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Mappers have to be sensitive and accurate and, if possible, fast and not too computationally demanding. The algorithms used for this step are called mappers. This step, which involves the accurate positioning of reads onto a reference genome sequence, is highly important because it determines the global quality of downstream analyses. A fundamental analysis steps in re-sequencing approaches is the mapping of the reads onto a reference genome. This technological evolution was paralleled by the development of new algorithms to deal with the quantity and the quality of reads produced. HTS has produced an explosion of knowledge in genetics and genomics thanks to the development of specific applications such as genome re-sequencing (whole genome sequencing and targeted sequencing). High-throughput sequencing (HTS) technology has recently shown a rapid and impressive development and this has led to the production of gigabases of sequence in a few hours for only a fraction of the former cost. This benchmark procedure can be used to evaluate existing or in-development mappers as well as to optimize parameters of a chosen mapper for any application and any sequencing platform.

#Facebook friends mapper torrenttz torrent#

The application of this procedure to Ion Torrent data from the whole genome sequencing of small genomes has allowed us to validate our benchmark procedure and demonstrate that it is helpful for selecting a mapper based on the intended application, questions to be addressed, and the technology used. ConclusionsДЄ benchmark procedure to compare HTS data mappers is introduced with a new definition for the mapping correctness as well as tools to generate simulated reads and evaluate mapping quality.

We applied our benchmark procedure to evaluate 14 mappers in the context of whole genome sequencing of small genomes with Ion Torrent data for which such a comparison has not yet been established. CuReSim and CuReSimEval, a tool to evaluate the mapping quality of the CuReSim simulated reads, are freely available. We developed CuReSim, a new read simulator, that is able to generate customized benchmark data for any kind of HTS technology by adjusting parameters to the error types. To measure robustness, we introduced a new definition for a correctly mapped read taking into account not only the expected start position of the read but also the end position and the number of indels and substitutions. In this paper, we present a benchmark procedure to compare mapping algorithms used in HTS using both real and simulated datasets and considering four evaluation criteria: computational resource and time requirements, robustness of mapping, ability to report positions for reads in repetitive regions, and ability to retrieve true genetic variation positions. Choosing a suitable mapper for a given technology and a given application is a subtle task because of the difficulty of evaluating mapping algorithms. A fundamental step in HTS data analysis is the mapping of reads onto reference sequences. The rapid evolution in high-throughput sequencing (HTS) technologies has opened up new perspectives in several research fields and led to the production of large volumes of sequence data.