Статті в журналах: "PRIORITIZE GENES"

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Gong, Lejun, Ronggen Yang, Chun Zhang, Quan Liu, Huakang Lee, and Geng Yang. "RE-RANKING FOR PRIORITIZATION OF DISEASE-RELATED GENES." Biomedical Engineering: Applications, Basis and Communications 28, no. 04 (August 2016): 1650027. http://dx.doi.org/10.4015/s1016237216500277.

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To explore the genetic complexity of disease, this paper focuses on the prioritization of susceptibility genes by re-ranking. This approach prioritizes disease-related genes based on the prior ranking. The genes in prior ranking are divided into seeds and candidates. And then these seed genes are used to prioritize candidates based on association rules. Experimental results show the approach is promising for finding new disease-related genes.

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Zhang, Yi, Tao Wang, Yan Wang, Kun Xia, Jinchen Li, and Zhongsheng Sun. "Targeted sequencing and integrative analysis to prioritize candidate genes in neurodevelopmental disorders." Molecular Neurobiology 58, no. 8 (April 15, 2021): 3863–73. http://dx.doi.org/10.1007/s12035-021-02377-y.

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AbstractNeurodevelopmental disorders (NDDs) are a group of diseases characterized by high heterogeneity and frequently co-occurring symptoms. The mutational spectrum in patients with NDDs is largely incomplete. Here, we sequenced 547 genes from 1102 patients with NDDs and validated 1271 potential functional variants, including 108 de novo variants (DNVs) in 78 autosomal genes and seven inherited hemizygous variants in six X chromosomal genes. Notably, 36 of these 78 genes are the first to be reported in Chinese patients with NDDs. By integrating our genetic data with public data, we prioritized 212 NDD candidate genes with FDR < 0.1, including 17 novel genes. The novel candidate genes interacted or were co-expressed with known candidate genes, forming a functional network involved in known pathways. We highlighted MSL2, which carried two de novo protein-truncating variants (p.L192Vfs*3 and p.S486Ifs*11) and was frequently connected with known candidate genes. This study provides the mutational spectrum of NDDs in China and prioritizes 212 NDD candidate genes for further functional validation and genetic counseling.

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Xin, Huang, Wang Changchen, Liu Lei, Yang Meirong, Zhang Ye, and Pan Bo. "The Phenolyzer Suite: Prioritizing the Candidate Genes Involved in Microtia." Annals of Otology, Rhinology & Laryngology 128, no. 6 (April 2, 2019): 556–62. http://dx.doi.org/10.1177/0003489419840052.

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Objective: Microtia is a congenital malformation of the external ear. Great progress about the genetic of microtia has been made in recent years. This article was to prioritize the potential candidate pathogenic genes of microtia based on existing studies and reports, with the purpose of narrowing the range of following study scientifically and quickly. Method: A computational tool called Phenolyzer (phenotype-based gene analyzer) was used to prioritize microtia genes. Microtia, as a query term, was input in the interface of Phenolyzer. After several steps, including disease match, gene query, gene score system, seed gene growth, and gene ranking, the final results about genetic information of microtia were provided. Then we tracked details of the top 10 genes ranked by Phenolyzer on the basis of previous reports. Results: We detected 10 348 genes associated with microtia or related syndromes, and 78 genes of those genes belonged to seed genes. Every gene was given a score, and the gene with higher scores was more likely influence microtia. The top 10 ranked genes included HOXA2, CHD7, CDT1, ORC1, ORC4, ORC6, CDC6, MED12, TWIST1, and GLI3. Otherwise, four gene-gene interactions were displayed. Conclusion: This article prioritized candidate genes of microtia for the first time. High-throughput methods provide tens of thousands of single-nucleotide variants, indels, and structural variants, and only a handful are relevant to microtia or associated syndromes. Combine the ranked potential pathogenic genes list from Phenolyzer with the results of samples provided by high-throughput methods, and more precise research directions are presented.

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Tranchevent, L. C., F. B. Capdevila, D. Nitsch, B. De Moor, P. De Causmaecker, and Y. Moreau. "A guide to web tools to prioritize candidate genes." Briefings in Bioinformatics 12, no. 1 (March 21, 2010): 22–32. http://dx.doi.org/10.1093/bib/bbq007.

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Rylander, Ragnar. "Genes and Agents: How to Prioritize to Prevent Disease." Archives of Environmental Health: An International Journal 50, no. 5 (October 1995): 333–34. http://dx.doi.org/10.1080/00039896.1995.9935963.

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Asefa, Nigus G., Zoha Kamali, Satyajit Pereira, Ahmad Vaez, Nomdo Jansonius, Arthur A. Bergen, and Harold Snieder. "Bioinformatic Prioritization and Functional Annotation of GWAS-Based Candidate Genes for Primary Open-Angle Glaucoma." Genes 13, no. 6 (June 13, 2022): 1055. http://dx.doi.org/10.3390/genes13061055.

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Background: Primary open-angle glaucoma (POAG) is the most prevalent glaucoma subtype, but its exact etiology is still unknown. In this study, we aimed to prioritize the most likely ‘causal’ genes and identify functional characteristics and underlying biological pathways of POAG candidate genes. Methods: We used the results of a large POAG genome-wide association analysis study from GERA and UK Biobank cohorts. First, we performed systematic gene-prioritization analyses based on: (i) nearest genes; (ii) nonsynonymous single-nucleotide polymorphisms; (iii) co-regulation analysis; (iv) transcriptome-wide association studies; and (v) epigenomic data. Next, we performed functional enrichment analyses to find overrepresented functional pathways and tissues. Results: We identified 142 prioritized genes, of which 64 were novel for POAG. BICC1, AFAP1, and ABCA1 were the most highly prioritized genes based on four or more lines of evidence. The most significant pathways were related to extracellular matrix turnover, transforming growth factor-β, blood vessel development, and retinoic acid receptor signaling. Ocular tissues such as sclera and trabecular meshwork showed enrichment in prioritized gene expression (>1.5 fold). We found pleiotropy of POAG with intraocular pressure and optic-disc parameters, as well as genetic correlation with hypertension and diabetes-related eye disease. Conclusions: Our findings contribute to a better understanding of the molecular mechanisms underlying glaucoma pathogenesis and have prioritized many novel candidate genes for functional follow-up studies.

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Cabrera-Andrade, Alejandro, Andrés López-Cortés, Gabriela Jaramillo-Koupermann, César Paz-y-Miño, Yunierkis Pérez-Castillo, Cristian R. Munteanu, Humbert González-Díaz, Alejandro Pazos, and Eduardo Tejera. "Gene Prioritization through Consensus Strategy, Enrichment Methodologies Analysis, and Networking for Osteosarcoma Pathogenesis." International Journal of Molecular Sciences 21, no. 3 (February 5, 2020): 1053. http://dx.doi.org/10.3390/ijms21031053.

