scholarly journals Automated Methods Enable Direct Computation on Phenotypic Descriptions for Novel Candidate Gene Prediction

2020 ◽  
Vol 10 ◽  
Author(s):  
Ian R. Braun ◽  
Carolyn J. Lawrence-Dill
Keyword(s):  
Candidate Gene ◽  
Gene Prediction ◽  
Direct Computation ◽  
Automated Methods

scholarly journals Integration of anatomy ontology data with protein–protein interaction networks improves the candidate gene prediction accuracy for anatomical entities

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Pasan C. Fernando ◽  
Paula M. Mabee ◽  
Erliang Zeng
Keyword(s):  
Candidate Gene ◽  
Candidate Genes ◽  
Gene Networks ◽  
Prediction Accuracy ◽  
Gene Prediction ◽  
Anatomical Entity ◽  
Anatomy Ontology ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Entity Relationships

Abstract Background Identification of genes responsible for anatomical entities is a major requirement in many fields including developmental biology, medicine, and agriculture. Current wet lab techniques used for this purpose, such as gene knockout, are high in resource and time consumption. Protein–protein interaction (PPI) networks are frequently used to predict disease genes for humans and gene candidates for molecular functions, but they are rarely used to predict genes for anatomical entities. Moreover, PPI networks suffer from network quality issues, which can be a limitation for their usage in predicting candidate genes. Therefore, we developed an integrative framework to improve the candidate gene prediction accuracy for anatomical entities by combining existing experimental knowledge about gene-anatomical entity relationships with PPI networks using anatomy ontology annotations. We hypothesized that this integration improves the quality of the PPI networks by reducing the number of false positive and false negative interactions and is better optimized to predict candidate genes for anatomical entities. We used existing Uberon anatomical entity annotations for zebrafish and mouse genes to construct gene networks by calculating semantic similarity between the genes. These anatomy-based gene networks were semantic networks, as they were constructed based on the anatomy ontology annotations that were obtained from the experimental data in the literature. We integrated these anatomy-based gene networks with mouse and zebrafish PPI networks retrieved from the STRING database and compared the performance of their network-based candidate gene predictions. Results According to evaluations of candidate gene prediction performance tested under four different semantic similarity calculation methods (Lin, Resnik, Schlicker, and Wang), the integrated networks, which were semantically improved PPI networks, showed better performances by having higher area under the curve values for receiver operating characteristic and precision-recall curves than PPI networks for both zebrafish and mouse. Conclusion Integration of existing experimental knowledge about gene-anatomical entity relationships with PPI networks via anatomy ontology improved the candidate gene prediction accuracy and optimized them for predicting candidate genes for anatomical entities.

QTL mapping for maize starch content and candidate gene prediction combined with co-expression network analysis

2019 ◽  
Vol 132 (7) ◽  
pp. 1931-1941 ◽  
Author(s):  
Feng Lin ◽  
Ling Zhou ◽  
Bing He ◽  
Xiaolin Zhang ◽  
Huixue Dai ◽  
...  
Keyword(s):  
Network Analysis ◽  
Qtl Mapping ◽  
Candidate Gene ◽  
Gene Prediction ◽  
Starch Content ◽  
Maize Starch

scholarly journals The Wheat GENIE3 Network Provides Biologically-Relevant Information in Polyploid Wheat

2020 ◽  
Vol 10 (10) ◽  
pp. 3675-3686 ◽  
Author(s):  
Sophie A. Harrington ◽  
Anna E. Backhaus ◽  
Ajit Singh ◽  
Keywan Hassani-Pak ◽  
Cristobal Uauy
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Candidate Gene ◽  
Web Application ◽  
Regulatory Networks ◽  
Target Genes ◽  
Gene Prediction ◽  
Relevant Information ◽  
Rna Seq ◽  
Biologically Relevant

Gene regulatory networks are powerful tools which facilitate hypothesis generation and candidate gene discovery. However, the extent to which the network predictions are biologically relevant is often unclear. Recently a GENIE3 network which predicted targets of wheat transcription factors was produced. Here we used an independent RNA-Seq dataset to test the predictions of the wheat GENIE3 network for the senescence-regulating transcription factor NAM-A1 (TraesCS6A02G108300). We re-analyzed the RNA-Seq data against the RefSeqv1.0 genome and identified a set of differentially expressed genes (DEGs) between the wild-type and nam-a1 mutant which recapitulated the known role of NAM-A1 in senescence and nutrient remobilisation. We found that the GENIE3-predicted target genes of NAM-A1 overlap significantly with the DEGs, more than would be expected by chance. Based on high levels of overlap between GENIE3-predicted target genes and the DEGs, we identified candidate senescence regulators. We then explored genome-wide trends in the network related to polyploidy and found that only homeologous transcription factors are likely to share predicted targets in common. However, homeologs which vary in expression levels across tissues are less likely to share predicted targets than those that do not, suggesting that they may be more likely to act in distinct pathways. This work demonstrates that the wheat GENIE3 network can provide biologically-relevant predictions of transcription factor targets, which can be used for candidate gene prediction and for global analyses of transcription factor function. The GENIE3 network has now been integrated into the KnetMiner web application, facilitating its use in future studies.

scholarly journals A Contemporary Review of AudioGene audioprofiling: A machine-based candidate gene prediction tool for autosomal dominant nonsyndromic hearing loss

2009 ◽  
Vol 119 (11) ◽  
pp. 2211-2215 ◽  
Author(s):  
Michael S. Hildebrand ◽  
Adam P. DeLuca ◽  
Kyle R. Taylor ◽  
David P. Hoskinson ◽  
In Ae Hur ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Candidate Gene ◽  
Autosomal Dominant ◽  
Gene Prediction ◽  
Prediction Tool ◽  
Nonsyndromic Hearing Loss

scholarly journals Fine Mapping and Candidate Gene Prediction of a Pleiotropic Quantitative Trait Locus for Yield-Related Trait in Zea mays

PLoS ONE ◽  
2012 ◽  
Vol 7 (11) ◽  
pp. e49836 ◽  
Author(s):  
Ruixiang Liu ◽  
Haitao Jia ◽  
Xiaoliang Cao ◽  
Jun Huang ◽  
Feng Li ◽  
...  
Keyword(s):  
Quantitative Trait Locus ◽  
Zea Mays ◽  
Candidate Gene ◽  
Fine Mapping ◽  
Quantitative Trait ◽  
Gene Prediction ◽  
Yield Related Trait ◽  
Trait Locus ◽  
Related Trait
Sign in / Sign up

Export Citation Format

Share Document