GeneNoteBook, a collaborative notebook for comparative genomics

Rens Holmer,Rene Geurts,Robin Van Velzen,Ton Bisseling,Sandra Smit,Dick De Ridder,Inanc Birol

doi:10.1093/bioinformatics/btz491

Rens Holmer, Rene Geurts + Show 5 more

Open Access

https://doi.org/10.1093/bioinformatics/btz491

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Abstract

SummaryAnalysis and comparison of genomic and transcriptomic datasets have become standard procedures in biological research. However, for non-model organisms no efficient tools exist to visually work with multiple genomes and their metadata, and to annotate such data in a collaborative way. Here we present GeneNoteBook: a web based collaborative notebook for comparative genomics. GeneNoteBook allows experimental and computational researchers to query, browse, visualize and curate bioinformatic analysis results for multiple genomes. GeneNoteBook is particularly suitable for the analysis of non-model organisms, as it allows for comparing newly sequenced genomes to those of model organisms.Availability and implementationGeneNoteBook is implemented as a node.js web application and depends on MongoDB and NCBI BLAST. Source code is available at https://github.com/genenotebook/genenotebook. Additionally, GeneNoteBook can be installed through Bioconda and as a Docker image. Full installation instructions and online documentation are available at https://genenotebook.github.io.Supplementary information Supplementary data are available at Bioinformatics online.

Highlights

Browsing, querying and comparing large genomic and transcriptomic datasets are indispensable aspects of genomic research
To enable quick and intuitive browsing and querying of genomic data for newly sequenced organisms we have developed GeneNoteBook: a collaborative web-based notebook for comparative genomics
BLAST results are linked to the gene table, which automatically allows querying and downloading data linked to the BLAST hits

Summary

Introduction

Browsing, querying and comparing large genomic and transcriptomic datasets are indispensable aspects of genomic research. As an example, integrating information on ortholog groups, protein domains and gene expression levels can provide valuable information on a gene’s hypothetical function For such integration, it is crucial to be able to browse, query and compare genomic data, and curate automated predictions. It is crucial to be able to browse, query and compare genomic data, and curate automated predictions This should ideally be a collaborative effort between experimental and computational researchers, and should be an efficient process that requires minimal configuration. Genome browsers are not very suitable for the integration of various data types, such as gene expression levels and ortholog groups Data warehouse systems, such as InterMine (Smith et al, 2012), provide more powerful query options but are relatively difficult to configure and generally do not come with data visualization options. Our application is designed for comparative analysis of genomic data and collaborative annotation of predicted genes with expert knowledge, by integrating genome annotations, gene expression data and gene evolutionary relationships

Features

Conclusion