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Computational tools for prioritizing candidate genes: boosting disease gene discovery
Abstract
At different stages of any research project, molecular biologists need to choose - often somewhat arbitrarily, even after careful statistical data analysis - which genes or proteins to investigate further experimentally and which to leave out because of limited resources. Computational methods that integrate complex, heterogeneous data sets - such as expression data, sequence information, functional annotation and the biomedical literature - allow prioritizing genes for future study in a more informed way. Such methods can substantially increase the yield of downstream studies and are becoming invaluable to researchers.
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“Topology-based methods have been shown to capture and leverage the dynamics of biological systems to enrich existing PPI networks [119].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [119]
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“Comparison with other methods In recent years, a variety ofmethods have been developed to infer disease genes from their connectivity patterns within the interactome (31,32).”
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Network Medicine in the Age of Biomedical Big Data
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BackgroundeXtasy: variant prioritization by genomic data fusion
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“However, these tools can be used to successfully prioritize disease genes 41 .”
MethodAdvances in understanding – genetic basis of intellectual disability
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“PolySearch has also been used to assist in disease– gene discovery (13,14), protein–protein interaction studies (15,16), microarray data analysis (17), metabolome annotation (9,11,12,18), biomarker discovery (19), as well as in building and assessing other biomedical text-mining tools (20,21).”
BackgroundMethodGenotype to phenotype via network analysis
Carter, H., Hofree, M., & Ideker, T. (2013) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2013.10.003 · Cited by 132
Protein–protein interactions and genetic diseases: The interactome
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Overexpression of blood microRNAs 103a, 30b, and 29a in l -dopa–treated patients with PD
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“…diagnosis in up to 25 % of cases in large cohorts of unselected, consecutive patients [6 – 8 , A number of strategies have emerged to rank the variants and the genes that they affect, with those most likely to cause disease ranked highest, through a process termed gene prioritization [16 – 18].”
BackgroundCandidate gene prioritization with Endeavour
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“Users can start a prioritization by (1) selecting the species of interest, (2) defining which genes are known to be associated with the process of interest, (3) selecting the data sources to be used in the process and (4) providing the candidate genes to prioritize.”
MethodSelf-citationJia, P., & Zhao, Z. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003460 · Cited by 114
“For a more comprehensive review, please refer to [3,4].”
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“[16]) and gene function prediction in various species [17, 18].”
BackgroundComprehensive integrative analyses identify GLT8D1 and CSNK2B as schizophrenia risk genes
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“The inference of causative genes is typically modeled as a one-class novelty detection problem [51].”
BackgroundPattern recognition in bioinformatics
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“Active learning is often used implicitly, when classifier predictions are ranked and the most confident ones are verified experimentally first [101, 102].”
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“We used random walk since it has been showed to outperform the others [41,127,128].”
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An unbiased evaluation of gene prioritization tools
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“As already discussed in Moreau et al. (2012), this field needs to consolidate through improved benchmarking efforts due to the lack of a ground truth for evaluating the performance of prioritization methods.”
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Walking on multiple disease-gene networks to prioritize candidate genes
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Network and Data Integration for Biomarker Signature Discovery via Network Smoothed T-Statistics
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Chapter 15: Disease Gene Prioritization
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“Computational methods - gene prioritization techniques, are therefore necessary to effectively translate the experimental data into legible disease-gene associations [11].”
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“Known PID genes can be utilized for training for some prediction models, which prioritize candidate genes according to the connection between the function, pathway, expression information on candidate genes and phenotype, including VASST, eXtasy, PHEVOR, Phen-Gen, Phenolyzer, Phenomizer, GeneCards and PosMed [8, 34–36…”
BackgroundA systems genetics resource and analysis of sleep regulation in the mouse
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“We outline that the reported results are based, according to the literature on gene prioritization, on retrospective benchmarks, and for this reason offer usually optimistic estimates of the generalization performances, since disease-associations are likely to be directly or indirectly incorporated in the gene-priorit…”
BackgroundMethodInfluentialUnderstanding mutational effects in digenic diseases
Gazzo, A. M., Raimondi, D., Daneels, D., Moreau, Y., Smits, G., Dooren, S. V., & Lenaerts, T. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx557 · Cited by 59
“Additionally, the DE profiles will be used in the context of a novel bioinformatics pipeline that aims to identify and rank novel digenic combinations as has been done for single genes (46).”