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Osteosarcoma is the most common subtype of primary bone cancer, affecting mostly adolescents. In recent years, several studies have focused on elucidating the molecular mechanisms of this sarcoma; however, its molecular etiology has still not been determined with precision. Therefore, we applied a consensus strategy with the use of several bioinformatics tools to prioritize genes involved in its pathogenesis. Subsequently, we assessed the physical interactions of the previously selected genes and applied a communality analysis to this protein–protein interaction network. The consensus strategy prioritized a total list of 553 genes. Our enrichment analysis validates several studies that describe the signaling pathways PI3K/AKT and MAPK/ERK as pathogenic. The gene ontology described TP53 as a principal signal transducer that chiefly mediates processes associated with cell cycle and DNA damage response It is interesting to note that the communality analysis clusters several members involved in metastasis events, such as MMP2 and MMP9, and genes associated with DNA repair complexes, like ATM, ATR, CHEK1, and RAD51. In this study, we have identified well-known pathogenic genes for osteosarcoma and prioritized genes that need to be further explored.

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Somepalli, Gowthami, Sarthak Sahoo, Arashdeep Singh, and Sridhar Hannenhalli. "Prioritizing and characterizing functionally relevant genes across human tissues." PLOS Computational Biology 17, no. 7 (July 16, 2021): e1009194. http://dx.doi.org/10.1371/journal.pcbi.1009194.

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Knowledge of genes that are critical to a tissue’s function remains difficult to ascertain and presents a major bottleneck toward a mechanistic understanding of genotype-phenotype links. Here, we present the first machine learning model–FUGUE–combining transcriptional and network features, to predict tissue-relevant genes across 30 human tissues. FUGUE achieves an average cross-validation auROC of 0.86 and auPRC of 0.50 (expected 0.09). In independent datasets, FUGUE accurately distinguishes tissue or cell type-specific genes, significantly outperforming the conventional metric based on tissue-specific expression alone. Comparison of tissue-relevant transcription factors across tissue recapitulate their developmental relationships. Interestingly, the tissue-relevant genes cluster on the genome within topologically associated domains and furthermore, are highly enriched for differentially expressed genes in the corresponding cancer type. We provide the prioritized gene lists in 30 human tissues and an open-source software to prioritize genes in a novel context given multi-sample transcriptomic data.

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Mahmood, Iqra, Asif Nadeem, Masroor Ellahi Babar, Muhammad Muddassir Ali, Maryam Javed, Aisha Siddiqa, Tanveer Hussain, and Muhammad Tariq Pervez. "Systematic and Integrated Analysis Approach to Prioritize Mastitis Resistant Genes." Pakistan Journal of Zoology 49, no. 1 (2016): 101–6. http://dx.doi.org/10.17582/journal.pjz/2017.49.1.101.106.

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Oliver, Karen L., Vesna Lukic, Natalie P. Thorne, Samuel F. Berkovic, Ingrid E. Scheffer, and Melanie Bahlo. "Harnessing Gene Expression Networks to Prioritize Candidate Epileptic Encephalopathy Genes." PLoS ONE 9, no. 7 (July 9, 2014): e102079. http://dx.doi.org/10.1371/journal.pone.0102079.

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11

Jiang, Rui. "Walking on multiple disease-gene networks to prioritize candidate genes." Journal of Molecular Cell Biology 7, no. 3 (February 13, 2015): 214–30. http://dx.doi.org/10.1093/jmcb/mjv008.

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Su, Yongchun, Yunfei Li, and Ping Ye. "Mammalian meiosis is more conserved by sex than by species: conserved co-expression networks of meiotic prophase." REPRODUCTION 142, no. 5 (November 2011): 675–87. http://dx.doi.org/10.1530/rep-11-0260.

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Despite the importance of meiosis to human reproduction, we know remarkably little about the genes and pathways that regulate meiotic progression through prophase in any mammalian species. Microarray expression profiles of mammalian gonads provide a valuable resource for probing gene networks. However, expression studies are confounded by mixed germ cell and somatic cell populations in the gonad and asynchronous germ cell populations. Further, widely used clustering methods for analyzing microarray profiles are unable to prioritize candidate genes for testing. To derive a comprehensive understanding of gene expression in mammalian meiotic prophase, we constructed conserved co-expression networks by linking expression profiles of male and female gonads across mouse and human. We demonstrate that conserved gene co-expression dramatically improved the accuracy of detecting known meiotic genes compared with using co-expression in individual studies. Interestingly, our results indicate that meiotic prophase is more conserved by sex than by species. The co-expression networks allowed us to identify genes involved in meiotic recombination, chromatin cohesion, and piRNA metabolism. Further, we were able to prioritize candidate genes based on quantitative co-expression links with known meiotic genes. Literature studies of these candidate genes suggest that some are human disease genes while others are associated with mammalian gonads. In conclusion, our co-expression networks provide a systematic understanding of cross-sex and cross-species conservations observed during meiotic prophase. This approach further allows us to prioritize candidate meiotic genes for in-depth mechanistic studies in the future.

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Perales-Patón, Javier, Tomás Di Domenico, Coral Fustero-Torre, Elena Piñeiro-Yáñez, Carlos Carretero-Puche, Héctor Tejero, Alfonso Valencia, Gonzalo Gómez-López, and Fátima Al-Shahrour. "vulcanSpot: a tool to prioritize therapeutic vulnerabilities in cancer." Bioinformatics 35, no. 22 (June 7, 2019): 4846–48. http://dx.doi.org/10.1093/bioinformatics/btz465.

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Abstract Motivation Genetic alterations lead to tumor progression and cell survival but also uncover cancer-specific vulnerabilities on gene dependencies that can be therapeutically exploited. Results vulcanSpot is a novel computational approach implemented to expand the therapeutic options in cancer beyond known-driver genes unlocking alternative ways to target undruggable genes. The method integrates genome-wide information provided by massive screening experiments to detect genetic vulnerabilities associated to tumors. Then, vulcanSpot prioritizes drugs to target cancer-specific gene dependencies using a weighted scoring system based on well known drug-gene relationships and drug repositioning strategies. Availability and implementation http://www.vulcanspot.org. Supplementary information Supplementary data are available at Bioinformatics online.

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Kumar, Rupesh, and Shazia Haider. "Protein network analysis to prioritize key genes in amyotrophic lateral sclerosis." IBRO Neuroscience Reports 12 (June 2022): 25–44. http://dx.doi.org/10.1016/j.ibneur.2021.12.002.

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Votava, James A., and Brian W. Parks. "Cross-species data integration to prioritize causal genes in lipid metabolism." Current Opinion in Lipidology 32, no. 2 (February 5, 2021): 141–46. http://dx.doi.org/10.1097/mol.0000000000000742.

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Chen, Zefu, Yu Zheng, Yongxin Yang, Yingzhao Huang, Sen Zhao, Hengqiang Zhao, Chenxi Yu, et al. "PhenoApt leverages clinical expertise to prioritize candidate genes via machine learning." American Journal of Human Genetics 109, no. 2 (February 2022): 270–81. http://dx.doi.org/10.1016/j.ajhg.2021.12.008.

