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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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From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [119]
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“KEY WORDS: gene prioritization; genome-wide association studies; bioinformatics databases”
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“Although symptoms play important role in modern biomedical diagnosis and disease classification, most modern biomedical research attempts to gain understanding of the molecular mechanism of disease phenotypes [3], including investigating the genotypes of disease/disease categories.”
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“Other previous efforts in data mining to link genes to disease have included a variety of implementations, including GeneDistiller [72], Endeavor [73], and many more further outlined in Moreau and Tranchevent [74].”
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“There is increasing evidence demonstrating that phenotypes are associated with genetic factors (GFs) and environmental factors (EFs) [1,2].”
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“Similar situations arise in gene regulatory networks or protein-protein interaction networks in computational biology where protein function prediction (Sharan et al. , 2007) or disease gene prioritization (Moreau & Tranchevent, 2012) have emerged as important tasks.”
BackgroundPositive and negative forms of replicability in gene network analysis
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ProphTools: general prioritization tools for heterogeneous biological networks
Navarro, C., Martínez, V., Blanco, A. J., & Cano, C. (2017) · GigaScience · doi:10.1093/gigascience/gix111 · Cited by 10
“Many of these approaches have been proposed for the identification of novel gene-disease potential associations [10] or drug-disease associations for drug repositioning [11].”
FunGeneNet: a web tool to estimate enrichment of functional interactions in experimental gene sets
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“Another way to solve this problem can be the extension of an experimental set of genes by gene-prioritization methods [60].”
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“In a recent review, Moreau and Tranchevent [ 54 ] discussed a large variety of computational tools for prioritizing candidate genes for boosting disease gene discovery.”
BackgroundDintor: functional annotation of genomic and proteomic data
Weichenberger, C. X., Blankenburg, H., Palermo, A., D’Elia, Y., König, E., Bernstein, E., & Domingues, F. S. (2015) · BMC Genomics · doi:10.1186/s12864-015-2279-5 · Cited by 10
“If newer data were used instead, we might benefit from a ”knowledge contamination” [49], where the publication that reported the disease association has later been incorporated into databases such as GO or Reactome, which are then used in the prioritization.”
MethodXie, F., He, M., He, L. F., Liu, K., Li, M., Hu, G., & Wen, Z. (2017) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2017.01.002 · Cited by 10
A Primer for Disease Gene Prioritization Using Next-Generation Sequencing Data
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“The breakthrough in next-generation sequencing (NGS) techniques has enabled rapid sequencing of whole genomes at low cost and has brought tremendous opportunities as well as challenges to biomedical research [1-5].”
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“Guilt by association” is not competitive with genetic association for identifying autism risk genes
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“gene prioritization approaches contribute to the determination of disease genes [8].”
BackgroundBCScreen: A gene panel to test for breast carcinogenesis in chemical safety screening
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“In a review on gene prioritization, Moreau and Trachevent [32], argue that computational approaches that integrate complex heterogeneous datasets offer a more thorough and unbiased assessment of candidate genes.”
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“Network analysis is a method for understanding complex relationships among multiple entities [20,21].”
MethodLiseron-Monfils, C., Olson, A. J., & Ware, D. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/326868 · Cited by 10
“The concept of metagene where the gene node in the network could represent the mRNA, its protein, or its promoter give the possibility to bundle the gene form under one unique term: metagene (Moreau and Tranchevent 2012) .”
BackgroundWang, Y., Xia, Z., Deng, J., Xie, X., Gong, M., & Ma, X. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04190-9 · Cited by 9
Enumerating consistent sub-graphs of directed acyclic graphs: an insight into biomedical ontologies
Peng, Y., Jiang, Y., & Predrag Radivojac, A. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty268 · Cited by 8
“diseasegene prioritization (Moreau and Tranchevent, 2012), since it does not exploit relationships between the terms.”
BackgroundDmitrzak‐Węglarz, M., Paszyńska, E., Bilska, K., Szcześniewska, P., Bryl, E., Duda, J., Dutkiewicz, A., Tyszkiewicz‐Nwafor, M., Czerski, P. M., Hanć, T., et al. (2021) · Genes · doi:10.3390/genes12091407 · Cited by 9
“This strategy is based on creating a ranking of the genes identified in silico by classifying them according to their potential importance in developing the analyzed trait [29].”
MethodBallouz, S., Liu, J. Y., Oti, M., Gaëta, B. A., Fatkin, D., Bahlo, M., & Wouters, M. A. (2013) · Molecular Genetics & Genomic Medicine · doi:10.1002/mgg3.40 · Cited by 9
“The results demonstrate that this computational approach is feasible and a valuable discovery tool for geneticists.”
BackgroundDisease-Gene Association Using a Genetic Algorithm
Tahmasebipour, K., & Houghten, S. K. (2014) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe.2014.38 · Cited by 9
“For further information on the various evidence, tools, and computational approaches so far developed and used to associate disease with genes, see [13], [31], [18], [37], [6] and [26].”
