An alarming increase in the Earth’s temperature, along with increasing water scarcity in recent years, is posing a threat to the future of global food security. In addition to this, addressing the issue of micronutrient deficiency in developing and underdeveloped nations is another formidable challenge faced by policymakers today. Enhancing the productivity and nutritional value of crops in the modern-day world is a concern that needs to be addressed efficiently to bridge the gap between demand and supply of food in the coming years. The nutritionally rich millets are low-maintenance, climate-resilient cereals that hold potential benefits for human health at minimal cost, thus providing a comprehensive solution for global food and nutritional security (Kumar et al., 2018). Nevertheless, despite the satisfaction that these crops bring to our table, sheer negligence has been observed in their cultivation and consumption (Himanshu et al., 2018). Also, the molecular aspects of stress tolerance in millets have largely remained unexplored (Babele et al., 2022).

Panicum sumatrense (Little millet) is one of the predominant yet lesser-studied, tetraploid species (2n = 4x = 36) of minor millets, essentially cultivated for its grain (Gomez and Gupta, 2003; Das et al., 2020). The crop is loaded with nutrients such as dietary fiber, protein, and minerals like iron (Fe = 32.20 ppm) and zinc (Zn = 32.40 ppm) (Girish et al., 2014; Vetriventhan et al., 2021) and performs exceptionally well under challenging environmental conditions and is invariably adapted to high temperature and moisture stress (Ajithkumar and Panneerselvam, 2014). Yet, to date, the crop has been considered an ‘orphan’ and has never fallen under the umbrella of extensive research until recent years. As a result of that, unlike rice, wheat, and other cereals, the crop has still not achieved breakthroughs in terms of gene discoveries, genetic improvement, and yield enhancement. Also, due to a lack of enough studies, ‘Omics’ information related to the crop has remained scarce (Natesan et al., 2020).

Transcriptomics is a promising approach that has transformed our outlook toward gene expression patterns at the cellular and molecular levels (Lowe et al., 2017). RNA-Seq, is one of the transcriptomic technologies and is known to resolve expression patterns of genes with exceptional clarity (Finotello and Di Camillo, 2015). It is evident from previous studies that, RNA-Seq has opened new opportunities for researchers and has played a pivotal role in mining a plethora of genes in a wide range of plant species, from parasitic to land plants and tree species. (Li et al., 2015; Howlader et al., 2020; Sobreiro et al., 2021). In millets as well, several genes, transcription factors, and metabolic pathways regulating biotic (Kulkarni et al., 2016) and abiotic stress tolerance (Das et al., 2020; Shivhare et al., 2020; Sun et al., 2020; Suresh et al., 2022), along with those involved in modulating metal-ion homeostasis (Chandra et al., 2020; Satyavathi et al., 2022) have also been tracked using RNA-Seq. This data-driven technology delivers enormous information upon intricate analysis and can help researchers if made publicly accessible. Foreseeing the potential of RNA-Seq data in understanding plants’ behavioral and developmental biology, scientific fraternities from all over the globe have been emphasizing to create web-based platforms which can be utilized to explore these information free of cost. FmMiRNADb, FmTFDb (Thulasinathan et al., 2022) Plant Public RNA-Seq Database (Yu et al., 2022), GRAND (Zhang et al., 2022), HpeNet (Sheng et al., 2020), TRANSNAP (Koshimizu et al., 2019), AgriSeqDB (Robinson et al., 2018), SeagrassDB (Sablok et al., 2018), and MOROKOSHI (Makita et al., 2015) are few of the popular plant expression databases developed for millets and other plants. Interestingly, even after a heedful screening, we were unable to detect any independent platform dedicated solely to little millet and this prompted us to create an interactive web-based transcriptomic resource for this non-model plant that can serve as a comprehensive guide to understanding the crop’s molecular biology. To our knowledge, the Little Millet Transcriptome Database version 1.0.0 (LMT^db v-1.0.0) is an exclusive, first-of-its-kind database made for little millet that runs on the Windows operating system. The database is an information-rich resource of transcriptome sequences and functions, plant metabolic networks, molecular markers, and differentially expressed genes of three tissue types, namely secondary leaf, flag leaf, and panicle under control and drought conditions, in which all data can be freely accessed for research purposes to attain global food and nutritional security.

The incomparable tolerance of millets toward abiotic stresses including drought, salinity, and heat along with their efficiency to sequester and load minerals from the soil to the grains make them an amenable system to understand the plant’s stress-responsive behavior and metal homeostasis at cellular, molecular and physiological levels (Bandyopadhyay et al., 2017). Next-generation sequencing approaches have been successfully harnessed to comprehend the stress and nutrition biology of both major and minor millets (Lata, 2015; Prabha et al., 2017; Pujar et al., 2020). In little millet, the de novo transcriptome analysis has recently been leveraged to identify genes involved in drought and salinity tolerance mechanisms (Das et al., 2020) Attempts have also been made to identify grain nutrient related and moisture stress-responsive gene orthologous (Chandel et al., 2017; Sushmitha et al., 2018) and genic microsatellite markers in little millet (Desai et al., 2021). Further, the chloroplast genome of little millet has been sequenced and employed to investigate the crop’s phylogenetic relationship with other members of Poaceae (Sebastin et al., 2018). However, the crop is still undermined as not much is known about molecular mechanisms that govern metal homeostasis and several other functions.

It is noteworthy that over the past decade gene discovery via transcriptomic approaches has been much more frequently observed than that through genomic approaches. Transcriptomics allows us to track changes in gene expression patterns over tissue types, environmental conditions, and developmental stages with great precision. With this idea, several researchers across the globe have developed open-access transcriptomic portals or databases for minor millets which are currently being explored for mining genes associated with biotic and abiotic stress tolerance, metal homeostasis, and also for yield and quality improvement. However, to our knowledge, no such unified browsing platform has been specifically designed and developed for little millet posing restrictions in employing forward genetic approaches to penetrate deeper into the molecular architecture of the crop. To bridge this gap, we developed the Little Millet transcriptome database (https://igkv.ac.in/xenom/index.aspx), encompassing extensive information on transcriptome assembly and annotation of little millet under one umbrella.

LMT^db provides its end-users a web interface to access various information related to the expressed part of little millet’s genome, such as DEGs, microsatellite markers, gene functions, and plant metabolic pathway information. The above data can be browsed, analyzed, and even downloaded upon registration, free of cost, and can contribute to functional as well as comparative Omic studies elucidating evolutionary and developmental biology of the crop. The information in this database is highly useful for the identification and characterization of novel genes, understanding the drought and tissue-specific gene expression patterns, and also in designing SSR-based fingerprinting studies in little millet as well as in other related minor millets. The database will serve as a comprehensive resource for breeders and scientists involved in addressing global food and nutritional security through crop improvement programs. In the forthcoming versions of LMT^db, hyperlinks to various bioinformatic tools will be added along with information related to SNP, Indels, protein-protein interaction, and gene families making it an even more extensive platform for Omic studies.

The original contributions presented in the study are publicly available. This data can be found here: NCBI accession PRJNA718414.

GC conceptualized the idea. GC, AP, SS, KB, AK, and RS curated, and formatted the data and worked on the design and development of the database. AS provided the experimental material. GC AM, MD, ArP, VP, and AV carried out the preliminary wet-lab experiments and data analysis. GC, AP, SS, KB, AK, and RS drafted the original manuscript. All authors contributed to the article and approved the submitted version.