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Cogill, S., & Wang, L. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw498 · Cited by 58
“…been used as negative controls in prioritization studies in an effort to reduce potential systematic bias (Thienpont et al., 2010; Erlich et al., 2011; Moreau and Tranchevent, 2012), and here we employ that same methodology by using non-ASD diseaseassociated genes as our negative instances.”
MethodCollective Prediction of Multiple Types of Links in Heterogeneous Information Networks
Cao, B., Kong, X., & Yu, P. S. (2014) · Proceedings · doi:10.1109/icdm.2014.25 · Cited by 57
“Prioritization of Candidate Disease Genes [22] Chemical Compound”
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“A number of computational methods have been developed to tackle the disease gene prioritization problem (Wang et al. , 2011; Moreau and Tranchevent, 2012), and have been shown to be useful.”
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Gao, L., Uzun, Y., Gao, P., He, B., Ma, X., Wang, J., Han, S., & Tan, K. (2018) · Nature Communications · doi:10.1038/s41467-018-03133-y · Cited by 55
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Contributions of Rare Gene Variants to Familial and Sporadic FSGS
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Predicting disease-related genes using integrated biomedical networks
Peng, J., Bai, K., Shang, X., Wang, G., Xue, H., Jin, S., Cheng, L., Wang, Y., & Chen, J. (2017) · BMC Genomics · doi:10.1186/s12864-016-3263-4 · Cited by 54
“However, these tools, including filtering methods based on a set of criteria [11], text mining of biomedical literature [12], integration of genomic data [13–15], semantic similarity [16–21] based disease gene prioritization [22] and network analysis based and highly robust approach [8, 23–26], remain pre-eminent [27].”
MethodGene Ontology Enrichment Improves Performances of Functional Similarity of Genes
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“…( FS ) between two genes is inferred from GO terms annotating the genes and is widely adopted in detecting and interpreting genetic interactions, functional interactions, protein-protein interactions 1 , biological pathways 2,3 , prioritization of disease genes 4 , and disease similarities 5 .”
BackgroundOTX2 Duplication Is Implicated in Hemifacial Microsomia
Zielinski, D., Markus, B., Sheikh, M. A., Gymrek, M., Chu, C. S., Zaks, M., Srinivasan, B. S., Hoffman, J. D., Aizenbud, D., & Erlich, Y. (2014) · PLoS ONE · doi:10.1371/journal.pone.0096788 · Cited by 54
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“Consequently, candidate gene prioritization seems to represent a valuable approach to validate genomic associations in silico and, more import-antly, to exacerbate the significance of molecular findings [1,2].”
BackgroundMethodDetecting somatic mosaicism: considerations and clinical implications
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Systematic noise degrades gene co-expression signals but can be corrected
Freytag, S., Gagnon-Bartsch, J. A., Speed, T. P., & Bahlo, M. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0745-3 · Cited by 53
“Gene prioritisation methods [29, 30] identify the most promising genes from a list of candidates lacking adequate support to corroborate their involvement in a specific disease pathogenesis.”
Protein function in precision medicine: deep understanding with machine learning
Rost, B., Predrag Radivojac, A., & Bromberg, Y. (2016) · FEBS Letters · doi:10.1002/1873-3468.12307 · Cited by 50
“An example of this is Google’s presentation of the search engine results or, closer to computational biology, disease gene prioritization [134, 135, 136, 137].”
BackgroundA large-scale benchmark of gene prioritization methods
Guala, D., & Sonnhammer, E. L. L. (2017) · Scientific Reports · doi:10.1038/srep46598 · Cited by 53
“Combination of complementary data types produces a more complete picture of the interaction landscape 20 and can mask inherent biases of individual data sources at the same time as cancelling out some of the existing noise.”
BackgroundNetwork-based ranking methods for prediction of novel disease associated microRNAs
Le, D. (2015) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2015.07.003 · Cited by 50
Ghosh, S., Vivar, J. C., Sarzynski, M. A., Sung, Y. J., Timmons, J. A., Bouchard, C., & Rankinen, T. (2013) · Journal of Applied Physiology · doi:10.1152/japplphysiol.01487.2012 · Cited by 51
Pazhohan, A., Amidi, F., Akbari-Asbagh, F., Seyedrezazadeh, E., Aftabı, Y., Abdolalizadeh, J., Khodarahmian, M., Khanlarkhani, N., & Sobhani, A. (2017) · The Journal of Steroid Biochemistry and Molecular Biology · doi:10.1016/j.jsbmb.2017.12.001 · Cited by 49
“All the prioritization procedure designed and performed as described by Moreau and Tranchevent [32].”
