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Saranya KR, Vimina ER, Pinto FR. TransNeT-CGP: A cluster-based comorbid gene prioritization by integrating transcriptomics and network-topological features. Comput Biol Chem 2024; 110:108038. [PMID: 38461796 DOI: 10.1016/j.compbiolchem.2024.108038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 01/11/2024] [Accepted: 02/25/2024] [Indexed: 03/12/2024]
Abstract
The local disruptions caused by the genes of one disease can influence the pathways associated with the other diseases resulting in comorbidity. For gene therapies, it is necessary to prioritize the key genes that regulate common biological mechanisms to tackle the issues caused by overlapping diseases. This work proposes a clustering-based computational approach for prioritising the comorbid genes within the overlapping disease modules by analyzing Protein-Protein Interaction networks. For this, a sub-network with gene interactions of the disease pair was extracted from the interactome. The edge weights are assigned by combining the pairwise gene expression correlation and betweenness centrality scores. Further, a weighted graph clustering algorithm is applied and dominant nodes of high-density clusters are ranked based on clustering coefficients and neighborhood connectivity. Case studies based on neurodegenerative diseases such as Amyotrophic Lateral Sclerosis- Spinal Muscular Atrophy (ALS-SMA) pair and cancers such as Ovarian Carcinoma-Invasive Ductal Breast Carcinoma (OC-IDBC) pair were conducted to examine the efficacy of the proposed method. To identify the mechanistic role of top-ranked genes, we used Functional and Pathway enrichment analysis, connectivity analysis with leave-one-out (LOO) method, analysis of associated disease-related protein complexes, and prioritization tools such as TOPPGENE and Heml2.0. From pathway analysis, it was observed that the top 10 genes obtained using the proposed method were associated with 10 pathways in ALS-SMA comorbidity and 15 in the case of OC-IDBC, while that in similar methods like SAPDSB and S2B were 4, 6 respectively for ALS-SMA and 9, 10 respectively for OC-IDBC. In both case studies, 70 % of the disease-specific benchmark protein complexes were linked to top-ranked genes of the proposed method while that of SAPDSB and S2B were 55 % and 60 % respectively. Additionally, it was found that the removal of the top 10 genes disconnect the network into 14 distinct components in the case of ALS-SMA and 9 in the case of OC-IDBC. The experimental results shows that the proposed method can be effectively used for identifying key genes in comorbidity and can offer insights about the intricate molecular relationship driving comorbid diseases.
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Affiliation(s)
- K R Saranya
- Department of Computer Science & IT, School of Computing, Amrita Vishwa Vidyapeetham, Kochi Campus, India.
| | - E R Vimina
- Department of Computer Science & IT, School of Computing, Amrita Vishwa Vidyapeetham, Kochi Campus, India.
| | - F R Pinto
- Chemistry and Biochemistry Department, Faculty of Sciences, University of Lisbon, Portugal.
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Nguyen T, Yue Z, Slominski R, Welner R, Zhang J, Chen JY. WINNER: A network biology tool for biomolecular characterization and prioritization. Front Big Data 2022; 5:1016606. [DOI: 10.3389/fdata.2022.1016606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 10/14/2022] [Indexed: 11/06/2022] Open
Abstract
Background and contributionIn network biology, molecular functions can be characterized by network-based inference, or “guilt-by-associations.” PageRank-like tools have been applied in the study of biomolecular interaction networks to obtain further the relative significance of all molecules in the network. However, there is a great deal of inherent noise in widely accessible data sets for gene-to-gene associations or protein-protein interactions. How to develop robust tests to expand, filter, and rank molecular entities in disease-specific networks remains an ad hoc data analysis process.ResultsWe describe a new biomolecular characterization and prioritization tool called Weighted In-Network Node Expansion and Ranking (WINNER). It takes the input of any molecular interaction network data and generates an optionally expanded network with all the nodes ranked according to their relevance to one another in the network. To help users assess the robustness of results, WINNER provides two different types of statistics. The first type is a node-expansion p-value, which helps evaluate the statistical significance of adding “non-seed” molecules to the original biomolecular interaction network consisting of “seed” molecules and molecular interactions. The second type is a node-ranking p-value, which helps evaluate the relative statistical significance of the contribution of each node to the overall network architecture. We validated the robustness of WINNER in ranking top molecules by spiking noises in several network permutation experiments. We have found that node degree–preservation randomization of the gene network produced normally distributed ranking scores, which outperform those made with other gene network randomization techniques. Furthermore, we validated that a more significant proportion of the WINNER-ranked genes was associated with disease biology than existing methods such as PageRank. We demonstrated the performance of WINNER with a few case studies, including Alzheimer's disease, breast cancer, myocardial infarctions, and Triple negative breast cancer (TNBC). In all these case studies, the expanded and top-ranked genes identified by WINNER reveal disease biology more significantly than those identified by other gene prioritizing software tools, including Ingenuity Pathway Analysis (IPA) and DiAMOND.ConclusionWINNER ranking strongly correlates to other ranking methods when the network covers sufficient node and edge information, indicating a high network quality. WINNER users can use this new tool to robustly evaluate a list of candidate genes, proteins, or metabolites produced from high-throughput biology experiments, as long as there is available gene/protein/metabolic network information.
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Liu Y, Yeung WSB, Chiu PCN, Cao D. Computational approaches for predicting variant impact: An overview from resources, principles to applications. Front Genet 2022; 13:981005. [PMID: 36246661 PMCID: PMC9559863 DOI: 10.3389/fgene.2022.981005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
One objective of human genetics is to unveil the variants that contribute to human diseases. With the rapid development and wide use of next-generation sequencing (NGS), massive genomic sequence data have been created, making personal genetic information available. Conventional experimental evidence is critical in establishing the relationship between sequence variants and phenotype but with low efficiency. Due to the lack of comprehensive databases and resources which present clinical and experimental evidence on genotype-phenotype relationship, as well as accumulating variants found from NGS, different computational tools that can predict the impact of the variants on phenotype have been greatly developed to bridge the gap. In this review, we present a brief introduction and discussion about the computational approaches for variant impact prediction. Following an innovative manner, we mainly focus on approaches for non-synonymous variants (nsSNVs) impact prediction and categorize them into six classes. Their underlying rationale and constraints, together with the concerns and remedies raised from comparative studies are discussed. We also present how the predictive approaches employed in different research. Although diverse constraints exist, the computational predictive approaches are indispensable in exploring genotype-phenotype relationship.
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Affiliation(s)
- Ye Liu
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
| | - William S. B. Yeung
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Department of Obstetrics and Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Philip C. N. Chiu
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- Department of Obstetrics and Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
- *Correspondence: Philip C. N. Chiu, ; Dandan Cao,
| | - Dandan Cao
- Shenzhen Key Laboratory of Fertility Regulation, Reproductive Medicine Center, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China
- *Correspondence: Philip C. N. Chiu, ; Dandan Cao,
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4
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Network-Based Approaches for Disease-Gene Association Prediction Using Protein-Protein Interaction Networks. Int J Mol Sci 2022; 23:ijms23137411. [PMID: 35806415 PMCID: PMC9266751 DOI: 10.3390/ijms23137411] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/25/2022] [Accepted: 06/30/2022] [Indexed: 01/02/2023] Open
Abstract
Genome-wide association studies (GWAS) can be used to infer genome intervals that are involved in genetic diseases. However, investigating a large number of putative mutations for GWAS is resource- and time-intensive. Network-based computational approaches are being used for efficient disease-gene association prediction. Network-based methods are based on the underlying assumption that the genes causing the same diseases are located close to each other in a molecular network, such as a protein-protein interaction (PPI) network. In this survey, we provide an overview of network-based disease-gene association prediction methods based on three categories: graph-theoretic algorithms, machine learning algorithms, and an integration of these two. We experimented with six selected methods to compare their prediction performance using a heterogeneous network constructed by combining a genome-wide weighted PPI network, an ontology-based disease network, and disease-gene associations. The experiment was conducted in two different settings according to the presence and absence of known disease-associated genes. The results revealed that HerGePred, an integrative method, outperformed in the presence of known disease-associated genes, whereas PRINCE, which adopted a network propagation algorithm, was the most competitive in the absence of known disease-associated genes. Overall, the results demonstrated that the integrative methods performed better than the methods using graph-theory only, and the methods using a heterogeneous network performed better than those using a homogeneous PPI network only.
