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Andrographolide, the principal secondary metabolite of Andrographis paniculata, displays a wide spectrum of medicinal activities. The content of andrographolide varies significantly in the species collected from different geographical regions. Therefore, this study aims at investigating the role of different abiotic factors and selecting suitable sites for the cultivation of A. paniculata with high andrographolide content using a multilayer perceptron artificial neural network (MLP-ANN) approach. A total of 150 accessions of A. paniculata collected from different regions of Odisha and West Bengal in eastern India showed a variation in andrographolide content in the range of 0.28–5.45% on a dry weight basis. The MLP-ANN was trained using climatic factors and soil nutrients as the input layer and the andrographolide content as the output layer. The best topological ANN architecture, consisting of 14 input neurons, 12 hidden neurons, and 1 output neuron, could predict the andrographolide content with 90% accuracy. The developed ANN model showed good predictive performance with a correlation coefficient (R2) of 0.9716 and a root-mean-square error (RMSE) of 0.18. The global sensitivity analysis revealed nitrogen followed by phosphorus and potassium as the predominant input variables influencing the andrographolide content. The andrographolide content could be increased from 3.38% to 4.90% by optimizing these sensitive factors. The result showed that the ANN approach is reliable for the prediction of suitable sites for the optimum andrographolide yield in A. paniculata.

The essential oil of Kaempferia galanga L. commonly known as sand ginger has increased its demand in national and international market for decades. Cinnamic acid esters like ethyl-p-methoxy cinnamate (EPMC) and ethyl cinnamate (EC) are major constituents in its essential oil. In spite of the high demand for the plant as raw material, identification of quality chemovars having high essential oil (EO) yield and constituents is still at an infant stage. With this in mind, we have evaluated the EO yield of 36 accessions from three provinces of Eastern India, which varied within a range of 0.41 ± 0.01 to 2.63 ± 0.03 v/w. Further, a total of 65 compounds were detected by gas chromatography and mass spectrometry (GC-MS) with area percentages varying from 76.16 to 97.3%. EPMC was found to be the major component in 14 accessions with area percentages varying from 10.7% to 41.1%, whereas other 22 accessions showed EC as the major constituent, varying from 16% to 29.1%. Further, a diversity study among accessions was performed by agglomerative hierarchical clustering (AHC) and principal component analysis (PCA) analysis based on the abundance of identified constituents, which categorized all 36 accessions into three clusters. Thus, the present study helps to identify quality chemovar K.g16 and K.g14 with respect to oil yield and constituents, respectively, which could be used to guide commercial cultivation and further improvement of the taxa.

Turmeric is an economically valued crop, because of its utility in the food, pharmaceutical industries and Ayurvedic medicine, attracts the attention in many areas of research work. In the present study, we executed resequencing through transcriptome assembly of the turmeric cultivar Suvarna (CL_Suv_10). Resequencing of Suvarna variety has generated 5 Gbases raw data with 75 bp paired-end sequence. The raw data has been submitted to SRA database of NCBI with accession number SRR4042181. Reads were assembled using Cufflinks-2.2.1 tool which ended up with 42994 numbers of transcripts. The length of transcripts ranged from 83 to15565, with a N50 value 1216 and median transcript length 773. The transcripts were annotated through number of databases. For the first time transcriptome profiling of cultivar Suvarna has been done, which could help towards identification of single nucleotide polymorphisms (SNPs) between Suvarna and other turmeric cultivars for its authentic identification.

Curcuma longa L. (Turmeric), of the family Zingiberaceae, is one of the economically as well as medicinally important plant species. It is a sterile, polyploid and vegetatively propagated spice crop cultivated usually in Southeast Asia. In the current study, we carried out re-sequencing through transcriptome profiling of Curcuma longa cv. Kedaram (Cl_Ked_6). We acquired a total of 1 GB raw data by resequencing through paired-end sequencing using Nextseq 500 platform. The raw data obtained in this study can be accessible in NCBI database with accession number of SRR3928562 with bioproject accession number PRJNA324755. Cufflinks-2.2.1 tool was used for transcriptome assembly which resulted in 39,554 numbers of transcripts. The transcript length ranged from 76 to 15,568, having N50 value of 1221 and median transcript length of 860. We annotated the transcripts using multiple databases. This data will be beneficial for studying sequence variations particularly SNPs between cultivars of turmeric towards authentic identification and discovery of novel functional transcripts in Kedaram.

Zingiber officinale Rosc., known as ginger, is an Asian crop, popularly used in every household kitchen and commercially used in bakery, beverage, food and pharmaceutical industries. The present study deals with de novo transcriptome assembly of an elite ginger cultivar Suruchi by next generation sequencing methodology. From the analysis 10.9 GB raw data was obtained which can be available in NCBI accession number SAMN03761185. We identified 41,969 transcripts using Trinity RNA-Seq from ginger rhizome of Suruchi variety from Odisha. The transcript length varied from 300 bp to 8404 bp with a total length of 3,96,40,526 bp and N50 of 1251 bp. To the best of our knowledge, this is the first transcriptome data of an elite ginger cultivar Suruchi released for Odisha state of India which will help molecular biologists to develop genetic markers for identification of cultivars.