MethodSoftPanel: a website for grouping diseases and related disorders for generation of customized panels
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“Based on the fact that these different data sources are complementary in nature, the integration of multiple data sources can improve the coverage and accuracy of prioritization [11].”
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“The network-based gene prioritization methods are the prioritization methods that work on biological data characterized as networks and rank the candidate genes using the network analysis techniques [2].”
BackgroundMethodKabeer, B. S. A., Subba, B., Rinchai, D., Toufiq, M., Khan, T., Yurieva, M., & Chaussabel, D. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1510431 · Cited by 9
Applications of molecular networks in biomedicine
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“Many researchers have demonstrated the performance of the boosting method and its optimization for genomic selection, gene interaction, and genetic disease diagnosis [104–107].”
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“The other strategy is to use bioinformatics methods to extract and prioritize disease candidate genes automatically [3,4].”
MethodAn integrated network of Arabidopsis growth regulators and its use for gene prioritization
Sabaghian, E., Drebert, Z. J., Inzé, D., & Saeys, Y. (2015) · Scientific Reports · doi:10.1038/srep17617 · Cited by 8
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“Several reviews have extensively described and evaluated the performance of varying methods for generating disease similarity metrics in the context of data integration strategies and modeling [35-41].”
BackgroundMethodGene Network for Identifying the Entropy Changes of Different Modules in Pediatric Sepsis
Yang, H. J., Zhang, P., & Wang, L. (2016) · Cellular Physiology and Biochemistry · doi:10.1159/000453169 · Cited by 8
GeneFishing to reconstruct context specific portraits of biological processes
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“An excellent review of developments up to 2012 and exposition of developments to come may be found in referenced literatures (6).”
BackgroundExtracting Gene Regulation Networks Using Linear-Chain Conditional Random Fields and Rules
Žitnik, S., Žitnik, M., Zupan, B., & Bajec, M. (2013) · Repository of the University of Ljubljana (University of Ljubljana) · Cited by 7
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“Fortunately, many computational tools are already available for prioritizing candidates (Moreau and Tranchevent, 2012), and could be of great use to biogerontologists.”
BackgroundSTIGMA: Single-cell tissue-specific gene prioritization using machine learning
Balachandran, S., Prada‐Medina, C. A., Mensah, M. A., Glaser, J., Kakar, N., Nagel, I., Pozojevic, J., Audain, E., Hitz, M., Kircher, M., et al. (2024) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.12.011 · Cited by 10
“7,8 Gene prioritization refers to arranging genes in the order of probability of association with a disease.”
BackgroundGene2DisCo: Gene to disease using disease commonalities
Frasca, M. (2017) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2017.08.001 · Cited by 7
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“The next step is the prioritization of the most promising genes for biological validation [16,17].”
BackgroundXie, Y., Jiang, W., Dong, W., Li, H., Jin, S. C., Brueckner, M. K., & Zhao, H. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010252 · Cited by 7
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“Sifting hundreds and thousands of gene or genetic variations associated with genes from genomic studies can also be daunting (Moreau and Tranchevent, 2012), e.g., even for a user to search for genes associated with cardiac arrhythmia (Rajab et al., 2010) within a 2-Mb region of chromosome 17 may…”
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“In lieu of or in addition to preconceived criteria, an investigator may want to take advantage of unrecognized patterns in public databases that involve the genes, which several web-based tools help to do (30).”
BackgroundIntegrated rare variant-based risk gene prioritization in disease case-control sequencing studies
Lin, J.-R., Zhang, Q., Cai, Y., Morrow, B. E., & Zhang, Z. D. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1007142 · Cited by 7
The conjunctive disjunctive graph node kernel for disease gene prioritization
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Self-citationA biomarker ensemble ranking framework for prioritizing depression candidate genes
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“[23] developed a computational tool for candidate gene prioritization that identified a set of genes or proteins to investigate further experiment.”
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Feature ranking in changing environments where new features are introduced
Degeest, A., Verleysen, M., & Frénay, B. (2015) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280533 · Cited by 6
Functional and Genomic Features of Human Genes Mutated in Neuropsychiatric Disorders
Forero, D. A., Quiroga, C. F. P., & Perry, G. L. (2016) · The Open Neurology Journal · doi:10.2174/1874205x01610010143 · Cited by 6
“These results could be useful for the future creation of computational tools [37] that allow prioritization of novel candidate genes (including ncRNAs [26, 38]) for NPD, incorporating several of the parameters that were found in this work as significant for NPD genes.”
BackgroundMimvec: a deep learning approach for analyzing the human phenome
Gan, M., Li, W., Zeng, W., Wang, X., & Jiang, R. (2017) · BMC Systems Biology · doi:10.1186/s12918-017-0451-z · Cited by 5
“This assumption has become the basis for designing gene prioritization methods, with such successful stories as PRINCE [21], pgFusion [18], pgWalk [17] and many others [15].”
BackgroundValentini, G., Köhler, S., Ré, M., Notaro, M., & Robinson, P. N. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16483-0_7 · Cited by 6
“This task is related but different from the classical disease-gene prioritization problem, in which genes are prioritized with respect to specific diseases [5].”