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Alghamdi SM, Schofield PN, Hoehndorf R. How much do model organism phenotypes contribute to the computational identification of human disease genes? Dis Model Mech 2022; 15:275986. [PMID: 35758016 PMCID: PMC9366895 DOI: 10.1242/dmm.049441] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 06/13/2022] [Indexed: 12/04/2022] Open
Abstract
Computing phenotypic similarity helps identify new disease genes and diagnose rare diseases. Genotype–phenotype data from orthologous genes in model organisms can compensate for lack of human data and increase genome coverage. In the past decade, cross-species phenotype comparisons have proven valuble, and several ontologies have been developed for this purpose. The relative contribution of different model organisms to computational identification of disease-associated genes is not fully explored. We used phenotype ontologies to semantically relate phenotypes resulting from loss-of-function mutations in model organisms to disease-associated phenotypes in humans. Semantic machine learning methods were used to measure the contribution of different model organisms to the identification of known human gene–disease associations. We found that mouse genotype–phenotype data provided the most important dataset in the identification of human disease genes by semantic similarity and machine learning over phenotype ontologies. Other model organisms' data did not improve identification over that obtained using the mouse alone, and therefore did not contribute significantly to this task. Our work impacts on the development of integrated phenotype ontologies, as well as for the use of model organism phenotypes in human genetic variant interpretation. This article has an associated First Person interview with the first author of the paper. Editor's choice: We investigated the use of model organism phenotypes in the computational identification of disease genes, identifying several data biases and concluding that mouse model phenotypes contribute most to computational disease gene identification.
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Affiliation(s)
- Sarah M Alghamdi
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, 4700 KAUST, 23955 Thuwal, Saudi Arabia
| | - Paul N Schofield
- Department of Physiology, Development & Neuroscience, University of Cambridge, Downing Street, CB2 3EG, Cambridge, UK
| | - Robert Hoehndorf
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, 4700 KAUST, 23955 Thuwal, Saudi Arabia
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Cohen ASA, Farrow EG, Abdelmoity AT, Alaimo JT, Amudhavalli SM, Anderson JT, Bansal L, Bartik L, Baybayan P, Belden B, Berrios CD, Biswell RL, Buczkowicz P, Buske O, Chakraborty S, Cheung WA, Coffman KA, Cooper AM, Cross LA, Curran T, Dang TTT, Elfrink MM, Engleman KL, Fecske ED, Fieser C, Fitzgerald K, Fleming EA, Gadea RN, Gannon JL, Gelineau-Morel RN, Gibson M, Goldstein J, Grundberg E, Halpin K, Harvey BS, Heese BA, Hein W, Herd SM, Hughes SS, Ilyas M, Jacobson J, Jenkins JL, Jiang S, Johnston JJ, Keeler K, Korlach J, Kussmann J, Lambert C, Lawson C, Le Pichon JB, Leeder JS, Little VC, Louiselle DA, Lypka M, McDonald BD, Miller N, Modrcin A, Nair A, Neal SH, Oermann CM, Pacicca DM, Pawar K, Posey NL, Price N, Puckett LMB, Quezada JF, Raje N, Rowell WJ, Rush ET, Sampath V, Saunders CJ, Schwager C, Schwend RM, Shaffer E, Smail C, Soden S, Strenk ME, Sullivan BR, Sweeney BR, Tam-Williams JB, Walter AM, Welsh H, Wenger AM, Willig LK, Yan Y, Younger ST, Zhou D, Zion TN, Thiffault I, Pastinen T. Genomic answers for children: Dynamic analyses of >1000 pediatric rare disease genomes. Genet Med 2022; 24:1336-1348. [PMID: 35305867 DOI: 10.1016/j.gim.2022.02.007] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 02/05/2022] [Accepted: 02/07/2022] [Indexed: 12/17/2022] Open
Abstract
PURPOSE This study aimed to provide comprehensive diagnostic and candidate analyses in a pediatric rare disease cohort through the Genomic Answers for Kids program. METHODS Extensive analyses of 960 families with suspected genetic disorders included short-read exome sequencing and short-read genome sequencing (srGS); PacBio HiFi long-read genome sequencing (HiFi-GS); variant calling for single nucleotide variants (SNV), structural variant (SV), and repeat variants; and machine-learning variant prioritization. Structured phenotypes, prioritized variants, and pedigrees were stored in PhenoTips database, with data sharing through controlled access the database of Genotypes and Phenotypes. RESULTS Diagnostic rates ranged from 11% in patients with prior negative genetic testing to 34.5% in naive patients. Incorporating SVs from genome sequencing added up to 13% of new diagnoses in previously unsolved cases. HiFi-GS yielded increased discovery rate with >4-fold more rare coding SVs compared with srGS. Variants and genes of unknown significance remain the most common finding (58% of nondiagnostic cases). CONCLUSION Computational prioritization is efficient for diagnostic SNVs. Thorough identification of non-SNVs remains challenging and is partly mitigated using HiFi-GS sequencing. Importantly, community research is supported by sharing real-time data to accelerate gene validation and by providing HiFi variant (SNV/SV) resources from >1000 human alleles to facilitate implementation of new sequencing platforms for rare disease diagnoses.
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Affiliation(s)
- Ana S A Cohen
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; Department of Pathology and Laboratory Medicine, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO
| | - Emily G Farrow
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | | | - Joseph T Alaimo
- Department of Pathology and Laboratory Medicine, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO
| | - Shivarajan M Amudhavalli
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - John T Anderson
- Department of Orthopaedic Surgery, Children's Mercy Kansas City, Kansas City, MO
| | - Lalit Bansal
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Lauren Bartik
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | | | - Bradley Belden
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | | | - Rebecca L Biswell
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | | | | | | | - Warren A Cheung
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Keith A Coffman
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Ashley M Cooper
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Laura A Cross
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - Tom Curran
- Children's Mercy Research Institute, Kansas City, MO
| | - Thuy Tien T Dang
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Mary M Elfrink
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | | | - Erin D Fecske
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Cynthia Fieser
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Keely Fitzgerald
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Emily A Fleming
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - Randi N Gadea
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | | | - Rose N Gelineau-Morel
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Margaret Gibson
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Jeffrey Goldstein
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Elin Grundberg
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Kelsee Halpin
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Brian S Harvey
- Department of Orthopaedic Surgery, Children's Mercy Kansas City, Kansas City, MO
| | - Bryce A Heese
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - Wendy Hein
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Suzanne M Herd
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Susan S Hughes
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - Mohammed Ilyas
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Jill Jacobson
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Janda L Jenkins
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | | | | | - Kathryn Keeler
- Department of Orthopaedic Surgery, Children's Mercy Kansas City, Kansas City, MO
| | - Jonas Korlach
- Pacific Biosciences of California, Inc, Menlo Park, CA
| | | | | | - Caitlin Lawson
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | | | | | - Vicki C Little
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | | | | | | | - Neil Miller
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Division of Allergy Immunology Pulmonary and Sleep Medicine, Children's Mercy Kansas City, Kansas City, MO
| | - Ann Modrcin
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Annapoorna Nair
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Shelby H Neal
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | | | - Donna M Pacicca
- Department of Orthopaedic Surgery, Children's Mercy Kansas City, Kansas City, MO
| | - Kailash Pawar
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Nyshele L Posey
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Nigel Price
- Department of Orthopaedic Surgery, Children's Mercy Kansas City, Kansas City, MO
| | - Laura M B Puckett
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Julio F Quezada
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Nikita Raje
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Division of Neonatology, Children's Mercy Kansas City, Kansas City, MO
| | | | - Eric T Rush
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Division of Genetics, Children's Mercy Kansas City, Kansas City, MO; Department of Internal Medicine, University of Kansas School of Medicine, Kansas City, MO
| | - Venkatesh Sampath
- Division of Neonatology, Children's Mercy Hospital Kansas City, Kansas City, MO
| | - Carol J Saunders
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; Department of Pathology and Laboratory Medicine, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO
| | - Caitlin Schwager
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - Richard M Schwend
- Department of Orthopaedic Surgery, Children's Mercy Kansas City, Kansas City, MO
| | - Elizabeth Shaffer
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Craig Smail
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Sarah Soden
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Meghan E Strenk
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | | | - Brooke R Sweeney
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | | | - Adam M Walter
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Holly Welsh
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | | | - Laurel K Willig
- Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Yun Yan
- UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO
| | - Scott T Younger
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO
| | - Dihong Zhou
- Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - Tricia N Zion
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Department of Pediatrics, Children's Mercy Kansas City, Kansas City, MO; Division of Genetics, Children's Mercy Kansas City, Kansas City, MO
| | - Isabelle Thiffault
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; Department of Pathology and Laboratory Medicine, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO.
