RMotifGen: random motif generator for DNA and protein sequences.

Eric C Rouchka,C Timothy Hardin

doi:10.1186/1471-2105-8-292

Abstract

BackgroundDetection of short, subtle conserved motif regions within a set of related DNA or amino acid sequences can lead to discoveries about important regulatory domains such as transcription factor and DNA binding sites as well as conserved protein domains. In order to help assess motif detection algorithms on motifs with varying properties and levels of conservation, we have developed a computational tool, rMotifGen, with the sole purpose of generating a number of random DNA or protein sequences containing short sequence motifs. Each motif consensus can be user-defined, randomly generated, or created from a position-specific scoring matrix (PSSM). Insertions and mutations within these motifs are created according to user-defined parameters and substitution matrices. The resulting sequences can be helpful in mutational simulations and in testing the limits of motif detection algorithms.ResultsTwo implementations of rMotifGen have been created, one providing a graphical user interface (GUI) for random motif construction, and the other serving as a command line interface. The second implementation has the added advantages of platform independence and being able to be called in a batch mode. rMotifGen was used to construct sample sets of sequences containing DNA motifs and amino acid motifs that were then tested against the Gibbs sampler and MEME packages.ConclusionrMotifGen provides an efficient and convenient method for creating random DNA or amino acid sequences with a variable number of motifs, where the instance of each motif can be incorporated using a position-specific scoring matrix (PSSM) or by creating an instance mutated from its corresponding consensus using an evolutionary model based on substitution matrices. rMotifGen is freely available at: .

Highlights

Detection of short, subtle conserved motif regions within a set of related DNA or amino acid sequences can lead to discoveries about important regulatory domains such as transcription factor and DNA binding sites as well as conserved protein domains
The benchmark dataset is limited only to DNA sequences, and those randomly created are done so using a stochastic approach rather than one directed by a mutational model. rMotifGen has been developed as a solution to test various aspects of these limitations by creating simulated DNA and protein sequences where the motifs within the amino acid sequences are allowed to mutate according to a substitution matrix model
A sample amino acid sequence set of ten sequences of length 500 residues with six allowed motifs per sequence was generated from rMotifGen

Summary

Introduction

Subtle conserved motif regions within a set of related DNA or amino acid sequences can lead to discoveries about important regulatory domains such as transcription factor and DNA binding sites as well as conserved protein domains. Tompa et al [22] describe the creation of a DNA benchmarking dataset using the binding sites of known promoter sequences from the TRANSFAC database [23] along with DNA motifs creating using a Markov chain Creation of this dataset is critically important for the assessment of motif detection approaches. RMotifGen has been developed as a solution to test various aspects of these limitations by creating simulated DNA and protein sequences where the motifs within the amino acid sequences are allowed to mutate according to a substitution matrix model The benchmark dataset is limited only to DNA sequences, and those randomly created are done so using a stochastic approach rather than one directed by a mutational model. rMotifGen has been developed as a solution to test various aspects of these limitations by creating simulated DNA and protein sequences where the motifs within the amino acid sequences are allowed to mutate according to a substitution matrix model

Results

Discussion

Conclusion