Next-Generation Sequencing for Crop Improvement

Manish Chugh ¹, Raman Yadav ², Ms. Prajkta S. Sarkale ³, Vijaykumar Bhanuse ⁴, Dr. Wasim A. Bagwan ⁵

¹Associate Professor,Department of Pharmacology, Arya College of Pharmacy,Jaipur, Rajasthan, India. Email: manish.chugh@aryajaipur.com
²Assistant Professor,School of Pharmacy,Noida International University,Uttar Pradesh 203201,India. Email:raman.yadav@niu.edu.in
³Assistant Professor, Krishna Institute of Science and Technology, Krishna Vishwa Vidyapeeth “Deemed to be University”, Taluka-Karad, Dist-Satara, Pin-415 539, Maharashtra, India Email:sprajktaenvse@gmail.com
⁴Assistant Professor, Department of Instrumentation and Control Engineering, Vishwakarma Institute of Technology, Pune, Maharashtra, 411037 Email:vijaykumar.bhanuse@vit.edu
⁵Assistant Professor, Krishna Institute of Science and Technology,Krishna Vishwa Vidyapeeth “Deemed to be University”,Taluka-Karad, Dist-Satara, Pin-415 539, Maharashtra, India Email:wasim.bagwan16@gmail.com

ABSTRACT

Next-Generation Sequencing (NGS) has changed the way farming research is done by giving high-throughput genetic data that makes it possible to develop exact methods for improving crops. As problems with global food security get worse, we need to quickly create crops that are high-yielding, immune to disease, and able to handle changes in temperature. Even though traditional breeding methods work, they take a long time and don't always make the best use of genetic diversity. The goal of this work is to speed up crop growth by using NGS technologies to find DNA markers linked to desire farming features. Even though there have been big steps forward, problems like large amounts of data, the need for a lot of computing power, and mistakes in genome assembly still make it hard to use NGS in many plant breeding projects. The main goal of this study is to find out how well NGS works at speeding up DNA finding for crop growth. Whole-genome sequencing (WGS), transcriptome sequencing (RNA-Seq), and genotyping-by-sequencing (GBS) are used in the study to find important genetic changes that affect food traits. Bioinformatics processes were used to look at sequencing data. These pipelines included sequence matching, variant finding, and functional classification. Findings show that NGS makes it easy to quickly find single nucleotide polymorphisms (SNPs) and gene expression patterns that are connected to being able to handle drought, being resistant to disease, and having higher nutritional value. Combining genetic selection with machine learning models makes it even easier to predict how traits will be passed down. These findings show that NGS has the ability to speed up breeding efforts and make crops more productive..

Keywords: Next-Generation Sequencing, Crop Improvement, Genomic Selection, Plant Breeding, Bioinformatics, Genetic Variability

How to cite this article:Chugh M, Yadav R, Sarkale PS, Bhanuse V, Bagwan WA., Next-Generation Sequencing for Crop Improvement .Int J Drug Deliv Technol. 2026;16(1s): 1248-1259; DOI: 10.25258/ijddt.16. 1248-1259