Nowadays, there is a strong interest to study autophagy mechanisms in plants, as it is one of the main cellular degradation and recycling pathways involved in the adaptation of organisms to stress conditions. We have isolated a number of peroxisome unusual positioning (peup) mutants, which display the accumulation of abnormal peroxisomes, and demonstrated that autophagy is involved in removing damaged organelles. These peup mutants also show defects of other autophagy-related processes, such as the recovery from dark-senescence, and also failed to induce vacuole-related vesicle formations during microautophagy under nutrient deprivations. Although most of peup mutants are identified as the mutants defective in autophagy-related (ATG) genes, the causative genes of several mutants are still staying undiscovered.

The aim of this study is to identify the causative gene of the peup33 mutant using Next Generation Sequencing (NGS) as a tool. Identification of mutations with NGS will allow us to save time compared to the conventional mapping method. Here we present the workflow of the experiment, the procedure of bioinformatic analysis and the software applied to the sequence data produced by NGS.