Abstract Motivation: The goal of any parentage analysis is to identify as many parent–offspring relationships as possible, while minimizing incorrect assignments. Existing methods can achieve these ends, but they require additional information in the form of demographic data, thousands of markers and/or estimates of genotyping error rates. For many non-model systems, it is simply not practical, cost-effective or logistically feasible to obtain this information. Here, we develop a Bayesian parentage method that only requires the sampled genotypes to account for genotyping error, missing data and false matches. Results: Extensive testing with microsatellite and SNP datasets reveals that our Bayesian parentage method reliably controls for the number of false assignments, irrespective of the genotyping error rate. When the number of loci is limiting, our approach maximizes the number of correct assignments by accounting for the frequencies of shared alleles. Comparisons with exclusion and likelihood-based methods on an empirical salmon dataset revealed that our Bayesian method had the highest ratio of correct to incorrect assignments. Availability: Our program SOLOMON is available as an R package from the CRAN website. SOLOMON comes with a fully functional graphical user interface, requiring no user knowledge about the R programming environment. In addition to performing Bayesian parentage analysis, SOLOMON includes Mendelian exclusion and a priori power analysis modules. Further information and user support can be found at https://sites.google.com/site/parentagemethods/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Functional divergence among proteins is often assumed to be strongly influenced by natural selection, as inferred from the ratio of non-synonymous nucleotide divergence (d(N)) to synonymous nucleotide divergence (d(S)). That is, the more a mutation changes protein function, the more likely it is to be either selected against or selectively favored, and because the d(N)/d(S) ratio is a measure of natural selection, this ratio can be used to predict the degree of functional divergence (d(F)). However, these hypotheses have rarely been experimentally tested. RESULTS: I present a novel method to address this issue, and demonstrate that divergence in bacteria-killing activity among animal antimicrobial peptides is positively correlated with the log of the d(N)/d(S) ratio. The primary cause of this pattern appears to be that positively selected substitutions change protein function more than neutral substitutions do. Thus, the d(N)/d(S) ratio is an accurate estimator of adaptive functional divergence. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data, including GenBank Accession numbers, are available at Bioinformatics online.