Sesame (Sesamum indicum L.) is a globally cultivated oilseed crop renowned for its historical significance and widespread growth in tropical and subtropical regions. With notable nutritional and medicinal attributes, sesame has shown promising effects in combating malnutrition cancer, diabetes, and other diseases like cardiovascular problems. However, sesame production faces significant challenges from environmental threats such as charcoal rot, drought, salinity, and waterlogging stress, resulting in economic losses for farmers. The scarcity of information on stress-resistance genes and pathways exacerbates these challenges. Despite its immense importance, there is currently no platform available to provide comprehensive information on sesame, which significantly hinders the mining of various stress-associated genes and the molecular breeding of sesame. To address this gap, here a free, web-accessible, and user-friendly genomic web resource (SesameGWR, http://backlin.cabgrid.res.in/sesameGWR/) has been developed This platform provides key insights into differentially expressed genes, transcription factors, miRNAs, and molecular markers like simple sequence repeats, single nucleotide polymorphisms, and insertions and deletions associated with both biotic and abiotic stresses.. The functional genomics information and annotations embedded in this web resource were predicted through RNA-seq data analysis. Considering the impact of climate change and the nutritional and medicinal importance of sesame, this study is of utmost importance in understanding stress responses. SesameGWR will serve as a valuable tool for developing climate-resilient sesame varieties, thereby enhancing the productivity of this ancient oilseed crop.

Next-generation sequencing (NGS) is a new technique for determining DNA/RNA sequences for the entire genome of interest at a fraction of the cost as compared to traditional sequencing methods. Nowadays, major plant and animal species are being sequenced using NGS platforms. These sequencing platforms produce massive amounts of biological data in various file formats, which may then be analyzed using a variety of computational tools. NGS can reveal a wealth of information on genetic variants, transcriptome dynamics, transcription factors, epigenetic changes, and more. The number of NGS applications is continually growing, necessitating more effective and innovative data storage, analysis, and visualization methods. In this chapter, we have covered the NGS data analysis pipeline, which included pre-processing of NGS data, genome and transcriptome assembly approaches like de novo assembly and reference-based assembly, gene prediction, and annotation along with identification of genetic marker as well as computational tools and software used for these analyses. NGS technologies are currently used for whole genome sequencing, investigation of genome diversity, metagenomics, epigenetics, discovery of non-coding RNAs and protein-binding sites, and gene-expression profiling by RNA sequencing. <!----> <!----> <!----> <!---->