scholarly journals Network-guided genetic screening: building, testing and using gene networks to predict gene function

2008 ◽  
Vol 7 (3) ◽  
pp. 217-227 ◽  
Author(s):  
B. Lehner ◽  
I. Lee
Keyword(s):  
Gene Function ◽  
Genetic Screening ◽  
Gene Networks ◽  
Predict Gene Function

scholarly journals Gene networks in Drosophila melanogaster: integrating experimental data to predict gene function

2009 ◽  
Vol 10 (9) ◽  
pp. R97 ◽  
Author(s):  
James C Costello ◽  
Mehmet M Dalkilic ◽  
Scott M Beason ◽  
Jeff R Gehlhausen ◽  
Rupali Patwardhan ◽  
...  
Keyword(s):  
Experimental Data ◽  
Drosophila Melanogaster ◽  
Gene Function ◽  
Gene Networks ◽  
Predict Gene Function

scholarly journals Patterns of diverse gene functions in genomic neighborhoods predict gene function and phenotype

2019 ◽  
Author(s):  
Matej Mihelčić ◽  
Tomislav Šmuc ◽  
Fran Supek
Keyword(s):  
Gene Function ◽  
Structural Variation ◽  
Predictive Power ◽  
State Of The Art ◽  
Gene Clustering ◽  
High Confidence ◽  
Gene Functions ◽  
Predict Gene Function ◽  
Guilt By Association ◽  
Clustering Patterns

AbstractGenes with similar roles in the cell are known to cluster on chromosomes, thus benefiting from coordinated regulation. This allows gene function to be inferred by transferring annotations from genomic neighbors, following the guilt-by-association principle. We performed a systematic search for co-occurrence of >1000 gene functions in genomic neighborhoods across 1669 prokaryotic, 49 fungal and 80 metazoan genomes, revealing prevalent patterns that cannot be explained by clustering of functionally similar genes. It is a very common occurrence that pairs of dissimilar gene functions – corresponding to semantically distant Gene Ontology terms – are significantly co-located on chromosomes. These neighborhood associations are often as conserved across genomes as the known associations between similar functions, suggesting selective benefits from clustering of certain diverse functions, which may conceivably play complementary roles in the cell. We propose a simple encoding of chromosomal gene order, the neighborhood function profiles (NFP), which draws on diverse gene clustering patterns to predict gene function and phenotype. NFPs yield a 26-46% increase in predictive power over state-of-the-art approaches that propagate function across neighborhoods, thus providing hundreds of novel, high-confidence gene function inferences per genome. Furthermore, we demonstrate that the effect of structural variation on gene function distribution across chromosomes may be used to predict phenotype of individuals from their genome sequence.

scholarly journals A roadmap for gene functional characterisation in wheat

2019 ◽  
Author(s):  
Nikolai M Adamski ◽  
Philippa Borrill ◽  
Jemima Brinton ◽  
Sophie Harrington ◽  
Clemence Marchal ◽  
...  
Keyword(s):  
Gene Function ◽  
Gene Networks ◽  
Crop Production ◽  
Crop Improvement ◽  
Basic Research ◽  
Reference Sequence ◽  
Background Information ◽  
World Population ◽  
Putative Gene ◽  
Functional Characterisation

To adapt to the challenges of climate change and the growing world population, it is vital to increase global crop production. Understanding the function of genes within staple crops will accelerate crop improvement by allowing targeted breeding approaches. Despite the importance of wheat, which provides 20 % of the calories consumed by humankind, a lack of genomic information and resources has hindered the functional characterisation of genes in this species. The recent release of a high-quality reference sequence for wheat underpins a suite of genetic and genomic resources that support basic research and breeding. These include accurate gene model annotations, gene expression atlases and gene networks that provide background information about putative gene function. In parallel, sequenced mutation populations, improved transformation protocols and structured natural populations provide rapid methods to study gene function directly. We highlight a case study exemplifying how to integrate these resources to study gene function in wheat and thereby accelerate improvement in this important crop. We hope that this review provides a helpful guide for plant scientists, especially those expanding into wheat research for the first time, to capitalise on the discoveries made in Arabidopsis and other plants. This will accelerate the improvement of wheat, a complex polyploid crop, of vital importance for food and nutrition security.

scholarly journals Cell-Cycle-Associated Expression Patterns Predict Gene Function in Mycobacteria

2020 ◽  
Vol 30 (20) ◽  
pp. 3961-3971.e6
Author(s):  
Aditya C. Bandekar ◽  
Sishir Subedi ◽  
Thomas R. Ioerger ◽  
Christopher M. Sassetti
Keyword(s):  
Cell Cycle ◽  
Gene Function ◽  
Expression Patterns ◽  
Predict Gene Function

scholarly journals PrismExp: Predicting Human Gene Function by Partitioning Massive RNA-seq Co-expression Data

