Bioinformatics approaches to integrate statistical methods and the agriculture industry

Abstract

Statistical analysis is one of the key cornerstones for the research and discovery pipeline in agriculture companies. It provides critical information for the decision making process. Robust analysis improves the efficiency of the discovery process and potentially leads to better agriculture products. Performing statistics analyses in the industry often encounter a different set of challenges in both technical and practical aspects, such as end-users are happy to accept an approximation solution with rapid turnaround time, instead of an optimal analysis that requires a new analytical approach. Therefore, a wide range of bioinformatics techniques and cloud computing are utilised in conjunction with statistical methods to address these issues. Several case studies will be discussed including, the usage of control charts to monitor the consistency of the screen pipelines, integrating greenhouse workflow and analysis using Amazon Web Services, and developing prediction models for aggregated data generated from a diverse variety of screening pipelines to make informative decisions.