02053nas a2200289   4500000000100000000000100001008004100002260000900043653002600052653002400078653002300102653002200125653001300147653001100160653001300171653002800184653001600212100002000228700001900248700001900267245008600286856005200372300001200424490000900436520130400445022001401749       2017                            d  c201710aComputational Biology10aCrops, Agricultural10aDatabases, Genetic10aGenetic Variation10aGenomics10aPlants10aSoftware10aUser-Computer Interface10aWeb Browser1 aSushma Naithani1 aMatthew Geniza1 aPankaj Jaiswal00aVariant Effect Prediction Analysis Using Resources Available at Gramene Database.  uhttps://dx.doi.org/10.1007/978-1-4939-6658-5_17  a279-2970 v15333 aThe goal of Gramene database ( www.gramene.org ) is to empower the plant research community in conducting comparative genomics studies across model plants and crops by employing a phylogenetic framework and orthology-based projections. Gramene database (release #49) provides resources for comparative plant genomics including well-annotated plant genomes (39 complete reference genomes and six partial genomes), genetic or structural variation data for 14 plant species, pathways for 58 plant species, and gene expression data for 14 species including Arabidopsis, rice, maize, soybean, wheat, etc. (fetched from EBI-EMBL Gene Expression Atlas database). Gramene also facilitates visualization and analysis of user-defined data in the context of species-specific Genome Browsers or pathways. This chapter describes basic navigation for Gramene users and illustrates how they can use the genome section to analyze the gene expression and nucleotide variation data generated in their labs. This includes (1) upload and display of genomic data onto a Genome Browser track, (2) analysis of variation data using online Variant Effect Predictor (VEP) tool for smaller data sets, and (3) the use of the stand-alone Perl scripts and command line protocols for variant effect prediction on larger data sets.  a1940-6029