| | - Tomi Pastinen
- Genomic Medicine Center, Children's Mercy Kansas City, Kansas City, MO; UKMC School of Medicine, University of Missouri Kansas City, Kansas City, MO; Children's Mercy Research Institute, Kansas City, MO.
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Yuan X, Wang J, Dai B, Sun Y, Zhang K, Chen F, Peng Q, Huang Y, Zhang X, Chen J, Xu X, Chuan J, Mu W, Li H, Fang P, Gong Q, Zhang P. Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases. Brief Bioinform 2022; 23:6521702. [PMID: 35134823 PMCID: PMC8921623 DOI: 10.1093/bib/bbac019] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 01/10/2022] [Accepted: 01/13/2022] [Indexed: 12/31/2022] Open
Abstract
It’s challenging work to identify disease-causing genes from the next-generation sequencing (NGS) data of patients with Mendelian disorders. To improve this situation, researchers have developed many phenotype-driven gene prioritization methods using a patient’s genotype and phenotype information, or phenotype information only as input to rank the candidate’s pathogenic genes. Evaluations of these ranking methods provide practitioners with convenience for choosing an appropriate tool for their workflows, but retrospective benchmarks are underpowered to provide statistically significant results in their attempt to differentiate. In this research, the performance of ten recognized causal-gene prioritization methods was benchmarked using 305 cases from the Deciphering Developmental Disorders (DDD) project and 209 in-house cases via a relatively unbiased methodology. The evaluation results show that methods using Human Phenotype Ontology (HPO) terms and Variant Call Format (VCF) files as input achieved better overall performance than those using phenotypic data alone. Besides, LIRICAL and AMELIE, two of the best methods in our benchmark experiments, complement each other in cases with the causal genes ranked highly, suggesting a possible integrative approach to further enhance the diagnostic efficiency. Our benchmarking provides valuable reference information to the computer-assisted rapid diagnosis in Mendelian diseases and sheds some light on the potential direction of future improvement on disease-causing gene prioritization methods.
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Affiliation(s)
- Xiao Yuan
- Changsha KingMed Center for Clinical Laboratory, Changsha, China.,Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, China.,Genetalks Biotech. Co., Ltd., Changsha, China
| | - Jing Wang
- Changsha KingMed Center for Clinical Laboratory, Changsha, China
| | - Bing Dai
- Changsha KingMed Center for Clinical Laboratory, Changsha, China
| | - Yanfang Sun
- Changsha KingMed Center for Clinical Laboratory, Changsha, China
| | - Keke Zhang
- Changsha KingMed Center for Clinical Laboratory, Changsha, China
| | - Fangfang Chen
- Changsha KingMed Center for Clinical Laboratory, Changsha, China
| | - Qian Peng
- Changsha KingMed Center for Clinical Laboratory, Changsha, China
| | - Yixuan Huang
- Beijing Geneworks Technology Co., Ltd., Beijing, China
| | - Xinlei Zhang
- Reproductive & Genetics Hospital of Citic & Xiangya, Changsha, China
| | - Junru Chen
- Genetalks Biotech. Co., Ltd., Changsha, China
| | - Xilin Xu
- Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, China
| | - Jun Chuan
- Changsha KingMed Center for Clinical Laboratory, Changsha, China.,Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, China
| | - Wenbo Mu
- Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, China
| | - Huiyuan Li
- Changsha KingMed Center for Clinical Laboratory, Changsha, China.,Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, China
| | - Ping Fang
- Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, China
| | - Qiang Gong
- Changsha KingMed Center for Clinical Laboratory, Changsha, China.,Guangzhou Kingmed Center for Clinical Laboratory, Guangzhou, China
| | - Peng Zhang
- Beijing Key Laboratory for Genetics of Birth Defects, Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
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8
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Schlüter A, Rodríguez-Palmero A, Verdura E, Vélez-Santamaría V, Ruiz M, Fourcade S, Planas-Serra L, Martínez JJ, Guilera C, Girós M, Artuch R, Yoldi ME, O'Callaghan M, García-Cazorla A, Armstrong J, Marti I, Rezola EM, Redin C, Mandel JL, Conejo D, Sierra-Córcoles C, Beltran S, Gut M, Vázquez E, Del Toro M, Troncoso M, Pérez-Jurado LA, Gutiérrez-Solana LG, López de Munain A, Casasnovas C, Aguilera-Albesa S, Macaya A, Pujol A. Diagnosis of Genetic White Matter Disorders by Singleton Whole-Exome and Genome Sequencing Using Interactome-Driven Prioritization. Neurology 2022; 98:e912-e923. [PMID: 35012964 PMCID: PMC8901178 DOI: 10.1212/wnl.0000000000013278] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 12/21/2021] [Indexed: 11/16/2022] Open
Abstract
Background and Objectives Genetic white matter disorders (GWMD) are of heterogeneous origin, with >100 causal genes identified to date. Classic targeted approaches achieve a molecular diagnosis in only half of all patients. We aimed to determine the clinical utility of singleton whole-exome sequencing and whole-genome sequencing (sWES-WGS) interpreted with a phenotype- and interactome-driven prioritization algorithm to diagnose GWMD while identifying novel phenotypes and candidate genes. Methods A case series of patients of all ages with undiagnosed GWMD despite extensive standard-of-care paraclinical studies were recruited between April 2017 and December 2019 in a collaborative study at the Bellvitge Biomedical Research Institute (IDIBELL) and neurology units of tertiary Spanish hospitals. We ran sWES and WGS and applied our interactome-prioritization algorithm based on the network expansion of a seed group of GWMD-related genes derived from the Human Phenotype Ontology terms of each patient. Results We evaluated 126 patients (101 children and 25 adults) with ages ranging from 1 month to 74 years. We obtained a first molecular diagnosis by singleton WES in 59% of cases, which increased to 68% after annual reanalysis, and reached 72% after WGS was performed in 16 of the remaining negative cases. We identified variants in 57 different genes among 91 diagnosed cases, with the most frequent being RNASEH2B, EIF2B5, POLR3A, and PLP1, and a dual diagnosis underlying complex phenotypes in 6 families, underscoring the importance of genomic analysis to solve these cases. We discovered 9 candidate genes causing novel diseases and propose additional putative novel candidate genes for yet-to-be discovered GWMD. Discussion Our strategy enables a high diagnostic yield and is a good alternative to trio WES/WGS for GWMD. It shortens the time to diagnosis compared to the classical targeted approach, thus optimizing appropriate management. Furthermore, the interactome-driven prioritization pipeline enables the discovery of novel disease-causing genes and phenotypes, and predicts novel putative candidate genes, shedding light on etiopathogenic mechanisms that are pivotal for myelin generation and maintenance.
