Detection of mutations at the whole-genome level is now possible by the use of high-throughput sequencing. However, determining mutations is a time-consuming process due to the number of false positives provided by mutation-detecting programs. AMAP (automated mutation analysis pipeline) was developed to overcome this issue. AMAP integrates a set of well-validated programs for mapping (BWA), removal of potential PCR duplicates (Picard), realignment (GATK) and detection of mutations (SAMtools, GATK, Pindel, BreakDancer and CNVnator). Thus, all types of mutations such as base substitution, deletion, insertion, translocation and chromosomal rearrangement can be detected by AMAP. In addition, AMAP automatically distinguishes false positives by comparing lists of candidate mutations in sequenced mutants. We tested AMAP by inputting already analyzed read data derived from three individual Arabidopsis thaliana mutants and confirmed that all true mutations were included in the list of candidate mutations. The result showed that the number of false positives was reduced to 12% of that obtained in a previous analysis that lacked a process of reducing false positives. Thus, AMAP will accelerate not only the analysis of mutation induction by individual mutagens but also the process of forward genetics.