The choice of technology and bioinformatics approach is critical in obtaining accurate and reliable information from next-generation sequencing (NGS) experiments. An increasing number of software and methodological guidelines are being published, but deciding upon which approach and experimental design to use can depend on the particularities of the species and on the aims of the study. This leaves researchers unable to produce informed decisions on these central questions. To address these issues, we developed pipeliner – a tool to evaluate, by simulation, the performance of NGS pipelines in resequencing studies. Pipeliner provides a graphical interface allowing the users to write and test their own bioinformatics pipelines with publicly available or custom software. It computes a number of statistics summarizing the performance in SNP calling, including the recovery, sensitivity and false discovery rate for heterozygous and homozygous SNP genotypes. Pipeliner can be used to answer many practical questions, for example, for a limited amount of NGS effort, how many more reliable SNPs can be detected by doubling coverage and halving sample size or what is the false discovery rate provided by different SNP calling algorithms and options. Pipeliner thus allows researchers to carefully plan their study's sampling design and compare the suitability of alternative bioinformatics approaches for their specific study systems. Pipeliner is written in C++ and is freely available from http://github.com/brunonevado/Pipeliner.