective computational algorithms to discover disease-causing genes would help unveil disease mechanisms and may enable better diagnosis and treatment. RESULTS: We have developed a novel method, named as DIGNiFI (Disease causIng GeNe FInder), which uses Protein-Protein Interaction (PPI) network-based features to discover and rank candidate disease-causing genes. Specifically, our approach computes topologically similar genes by taking into account both local and global connected paths in PPI networks via Direct Neighbors and Local Random Walks, respectively. Furthermore, since genes with similar phenotypes tend to be functionally related, we have integrated PPI data with gene ontology (GO) annotations and protein complex data to further improve the performance of this approach. Results of 128 orphan diseases with 1184 known disease genes collected from the Orphanet show that our proposed methods outperform existing state-of-the-art methods for discovering candidate disease-causing genes. We also show that further performance improvement can be achieved when enriching the human-curated PPI network data with text-mined interactions from the biomedical literature. Finally, we demonstrate the utility of our approach by applying our method to identifying novel candidate genes for a set of four inherited retinal dystrophies. In this study, we found the top predictions for these retinal dystrophies consistent with literature reports and online databases of other retinal dystrophies. CONCLUSIONS: Our method successfully prioritizes orphan-disease-causative genes. This method has great potential to benefit the field of orphan disease research, where resources are scarce and greatly needed. |
format | Online Article Text |
id | pubmed-5374555 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-53745552017-03-31 DIGNiFI: Discovering causative genes for orphan diseases using protein-protein interaction networks Liu, Xiaoxia Yang, Zhihao Lin, Hongfei Simmons, Michael Lu, Zhiyong BMC Syst Biol Research BACKGROUND: An orphan disease is any disease that affects a small percentage of the population. Orphan diseases are a great burden to patients and society, and most of them are genetic in origin. Unfortunately, our current understanding of the genes responsible for inherited orphan diseases is still quite limited. Developing effective computational algorithms to discover disease-causing genes would help unveil disease mechanisms and may enable better diagnosis and treatment. RESULTS: We have developed a novel method, named as DIGNiFI (Disease causIng GeNe FInder), which uses Protein-Protein Interaction (PPI) network-based features to discover and rank candidate disease-causing genes. Specifically, our approach computes topologically similar genes by taking into account both local and global connected paths in PPI networks via Direct Neighbors and Local Random Walks, respectively. Furthermore, since genes with similar phenotypes tend to be functionally related, we have integrated PPI data with gene ontology (GO) annotations and protein complex data to further improve the performance of this approach. Results of 128 orphan diseases with 1184 known disease genes collected from the Orphanet show that our proposed methods outperform existing state-of-the-art methods for discovering candidate disease-causing genes. We also show that further performance improvement can be achieved when enriching the human-curated PPI network data with text-mined interactions from the biomedical literature. Finally, we demonstrate the utility of our approach by applying our method to identifying novel candidate genes for a set of four inherited retinal dystrophies. In this study, we found the top predictions for these retinal dystrophies consistent with literature reports and online databases of other retinal dystrophies. CONCLUSIONS: Our method successfully prioritizes orphan-disease-causative genes. This method has great potential to benefit the field of orphan disease research, where resources are scarce and greatly needed. BioMed Central 2017-03-14 /pmc/articles/PMC5374555/ /pubmed/28361678 http://dx.doi.org/10.1186/s12918-017-0402-8 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Liu, Xiaoxia Yang, Zhihao Lin, Hongfei Simmons, Michael Lu, Zhiyong DIGNiFI: Discovering causative genes for orphan diseases using protein-protein interaction networks |
title | DIGNiFI: Discovering causative genes for orphan diseases using protein-protein interaction networks |
title_full | DIGNiFI: Discovering causative genes for orphan diseases using protein-protein interaction networks |
title_fullStr | DIGNiFI: Discovering causative genes for orphan diseases using protein-protein interaction networks |
title_full_unstemmed | DIGNiFI: Discovering causative genes for orphan diseases using protein-protein interaction networks |
title_short | DIGNiFI: Discovering causative genes for orphan diseases using protein-protein interaction networks |
title_sort | dignifi: discovering causative genes for orphan diseases using protein-protein interaction networks |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5374555/ https://www.ncbi.nlm.nih.gov/pubmed/28361678 http://dx.doi.org/10.1186/s12918-017-0402-8 |
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