MOTIVATION: Genotyping by sequencing is a powerful tool for investigating genetic variation in plants, but many economically important plants are allopolyploids, where homoeologous similarity obscures the subgenomic origin of reads and confounds allelic and homoeologous SNPs. Recent polyploid genotyping methods use allelic frequencies, rate of heterozygosity, parental cross or other information to resolve read assignment, but good subgenomic references offer the most direct information. The typical strategy aligns reads to the joint reference, performs diploid genotyping within each subgenome, and filters the results, but persistent read misassignment results in an excess of false heterozygous calls. RESULTS: We introduce the Comprehensive Allopolyploid Genotyper (CAPG), which formulates an explicit likelihood to weight read alignments against both subgenomic references and genotype individual allopolyploids from whole-genome resequencing data. We demonstrate CAPG in allotetraploids, where it performs better than Genome Analysis Toolkit's HaplotypeCaller applied to reads aligned to the combined subgenomic references. AVAILABILITY AND IMPLEMENTATION: Code and tutorials are available at https://github.com/Kkulkarni1/CAPG.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The ability to dynamically discover and invoke a Web service is a critical aspect of service oriented architectures. An important component of the discovery process is the matchmaking algorithm itself. In order to overcome the limitations of a syntax-based search, matchmaking algorithms based on semantic techniques have been proposed. Most of them are based on an algorithm originally proposed by M. Paolucci, et al. [19]. In this paper, we analyze this original algorithm and identify some correctness issues with it. We illustrate how these issues are an outcome of the greedy approach adopted by the algorithm. We propose a more exhaustive matchmaking algorithm, based on the concept of matching bipartite graphs, to overcome the problems faced with the original algorithm. We analyze the complexity of both the algorithms and present performance results based on our implementation of both these algorithms. We show that the complexity of our algorithm is equivalent to that of the original algorithm in spite of the improvements we have made to address the correctness issues.