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Affiliation(s)
- Agatha Schlüter
- Neurometabolic Diseases Laboratory, Bellvitge Biomedical Research Institute (IDIBELL), 08908 L'Hospitalet de Llobregat, Barcelona, Spain.,Center for Biomedical Research on Rare Diseases (CIBERER), ISCIII, Spain
| | - Agustí Rodríguez-Palmero
- Neurometabolic Diseases Laboratory, Bellvitge Biomedical Research Institute (IDIBELL), 08908 L'Hospitalet de Llobregat, Barcelona, Spain.,Center for Biomedical Research on Rare Diseases (CIBERER), ISCIII, Spain.,Pediatric Neurology Unit, Department of Pediatrics. Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona, Spain
| | - Edgard Verdura
- Neurometabolic Diseases Laboratory, Bellvitge Biomedical Research Institute (IDIBELL), 08908 L'Hospitalet de Llobregat, Barcelona, Spain.,Center for Biomedical Research on Rare Diseases (CIBERER), ISCIII, Spain
| | - Valentina Vélez-Santamaría
- Neurometabolic Diseases Laboratory, Bellvitge Biomedical Research Institute (IDIBELL), 08908 L'Hospitalet de Llobregat, Barcelona, Spain.,Neuromuscular Unit, Neurology Department, Hospital Universitari de Bellvitge, Universitat de Barcelona, Hospitalet de Llobregat, Spain
| | - Montserrat Ruiz
- Neurometabolic Diseases Laboratory, Bellvitge Biomedical Research Institute (IDIBELL), 08908 L'Hospitalet de Llobregat, Barcelona, Spain.,Center for Biomedical Research on Rare Diseases (CIBERER), ISCIII, Spain
| | - Stéphane Fourcade
- Neurometabolic Diseases Laboratory, Bellvitge Biomedical Research Institute (IDIBELL), 08908 L'Hospitalet de Llobregat, Barcelona, Spain.,Center for Biomedical Research on Rare Diseases (CIBERER), ISCIII, Spain
| | - Laura Planas-Serra
- Neurometabolic Diseases Laboratory, Bellvitge Biomedical Research Institute (IDIBELL), 08908 L'Hospitalet de Llobregat, Barcelona, Spain.,Center for Biomedical Research on Rare Diseases (CIBERER), ISCIII, Spain
| | - Juan José Martínez
- Neurometabolic Diseases Laboratory, Bellvitge Biomedical Research Institute (IDIBELL), 08908 L'Hospitalet de Llobregat, Barcelona, Spain.,Center for Biomedical Research on Rare Diseases (CIBERER), ISCIII, Spain
| | - Cristina Guilera
- Neurometabolic Diseases Laboratory, Bellvitge Biomedical Research Institute (IDIBELL), 08908 L'Hospitalet de Llobregat, Barcelona, Spain.,Center for Biomedical Research on Rare Diseases (CIBERER), ISCIII, Spain
| | - Marisa Girós
- Secció d'Errors Congènits del Metabolisme-IBC, Servei de Bioquímica i Genètica Molecular, Hospital Clínic, IDIBAPS, CIBERER, Barcelona, Spain
| | - Rafael Artuch
- Center for Biomedical Research on Rare Diseases (CIBERER), ISCIII, Spain.,Institut de Recerca Pediàtrica-Hospital Sant Joan de Déu (IRP-HSJD), Barcelona, Spain
| | - María Eugenia Yoldi
- Pediatric Neurology Unit, Department of Pediatrics, Navarra Health Service, Navarrabiomed Research Foundation, Pamplona, Spain
| | - Mar O'Callaghan
- Center for Biomedical Research on Rare Diseases (CIBERER), ISCIII, Spain.,Institut de Recerca Pediàtrica-Hospital Sant Joan de Déu (IRP-HSJD), Barcelona, Spain
| | - Angels García-Cazorla
- Center for Biomedical Research on Rare Diseases (CIBERER), ISCIII, Spain.,Institut de Recerca Pediàtrica-Hospital Sant Joan de Déu (IRP-HSJD), Barcelona, Spain
| | - Judith Armstrong
- Center for Biomedical Research on Rare Diseases (CIBERER), ISCIII, Spain.,Molecular and Genetics Medicine Section, Hospital Sant Joan de Déu, Barcelona, Spain
| | - Itxaso Marti
- Department of Neuropediatrics, Hospital Universitario Donostia, San Sebastián, Spain.,Biodonostia Health Research Institute (Biodonostia HRI), San Sebastián, Spain.,University of the Basque Country (UPV-EHU), San Sebastian, Spain.,Centro de Investigación Biomédica en Red para Enfermedades Neurodegenerativas (CIBERNED), Carlos III Health Institute, Madrid, Spain
| | - Elisabet Mondragón Rezola
- Biodonostia Health Research Institute (Biodonostia HRI), San Sebastián, Spain.,Centro de Investigación Biomédica en Red para Enfermedades Neurodegenerativas (CIBERNED), Carlos III Health Institute, Madrid, Spain.,Department of Neurology, Hospital Universitario Donostia, San Sebastián, Spain
| | - Claire Redin
- Département de Médecine translationnelle et Neurogénétique, IGBMC, CNRS UMR 7104/INSERM U964/Université de Strasbourg, Illkirch, France
| | - Jean Louis Mandel
- Département de Médecine translationnelle et Neurogénétique, IGBMC, CNRS UMR 7104/INSERM U964/Université de Strasbourg, Illkirch, France.,Laboratoire de Diagnostic Génétique, Hôpitaux Universitaires de Strasbourg, Strasbourg, France.,Chaire de Génétique Humaine, Collège de France, Illkirch, France
| | - David Conejo
- Complejo asistencial universitario de Burgos, Burgos, Spain
| | | | - Sergi Beltran
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Marta Gut
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Elida Vázquez
- Department of Pediatric Radiology, Hospital Materno-Infantil Vall d'Hebrón, Barcelona, Spain
| | - Mireia Del Toro
- Center for Biomedical Research on Rare Diseases (CIBERER), ISCIII, Spain.,Pediatric Neurology Department, Vall d'Hebron University Hospital, Universitat Autónoma de Barcelona, Spain
| | - Mónica Troncoso
- Pediatric Neurology, Hospital Clínico San Borja Arriarán, Central Campus Universidad de Chile, Chile
| | - Luis A Pérez-Jurado
- Center for Biomedical Research on Rare Diseases (CIBERER), ISCIII, Spain.,Genetics Service, Hospital del Mar Research Institute (IMIM), Barcelona, Spain.,Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Luis G Gutiérrez-Solana
- Department of Paediatric Neurology, Children's University Hospital Niño Jesús, Madrid, Spain
| | - Adolfo López de Munain
- Biodonostia Health Research Institute (Biodonostia HRI), San Sebastián, Spain.,University of the Basque Country (UPV-EHU), San Sebastian, Spain.,Centro de Investigación Biomédica en Red para Enfermedades Neurodegenerativas (CIBERNED), Carlos III Health Institute, Madrid, Spain.,Department of Neurology, Hospital Universitario Donostia, San Sebastián, Spain
| | - Carlos Casasnovas
- Neurometabolic Diseases Laboratory, Bellvitge Biomedical Research Institute (IDIBELL), 08908 L'Hospitalet de Llobregat, Barcelona, Spain.,Neuromuscular Unit, Neurology Department, Hospital Universitari de Bellvitge, Universitat de Barcelona, Hospitalet de Llobregat, Spain
| | - Sergio Aguilera-Albesa
- Pediatric Neurology Unit, Department of Pediatrics, Navarra Health Service, Navarrabiomed Research Foundation, Pamplona, Spain
| | - Alfons Macaya
- Center for Biomedical Research on Rare Diseases (CIBERER), ISCIII, Spain.,Pediatric Neurology Department, Vall d'Hebron University Hospital, Universitat Autónoma de Barcelona, Spain.,Pediatric Neurology Research Group, Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Aurora Pujol
- Neurometabolic Diseases Laboratory, Bellvitge Biomedical Research Institute (IDIBELL), 08908 L'Hospitalet de Llobregat, Barcelona, Spain .,Center for Biomedical Research on Rare Diseases (CIBERER), ISCIII, Spain.,Catalan Institution of Research and Advanced Studies (ICREA), Barcelona, Catalonia, Spain
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9
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Althagafi A, Alsubaie L, Kathiresan N, Mineta K, Aloraini T, Al Mutairi F, Alfadhel M, Gojobori T, Alfares A, Hoehndorf R. DeepSVP: integration of genotype and phenotype for structural variant prioritization using deep learning. Bioinformatics 2021; 38:1677-1684. [PMID: 34951628 PMCID: PMC8896633 DOI: 10.1093/bioinformatics/btab859] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 12/07/2021] [Accepted: 12/21/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Structural genomic variants account for much of human variability and are involved in several diseases. Structural variants are complex and may affect coding regions of multiple genes, or affect the functions of genomic regions in different ways from single nucleotide variants. Interpreting the phenotypic consequences of structural variants relies on information about gene functions, haploinsufficiency or triplosensitivity and other genomic features. Phenotype-based methods to identifying variants that are involved in genetic diseases combine molecular features with prior knowledge about the phenotypic consequences of altering gene functions. While phenotype-based methods have been applied successfully to single nucleotide variants as well as short insertions and deletions, the complexity of structural variants makes it more challenging to link them to phenotypes. Furthermore, structural variants can affect a large number of coding regions, and phenotype information may not be available for all of them. RESULTS We developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic and gene functions information. We incorporate phenotypes linked to genes, functions of gene products, gene expression in individual cell types and anatomical sites of expression, and systematically relate them to their phenotypic consequences through ontologies and machine learning. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families. AVAILABILITY AND IMPLEMENTATION https://github.com/bio-ontology-research-group/DeepSVP. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Azza Althagafi