Sharma, A., Kitsak, M., Cho, M. H., Ameli, A., Zhou, X., Jiang, Z., Crapo, J. D., Beaty, T. H., Menche, J., Bakke, P. S., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-32173-z · Cited by 48
Using Interactome Big Data to Crack Genetic Mysteries and Enhance Future Crop Breeding
Wu, L., Han, L., Li, Q., Wang, G., Zhang, H., & Li, L. (2020) · Molecular Plant · doi:10.1016/j.molp.2020.12.012 · Cited by 49
PHENOstruct: Prediction of human phenotype ontology terms using heterogeneous data sources
Kahanda, I., Funk, C., Verspoor, K. M., & Ben‐Hur, A. (2015) · F1000Research · doi:10.12688/f1000research.6670.1 · Cited by 47
“While there is a plethora of computational approaches for the related task of prediction of gene-disease associations 8 , no computational method that directly predicts gene-HPO term associations exists at this time.”
BackgroundPrioritization of Candidate Genes for Periodontitis Using Multiple Computational Tools
Zhan, Y., Zhang, R., Lv, H., Song, X., Xu, X., Chai, L., Lv, W., Shang, Z., Jiang, Y., & Zhang, R. (2014) · Journal of Periodontology · doi:10.1902/jop.2014.130523 · Cited by 46
Rezaei–Tavirani, M., Rezaei-Tavirani, S., Mansouri, M., Rostami‐Nejad, M., & Rezaei–Tavirani, M. (2017) · Europe PMC (PubMed Central) · doi:10.22034/apjcp.2017.18.12.3357 · Cited by 47
Zhong, R., Liu, L., Zou, L., Zhu, Y., Chen, W., Zhu, B., Shen, N., Rui, R., Long, L., Ke, J., et al. (2013) · Molecular Carcinogenesis · doi:10.1002/mc.22043 · Cited by 46
“To advance the identification of the disease-causing variations within promising candidate genes, bioinformatic approaches are applied to prioritize SNPs based on their potential functionality in follow-up studies [16].”
MethodIntegrating Imaging and Omics: Computational Methods and Challenges
Hèriché, J.-K., Alexander, S., & Ellenberg, J. (2019) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-080917-013328 · Cited by 46
ProphNet: A generic prioritization method through propagation of information
Martínez, V., Cano, C., & Blanco, A. J. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-s1-s5 · Cited by 46
A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases
Zolotareva, O., & Kleine, M. (2019) · Journal of Integrative Bioinformatics · doi:10.1515/jib-2018-0069 · Cited by 46
Bargsten, J. W., Nap, J., Sanchez‐Perez, G. F., & van Dijk, A. D. J. (2014) · BMC Plant Biology · doi:10.1186/s12870-014-0330-3 · Cited by 49
“Such prioritization is well developed in human disease genetics, where several criteria, such as the putative deleteriousness of a variant, evolutionary conservation, and known biological pathways, are taken into account [11-23].”