BackgroundKim, H., Choi, S., & Park, S. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2618927 · Cited by 6
“Moreau [4] introduced statistical benchmarking, which evaluates how well a method discovers known disease-gene associations for assessment of the gene prioritization.”
BackgroundMGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization
Su, L., Liu, G., Bai, T., Meng, X., & Ma, Q. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2216-0 · Cited by 6
“Other methods rank candidate genes by exploiting either local or global network information [2].”
MethodNetwork-based prediction of polygenic disease genes involved in cell motility
Bern, M., King, A., Applewhite, D. A., & Ritz, A. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2834-1 · Cited by 6
A systematic comparison of data- and knowledge-driven approaches to disease subtype discovery
Rintala, T., Federico, A., Latonen, L., Greco, D., & Fortino, V. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab314 · Cited by 6
“RWR is a type of network diffusion algorithm that can be used to boost the discovery of disease-relevant genes [49].”
BackgroundA study of gene prioritization algorithms on PPI networks
Sinsha, K. P., & Chitturi, B. (2016) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2016.7732164 · Cited by 5
“The utility of boosting in GP was shown in [26], [27].”
BackgroundIdentifying genes that mediate anthracyline toxicity in immune cells
Frick, A., Suzuki, O., Benton, C. S., Parks, B., Fedoriw, Y. D., Richards, K. L., Thomas, R. S., & Wiltshire, T. (2015) · Frontiers in Pharmacology · doi:10.3389/fphar.2015.00062 · Cited by 5
“Splenocytes were isolated using standard procedures; spleens were excised and mechanically dissociated into a single-cell suspension, after which red blood cells were removed using ammonium-chloride-potassium lysing buffer (Gibco, Grand Island, NY, USA).”
BackgroundMethodgeneDRAGNN: Gene Disease Prioritization using Graph Neural Networks
Altabaa, A., Huang, D., Byles-Ho, C., Khatib, H., Sosa, F., & Hu, T. (2022) · IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) · doi:10.1109/cibcb55180.2022.9863043 · Cited by 7
Evolutionary history of MEK1 illuminates the nature of deleterious mutations
Andrianova, E. P., Marmion, R. A., Shvartsman, S. Y., & Zhulin, I. B. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2304184120 · Cited by 5
“The majority of disease genes have been duplicated in their evolutionary history, and the role of paralogs in disease has been a subject of debate (7).”
BackgroundMethodGenToS: Use of Orthologous Gene Information to Prioritize Signals from Human GWAS
Hoppmann, A., Schlosser, P., Backofen, R., Lausch, E. U., & Köttgen, A. (2016) · PLoS ONE · doi:10.1371/journal.pone.0162466 · Cited by 5
Using Gene Ontology to Annotate and Prioritize Microarray Data
Milano, M. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1839-4_18 · Cited by 5
A Comparative Study and Analysis of DNA Sequence Classifiers for Predicting Human Diseases
Sakthivel, N. K., Gopalan, N. P., & Subasree, S. (2016) · International Conference on Informatics and Analytics · doi:10.1145/2980258.2982038 · Cited by 5
“The detailed information on various available computational methods and tools can be found at [1,4,12,13,14,15,16].”
MethodSaridaki, A., Antonakos, G., Hager-Theodorides, A. L., Zoidis, E., Tsiamis, G., Bourtzis, K., & Kominakis, A. (2018) · Livestock Science · doi:10.1016/j.livsci.2018.11.020 · Cited by 5
Integrating gene expression biomarker predictions into networks of adverse outcome pathways
Corton, J. C. (2019) · Current Opinion in Toxicology · doi:10.1016/j.cotox.2019.05.006 · Cited by 5
Nazarieh, M., & Helms, V. (2019) · Scientific Reports · doi:10.1038/s41598-019-55954-6 · Cited by 5
Zhang, J., Zhang, L., Yang, G., Wu, D., Jiang, L., Huang, L., Wen, Z., & Li, M. (2013) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2013.05.004 · Cited by 5
Milano, M. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-809633-8.20406-8 · Cited by 5
Shi, K., Gao, L., & Wang, B. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.12.120 · Cited by 4
Incorporating Multi-Scale Module Kernel for Disease-Gene Identification in Biological Networks
Xiang, J., Zeng, K., Chen, S., Meng, X., Zheng, R., Zheng, Y., Long, Y., & Li, M. (2025) · Tsinghua Science & Technology · doi:10.26599/tst.2025.9010088 · Cited by 4
Factors affecting interactome-based prediction of human genes associated with clinical signs
González‐Pérez, S., Pazos, F., & Chagoyen, M. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1754-1 · Cited by 4
“To save resources and maximize success, candidate genes obtained by genetic/genomic approaches can be prioritized with computational methods that consider a variety of previous information [2].”
Le, D., & Tran, T. T. H. (2018) · F1000Research · doi:10.12688/f1000research.14810.1 · Cited by 4
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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