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia,Computer Science Department, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
| | - Lamia Alsubaie
- Department of Pathology and Laboratory Medicine, King Abdulaziz Medical City (KAMC), Riyadh, Saudi Arabia,Center for Genetics and Inherited Diseases, Taibah University, Almadinah Almunwarah, Saudi Arabia
| | | | - Katsuhiko Mineta
- Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
| | - Taghrid Aloraini
- Department of Pathology and Laboratory Medicine, King Abdulaziz Medical City (KAMC), Riyadh, Saudi Arabia,King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Centre, Ministry of National Guard-Health Affairs (MNG-HA), Riyadh, Saudi Arabia
| | - Fuad Al Mutairi
- Genetics & Precision Medicine Department, King Abdulaziz Medical City, Ministry of National Guard-Health Affairs (MNG-HA), Riyadh, Saudi Arabia,King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Centre, Ministry of National Guard-Health Affairs (MNG-HA), Riyadh, Saudi Arabia
| | - Majid Alfadhel
- Genetics & Precision Medicine Department, King Abdulaziz Medical City, Ministry of National Guard-Health Affairs (MNG-HA), Riyadh, Saudi Arabia,King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Centre, Ministry of National Guard-Health Affairs (MNG-HA), Riyadh, Saudi Arabia
| | - Takashi Gojobori
- KCBRC, Biological and Environmental Science and Engineering Division (BESE), KAUST, Thuwal, Saudi Arabia
| | - Ahmad Alfares
- Department of Pathology and Laboratory Medicine, King Abdulaziz Medical City (KAMC), Riyadh, Saudi Arabia,King Saud bin Abdulaziz University for Health Sciences, King Abdullah International Medical Research Centre, Ministry of National Guard-Health Affairs (MNG-HA), Riyadh, Saudi Arabia,Department of Pediatrics, College of Medicine, Qassim University, Qassim, Saudi Arabia
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10
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Kulmanov M, Smaili FZ, Gao X, Hoehndorf R. Semantic similarity and machine learning with ontologies. Brief Bioinform 2021; 22:bbaa199. [PMID: 33049044 PMCID: PMC8293838 DOI: 10.1093/bib/bbaa199] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 08/03/2020] [Accepted: 08/04/2020] [Indexed: 12/13/2022] Open
Abstract
Ontologies have long been employed in the life sciences to formally represent and reason over domain knowledge and they are employed in almost every major biological database. Recently, ontologies are increasingly being used to provide background knowledge in similarity-based analysis and machine learning models. The methods employed to combine ontologies and machine learning are still novel and actively being developed. We provide an overview over the methods that use ontologies to compute similarity and incorporate them in machine learning methods; in particular, we outline how semantic similarity measures and ontology embeddings can exploit the background knowledge in ontologies and how ontologies can provide constraints that improve machine learning models. The methods and experiments we describe are available as a set of executable notebooks, and we also provide a set of slides and additional resources at https://github.com/bio-ontology-research-group/machine-learning-with-ontologies.
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Affiliation(s)
| | | | - Xin Gao
- Computational Bioscience Research Center and lead of the Structural and Functional Bioinformatics Group at King Abdullah University of Science and Technology
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11
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Chen J, Althagafi A, Hoehndorf R. Predicting candidate genes from phenotypes, functions and anatomical site of expression. Bioinformatics 2021; 37:853-860. [PMID: 33051643 PMCID: PMC8248315 DOI: 10.1093/bioinformatics/btaa879] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 08/26/2020] [Accepted: 09/28/2020] [Indexed: 12/30/2022] Open
Abstract
Motivation Over the past years, many computational methods have been developed to
incorporate information about phenotypes for disease–gene
prioritization task. These methods generally compute the similarity between
a patient’s phenotypes and a database of gene-phenotype to find the
most phenotypically similar match. The main limitation in these methods is
their reliance on knowledge about phenotypes associated with particular
genes, which is not complete in humans as well as in many model organisms,
such as the mouse and fish. Information about functions of gene products and
anatomical site of gene expression is available for more genes and can also
be related to phenotypes through ontologies and machine-learning models. Results We developed a novel graph-based machine-learning method for biomedical
ontologies, which is able to exploit axioms in ontologies and other
graph-structured data. Using our machine-learning method, we embed genes
based on their associated phenotypes, functions of the gene products and
anatomical location of gene expression. We then develop a machine-learning
model to predict gene–disease associations based on the associations
between genes and multiple biomedical ontologies, and this model
significantly improves over state-of-the-art methods. Furthermore, we extend
phenotype-based gene prioritization methods significantly to all genes,
which are associated with phenotypes, functions or site of expression. Availability and implementation Software and data are available at https://github.com/bio-ontology-research-group/DL2Vec. Supplementary information Supplementary data
are available at Bioinformatics online.
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Affiliation(s)
- Jun Chen
- Computational Bioscience Research Center (CBRC), Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
| | - Azza Althagafi
- Computational Bioscience Research Center (CBRC), Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia.,Computer Science Department, College of Computers and Information Technology, Taif University, Taif 26571, Saudi Arabia
| | - Robert Hoehndorf
- Computational Bioscience Research Center (CBRC), Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia
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12
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Boizard F, Buffin-Meyer B, Aligon J, Teste O, Schanstra JP, Klein J. PRYNT: a tool for prioritization of disease candidates from proteomics data using a combination of shortest-path and random walk algorithms. Sci Rep 2021; 11:5764. [PMID: 33707596 PMCID: PMC7952700 DOI: 10.1038/s41598-021-85135-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Accepted: 01/29/2021] [Indexed: 11/14/2022] Open
Abstract
The urinary proteome is a promising pool of biomarkers of kidney disease. However, the protein changes observed in urine only partially reflect the deregulated mechanisms within kidney tissue. In order to improve on the mechanistic insight based on the urinary protein changes, we developed a new prioritization strategy called PRYNT (PRioritization bY protein NeTwork) that employs a combination of two closeness-based algorithms, shortest-path and random walk, and a contextualized protein-protein interaction (PPI) network, mainly based on clique consolidation of STRING network. To assess the performance of our approach, we evaluated both precision and specificity of PRYNT in prioritizing kidney disease candidates. Using four urinary proteome datasets, PRYNT prioritization performed better than other prioritization methods and tools available in the literature. Moreover, PRYNT performed to a similar, but complementary, extent compared to the upstream regulator analysis from the commercial Ingenuity Pathway Analysis software. In conclusion, PRYNT appears to be a valuable freely accessible tool to predict key proteins indirectly from urinary proteome data. In the future, PRYNT approach could be applied to other biofluids, molecular traits and diseases. The source code is freely available on GitHub at: https://github.com/Boizard/PRYNT and has been integrated as an interactive web apps to improved accessibility ( https://github.com/Boizard/PRYNT/tree/master/AppPRYNT ).
