MOTIVATION: Next-generation sequencing (NGS) technologies generate large amounts of short read data for many different organisms. The fact that NGS reads are generally short makes it challenging to assemble the reads and reconstruct the original genome sequence. For clustering genomes using such NGS data, word-count based alignment-free sequence comparison is a promising approach, but for this approach, the underlying expected word counts are essential.A plausible model for this underlying distribution of word counts is given through modeling the DNA sequence as a Markov chain (MC). For single long sequences, efficient statistics are available to estimate the order of MCs and the transition probability matrix for the sequences. As NGS data do not provide a single long sequence, inference methods on Markovian properties of sequences based on single long sequences cannot be directly used for NGS short read data. RESULTS: Here we derive a normal approximation for such word counts. We also show that the traditional Chi-square statistic has an approximate gamma distribution ,: using the Lander-Waterman model for physical mapping. We propose several methods to estimate the order of the MC based on NGS reads and evaluate those using simulations. We illustrate the applications of our results by clustering genomic sequences of several vertebrate and tree species based on NGS reads using alignment-free sequence dissimilarity measures. We find that the estimated order of the MC has a considerable effect on the clustering results ,: and that the clustering results that use a N: MC of the estimated order give a plausible clustering of the species. AVAILABILITY AND IMPLEMENTATION: Our implementation of the statistics developed here is available as R package 'NGS.MC' at http://www-rcf.usc.edu/∼fsun/Programs/NGS-MC/NGS-MC.html CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

As Semantic Web technologies mature and new releases of key elements, such as SPARQL 1.1 and OWL 2.0, become available, the Life Sciences continue to push the boundaries of these technologies with ever more sophisticated tools and applications. Unsurprisingly, therefore, interest in the SWAT4LS (Semantic Web Applications and Tools for the Life Sciences) activities have remained high, as was evident during the third international SWAT4LS workshop held in Berlin in December 2010. Contributors to this workshop were invited to submit extended versions of their papers, the best of which are now made available in the special supplement of BMC Bioinformatics. The papers reflect the wide range of work in this area, covering the storage and querying of Life Sciences data in RDF triple stores, tools for the development of biomedical ontologies and the semantics-based integration of Life Sciences as well as clinicial data.