Recent advances in DNA sequencing technology have caused an exponential growth of publicly available genomic sequence data. A particularly voluminous, frequently used static data set are whole genome alignments. The first lossless compression algorithm for such data sets based on well-established statistical evolutionary models and prediction techniques from lossless binary image compression is introduced. The compression rate is improved by a factor of 1.6 compared to the currently used Lempel-Ziv (LZ) compression.

Rapid development of DNA sequencing technologies exponentially increases the amount of publicly available genomic data. Whole genome multiple sequence alignments represent a particularly voluminous, frequently downloaded static dataset. In this work we propose an asymmetric source coding scheme for such alignments using evolutionary prediction in combination with lossless black and white image compression. Compared to the Lempel-Ziv algorithm used so far the compression rates are almost halved.

BACKGROUND: Comparative genomics aims to detect signals of evolutionary conservation as an indicator of functional constraint. Surprisingly, results of the ENCODE project revealed that about half of the experimentally verified functional elements found in non-coding DNA were classified as unconstrained by computational predictions. Following this observation, it has been hypothesized that this may be partly explained by biased estimates on neutral evolutionary rates used by existing sequence conservation metrics. All methods we are aware of rely on a comparison with the neutral rate and conservation is estimated by measuring the deviation of a particular genomic region from this rate. Consequently, it is a reasonable assumption that inaccurate neutral rate estimates may lead to biased conservation and constraint estimates. RESULTS: We propose a conservation signal that is produced by local Maximum Likelihood estimation of evolutionary parameters using an optimized sliding window and present a Kullback-Leibler projection that allows multiple different estimated parameters to be transformed into a conservation measure. This conservation measure does not rely on assumptions about neutral evolutionary substitution rates and little a priori assumptions on the properties of the conserved regions are imposed. We show the accuracy of our approach (KuLCons) on synthetic data and compare it to the scores generated by state-of-the-art methods (phastCons, GERP, SCONE) in an ENCODE region. We find that KuLCons is most often in agreement with the conservation/constraint signatures detected by GERP and SCONE while qualitatively very different patterns from phastCons are observed. Opposed to standard methods KuLCons can be extended to more complex evolutionary models, e.g. taking insertion and deletion events into account and corresponding results show that scores obtained under this model can diverge significantly from scores using the simpler model. CONCLUSION: Our results suggest that discriminating among the different degrees of conservation is possible without making assumptions about neutral rates. We find, however, that it cannot be expected to discover considerably different constraint regions than GERP and SCONE. Consequently, we conclude that the reported discrepancies between experimentally verified functional and computationally identified constraint elements are likely not to be explained by biased neutral rate estimates.

Possibilities of using mutual information for classification and content recognition are exploited. Two different mutual information based distance measures are proposed, one for classification and one for content recognition. The measure proposed for classification is shown to be a metric. The influence of compression based estimation methods on the proposed measures is investigated. Several examples of successful applications in the field of genetics are presented.

Methods used in information theory can be used in genomic analysis to provide a more meaningful insight into the genetic process. Using an information transfer model between certain polymorphisms in the human genome (SNP) and certain diseases (e.g. Alzheimer), Shannon's mutual information can identify the relevant SNP. Several compression algorithms approximating the entropy of DNA sequences are used to distinguish between introns and exons. Certain distance measures derived from DNA sequences allow classification leading to evolutionary trees. They can also be used for content recognition.