2021 ◽  
Author(s):  
Alexander Lachmann ◽  
Kaeli Rizzo ◽  
Alon Bartal ◽  
Minji Jeon ◽  
Daniel J. B. Clarke ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Gene Function ◽  
Covariance Structure ◽  
Tissue Type ◽  
Specific Gene ◽  
Expression Data ◽  
Rna Seq ◽  
Protein Protein Interactions ◽  
Predict Gene Function ◽  
Mammalian Genes

Gene co-expression correlations from mRNA-sequencing (RNA-seq) can be used to predict gene function based on the covariance structure that exists within such data. In the past, we showed that RNA-seq co-expression data is highly predictive of gene function and protein-protein interactions. We demonstrated that the performance of such predictions is dependent on the source of the gene expression data. Furthermore, since genes function in different cellular contexts, predictions derived from tissue-specific gene co-expression data outperform predictions derived from cross-tissue gene co-expression data. However, the identification of the optimal tissue type to maximize gene function predictions for all mammalian genes is not trivial. Here we introduce and validate an approach we term Partitioning RNA-seq data Into Segments for Massive co-EXpression-based gene function Predictions (PrismExp), for improved gene function prediction based on RNA-seq co-expression data. With coexpression data from ARCHS4, we apply PrismExp to predict a wide variety of gene functions, including pathway membership, phenotypic associations, and protein-protein interactions. PrismExp outperforms the cross-tissue co-expression correlation matrix approach on all tested domains. Hence, PrismExp can enhance machine learning methods that utilize RNA-seq coexpression correlations to impute knowledge about understudied genes and proteins.

scholarly journals Bacteria.guru: comparative transcriptomics and co-expression database for bacterial pathogens

2021 ◽  
Author(s):  
Peng Ken Lim ◽  
Emilia E. Davey ◽  
Sean Wee ◽  
Wei Song Seetoh ◽  
Jong Ching Goh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Function ◽  
Expression Profiles ◽  
Bacterial Pathogens ◽  
Gene Expression Profiles ◽  
Gene Families ◽  
Resistance Mechanisms ◽  
Bacterial Pathogenicity ◽  
Predict Gene Function ◽  
Compare Gene Expression

The bacterial kingdom comprises unicellular prokaryotes able to establish symbioses from mutualism to parasitism. To combat bacterial pathogenicity, we need an enhanced understanding of gene function and regulation, which will mediate the development of novel antimicrobials. Gene expression can predict gene function, but there lacks a database enabling expansive inter- and intraspecific exploration of gene expression profiles and co-expression networks for bacteria. To address this, we integrated the genomic and transcriptomic data of the 17 most notorious and studied bacterial pathogens, creating bacteria.guru, an interactive database that can identify, visualize, and compare gene expression profiles, co-expression networks, functionally enriched clusters, and gene families across species. Through illustrating antibiotic resistance mechanisms in P. aeruginosa, we demonstrate that bacteria.guru could potentially aid the discovery of multi-faceted antibiotic targets. Hence, we believe bacteria.guru will facilitate future bacterial research. Availability: The database and co-expression networks are freely available from https://bacteria.guru/. The sample annotations are found in the supplemental data.

scholarly journals A roadmap for gene functional characterisation in wheat

2019 ◽  
Author(s):  
Nikolai M Adamski ◽  
Philippa Borrill ◽  
Jemima Brinton ◽  
Sophie Harrington ◽  
Clemence Marchal ◽  
...  
Keyword(s):  
Gene Function ◽  
Gene Networks ◽  
Crop Production ◽  
Crop Improvement ◽  
Basic Research ◽  
Reference Sequence ◽  
Background Information ◽  
World Population ◽  
Putative Gene ◽  
Functional Characterisation

To adapt to the challenges of climate change and the growing world population, it is vital to increase global crop production. Understanding the function of genes within staple crops will accelerate crop improvement by allowing targeted breeding approaches. Despite the importance of wheat, which provides 20 % of the calories consumed by humankind, a lack of genomic information and resources has hindered the functional characterisation of genes in this species. The recent release of a high-quality reference sequence for wheat underpins a suite of genetic and genomic resources that support basic research and breeding. These include accurate gene model annotations, gene expression atlases and gene networks that provide background information about putative gene function. In parallel, sequenced mutation populations, improved transformation protocols and structured natural populations provide rapid methods to study gene function directly. We highlight a case study exemplifying how to integrate these resources to study gene function in wheat and thereby accelerate improvement in this important crop. We hope that this review provides a helpful guide for plant scientists, especially those expanding into wheat research for the first time, to capitalise on the discoveries made in Arabidopsis and other plants. This will accelerate the improvement of wheat, a complex polyploid crop, of vital importance for food and nutrition security.

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