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Affiliation(s)
- Franck Boizard
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1297, Institute of Cardiovascular and Metabolic Disease, 31432, Toulouse, France
- Université Toulouse III Paul-Sabatier, 31330, Toulouse, France
| | - Bénédicte Buffin-Meyer
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1297, Institute of Cardiovascular and Metabolic Disease, 31432, Toulouse, France
- Université Toulouse III Paul-Sabatier, 31330, Toulouse, France
| | - Julien Aligon
- Université de Toulouse, UT1, IRIT, (CNRS/UMR 5505), Toulouse, France
| | - Olivier Teste
- Université de Toulouse, UT2J, IRIT, (CNRS/UMR 5505), Toulouse, France
| | - Joost P Schanstra
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1297, Institute of Cardiovascular and Metabolic Disease, 31432, Toulouse, France
- Université Toulouse III Paul-Sabatier, 31330, Toulouse, France
| | - Julie Klein
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1297, Institute of Cardiovascular and Metabolic Disease, 31432, Toulouse, France.
- Université Toulouse III Paul-Sabatier, 31330, Toulouse, France.
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13
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DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier. PLoS Comput Biol 2020; 16:e1008453. [PMID: 33206638 PMCID: PMC7710064 DOI: 10.1371/journal.pcbi.1008453] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 12/02/2020] [Accepted: 10/20/2020] [Indexed: 12/21/2022] Open
Abstract
Predicting the phenotypes resulting from molecular perturbations is one of the key challenges in genetics. Both forward and reverse genetic screen are employed to identify the molecular mechanisms underlying phenotypes and disease, and these resulted in a large number of genotype–phenotype association being available for humans and model organisms. Combined with recent advances in machine learning, it may now be possible to predict human phenotypes resulting from particular molecular aberrations. We developed DeepPheno, a neural network based hierarchical multi-class multi-label classification method for predicting the phenotypes resulting from loss-of-function in single genes. DeepPheno uses the functional annotations with gene products to predict the phenotypes resulting from a loss-of-function; additionally, we employ a two-step procedure in which we predict these functions first and then predict phenotypes. Prediction of phenotypes is ontology-based and we propose a novel ontology-based classifier suitable for very large hierarchical classification tasks. These methods allow us to predict phenotypes associated with any known protein-coding gene. We evaluate our approach using evaluation metrics established by the CAFA challenge and compare with top performing CAFA2 methods as well as several state of the art phenotype prediction approaches, demonstrating the improvement of DeepPheno over established methods. Furthermore, we show that predictions generated by DeepPheno are applicable to predicting gene–disease associations based on comparing phenotypes, and that a large number of new predictions made by DeepPheno have recently been added as phenotype databases. Gene–phenotype associations can help to understand the underlying mechanisms of many genetic diseases. However, experimental identification, often involving animal models, is time consuming and expensive. Computational methods that predict gene–phenotype associations can be used instead. We developed DeepPheno, a novel approach for predicting the phenotypes resulting from a loss of function of a single gene. We use gene functions and gene expression as information to prediction phenotypes. Our method uses a neural network classifier that is able to account for hierarchical dependencies between phenotypes. We extensively evaluate our method and compare it with related approaches, and we show that DeepPheno results in better performance in several evaluations. Furthermore, we found that many of the new predictions made by our method have been added to phenotype association databases released one year later. Overall, DeepPheno simulates some aspects of human physiology and how molecular and physiological alterations lead to abnormal phenotypes.
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14
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Köhler S, Carmody L, Vasilevsky N, Jacobsen JOB, Danis D, Gourdine JP, Gargano M, Harris NL, Matentzoglu N, McMurry JA, Osumi-Sutherland D, Cipriani V, Balhoff JP, Conlin T, Blau H, Baynam G, Palmer R, Gratian D, Dawkins H, Segal M, Jansen AC, Muaz A, Chang WH, Bergerson J, Laulederkind SJF, Yüksel Z, Beltran S, Freeman AF, Sergouniotis PI, Durkin D, Storm AL, Hanauer M, Brudno M, Bello SM, Sincan M, Rageth K, Wheeler MT, Oegema R, Lourghi H, Della Rocca MG, Thompson R, Castellanos F, Priest J, Cunningham-Rundles C, Hegde A, Lovering RC, Hajek C, Olry A, Notarangelo L, Similuk M, Zhang XA, Gómez-Andrés D, Lochmüller H, Dollfus H, Rosenzweig S, Marwaha S, Rath A, Sullivan K, Smith C, Milner JD, Leroux D, Boerkoel CF, Klion A, Carter MC, Groza T, Smedley D, Haendel MA, Mungall C, Robinson PN. Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources. Nucleic Acids Res 2020; 47:D1018-D1027. [PMID: 30476213 PMCID: PMC6324074 DOI: 10.1093/nar/gky1105] [Citation(s) in RCA: 409] [Impact Index Per Article: 102.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 10/24/2018] [Indexed: 12/12/2022] Open
Abstract
The Human Phenotype Ontology (HPO)—a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases—is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO’s interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.human-phenotype-ontology.org) simplifies browsing terms and exploring clinical features, diseases, and human genes.
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Affiliation(s)
- Sebastian Köhler
- Charité Centrum für Therapieforschung, Charité-Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin 10117, Germany.,Einstein Center Digital Future, Berlin 10117, Germany.,Monarch Initiative, monarchinitiative.org
| | - Leigh Carmody
- Monarch Initiative, monarchinitiative.org.,The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Nicole Vasilevsky
- Monarch Initiative, monarchinitiative.org.,Oregon Health & Science University, Portland, OR 97217, USA
| | - Julius O B Jacobsen
- Monarch Initiative, monarchinitiative.org.,Genomics England, Queen Mary University of London, Dawson Hall, Charterhouse Square, London EC1M 6BQ, UK
| | - Daniel Danis
- Monarch Initiative, monarchinitiative.org.,The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Jean-Philippe Gourdine
- Monarch Initiative, monarchinitiative.org.,Oregon Health & Science University, Portland, OR 97217, USA
| | - Michael Gargano
- Monarch Initiative, monarchinitiative.org.,The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Nomi L Harris
- Monarch Initiative, monarchinitiative.org.,Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Nicolas Matentzoglu
- Monarch Initiative, monarchinitiative.org.,European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK
| | - Julie A McMurry
- Monarch Initiative, monarchinitiative.org.,Linus Pauling institute, Oregon State University, Corvallis, OR, USA
| | - David Osumi-Sutherland
- Monarch Initiative, monarchinitiative.org.,European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge, UK
| | - Valentina Cipriani
- Monarch Initiative, monarchinitiative.org.,William Harvey Research Institute, Queen Mary University College of London.,UCL Genetics Institute, University College of London.,UCL Institute of Ophthalmology, University College of London
| | - James P Balhoff
- Monarch Initiative, monarchinitiative.org.,Renaissance Computing Institute, University of North Carolina at Chapel Hill
| | - Tom Conlin
- Monarch Initiative, monarchinitiative.org.,Linus Pauling institute, Oregon State University, Corvallis, OR, USA
| | - Hannah Blau
- Monarch Initiative, monarchinitiative.org.,The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Gareth Baynam
- Western Australian Register of Developmental Anomalies and Genetic Services of Western Australia, Department of Health, Government of Western Australia, WA, Australia.,School of Paediatrics and Telethon Kids Institute, University of Western Australia, Perth, WA, Australia.,Institute for Immunology and Infectious Diseases, Murdoch University, Perth, WA, Australia.,Spatial Sciences, Department of Science and Engineering, Curtin University, Perth, WA, Australia.,The Office of Population Health Genomics, Department of Health, Government of Western Australia, Perth, WA, Australia
| | - Richard Palmer
- Spatial Sciences, Department of Science and Engineering, Curtin University, Perth, WA, Australia
| | - Dylan Gratian
- Western Australian Register of Developmental Anomalies and Genetic Services of Western Australia, Department of Health, Government of Western Australia, WA, Australia
| | - Hugh Dawkins
- The Office of Population Health Genomics, Department of Health, Government of Western Australia, Perth, WA, Australia
| | | | - Anna C Jansen
- Neurogenetics Research Group, Vrije Universiteit Brussel, Brussels, Belgium.,Pediatric Neurology Unit, Department of Pediatrics, UZ Brussel, Brussels, Belgium