BackgroundPaliwal, S., de Giorgio, A., Neil, D., Michel, J.-B., & Lacoste, A. M. B. (2020) · Scientific Reports · doi:10.1038/s41598-020-74922-z · Cited by 43
Identification of potential endocrine disrupting chemicals using gene expression biomarkers
Corton, J. C., Kleinstreuer, N., & Judson, R. (2019) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2019.114683 · Cited by 43
Phenome-driven disease genetics prediction toward drug discovery
Chen, Y., Li, L., Zhang, G., & Xu, R. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv245 · Cited by 45
“…network using 37 039 binary interactions among 9465 genes in the Human Protein Reference Database, which has high coverage and accuracy (Kann, 2010; Moreau and Tranchevent, 2012; Prasad et al., 2009) and has been used in many disease gene discovery studies (Köhler et al., 2008; Li and Patra,…”
MethodFiltering for Compound Heterozygous Sequence Variants in Non-Consanguineous Pedigrees
Kamphans, T., Sabri, P., Zhu, N., Heinrich, V., Mundlos, S., Robinson, P. N., Parkhomchuk, D., & Krawitz, P. M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0070151 · Cited by 42
Network-based cancer precision medicine: A new emerging paradigm
Tan, A., Huang, H., Zhang, P., & Li, S. (2019) · Cancer Letters · doi:10.1016/j.canlet.2019.05.015 · Cited by 43
Alemán, A., Garcia‐García, F., Salavert, F., Medina, I., & Dopazo, J. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku407 · Cited by 40
Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods
Notaro, M., Schubach, M., Robinson, P. N., & Valentini, G. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1854-y · Cited by 41
“While the problem of the prediction of gene–disease associations has been widely investigated [9], the related problem of gene–HPO term prediction has been only considered in a few studies [10], despite the fact that no HPO term associations are known for most human genes, and the quickly growing application of the HP…”
Luo, X., Huang, L., Han, L., Luo, Z., Hu, F., Tieu, R., & Gan, L. (2014) · Schizophrenia Bulletin · doi:10.1093/schbul/sbu045 · Cited by 38
Computational Disease Gene Prioritization: An Appraisal
Gill, N., Singh, S. N., & Aseri, T. C. (2014) · Journal of Computational Biology · doi:10.1089/cmb.2013.0158 · Cited by 41
“In these methods, the candidate genes are ranked on the basis of their distance from the seed genes (Moreau and Tranchevent, 2012).”
InfluentialJumping across biomedical contexts using compressive data fusion
Žitnik, M., & Zupan, B. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw247 · Cited by 39
“In recent years, there is increasing evidence that gene-disease association prediction and disease module detection can benefit from integrative data analysis (Moreau and Tranchevent, 2012; Ritchie et al. , 2015).”
Ni, J., Koyutürk, M., Tong, H., Haines, J. L., Xu, R., & Zhang, X. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1317-x · Cited by 39
Gene prioritization using Bayesian matrix factorization with genomic and phenotypic side information
Zakeri, P., Simm, J., Arany, Á., ElShal, S., & Moreau, Y. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty289 · Cited by 39
“1 Evaluation method Receiver Operating Characteristic (ROC) curve is often used to evaluate the performance of gene prioritization methods (Aerts et al., 2006; Chen et al., 2009; De Bie et al., 2007; Moreau and Tranchevent, 2012).”
MethodSelf-citationHassani‐Pak, K., & Rawlings, C. (2017) · Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics · doi:10.1515/jib-2016-0002 · Cited by 38
“It has been recognised that computational tools are needed that can systematically mine the wealth of biomedical information to boost candidate gene discovery [4].”
BackgroundDoğan, T. (2018) · PeerJ · doi:10.7717/peerj.5298 · Cited by 38
Analyzing and Visualizing Genomic Complexity for the Derivation of the Emergent Molecular Networks
Koutsandreas, T., Binenbaum, I., Pilalis, E. D., Valavanis, I. K., Papadodima, O., & Chatziioannou, A. A. (2016) · International Journal of Monitoring and Surveillance Technologies Research · doi:10.4018/ijmstr.2016040103 · Cited by 37
Metadata
- Authors
- Moreau, Y., & Tranchevent, L.-C.
- Affiliations
- KU Leuven (BE)
- Corresponding author
- Moreau, Yves
- Tranchevent, Léon-Charles
- Venue
- Nature Reviews Genetics
- Published
- 2012-07-03
- Volume
- 13
- Issue
- 8
- Pages
- 523-536
- ISSN
- 1471-0056 (ISSN-L)
- 1471-0064
- OpenAlex
- W2090247362
- Cited by
- 417
- References
- 134
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 6 · 2023: 15 · 2022: 19 · 2021: 21 · 2020: 31 · 2019: 36 · 2018: 36 · 2017: 42 · 2016: 45 · 2015: 49 · 2014: 52 · 2013: 41 · 2012: 15
- FWCI
- 4.58
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- FP7 Health
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Keywords
- gene prioritization, candidate genes, disease gene discovery, computational tools, heterogeneous data integration, expression data, functional annotation, biomedical text mining, genetic linkage, omics data
- MeSH terms
- Animals, Humans, Models, Genetic, Computational Biology, Genetic Predisposition to Disease, Databases, Genetic, Mice, Genetic Association Studies, Haploinsufficiency
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