KnetMaps: a BioJS component to visualize biological knowledge networks

Singh, Ajit; Rawlings, Christopher J.; Hassani‐Pak, Keywan

doi:10.12688/f1000research.16605.1

Cited by 9 publications

(4 citation statements)

References 13 publications

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“…Daisychain can host multiple independent homology databases, with data and metadata stored in Neo4j graph databases. The web application is built upon KnetMaps.js [32], (https://github.com/Rothamsted/knetmaps. js (accessed on 21 August 2021)) which uses cytoscape.JS 2.4.0 [33] and jQuery 1.11.2 (https://jquery.com/ (accessed on 21 August 2021)).…”

Section: Methodsmentioning

confidence: 99%

Daisychain: Search and Interactive Visualisation of Homologs in Genome Assemblies

et al. 2021

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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.

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Section: Methodsmentioning

confidence: 99%

Daisychain: Search and Interactive Visualisation of Homologs in Genome Assemblies

et al. 2021

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“…2B. This tool was adapted from KnetMaps [39]. -The SPARQL Editor is a query editor that provides an interactive environment for formulating SPARQL queries.…”

Section: Data Access and Applicationsmentioning

confidence: 99%

AgroLD: A Knowledge Graph for the Plant Sciences

Larmande¹,

Todorov²

2021

The Semantic Web – ISWC 2021

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Recent advances in sequencing technologies and high-throughput phenotyping have revolutionized the analysis in the field of the plant sciences. However, there is an urgent need to effectively integrate and assimilate complementary information to understand the biological system in its entirety. We have developed AgroLD, a knowledge graph that exploits Semantic Web technologies to integrate information on plant species and in this way facilitate the formulation and validation of new scientific hypotheses. AgroLD contains around 900M triples created by annotating and integrating more than 100 datasets coming from 15 data sources. Our objective is to offer a domain specific knowledge platform to answer complex biological and plant sciences questions related to the implication of genes in, for instance, plant disease resistance or adaptative responses to climate change. In this paper, we present results of the project, which focused on genomics, proteomics and phenomics. We present the AgroLD pipeline for lifting the data, the open source tools developed for these purposes, as well as the web application allowing to explore the data.

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“…We have previously described our approaches (Figure 1 ) to build genome‐scale KGs with the KnetBuilder ( https://github.com/Rothamsted/knetbuilder ) data integration platform (Hassani‐Pak et al, 2016 ), to extend KGs with novel gene–phenotype relations from the literature (Hassani‐Pak et al, 2010 ), to publish KGs as standardized and interoperable data based on FAIR principles (Brandizi et al, 2018a ) and to visualize biological knowledge networks in an interactive web application (Singh et al, 2018 ). Our data integration approach to build KGs is based on an intelligent data model with just enough semantics to capture complex biological relationships between genes, traits, diseases and many more information types derived from curated or predicted information sources.…”

Section: Introductionmentioning

confidence: 99%

KnetMiner: a comprehensive approach for supporting evidence‐based gene discovery and complex trait analysis across species

Hassani‐Pak

Singh

Brandizi

et al. 2021

Plant Biotechnology Journal

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The generation of new ideas and scientific hypotheses is often the result of extensive literature and database searches, but, with the growing wealth of public and private knowledge, the process of searching diverse and interconnected data to generate new insights into genes, gene networks, traits and diseases is becoming both more complex and more time-consuming. To guide this technically challenging data integration task and to make gene discovery and hypotheses generation easier for researchers, we have developed a comprehensive software package called KnetMiner which is open-source and containerized for easy use. KnetMiner is an integrated, intelligent, interactive gene and gene network discovery platform that supports scientists explore and understand the biological stories of complex traits and diseases across species. It features fast algorithms for generating rich interactive gene networks and prioritizing candidate genes based on knowledge mining approaches. KnetMiner is used in many plant science institutions and has been adopted by several plant breeding organizations to accelerate gene discovery. The software is generic and customizable and can therefore be readily applied to new species and data types; for example, it has been applied to pest insects and fungal pathogens; and most recently repurposed to support COVID-19 research. Here, we give an overview of the main approaches behind KnetMiner and we report plant-centric case studies for identifying genes, gene networks and trait relationships in Triticum aestivum (bread wheat), as well as, an evidence-based approach to rank candidate genes under a large Arabidopsis thaliana QTL.

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KnetMaps: a BioJS component to visualize biological knowledge networks

Cited by 9 publications

References 13 publications

Daisychain: Search and Interactive Visualisation of Homologs in Genome Assemblies

Daisychain: Search and Interactive Visualisation of Homologs in Genome Assemblies

AgroLD: A Knowledge Graph for the Plant Sciences

KnetMiner: a comprehensive approach for supporting evidence‐based gene discovery and complex trait analysis across species

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