Abstract
BackgroundThere are many programs available for generating simulated whole-genome shotgun sequence reads. The data generated by many of these programs follow predefined models, which limits their use to the authors' original intentions. For example, many models assume that read lengths follow a uniform or normal distribution. Other programs generate models from actual sequencing data, but are limited to reads from single-genome studies. To our knowledge, there are no programs that allow a user to generate simulated data following non-parametric read-length distributions and quality profiles based on empirically-derived information from metagenomics sequencing data.ResultsWe present BEAR (Better Emulation for Artificial Reads), a program that uses a machine-learning approach to generate reads with lengths and quality values that closely match empirically-derived distributions. BEAR can emulate reads from various sequencing platforms, including Illumina, 454, and Ion Torrent. BEAR requires minimal user input, as it automatically determines appropriate parameter settings from user-supplied data. BEAR also uses a unique method for deriving run-specific error rates, and extracts useful statistics from the metagenomic data itself, such as quality-error models. Many existing simulators are specific to a particular sequencing technology; however, BEAR is not restricted in this way. Because of its flexibility, BEAR is particularly useful for emulating the behaviour of technologies like Ion Torrent, for which no dedicated sequencing simulators are currently available. BEAR is also the first metagenomic sequencing simulator program that automates the process of generating abundances, which can be an arduous task.ConclusionsBEAR is useful for evaluating data processing tools in genomics. It has many advantages over existing comparable software, such as generating more realistic reads and being independent of sequencing technology, and has features particularly useful for metagenomics work.
Highlights
A common problem in metagenomic studies is that given real data (e.g., whole genome shotgun (WGS) sequences generated by next-generation sequencing (NGS) technologies), it is difficult to know if the bioinformatics analyses generate correct or complete results
This paper presented BEAR, a tool for generating simulated reads based on empirically-derived read length distributions and quality scores
The approach used by BEAR for generating data eliminates the need for parameter tuning, allowing for an easy-to-use interface; the user need only provide a sample of data that has the desired properties of the reads to be emulated
Summary
A common problem in metagenomic studies is that given real data (e.g., whole genome shotgun (WGS) sequences generated by next-generation sequencing (NGS) technologies), it is difficult to know if the bioinformatics analyses generate correct or complete results. It is difficult to determine the usefulness of obtained results when their correctness is unknown Even for problems such as de novo genome assembly, a simpler problem than metagenomic assembly, there is still debate as to which features make a “good” assembly due to significant variability in results between programs (e.g., high variability in average contig length and N50 values between programs) [3]. Other programs generate models from actual sequencing data, but are limited to reads from singlegenome studies. There are no programs that allow a user to generate simulated data following non-parametric read-length distributions and quality profiles based on empirically-derived information from metagenomics sequencing data
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