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Schaefer, Robert J., Jean-Michel Michno, Joseph Jeffers, Owen Hoekenga, Brian Dilkes, Ivan Baxter, and Chad L. Myers. "Integrating Coexpression Networks with GWAS to Prioritize Causal Genes in Maize." Plant Cell 30, no. 12 (November 9, 2018): 2922–42. http://dx.doi.org/10.1105/tpc.18.00299.

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Lin, Fan, Jue Fan, and Seung Y. Rhee. "QTG-Finder: A Machine-Learning Based Algorithm To Prioritize Causal Genes of Quantitative Trait Loci in Arabidopsis and Rice." G3: Genes|Genomes|Genetics 9, no. 10 (July 29, 2019): 3129–38. http://dx.doi.org/10.1534/g3.119.400319.

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Linkage mapping is one of the most commonly used methods to identify genetic loci that determine a trait. However, the loci identified by linkage mapping may contain hundreds of candidate genes and require a time-consuming and labor-intensive fine mapping process to find the causal gene controlling the trait. With the availability of a rich assortment of genomic and functional genomic data, it is possible to develop a computational method to facilitate faster identification of causal genes. We developed QTG-Finder, a machine learning based algorithm to prioritize causal genes by ranking genes within a quantitative trait locus (QTL). Two predictive models were trained separately based on known causal genes in Arabidopsis and rice. An independent validation analysis showed that the models could recall about 64% of Arabidopsis and 79% of rice causal genes when the top 20% ranked genes were considered. The top 20% ranked genes can range from 10 to 100 genes, depending on the size of a QTL. The models can prioritize different types of traits though at different efficiency. We also identified several important features of causal genes including paralog copy number, being a transporter, being a transcription factor, and containing SNPs that cause premature stop codon. This work lays the foundation for systematically understanding characteristics of causal genes and establishes a pipeline to predict causal genes based on public data.

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O'Mara, Tracy A., Kaltin Ferguson, Paul Fahey, Louise Marquart, Hannah P. Yang, Jolanta Lissowska, Stephen Chanock, et al. "CHEK2, MGMT, SULT1E1 and SULT1A1 Polymorphisms and Endometrial Cancer Risk." Twin Research and Human Genetics 14, no. 4 (August 1, 2011): 328–32. http://dx.doi.org/10.1375/twin.14.4.328.

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Several single nucleotide polymorphisms (SNPs) in candidate genes of DNA repair and hormone pathways have been reported to be associated with endometrial cancer risk. We sought to confirm these associations in two endometrial cancer case-control sample sets and used additional data from an existing genome-wide association study to prioritize an additional SNP for further study. Five SNPs from the CHEK2, MGMT, SULT1E1 and SULT1A1 genes, genotyped in a total of 1597 cases and 1507 controls from two case-control studies, the Australian National Endometrial Cancer Study and the Polish Endometrial Cancer Study, were assessed for association with endometrial cancer risk using logistic regression analysis. Imputed data was drawn for CHEK2 rs8135424 for 666 cases from the Study of Epidemiology and Risk factors in Cancer Heredity study and 5190 controls from the Wellcome Trust Case Control Consortium. We observed no association between SNPs in the MGMT, SULT1E1 and SULT1A1 genes and endometrial cancer risk. The A allele of the rs8135424 CHEK2 SNP was associated with decreased risk of endometrial cancer (adjusted per-allele OR 0.83; 95%CI 0.70-0.98; p = .03) however this finding was opposite to that previously published. Imputed data for CHEK2 rs8135424 supported the direction of effect reported in this study (OR 0.85; 95% CI 0.65–1.10). Previously reported endometrial cancer risk associations with SNPs from in genes involved in estrogen metabolism and DNA repair were not replicated in our larger study population. This study highlights the need for replication of candidate gene SNP studies using large sample groups, to confirm risk associations and better prioritize downstream studies to assess the causal relationship between genetic variants and cancer risk. Our findings suggest that the CHEK2 SNP rs8135424 be prioritized for further study as a genetic factor associated with risk of endometrial cancer.

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Suratanee, Apichat, Chidchanok Chokrathok, Panita Chutimanukul, Nopphawitchayaphong Khrueasan, Teerapong Buaboocha, Supachitra Chadchawan, and Kitiporn Plaimas. "Two-State Co-Expression Network Analysis to Identify Genes Related to Salt Tolerance in Thai rice." Genes 9, no. 12 (November 29, 2018): 594. http://dx.doi.org/10.3390/genes9120594.

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Khao Dawk Mali 105 (KDML105) rice is one of the most important crops of Thailand. It is a challenging task to identify the genes responding to salinity in KDML105 rice. The analysis of the gene co-expression network has been widely performed to prioritize significant genes, in order to select the key genes in a specific condition. In this work, we analyzed the two-state co-expression networks of KDML105 rice under salt-stress and normal grown conditions. The clustering coefficient was applied to both networks and exhibited significantly different structures between the salt-stress state network and the original (normal-grown) network. With higher clustering coefficients, the genes that responded to the salt stress formed a dense cluster. To prioritize and select the genes responding to the salinity, we investigated genes with small partners under normal conditions that were highly expressed and were co-working with many more partners under salt-stress conditions. The results showed that the genes responding to the abiotic stimulus and relating to the generation of the precursor metabolites and energy were the great candidates, as salt tolerant marker genes. In conclusion, in the case of the complexity of the environmental conditions, gaining more information in order to deal with the co-expression network provides better candidates for further analysis.

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Razaghi-Moghadam, Zahra, Razieh Abdollahi, Sama Goliaei, and Morteza Ebrahimi. "HybridRanker: Integrating network topology and biomedical knowledge to prioritize cancer candidate genes." Journal of Biomedical Informatics 64 (December 2016): 139–46. http://dx.doi.org/10.1016/j.jbi.2016.10.003.

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Zazuli, Zulfan, Lalu Muhammad Irham, Wirawan Adikusuma, and Nur Melani Sari. "Identification of Potential Treatments for Acute Lymphoblastic Leukemia through Integrated Genomic Network Analysis." Pharmaceuticals 15, no. 12 (December 14, 2022): 1562. http://dx.doi.org/10.3390/ph15121562.

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The advancement of high-throughput sequencing and genomic analysis revealed that acute lymphoblastic leukemia (ALL) is a genetically heterogeneous disease. The abundance of such genetic data in ALL can also be utilized to identify potential targets for drug discovery and even drug repurposing. We aimed to determine potential genes for drug development and further guide the identification of candidate drugs repurposed for treating ALL through integrated genomic network analysis. Genetic variants associated with ALL were retrieved from the GWAS Catalog. We further applied a genomic-driven drug repurposing approach based on the six functional annotations to prioritize crucial biological ALL-related genes based on the scoring system. Lastly, we identified the potential drugs in which the mechanisms overlapped with the therapeutic targets and prioritized the candidate drugs using Connectivity Map (CMap) analysis. Forty-two genes were considered biological ALL-risk genes with ARID5B topping the list. Based on potentially druggable genes that we identified, palbociclib, sirolimus, and tacrolimus were under clinical trial for ALL. Additionally, chlorprothixene, sirolimus, dihydroergocristine, papaverine, and tamoxifen are the top five drug repositioning candidates for ALL according to the CMap score with dasatinib as a comparator. In conclusion, this study determines the practicability and the potential of integrated genomic network analysis in driving drug discovery in ALL.

