MOTIVATION: Why recombination? is one of the central questions in biology. This has led to a host of methods for quantifying recombination from sequence data. These methods are usually based on aligned DNA sequences. Here, we propose an efficient alignment-free alternative. RESULTS: Our method is based on the distribution of match lengths, which we look up using enhanced suffix arrays. By eliminating the alignment step, the test becomes fast enough for application to whole bacterial genomes. Using simulations we show that our test has similar power as established tests when applied to long pairs of sequences. When applied to 58 genomes of Escherichia coli, we pick up the strongest recombination signal from a 125 kb horizontal gene transfer engineered 20 years ago. AVAILABILITY AND IMPLEMENTATION: We have implemented our method in the command-line program rush. Its C sources and documentation are available under the GNU General Public License from http://guanine.evolbio.mpg.de/rush/.

BACKGROUND: Systematic measurement of genetic interactions by combinatorial RNAi (co-RNAi) is a powerful tool for mapping functional modules and discovering components. It also provides insights into the role of epistasis on the way from genotype to phenotype. The interpretation of co-RNAi data requires computational and statistical analysis in order to detect interactions reliably and sensitively. RESULTS: We present a comprehensive approach to the analysis of univariate phenotype measurements, such as cell growth. The method is based on a quantitative model and is demonstrated on two example Drosophila cell culture data sets. We discuss adjustments for technical variability, data quality assessment, model parameter fitting and fit diagnostics, choice of scale, and assessment of statistical significance. CONCLUSIONS: As a result, we obtain quantitative genetic interactions and interaction networks reflecting known biological relationships between target genes. The reliable extraction of presence, absence, and strength of interactions provides insights into molecular mechanisms.