BMC BioinformaticsBioinformaticsPeer-reviewed journal article
Plant Co-expression Annotation Resource: a webserver for identifying targets for genetically modified crop breeding pipelines
Abstract
BackgroundThe development of genetically modified crops (GM) includes the discovery of candidate genes through bioinformatics analysis using genomics data, gene expression, and others. Proteins of unknown function (PUFs) are interesting targets for GM crops breeding pipelines for the novelty associated to such targets and also to avoid copyright protections. One method of inferring the putative function of PUFs is by relating them to factors of interest such as abiotic stresses using orthology and co-expression networks, in a guilt-by-association manner. ResultsIn this regard, we have downloaded, analyzed, and processed genomics data of 53 angiosperms, totaling 1,862,010 genes and 2,332,974 RNA. Diamond and InterproScan were used to discover 72,266 PUFs for all organisms. RNA-seq datasets related to abiotic stresses were downloaded from NCBI/GEO. The RNA-seq data was used as input to the LSTrAP software to construct co-expression networks. LSTrAP also created clusters of transcripts with correlated expression, whose members are more probably related to the molecular mechanisms associated to abiotic stresses in the plants. Orthologous groups were created (OrhtoMCL) using all 2,332,974 proteins in order to associate PUFs to abiotic stress related clusters of co-expression and therefore infer their function in a guilt-by-association manner. ConclusionA freely available web resource named "Plant Co-expression Annotation Resource" (https://www.machado.cnptia.embrapa.br/plantannot), Plantannot, was created to provide indexed queries to search for PUF putatively associated to abiotic stresses. The web interface also allows browsing, querying and retrieving of public genomics data from 53 plants. We hope Plantannot to be useful for researchers trying to obtain novel GM crops resistant to climate change hazards.
- organism: plant (other)
- assay: bulk RNA-seq
- research focus: database / resource
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: cell type annotation
- approach: statistical model
- approach: experimental assay
- Citations
- 2
- FWCI
- 0.08
- Field percentile
- 30th percentile
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