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Fadaka, Adewale Oluwaseun, Ashwil Klein, and Ashley Pretorius. "In silico identification of microRNAs as candidate colorectal cancer biomarkers." Tumor Biology 41, no. 11 (November 2019): 101042831988372. http://dx.doi.org/10.1177/1010428319883721.

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The involvement of microRNA in cancers plays a significant role in their pathogenesis. Specific expressions of these non-coding RNAs also serve as biomarkers for early colorectal cancer diagnosis, but their laboratory/molecular identification is challenging and expensive. The aim of this study was to identify potential microRNAs for colorectal cancer diagnosis using in silico approach. Sequence similarity search was employed to obtain the candidate microRNA from the datasets, and three target prediction software were employed to determine their target genes. To determine the involvement of these microRNAs in colorectal cancer, the microRNA gene list obtained was used alongside with colorectal cancer expressed genes from gbCRC and CoReCG databases for gene intersection analysis. The involvement of these genes in the cancer subtype was further strengthened with the DAVID database. KEGG and Gene Ontology were used for the pathway and functional analysis, while STRING was employed for the interactions of protein network and further visualized by Cytoscape. The cBioPortal database was used to prioritize the target genes; prognostic and expression analysis were finally performed on the candidate microRNAs and the prioritized targets. This study, therefore, identified five candidate microRNAs, two hub genes (CTNNB1 and epidermal growth factor receptor), and seven significant target genes associated with colorectal cancer. The molecular validation studies are ongoing to ascertain the biological fitness of these findings.

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Srivastava, Neha, Bhartendu Nath Mishra, and Prachi Srivastava. "Protein Network Analysis to Prioritize Key Genes and Pathway for Stress-Mediated Neurodegeneration." Open Bioinformatics Journal 11, no. 1 (October 18, 2018): 240–51. http://dx.doi.org/10.2174/1875036201811010240.

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Background:Oxidative Stress (OS) has been implicated in the pathophysiology of many neurodegenerative diseases. OS can cause cellular damage that results in cell death due to overproduction of reactive oxygen species (ROS) that may play the crucial role in the disease progression. An impaired mechanism in correlation with reduced expression of antioxidant proteins is the very common feature among most of the age-related disorders. Variousin-vitroandin-vivostudies suggest the major contribution of oxidative stress in neurodegeneration. Role of Nrf2 gene is well established as a neuroprotective gene especially in concern with stress-mediated neurodegeneration. Nrf2 is a bZIP transcription factor that forms the heterodimer with small Maf protein and transcription factor AP1 that regulates transcription by binding to ARE which coordinates the transcription of genes involved in phase II detoxification and an antioxidant defense that is used to protect the cell from oxidative stress.Aim:The currentinsilicostudy was attempted to prioritize key genes and pathway in stress-mediated neurodegeneration through network-based analysis.Methods:Protein-protein interaction network was constructed and analyzed using 63 Nrf2 regulating candidate genes obtained from NCBI database based on literature studies usingSTRING 10.0database andCytoscape v 3.6.0software plug-inNetwork Analyzer.Further, the functional enrichment analysis of identified gene was done usingPANTHER GENE ONTOLOGYsoftware and DAVID tool.Results:Based on network topological parameter, TP53, JUN, MYC, NFE2L2, AKT1, PIK3CA & UBC were identified as the key gene in the network. Among them, TP53 gene was obtained as a super hub gene with the highest Betweenness Centrality (BC) and node degree. The functional enrichment analysis was done usingPANTHER GENE ONTOLOGYsoftware and DAVID tool reveals their significant role in neurotrophin signaling pathway, MAPK signaling pathway, cellular response to stress & in the regulation of stress.Conclusion:The network analysis will help in prioritizing genes in the pathway that helps in understanding the underlying mechanism of disease. Thus, further study on these genes and their biological mechanism and pathway may, therefore, provide a potential target for the treatment of stress-mediated neurodegeneration.

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Zhang, Tiejun, and Di Zhang. "Integrating omics data and protein interaction networks to prioritize driver genes in cancer." Oncotarget 8, no. 35 (July 22, 2017): 58050–60. http://dx.doi.org/10.18632/oncotarget.19481.

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Wu, Mengmeng, Wanwen Zeng, Wenqiang Liu, Hairong Lv, Ting Chen, and Rui Jiang. "Leveraging multiple gene networks to prioritize GWAS candidate genes via network representation learning." Methods 145 (August 2018): 41–50. http://dx.doi.org/10.1016/j.ymeth.2018.06.002.

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Himmelstein, Daniel S., and Sergio E. Baranzini. "Heterogeneous Network Edge Prediction: A Data Integration Approach to Prioritize Disease-Associated Genes." PLOS Computational Biology 11, no. 7 (July 9, 2015): e1004259. http://dx.doi.org/10.1371/journal.pcbi.1004259.

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Lin, Fan, Elena Z. Lazarus, and Seung Y. Rhee. "QTG-Finder2: A Generalized Machine-Learning Algorithm for Prioritizing QTL Causal Genes in Plants." G3: Genes|Genomes|Genetics 10, no. 7 (May 19, 2020): 2411–21. http://dx.doi.org/10.1534/g3.120.401122.

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Linkage mapping has been widely used to identify quantitative trait loci (QTL) in many plants and usually requires a time-consuming and labor-intensive fine mapping process to find the causal gene underlying the QTL. Previously, we described QTG-Finder, a machine-learning algorithm to rationally prioritize candidate causal genes in QTLs. While it showed good performance, QTG-Finder could only be used in Arabidopsis and rice because of the limited number of known causal genes in other species. Here we tested the feasibility of enabling QTG-Finder to work on species that have few or no known causal genes by using orthologs of known causal genes as the training set. The model trained with orthologs could recall about 64% of Arabidopsis and 83% of rice causal genes when the top 20% ranked genes were considered, which is similar to the performance of models trained with known causal genes. The average precision was 0.027 for Arabidopsis and 0.029 for rice. We further extended the algorithm to include polymorphisms in conserved non-coding sequences and gene presence/absence variation as additional features. Using this algorithm, QTG-Finder2, we trained and cross-validated Sorghum bicolor and Setaria viridis models. The S. bicolor model was validated by causal genes curated from the literature and could recall 70% of causal genes when the top 20% ranked genes were considered. In addition, we applied the S. viridis model and public transcriptome data to prioritize a plant height QTL and identified 13 candidate genes. QTL-Finder2 can accelerate the discovery of causal genes in any plant species and facilitate agricultural trait improvement.

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Chang, Ji-Wei, Yuduan Ding, Muhammad Tahir ul Qamar, Yin Shen, Junxiang Gao, and Ling-Ling Chen. "A deep learning model based on sparse auto-encoder for prioritizing cancer-related genes and drug target combinations." Carcinogenesis 40, no. 5 (April 4, 2019): 624–32. http://dx.doi.org/10.1093/carcin/bgz044.

