Next-generation sequencing (NGS) techniques are the current standard for the generation of genomic data, producing ever-increasing amounts of information rapidly and at a low cost. These techniques allow us to sequence DNA and RNA very quickly, facilitating the acquisition of massive genomic, transcriptomic, DNA-protein interaction and epigenomic data sets; they are also radically changing the way we look at genomes [1] [2] [3] . Given their higher degree of parallelism and smaller reaction volumes compared to conventional Sanger sequencing, NGS methods offer larger amounts of data, shorter sequencing times and reduced costs, albeit at the expense of increased error rates and shorter read lengths 4 . NGS clearly facilitates the accumulation of large data sets, but the downstream processing of these data is still an important bottleneck 5 . Not surprisingly, NGS data give rise to numerous bioinformatics challenges, including storage, transmission, manipulation and analysis. Improved computational methods and more efficient software tools are constantly being developed to provide faster processing and more accurate inferences. However, it is essential that these methods are benchmarked against existing tools with similar functionality, to show their superiority at least in some aspect. In general, computational methods can be benchmarked using empirical and/or simulated data. Although validation with empirical data is essential, as it represents real scenarios, the true process underlying it is usually unknown, thus complicating its use for the assessment of accuracy (that is, how close the estimated value is to the 'true' value). Alternatively, in silico data allow us to generate as much data as desired and under controlled scenarios with predefined parameters for which the true values are known, thus nicely complementing validation with real data 6, 7 . Therefore, computer simulation of genetic and genomic data has become increasingly popular for assessing and validating biological models or for gaining an understanding of specific data sets. Simulations alone can be used as guidance for the development of new computational tools 8 , for debugging and for evaluating software performance 9, 10 . Computer simu lations also allow us to generate new hypotheses 11 , help in the design of sequencing projects 12, 13 and are essential to verify distinct inferences, such as the correctness of an assembly 14 , the accuracy of gene prediction 15 or the power to reconstruct accurate geno types and haplotypes 2, 16 . Several computational tools for the simulation of NGS data have been developed in the past few years. These tools have very diverse input requirements and functionalities, which makes it quite difficult to choose the most appropriate one for the problem at hand. Here we present, to our knowledge, the first review of available software tools for the simulation of genomic NGS data. Note that we focus on the simulation of DNA sequences and do not discuss RNA sequencing (RNAseq) simulation, which has its own characteristics. We review 23 NGS simulation tools that were either recently published or developed, that were -in most cases -still maintained and that were freely available. We discuss their various features, such as the required input, the inter action with the user, the sequencing platforms, the type of reads, the error models, the possibility of introducing coverage bias, the simulation of genomic variants and the output provided. This is done within the framework of potential applications, providing readers with guidelines for the identification of the NGS simulators that are best suited for their purposes (FIG. 1) . Abstract | Computer simulation of genomic data has become increasingly popular for assessing and validating biological models or for gaining an understanding of specific data sets. Several computational tools for the simulation of next-generation sequencing (NGS) data have been developed in recent years, which could be used to compare existing and new NGS analytical pipelines. Here we review 23 of these tools, highlighting their distinct functionality, requirements and potential applications. We also provide a decision tree for the informed selection of an appropriate NGS simulation tool for the specific question at hand. R E V I E W S NATURE REVIEWS | GENETICS VOLUME 17 | AUGUST 2016 | 459 © 2 0 1 6 M a c m i l l a n P u b l i s h e r s L i m i t e d . A l l r i g h t s r e s e r v e d .
These limitations complicate the use of real data for assessing the accuracy of tools and other performance measures REF .
A comparison of tools for the simulation of genomic next-generation sequencing data
Nature Reviews Genetics
Nature Reviews Genetics
Biology Medicine