scholarly journals FOXO3 cell resilience gene neighborhood

Aging ◽  
2017 ◽  
Vol 9 (12) ◽  
pp. 2467-2468 ◽  
Author(s):  
Timothy A. Donlon ◽  
Bradley J. Willcox ◽  
Brian J. Morris
Keyword(s):  
Gene Neighborhood

scholarly journals TREND: a platform for exploring protein function in prokaryotes based on phylogenetic, domain architecture and gene neighborhood analyses

2020 ◽  
Vol 48 (W1) ◽  
pp. W72-W76 ◽  
Author(s):  
Vadim M Gumerov ◽  
Igor B Zhulin
Keyword(s):  
Protein Function ◽  
Computational Study ◽  
Function Analysis ◽  
Domain Architecture ◽  
Protein Domain ◽  
Homologous Proteins ◽  
Gene Neighborhood ◽  
Protein Domain Architecture ◽  
Key Steps

Abstract Key steps in a computational study of protein function involve analysis of (i) relationships between homologous proteins, (ii) protein domain architecture and (iii) gene neighborhoods the corresponding proteins are encoded in. Each of these steps requires a separate computational task and sets of tools. Currently in order to relate protein features and gene neighborhoods information to phylogeny, researchers need to prepare all the necessary data and combine them by hand, which is time-consuming and error-prone. Here, we present a new platform, TREND (tree-based exploration of neighborhoods and domains), which can perform all the necessary steps in automated fashion and put the derived information into phylogenomic context, thus making evolutionary based protein function analysis more efficient. A rich set of adjustable components allows a user to run the computational steps specific to his task. TREND is freely available at http://trend.zhulinlab.org.

scholarly journals OrthoGNC: A Software for Accurate Identification of Orthologs Based on Gene Neighborhood Conservation

2017 ◽  
Vol 15 (6) ◽  
pp. 361-370 ◽  
Author(s):  
Soheil Jahangiri-Tazehkand ◽  
Limsoon Wong ◽  
Changiz Eslahchi
Keyword(s):  
Accurate Identification ◽  
Gene Neighborhood

scholarly journals G-NEST: a gene neighborhood scoring tool to identify co-conserved, co-expressed genes

2012 ◽  
Vol 13 (1) ◽  
Author(s):  
Danielle G Lemay ◽  
William F Martin ◽  
Angie S Hinrichs ◽  
Monique Rijnkels ◽  
J Bruce German ◽  
...  
Keyword(s):  
Gene Neighborhood ◽  
Scoring Tool

scholarly journals Daisychain: Search and Interactive Visualisation of Homologs in Genome Assemblies

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2587
Author(s):  
Oliver Schliebs ◽  
Chon-Kit Kenneth Chan ◽  
Philipp E. Bayer ◽  
Jakob Petereit ◽  
Ajit Singh ◽  
...  
Keyword(s):  
User Interaction ◽  
Data Publishing ◽  
Web Interface ◽  
Genome Sequences ◽  
Gene Neighborhood ◽  
Gene Homology ◽  
Search Tool ◽  
Genome Assemblies ◽  
Interactive Visualisation ◽  
Local Synteny

Daisychain is an interactive graph visualisation and search tool for custom-built gene homology databases. The main goal of Daisychain is to allow researchers working with specific genes to identify homologs in other annotation releases. The gene-centric representation includes local gene neighborhood to distinguish orthologs and paralogs by local synteny. The software supports genome sequences in FASTA format and GFF3 formatted annotation files, and the process of building the homology database requires a minimum amount of user interaction. Daisychain includes an integrated web viewer that can be used for both data analysis and data publishing. The web interface extends KnetMaps.js and is based on JavaScript.

scholarly journals Detecting operons in bacterial genomes via visual representation learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rida Assaf ◽  
Fangfang Xia ◽  
Rick Stevens
Keyword(s):  
Regulatory Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Critical Role ◽  
Visual Representations ◽  
Representation Learning ◽  
Genomic Region ◽  
Bacterial Genomes ◽  
Gene Neighborhood ◽  
Augmentation Techniques

AbstractContiguous genes in prokaryotes are often arranged into operons. Detecting operons plays a critical role in inferring gene functionality and regulatory networks. Human experts annotate operons by visually inspecting gene neighborhoods across pileups of related genomes. These visual representations capture the inter-genic distance, strand direction, gene size, functional relatedness, and gene neighborhood conservation, which are the most prominent operon features mentioned in the literature. By studying these features, an expert can then decide whether a genomic region is part of an operon. We propose a deep learning based method named Operon Hunter that uses visual representations of genomic fragments to make operon predictions. Using transfer learning and data augmentation techniques facilitates leveraging the powerful neural networks trained on image datasets by re-training them on a more limited dataset of extensively validated operons. Our method outperforms the previously reported state-of-the-art tools, especially when it comes to predicting full operons and their boundaries accurately. Furthermore, our approach makes it possible to visually identify the features influencing the network’s decisions to be subsequently cross-checked by human experts.

scholarly journals Assessment of genome annotation using gene function similarity within the gene neighborhood

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Se-Ran Jun ◽  
Intawat Nookaew ◽  
Loren Hauser ◽  
Andrey Gorin
Keyword(s):  
Gene Function ◽  
Genome Annotation ◽  
Gene Neighborhood ◽  
Function Similarity
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