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Abstract Prioritization of cancer-related genes from gene expression profiles and proteomic data is vital to improve the targeted therapies research. Although computational approaches have been complementing high-throughput biological experiments on the understanding of human diseases, it still remains a big challenge to accurately discover cancer-related proteins/genes via automatic learning from large-scale protein/gene expression data and protein–protein interaction data. Most of the existing methods are based on network construction combined with gene expression profiles, which ignore the diversity between normal samples and disease cell lines. In this study, we introduced a deep learning model based on a sparse auto-encoder to learn the specific characteristics of protein interactions in cancer cell lines integrated with protein expression data. The model showed learning ability to identify cancer-related proteins/genes from the input of different protein expression profiles by extracting the characteristics of protein interaction information, which could also predict cancer-related protein combinations. Comparing with other reported methods including differential expression and network-based methods, our model got the highest area under the curve value (>0.8) in predicting cancer-related genes. Our study prioritized ~500 high-confidence cancer-related genes; among these genes, 211 already known cancer drug targets were found, which supported the accuracy of our method. The above results indicated that the proposed auto-encoder model could computationally prioritize candidate proteins/genes involved in cancer and improve the targeted therapies research.

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Hartanto, Margi, Ronny V. L. Joosen, Basten L. Snoek, Leo A. J. Willems, Mark G. Sterken, Dick de Ridder, Henk W. M. Hilhorst, Wilco Ligterink, and Harm Nijveen. "Network Analysis Prioritizes DEWAX and ICE1 as the Candidate Genes for Major eQTL Hotspots in Seed Germination of Arabidopsis thaliana." G3: Genes|Genomes|Genetics 10, no. 11 (September 22, 2020): 4215–26. http://dx.doi.org/10.1534/g3.120.401477.

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Seed germination is characterized by a constant change of gene expression across different time points. These changes are related to specific processes, which eventually determine the onset of seed germination. To get a better understanding on the regulation of gene expression during seed germination, we performed a quantitative trait locus mapping of gene expression (eQTL) at four important seed germination stages (primary dormant, after-ripened, six-hour after imbibition, and radicle protrusion stage) using Arabidopsis thaliana Bay x Sha recombinant inbred lines (RILs). The mapping displayed the distinctness of the eQTL landscape for each stage. We found several eQTL hotspots across stages associated with the regulation of expression of a large number of genes. Interestingly, an eQTL hotspot on chromosome five collocates with hotspots for phenotypic and metabolic QTL in the same population. Finally, we constructed a gene co-expression network to prioritize the regulatory genes for two major eQTL hotspots. The network analysis prioritizes transcription factors DEWAX and ICE1 as the most likely regulatory genes for the hotspot. Together, we have revealed that the genetic regulation of gene expression is dynamic along the course of seed germination.

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McGuirl, Melissa R., Samuel Pattillo Smith, Björn Sandstede, and Sohini Ramachandran. "Detecting Shared Genetic Architecture Among Multiple Phenotypes by Hierarchical Clustering of Gene-Level Association Statistics." Genetics 215, no. 2 (April 3, 2020): 511–29. http://dx.doi.org/10.1534/genetics.120.303096.

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Emerging large-scale biobanks pairing genotype data with phenotype data present new opportunities to prioritize shared genetic associations across multiple phenotypes for molecular validation. Past research, by our group and others, has shown gene-level tests of association produce biologically interpretable characterization of the genetic architecture of a given phenotype. Here, we present a new method, Ward clustering to identify Internal Node branch length outliers using Gene Scores (WINGS), for identifying shared genetic architecture among multiple phenotypes. The objective of WINGS is to identify groups of phenotypes, or “clusters,” sharing a core set of genes enriched for mutations in cases. We validate WINGS using extensive simulation studies and then combine gene-level association tests with WINGS to identify shared genetic architecture among 81 case-control and seven quantitative phenotypes in 349,468 European-ancestry individuals from the UK Biobank. We identify eight prioritized phenotype clusters and recover multiple published gene-level associations within prioritized clusters.

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Bonnot, Titouan, and Dawn H. Nagel. "Time of the day prioritizes the pool of translating mRNAs in response to heat stress." Plant Cell 33, no. 7 (April 19, 2021): 2164–82. http://dx.doi.org/10.1093/plcell/koab113.

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Abstract The circadian clock helps organisms to anticipate and coordinate gene regulatory responses to changes in environmental stimuli. Under growth limiting temperatures, the time of the day modulates the accumulation of polyadenylated mRNAs. In response to heat stress, plants will conserve energy and selectively translate mRNAs. How the clock and/or the time of the day regulates polyadenylated mRNAs bound by ribosomes in response to heat stress is unknown. In-depth analysis of Arabidopsis thaliana translating mRNAs found that the time of the day gates the response of approximately one-third of the circadian-regulated heat-responsive translatome. Specifically, the time of the day and heat stress interact to prioritize the pool of mRNAs in cue to be translated. For a subset of mRNAs, we observed a stronger gated response during the day, and preferentially before the peak of expression. We propose previously overlooked transcription factors (TFs) as regulatory nodes and show that the clock plays a role in the temperature response for select TFs. When the stress was removed, the redefined priorities for translation recovered within 1 h, though slower recovery was observed for abiotic stress regulators. Through hierarchical network connections between clock genes and prioritized TFs, our work provides a framework to target key nodes underlying heat stress tolerance throughout the day.

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Razzaghdoust, Abolfazl, Shahabedin Rahmatizadeh, Bahram Mofid, Samad Muhammadnejad, Mahmoud Parvin, Peyman Mohammadi Torbati, and Abbas Basiri. "Data-Driven Discovery of Molecular Targets for Antibody-Drug Conjugates in Cancer Treatment." BioMed Research International 2021 (January 2, 2021): 1–9. http://dx.doi.org/10.1155/2021/2670573.

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Antibody-drug conjugate therapy has attracted considerable attention in recent years. Since the selection of appropriate targets is a critical aspect of antibody-drug conjugate research and development, a big data research for discovery of candidate targets per tumor type is outstanding and of high interest. Thus, the purpose of this study was to identify and prioritize candidate antibody-drug conjugate targets with translational potential across common types of cancer by mining the Human Protein Atlas, as a unique big data resource. To perform a multifaceted screening process, XML and TSV files including immunohistochemistry expression data for 45 normal tissues and 20 tumor types were downloaded from the Human Protein Atlas website. For genes without high protein expression across critical normal tissues, a quasi H -score (range, 0-300) was computed per tumor type. All genes with a quasi H − score ≥ 150 were extracted. Of these, genes with cell surface localization were selected and included in a multilevel validation process. Among 19670 genes that encode proteins, 5520 membrane protein-coding genes were included in this study. During a multistep data mining procedure, 332 potential targets were identified based on the level of the protein expression across critical normal tissues and 20 tumor types. After validation, 23 cell surface proteins were identified and prioritized as candidate antibody-drug conjugate targets of which two have interestingly been approved by the FDA for use in solid tumors, one has been approved for lymphoma, and four have currently been entered in clinical trials. In conclusion, we identified and prioritized several candidate targets with translational potential, which may yield new clinically effective and safe antibody-drug conjugates. This large-scale antibody-based proteomic study allows us to go beyond the RNA-seq studies, facilitates bench-to-clinic research of targeted anticancer therapeutics, and offers valuable insights into the development of new antibody-drug conjugates.