| | - Ahmed Muaz
- Monarch Initiative, monarchinitiative.org.,Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia
| | - Willie H Chang
- Centre for Computational Medicine, Hospital for Sick Children and Department of Computer Science, University of Toronto, Toronto, Canada
| | - Jenna Bergerson
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Stanley J F Laulederkind
- Rat Genome Database, Department of Biomedical Engineering, Medical College of Wisconsin & Marquette University, 8701 Watertown Plank Road Milwaukee, WI 53226, USA
| | | | - Sergi Beltran
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Baldiri Reixac 4, Barcelona 08028, Spain.,Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Alexandra F Freeman
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | | | - Daniel Durkin
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Andrea L Storm
- ICF, Rockville, MD, USA.,National Center for Advancing Translational Sciences, Office of Rare Diseases Research, National Institutes of Health, Bethesda, MD, USA
| | - Marc Hanauer
- INSERM, US14-Orphanet, Plateforme Maladies Rares, 75014 Paris, France
| | - Michael Brudno
- Centre for Computational Medicine, Hospital for Sick Children and Department of Computer Science, University of Toronto, Toronto, Canada
| | | | - Murat Sincan
- Sanford Imagenetics, Sanford Health, Sioux Falls, SD, USA
| | - Kayli Rageth
- Sanford Imagenetics, Sanford Health, Sioux Falls, SD, USA
| | - Matthew T Wheeler
- Center for Undiagnosed Diseases, Stanford University School of Medicine, Stanford, CA, USA
| | - Renske Oegema
- Department of Genetics, University Medical Center Utrecht, the Netherlands
| | - Halima Lourghi
- INSERM, US14-Orphanet, Plateforme Maladies Rares, 75014 Paris, France
| | - Maria G Della Rocca
- ICF, Rockville, MD, USA.,National Center for Advancing Translational Sciences, Office of Rare Diseases Research, National Institutes of Health, Bethesda, MD, USA
| | - Rachel Thompson
- Institute of Genetic Medicine, Newcastle University, Newcastle upon Tyne, UK
| | | | - James Priest
- Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Ayushi Hegde
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Ruth C Lovering
- Institute of Cardiovascular Science, University College London, UK
| | | | - Annie Olry
- INSERM, US14-Orphanet, Plateforme Maladies Rares, 75014 Paris, France
| | - Luigi Notarangelo
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Morgan Similuk
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Xingmin A Zhang
- Monarch Initiative, monarchinitiative.org.,The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - David Gómez-Andrés
- Child Neurology Unit. Hospital Universitari Vall d'Hebron, Vall d'Hebron Research Institute (VHIR), Barcelona, Spain
| | - Hanns Lochmüller
- CNAG-CRG, Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Baldiri Reixac 4, Barcelona 08028, Spain.,Department of Neuropediatrics and Muscle Disorders, Medical Center-University of Freiburg, Faculty of Medicine, Freiburg, Germany.,Children's Hospital of Eastern Ontario Research Institute, University of Ottawa, Ottawa, Canada.,Division of Neurology, Department of Medicine, The Ottawa Hospital, Ottawa, Canada
| | - Hélène Dollfus
- Centre for Rare Eye Diseases CARGO, SENSGENE FSMR Network, Strasbourg University Hospital, Strasbourg, France
| | - Sergio Rosenzweig
- Immunology Service, Department of Laboratory Medicine, NIH Clinical Center, Bethesda, MD, USA
| | - Shruti Marwaha
- Center for Undiagnosed Diseases, Stanford University School of Medicine, Stanford, CA, USA
| | - Ana Rath
- INSERM, US14-Orphanet, Plateforme Maladies Rares, 75014 Paris, France
| | - Kathleen Sullivan
- Department of Pediatrics, Division of Allergy Immunology, The Children's Hospital of Philadelphia, University of Pennsylvania Perelman School of Medicine, 3615 Civic Center Boulevard, Philadelphia, PA 19104, USA
| | | | - Joshua D Milner
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Dorothée Leroux
- Centre for Rare Eye Diseases CARGO, SENSGENE FSMR Network, Strasbourg University Hospital, Strasbourg, France
| | | | - Amy Klion
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Melody C Carter
- National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Tudor Groza
- Monarch Initiative, monarchinitiative.org.,Garvan Institute of Medical Research, Darlinghurst, Sydney, NSW 2010, Australia
| | - Damian Smedley
- Monarch Initiative, monarchinitiative.org.,Genomics England, Queen Mary University of London, Dawson Hall, Charterhouse Square, London EC1M 6BQ, UK
| | - Melissa A Haendel
- Monarch Initiative, monarchinitiative.org.,Oregon Health & Science University, Portland, OR 97217, USA.,Linus Pauling institute, Oregon State University, Corvallis, OR, USA
| | - Chris Mungall
- Monarch Initiative, monarchinitiative.org.,Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Peter N Robinson
- Monarch Initiative, monarchinitiative.org.,The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.,Institute for Systems Genomics, University of Connecticut, Farmington, CT, USA
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15
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Shang H, Liu ZP. Network-based prioritization of cancer genes by integrative ranks from multi-omics data. Comput Biol Med 2020; 119:103692. [PMID: 32339126 DOI: 10.1016/j.compbiomed.2020.103692] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 02/10/2020] [Accepted: 02/29/2020] [Indexed: 10/24/2022]
Abstract
Finding disease genes related to cancer is of great importance for diagnosis and treatment. With the development of high-throughput technologies, more and more multiple-level omics data have become available. Thus, it is urgent to develop computational methods to identify cancer genes by integrating these data. We propose an integrative rank-based method called iRank to prioritize cancer genes by integrating multi-omics data in a unified network-based framework. The method was used to identify the disease genes of hepatocellular carcinoma (HCC) in humans using the multi-omics data for HCC from TCGA after building up integrated networks in the corresponding molecular levels. The kernel of iRank is based on an improved PageRank algorithm with constraints. To demonstrate the validity and the effectiveness of the method, we performed experiments for comparison between single-level omics data and multiple omics data as well as with other algorithms: random walk (RW), random walk with restart on heterogeneous network (RWH), PRINCE and PhenoRank. We also performed a case study on another cancer, prostate adenocarcinoma (PRAD). The results indicate the effectiveness and efficiency of iRank which demonstrates the significance of integrating multi-omics data and multiplex networks in cancer gene prioritization.
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Affiliation(s)
- Haixia Shang
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
| | - Zhi-Ping Liu
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China; Center of Intelligent Medicine, Shandong University, Jinan, Shandong 250061, China.
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16
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Finding Diagnostically Useful Patterns in Quantitative Phenotypic Data. Am J Hum Genet 2019; 105:933-946. [PMID: 31607427 PMCID: PMC6848993 DOI: 10.1016/j.ajhg.2019.09.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 09/13/2019] [Indexed: 12/11/2022] Open
Abstract
Trio-based whole-exome sequence (WES) data have established confident genetic diagnoses in ∼40% of previously undiagnosed individuals recruited to the Deciphering Developmental Disorders (DDD) study. Here we aim to use the breadth of phenotypic information recorded in DDD to augment diagnosis and disease variant discovery in probands. Median Euclidean distances (mEuD) were employed as a simple measure of similarity of quantitative phenotypic data within sets of ≥10 individuals with plausibly causative de novo mutations (DNM) in 28 different developmental disorder genes. 13/28 (46.4%) showed significant similarity for growth or developmental milestone metrics, 10/28 (35.7%) showed similarity in HPO term usage, and 12/28 (43%) showed no phenotypic similarity. Pairwise comparisons of individuals with high-impact inherited variants to the 32 individuals with causative DNM in ANKRD11 using only growth z-scores highlighted 5 likely causative inherited variants and two unrecognized DNM resulting in an 18% diagnostic uplift for this gene. Using an independent approach, naive Bayes classification of growth and developmental data produced reasonably discriminative models for the 24 DNM genes with sufficiently complete data. An unsupervised naive Bayes classification of 6,993 probands with WES data and sufficient phenotypic information defined 23 in silico syndromes (ISSs) and was used to test a “phenotype first” approach to the discovery of causative genotypes using WES variants strictly filtered on allele frequency, mutation consequence, and evidence of constraint in humans. This highlighted heterozygous de novo nonsynonymous variants in SPTBN2 as causative in three DDD probands.
