CorGen—measuring and generating long-range correlations for DNA sequence analysis

CorGen is a web server that measures long-range correlations in the base composition of DNA and generates random sequences with the same correlation parameters. Long-range correlations are characterized by a power-law decay of the auto correlation function of the GC-content. The widespread presence of such correlations in eukaryotic genomes calls for their incorporation into accurate null models of eukaryotic DNA in computational biology. For example, the score statistics of sequence alignment and the performance of motif finding algorithms are significantly affected by the presence of genomic long-range correlations. We use an expansion-randomization dynamics to efficiently generate the correlated random sequences. The server is available a

Alignment Statistics for Long-Range Correlated Genomic Sequences

It is well known that the base composition along eukaryotic genomes is long-range correlated. Here, we investigate the effect of such long-range correlations on alignment score statistics. We model the correlated score-landscape by means of a Gaussian approximation. In this framework, we can calculate the corrections to the scale parameter λ of the extreme value distribution of alignment scores. To evaluate our approximate analytic results, we perform a detailed numerical study based on a simple algorithm to efficiently generate long-range correlated random sequences. We find that the mean and the exponential tail of the score distribution are in fact influenced by the correlations along the sequences. Therefore, the significance of measured alignment scores in biological sequences will change upon incorporation of the correlations in the null model