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Shi, Xingjie, Xiaoran Chai, Yi Yang, Qing Cheng, Yuling Jiao, Haoyue Chen, Jian Huang, Can Yang, and Jin Liu. "A tissue-specific collaborative mixed model for jointly analyzing multiple tissues in transcriptome-wide association studies." Nucleic Acids Research 48, no. 19 (September 26, 2020): e109-e109. http://dx.doi.org/10.1093/nar/gkaa767.

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Abstract Transcriptome-wide association studies (TWASs) integrate expression quantitative trait loci (eQTLs) studies with genome-wide association studies (GWASs) to prioritize candidate target genes for complex traits. Several statistical methods have been recently proposed to improve the performance of TWASs in gene prioritization by integrating the expression regulatory information imputed from multiple tissues, and made significant achievements in improving the ability to detect gene-trait associations. Unfortunately, most existing multi-tissue methods focus on prioritization of candidate genes, and cannot directly infer the specific functional effects of candidate genes across different tissues. Here, we propose a tissue-specific collaborative mixed model (TisCoMM) for TWASs, leveraging the co-regulation of genetic variations across different tissues explicitly via a unified probabilistic model. TisCoMM not only performs hypothesis testing to prioritize gene-trait associations, but also detects the tissue-specific role of candidate target genes in complex traits. To make full use of widely available GWASs summary statistics, we extend TisCoMM to use summary-level data, namely, TisCoMM-S2. Using extensive simulation studies, we show that type I error is controlled at the nominal level, the statistical power of identifying associated genes is greatly improved, and the false-positive rate (FPR) for non-causal tissues is well controlled at decent levels. We further illustrate the benefits of our methods in applications to summary-level GWASs data of 33 complex traits. Notably, apart from better identifying potential trait-associated genes, we can elucidate the tissue-specific role of candidate target genes. The follow-up pathway analysis from tissue-specific genes for asthma shows that the immune system plays an essential function for asthma development in both thyroid and lung tissues.

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Alexandre, Pâmela A., Nicholas J. Hudson, Sigrid A. Lehnert, Marina R. S. Fortes, Marina Naval-Sánchez, Loan T. Nguyen, Laercio R. Porto-Neto, and Antonio Reverter. "Genome-Wide Co-Expression Distributions as a Metric to Prioritize Genes of Functional Importance." Genes 11, no. 10 (October 20, 2020): 1231. http://dx.doi.org/10.3390/genes11101231.

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Genome-wide gene expression analysis are routinely used to gain a systems-level understanding of complex processes, including network connectivity. Network connectivity tends to be built on a small subset of extremely high co-expression signals that are deemed significant, but this overlooks the vast majority of pairwise signals. Here, we developed a computational pipeline to assign to every gene its pair-wise genome-wide co-expression distribution to one of 8 template distributions shapes varying between unimodal, bimodal, skewed, or symmetrical, representing different proportions of positive and negative correlations. We then used a hypergeometric test to determine if specific genes (regulators versus non-regulators) and properties (differentially expressed or not) are associated with a particular distribution shape. We applied our methodology to five publicly available RNA sequencing (RNA-seq) datasets from four organisms in different physiological conditions and tissues. Our results suggest that genes can be assigned consistently to pre-defined distribution shapes, regarding the enrichment of differential expression and regulatory genes, in situations involving contrasting phenotypes, time-series, or physiological baseline data. There is indeed a striking additional biological signal present in the genome-wide distribution of co-expression values which would be overlooked by currently adopted approaches. Our method can be applied to extract further information from transcriptomic data and help uncover the molecular mechanisms involved in the regulation of complex biological process and phenotypes.

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Zhang, Wangshu, Fengzhu Sun, and Rui Jiang. "Integrating multiple protein-protein interaction networks to prioritize disease genes: a Bayesian regression approach." BMC Bioinformatics 12, Suppl 1 (2011): S11. http://dx.doi.org/10.1186/1471-2105-12-s1-s11.

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Liu, Yining, Jingchun Sun, and Min Zhao. "Literature-based knowledgebase of pancreatic cancer gene to prioritize the key genes and pathways." Journal of Genetics and Genomics 43, no. 9 (September 2016): 569–71. http://dx.doi.org/10.1016/j.jgg.2016.04.006.

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Zheng, Chunlei, and Rong Xu. "The Alzheimer’s comorbidity phenome: mining from a large patient database and phenome-driven genetics prediction." JAMIA Open 2, no. 1 (December 19, 2018): 131–38. http://dx.doi.org/10.1093/jamiaopen/ooy050.

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Abstract Objective Alzheimer’s disease (AD) is a severe neurodegenerative disorder and has become a global public health problem. Intensive research has been conducted for AD. But the pathophysiology of AD is still not elucidated. Disease comorbidity often associates diseases with overlapping patterns of genetic markers. This may inform a common etiology and suggest essential protein targets. US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) collects large-scale postmarketing surveillance data that provide a unique opportunity to investigate disease co-occurrence pattern. We aim to construct a heterogeneous network that integrates disease comorbidity network (DCN) from FAERS with protein–protein interaction (PPI) to prioritize the AD risk genes using network-based ranking algorithm. Materials and Methods We built a DCN based on indication data from FAERS using association rule mining. DCN was further integrated with PPI network. We used random walk with restart ranking algorithm to prioritize AD risk genes. Results We evaluated the performance of our approach using AD risk genes curated from genetic association studies. Our approach achieved an area under a receiver operating characteristic curve of 0.770. Top 500 ranked genes achieved 5.53-fold enrichment for known AD risk genes as compared to random expectation. Pathway enrichment analysis using top-ranked genes revealed that two novel pathways, ERBB and coagulation pathways, might be involved in AD pathogenesis. Conclusion We innovatively leveraged FAERS, a comprehensive data resource for FDA postmarket drug safety surveillance, for large-scale AD comorbidity mining. This exploratory study demonstrated the potential of disease-comorbidities mining from FAERS in AD genetics discovery.

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Kanduri, Chakravarthi, and Irma Järvelä. "GenRank: a R/Bioconductor package for prioritization of candidate genes." F1000Research 6 (April 11, 2017): 463. http://dx.doi.org/10.12688/f1000research.11223.1.

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Modern high-throughput studies often yield long lists of genes, a fraction of which are of high relevance to the phenotype of interest. To prioritize the candidate genes of complex genetic traits, our R/Bioconductor package GenRank ranks genes based on convergent evidence obtained from multiple layers of independent evidence. We implemented three methods to rank genes that integrate gene-level data generated from multiple layers of evidence: (a) the convergent evidence (CE) method aggregates evidence based on a weighted vote counting method; (b) the rank product (RP) method performs a meta-analysis of microarray-based gene expression data, and (c) the traditional method combines p-values. The methods are implemented in R and are available as a package in the Bioconductor repository (http://bioconductor.org/packages/GenRank/).