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17
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Aguilar D, Lemonnier N, Koppelman GH, Melén E, Oliva B, Pinart M, Guerra S, Bousquet J, Anto JM. Understanding allergic multimorbidity within the non-eosinophilic interactome. PLoS One 2019; 14:e0224448. [PMID: 31693680 PMCID: PMC6834334 DOI: 10.1371/journal.pone.0224448] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 10/14/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The mechanisms explaining multimorbidity between asthma, dermatitis and rhinitis (allergic multimorbidity) are not well known. We investigated these mechanisms and their specificity in distinct cell types by means of an interactome-based analysis of expression data. METHODS Genes associated to the diseases were identified using data mining approaches, and their multimorbidity mechanisms in distinct cell types were characterized by means of an in silico analysis of the topology of the human interactome. RESULTS We characterized specific pathomechanisms for multimorbidities between asthma, dermatitis and rhinitis for distinct emergent non-eosinophilic cell types. We observed differential roles for cytokine signaling, TLR-mediated signaling and metabolic pathways for multimorbidities across distinct cell types. Furthermore, we also identified individual genes potentially associated to multimorbidity mechanisms. CONCLUSIONS Our results support the existence of differentiated multimorbidity mechanisms between asthma, dermatitis and rhinitis at cell type level, as well as mechanisms common to distinct cell types. These results will help understanding the biology underlying allergic multimorbidity, assisting in the design of new clinical studies.
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MESH Headings
- Asthma/epidemiology
- Asthma/genetics
- Asthma/immunology
- Blood Cells/immunology
- Blood Cells/metabolism
- Cytokines/immunology
- Cytokines/metabolism
- Datasets as Topic
- Dermatitis, Allergic Contact/epidemiology
- Dermatitis, Allergic Contact/genetics
- Dermatitis, Allergic Contact/immunology
- Dermatitis, Atopic/epidemiology
- Dermatitis, Atopic/genetics
- Dermatitis, Atopic/immunology
- Gene Expression Profiling
- Humans
- Immunity, Cellular/genetics
- Multimorbidity
- Protein Interaction Maps/genetics
- Protein Interaction Maps/immunology
- Rhinitis, Allergic/epidemiology
- Rhinitis, Allergic/genetics
- Rhinitis, Allergic/immunology
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Affiliation(s)
- Daniel Aguilar
- Biomedical Research Networking Center in Hepatic and Digestive Diseases (CIBEREHD), Instituto de Salud Carlos III, Barcelona, Spain
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
- 6AM Data Mining, Barcelona, Spain
| | - Nathanael Lemonnier
- Institute for Advanced Biosciences, Inserm U 1209 CNRS UMR 5309 Université Grenoble Alpes, Site Santé, Allée des Alpes, La Tronche, France
| | - Gerard H. Koppelman
- University of Groningen, University Medical Center Groningen, Beatrix Children’s Hospital, Department of Pediatric Pulmonology and Pediatric Allergology, Groningen, Netherlands
- University of Groningen, University Medical Center Groningen, GRIAC Research Institute
| | - Erik Melén
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Baldo Oliva
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Mariona Pinart
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
| | - Stefano Guerra
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
- Asthma and Airway Disease Research Center, University of Arizona, Tucson, Arizona, United States of America
| | - Jean Bousquet
- Hopital Arnaud de Villeneuve University Hospital, Montpellier, France
- Charité, Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Comprehensive Allergy Center, Department of Dermatology and Allergy, Berlin, Germany
| | - Josep M. Anto
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
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18
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Zolotareva O, Kleine M. A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases. J Integr Bioinform 2019; 16:/j/jib.ahead-of-print/jib-2018-0069/jib-2018-0069.xml. [PMID: 31494632 PMCID: PMC7074139 DOI: 10.1515/jib-2018-0069] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Accepted: 07/12/2019] [Indexed: 12/16/2022] Open
Abstract
Modern high-throughput experiments provide us with numerous potential associations between genes and diseases. Experimental validation of all the discovered associations, let alone all the possible interactions between them, is time-consuming and expensive. To facilitate the discovery of causative genes, various approaches for prioritization of genes according to their relevance for a given disease have been developed. In this article, we explain the gene prioritization problem and provide an overview of computational tools for gene prioritization. Among about a hundred of published gene prioritization tools, we select and briefly describe 14 most up-to-date and user-friendly. Also, we discuss the advantages and disadvantages of existing tools, challenges of their validation, and the directions for future research.
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Affiliation(s)
- Olga Zolotareva
- Bielefeld University, Faculty of Technology and Center for Biotechnology, International Research Training Group "Computational Methods for the Analysis of the Diversity and Dynamics of Genomes" and Genome Informatics, Universitätsstraße 25, Bielefeld, Germany
| | - Maren Kleine
- Bielefeld University, Faculty of Technology, Bioinformatics/Medical Informatics Department, Universitätsstraße 25, Bielefeld, Germany
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19
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Boudellioua I, Kulmanov M, Schofield PN, Gkoutos GV, Hoehndorf R. DeepPVP: phenotype-based prioritization of causative variants using deep learning. BMC Bioinformatics 2019; 20:65. [PMID: 30727941 PMCID: PMC6364462 DOI: 10.1186/s12859-019-2633-8] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Accepted: 01/17/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient's phenotype. RESULTS We have developed DeepPVP, a variant prioritization method that combined automated inference with deep neural networks to identify the likely causative variants in whole exome or whole genome sequence data. We demonstrate that DeepPVP performs significantly better than existing methods, including phenotype-based methods that use similar features. DeepPVP is freely available at https://github.com/bio-ontology-research-group/phenomenet-vp . CONCLUSIONS DeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.
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Affiliation(s)
- Imane Boudellioua
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, 4700 KAUST, Thuwal, 23955-6900, Kingdom of Saudi Arabia.,Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology, 4700 KAUST, PO Box 2882, Thuwal, 23955-6900, Kingdom of Saudi Arabia
| | - Maxat Kulmanov
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, 4700 KAUST, Thuwal, 23955-6900, Kingdom of Saudi Arabia.,Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology, 4700 KAUST, PO Box 2882, Thuwal, 23955-6900, Kingdom of Saudi Arabia
| | - Paul N Schofield
- Department of Physiology, Development & Neuroscience, University of Cambridge, Downing Street, Cambridge, CB2 3EG, UK
| | - Georgios V Gkoutos
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, B15 2TT, UK.,Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, Birmingham, B15 2TT, UK.,NIHR Experimental Cancer Medicine Centre, Birmingham, B15 2TT, UK.,NIHR Surgical Reconstruction and Microbiology, Birmingham, B15 2TT, UK.,NIHR Biomedical Research Centre, Birmingham, B15 2TT, UK.,MRC Health Data Research UK, Birmingham, B15 2TT, UK
| | - Robert Hoehndorf
- Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, 4700 KAUST, Thuwal, 23955-6900, Kingdom of Saudi Arabia. .,Computer, Electrical and Mathematical Sciences & Engineering Division (CEMSE), King Abdullah University of Science and Technology, 4700 KAUST, PO Box 2882, Thuwal, 23955-6900, Kingdom of Saudi Arabia.
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