We assess the impacts of different quality control methods on the Ficus NGS datasets by doing de novo assembly and evaluate certain assembly quality indicators.We focus on the relatively new area of error correction and digital normalization, and find that for low-coverage datasets, error correction may facilitate de novo assembly, while for relatively high-coverage datasets, digital normalization can reduce time and memory usage of de novo assembly, as well as enhance assembly quality.