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Ning, Kaida, Kyle Gettler, Wei Zhang, Sok Meng Ng, B. Monica Bowen, Jeffrey Hyams, Michael C. Stephens, et al. "Improved integrative framework combining association data with gene expression features to prioritize Crohn's disease genes." Human Molecular Genetics 24, no. 14 (May 1, 2015): 4147–57. http://dx.doi.org/10.1093/hmg/ddv142.

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Thibodeau, Asa, and Dong-Guk Shin. "TriPOINT: a software tool to prioritize important genes in pathways and their non-coding regulators." Bioinformatics 35, no. 15 (December 19, 2018): 2686–89. http://dx.doi.org/10.1093/bioinformatics/bty998.

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Abstract Summary Current approaches for pathway analyses focus on representing gene expression levels on graph representations of pathways and conducting pathway enrichment among differentially expressed genes. However, gene expression levels by themselves do not reflect the overall picture as non-coding factors play an important role to regulate gene expression. To incorporate these non-coding factors into pathway analyses and to systematically prioritize genes in a pathway we introduce a new software: Triangulation of Perturbation Origins and Identification of Non-Coding Targets. Triangulation of Perturbation Origins and Identification of Non-Coding Targets is a pathway analysis tool, implemented in Java that identifies the significance of a gene under a condition (e.g. a disease phenotype) by studying graph representations of pathways, analyzing upstream and downstream gene interactions and integrating non-coding regions that may be regulating gene expression levels. Availability and implementation The TriPOINT open source software is freely available at https://github.uconn.edu/ajt06004/TriPOINT under the GPL v3.0 license. Supplementary information Supplementary data are available at Bioinformatics online.

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Almeida-Silva, Fabricio, and Thiago M. Venancio. "cageminer: an R/Bioconductor package to prioritize candidate genes by integrating GWAS and gene coexpression networks." in silico Plants, August 24, 2022. http://dx.doi.org/10.1093/insilicoplants/diac018.

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Abstract Although genome-wide association studies (GWAS) identify variants associated with traits of interest, they often fail in identifying causative genes underlying a given phenotype. Integrating GWAS and gene coexpression networks can help prioritize high-confidence candidate genes, as the expression profiles of trait-associated genes can be used to mine novel candidates. Here, we present cageminer, an R package to prioritize candidate genes through the integration of GWAS and coexpression networks. Genes are considered high-confidence candidates if they pass all three filtering criteria implemented in cageminer, namely physical proximity to (or linkage disequilibrium with) SNPs, coexpression with known trait-associated genes, and significant changes in expression levels in conditions of interest. Prioritized candidates can also be scored and ranked to select targets for experimental validation. By applying cageminer to a real data set of Capsicum annuum response to Phytophthora infection (RNA-seq and SNPs from an association panel), we demonstrate that it can effectively prioritize candidates, leading to a significant reduction in candidate gene lists.

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Ruan, Peifeng, and Shuang Wang. "DiSNEP: a Disease-Specific gene Network Enhancement to improve Prioritizing candidate disease genes." Briefings in Bioinformatics, October 16, 2020. http://dx.doi.org/10.1093/bib/bbaa241.

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Abstract Biological network-based strategies are useful in prioritizing genes associated with diseases. Several comprehensive human gene networks such as STRING, GIANT and HumanNet were developed and used in network-assisted algorithms to identify disease-associated genes. However, none of these networks are disease-specific and may not accurately reflect gene interactions for a specific disease. Aiming to improve disease gene prioritization using networks, we propose a Disease-Specific Network Enhancement Prioritization (DiSNEP) framework. DiSNEP first enhances a comprehensive gene network specifically for a disease through a diffusion process on a gene–gene similarity matrix derived from disease omics data. The enhanced disease-specific gene network thus better reflects true gene interactions for the disease and may improve prioritizing disease-associated genes subsequently. In simulations, DiSNEP that uses an enhanced disease-specific network prioritizes more true signal genes than comparison methods using a general gene network or without prioritization. Applications to prioritize cancer-associated gene expression and DNA methylation signal genes for five cancer types from The Cancer Genome Atlas (TCGA) project suggest that more prioritized candidate genes by DiSNEP are cancer-related according to the DisGeNET database than those prioritized by the comparison methods, consistently across all five cancer types considered, and for both gene expression and DNA methylation signal genes.

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Xu, Zhuoran, Luigi Marchionni, and Shuang Wang. "MultiNEP: a Multi-omics Network Enhancement framework for Prioritizing disease genes and metabolites simultaneously." Bioinformatics, May 22, 2023. http://dx.doi.org/10.1093/bioinformatics/btad333.

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Abstract Motivation Many studies have successfully used network information to prioritize candidate omics profiles associated with diseases. The metabolome, as the link between genotypes and phenotypes, has accumulated growing attention. Using a ”multi-omics” network constructed with a gene-gene network, a metabolite-metabolite network, and a gene-metabolite network to simultaneously prioritize candidate disease-associated metabolites and gene expressions could further utilize gene-metabolite interactions that are not used when prioritizing them separately. However, the number of metabolites is usually 100 times fewer than that of genes. Without accounting for this imbalance issue, we cannot effectively use gene-metabolite interactions when simultaneously prioritizing disease-associated metabolites and genes. Results Here we developed a Multi-omics Network Enhancement Prioritization (MultiNEP) framework with a weighting scheme to reweight contributions of different sub-networks in a multi-omics network to effectively prioritize candidate disease-associated metabolites and genes simultaneously. In simulation studies, MultiNEP outperforms competing methods that do not address network imbalances and identifies more true signal genes and metabolites simultaneously when we down-weight relative contributions of the gene-gene network and up-weight that of the metabolite-metabolite network to the gene-metabolite network. Applications to two human cancer cohorts show that MultiNEP prioritizes more cancer-related genes by effectively using both within- and between-omics interactions after handling network imbalance. Availability The developed MultiNEP framework is implemented in an R package and available at: https://github.com/Karenxzr/MultiNep

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Chen, Yong, Xuebing Wu, and Rui Jiang. "Integrating human omics data to prioritize candidate genes." BMC Medical Genomics 6, no. 1 (December 2013). http://dx.doi.org/10.1186/1755-8794-6-57.

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Hao, Ke, Raili Ermel, Katyayani Sukhavasi, Haoxiang Cheng, Lijiang Ma, Ling Li, Letizia Amadori, et al. "Integrative Prioritization of Causal Genes for Coronary Artery Disease." Circulation: Genomic and Precision Medicine 15, no. 1 (February 2022). http://dx.doi.org/10.1161/circgen.121.003365.

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Background: Hundreds of candidate genes have been associated with coronary artery disease (CAD) through genome-wide association studies. However, a systematic way to understand the causal mechanism(s) of these genes, and a means to prioritize them for further study, has been lacking. This represents a major roadblock for developing novel disease- and gene-specific therapies for patients with CAD. Recently, powerful integrative genomics analyses pipelines have emerged to identify and prioritize candidate causal genes by integrating tissue/cell-specific gene expression data with genome-wide association study data sets. Methods: We aimed to develop a comprehensive integrative genomics analyses pipeline for CAD and to provide a prioritized list of causal CAD genes. To this end, we leveraged several complimentary informatics approaches to integrate summary statistics from CAD genome-wide association studies (from UK Biobank and CARDIoGRAMplusC4D) with transcriptomic and expression quantitative trait loci data from 9 cardiometabolic tissue/cell types in the STARNET study (Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task). Results: We identified 162 unique candidate causal CAD genes, which exerted their effect from between one and up to 7 disease-relevant tissues/cell types, including the arterial wall, blood, liver, skeletal muscle, adipose, foam cells, and macrophages. When their causal effect was ranked, the top candidate causal CAD genes were CDKN2B (associated with the 9p21.3 risk locus) and PHACTR1 ; both exerting their causal effect in the arterial wall. A majority of candidate causal genes were represented in cross-tissue gene regulatory co-expression networks that are involved with CAD, with 22/162 being key drivers in those networks. Conclusions: We identified and prioritized candidate causal CAD genes, also localizing their tissue(s) of causal effect. These results should serve as a resource and facilitate targeted studies to identify the functional impact of top causal CAD genes.

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Dutta, Tithi, Sayantan Mitra, Arpan Saha, Kausik Ganguly, Tushar Pyne, and Mainak Sengupta. "A comprehensive meta-analysis and prioritization study to identify vitiligo associated coding and non-coding SNV candidates using web-based bioinformatics tools." Scientific Reports 12, no. 1 (August 25, 2022). http://dx.doi.org/10.1038/s41598-022-18766-9.

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AbstractVitiligo is a prevalent depigmentation disorder affecting around 1% of the general population. So far, various Genome Wide Association Studies (GWAS) and Candidate Gene Association Studies (CGAS) have identified several single nucleotide variants (SNVs) as a risk factor for vitiligo. Nonetheless, little has been discerned regarding their direct functional significance to the disease pathogenesis. In this study, we did extensive data mining and downstream analysis using several experimentally validated datasets like GTEx Portal and web tools like rSNPBase, RegulomeDB, HaploReg and STRING to prioritize 13 SNVs from a set of 291SNVs that have been previously reported to be associated with vitiligo. We also prioritized their underlying/target genes and tried annotating their functional contribution to vitiligo pathogenesis. Our analysis revealed genes like FGFR10P, SUOX, CDK5RAP1 and RERE that have never been implicated in vitiligo previously to have strong potentials to contribute to the disease pathogenesis. The study is the first of its kind to prioritize and functionally annotate vitiligo-associated GWAS and CGAS SNVs and their underlying/target genes, based on functional data available in the public domain database.

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Buonaiuto, Silvia, Immacolata Di Biase, Valentina Aleotti, Amin Ravaei, Adriano De Marino, Gianluca Damaggio, Marco Chierici, et al. "Prioritization of putatively detrimental variants in euploid miscarriages." Scientific Reports 12, no. 1 (February 7, 2022). http://dx.doi.org/10.1038/s41598-022-05737-3.

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AbstractMiscarriage is the spontaneous termination of a pregnancy before 24 weeks of gestation. We studied the genome of euploid miscarried embryos from mothers in the range of healthy adult individuals to understand genetic susceptibility to miscarriage not caused by chromosomal aneuploidies. We developed gp , a pipeline that we used to prioritize 439 unique variants in 399 genes, including genes known to be associated with miscarriages. Among the prioritized genes we found STAG2 coding for the cohesin complex subunit, for which inactivation in mouse is lethal, and TLE4 a target of Notch and Wnt, physically interacting with a region on chromosome 9 associated to miscarriages.

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Yepes, Sally, Margaret A. Tucker, Hela Koka, Yanzi Xiao, Kristine Jones, Aurelie Vogt, Laurie Burdette, et al. "Using whole-exome sequencing and protein interaction networks to prioritize candidate genes for germline cutaneous melanoma susceptibility." Scientific Reports 10, no. 1 (October 14, 2020). http://dx.doi.org/10.1038/s41598-020-74293-5.

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Abstract Although next-generation sequencing has demonstrated great potential for novel gene discovery, confirming disease-causing genes after initial discovery remains challenging. Here, we applied a network analysis approach to prioritize candidate genes identified from whole-exome sequencing analysis of 98 cutaneous melanoma patients from 27 families. Using a network propagation method, we ranked candidate genes by their similarity to known disease genes in protein–protein interaction networks and identified gene clusters with functional connectivity. Using this approach, we identified several new candidate susceptibility genes that warrant future investigations such as NGLY1, IL1RN, FABP2, PRKDC, and PROSER2. The propagated network analysis also allowed us to link families that did not have common underlying genes but that carried variants in genes that interact on protein–protein interaction networks. In conclusion, our study provided an analysis perspective for gene prioritization in the context of genetic heterogeneity across families and prioritized top potential candidate susceptibility genes in our dataset.

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Hoffmann, Markus, Nico Trummer, Leon Schwartz, Jakub Jankowski, Hye Kyung Lee, Lina-Liv Willruth, Olga Lazareva, et al. "TF-Prioritizer: a Java pipeline to prioritize condition-specific transcription factors." GigaScience 12 (December 28, 2022). http://dx.doi.org/10.1093/gigascience/giad026.

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Abstract Background Eukaryotic gene expression is controlled by cis-regulatory elements (CREs), including promoters and enhancers, which are bound by transcription factors (TFs). Differential expression of TFs and their binding affinity at putative CREs determine tissue- and developmental-specific transcriptional activity. Consolidating genomic datasets can offer further insights into the accessibility of CREs, TF activity, and, thus, gene regulation. However, the integration and analysis of multimodal datasets are hampered by considerable technical challenges. While methods for highlighting differential TF activity from combined chromatin state data (e.g., chromatin immunoprecipitation [ChIP], ATAC, or DNase sequencing) and RNA sequencing data exist, they do not offer convenient usability, have limited support for large-scale data processing, and provide only minimal functionality for visually interpreting results. Results We developed TF-Prioritizer, an automated pipeline that prioritizes condition-specific TFs from multimodal data and generates an interactive web report. We demonstrated its potential by identifying known TFs along with their target genes, as well as previously unreported TFs active in lactating mouse mammary glands. Additionally, we studied a variety of ENCODE datasets for cell lines K562 and MCF-7, including 12 histone modification ChIP sequencing as well as ATAC and DNase sequencing datasets, where we observe and discuss assay-specific differences. Conclusion TF-Prioritizer accepts ATAC, DNase, or ChIP sequencing and RNA sequencing data as input and identifies TFs with differential activity, thus offering an understanding of genome-wide gene regulation, potential pathogenesis, and therapeutic targets in biomedical research.

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