MOTIVATION: Next generation sequencing technology considerably changed the way we screen for pathogenic mutations in rare Mendelian disorders. However, the identification of the disease-causing mutation amongst thousands of variants of partly unknown relevance is still challenging and efficient techniques that reduce the genomic search space play a decisive role. Often segregation- or linkage analysis are used to prioritize candidates, however, these approaches require correct information about the degree of relationship among the sequenced samples. For quality assurance an automated control of pedigree structures and sample assignment is therefore highly desirable in order to detect label mix-ups that might otherwise corrupt downstream analysis. RESULTS: We developed an algorithm based on likelihood ratios that discriminates between different classes of relationship for an arbitrary number of genotyped samples. By identifying the most likely class we are able to reconstruct entire pedigrees iteratively, even for highly consanguineous families. We tested our approach on exome data of different sequencing studies and achieved high precision for all pedigree predictions. By analyzing the precision for varying degrees of relatedness or inbreeding we could show that a prediction is robust down to magnitudes of a few hundred loci. AVAILABILITY AND IMPLEMENTATION: A java standalone application that computes the relationships between multiple samples as well as a Rscript that visualizes the pedigree information is available for download as well as a web service at www.gene-talk.de CONTACT: [email protected] information: Supplementary data are available at Bioinformatics online.

MOTIVATION: When analyzing a case group of patients with ultra-rare disorders the ethnicities are often diverse and the data quality might vary. The population substructure in the case group as well as the heterogeneous data quality can cause substantial inflation of test statistics and result in spurious associations in case-control studies if not properly adjusted for. Existing techniques to correct for confounding effects were especially developed for common variants and are not applicable to rare variants. RESULTS: We analyzed strategies to select suitable controls for cases that are based on similarity metrics that vary in their weighting schemes. We simulated different disease entities on real exome data and show that a similarity-based selection scheme can help to reduce false positive associations and to optimize the performance of the statistical tests. Especially when data quality as well as ethnicities vary a lot in the case group, a matching approach that puts more weight on rare variants shows the best performance. We reanalyzed collections of unrelated patients with Kabuki make-up syndrome, Hyperphosphatasia with Mental Retardation syndrome and Catel-Manzke syndrome for which the disease genes were recently described. We show that rare variant association tests are more sensitive and specific in identifying the disease gene than intersection filters and should thus be considered as a favorable approach in analyzing even small patient cohorts. AVAILABILITY AND IMPLEMENTATION: Datasets used in our analysis are available at ftp://ftp.1000genomes.ebi.ac.uk./vol1/ftp/ CONTACT: : [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Next-generation sequencing has become a powerful tool in personalized medicine. Exomes or even whole genomes of patients suffering from rare diseases are screened for sequence variants. After filtering out common polymorphisms, the assessment and interpretation of detected personal variants in the clinical context is an often time-consuming effort. We have developed GeneTalk, a web-based platform that serves as an expert exchange network for the assessment of personal and potentially disease-relevant sequence variants. GeneTalk assists a clinical geneticist who is searching for information about specific sequence variants and connects this user to other users with expertise for the same sequence variant. AVAILABILITY: GeneTalk is available at www.gene-talk.de. Users can login without registering in a demo account. CONTACT: [email protected].

We propose a new method for defragmenting the module layout of a reconfigurable device, enabled by a novel approach for dealing with communication needs between relocated modules and with inhomogeneities found in commonly used FPGAs. Our method is based on dynamic relocation of module positions during runtime, with only very little reconfiguration overhead; the objective is to maximize the length of contiguous free space that is available for new modules. We describe a number of algorithmic aspects of good defragmentation, and present an optimization method based on tabu search. Experimental results indicate that we can improve the quality of module layout by roughly 50% over the static layout. Among other benefits, this improvement avoids unnecessary rejections of modules.

We propose a new method for defragmenting the module layout of a reconfigurable device, enabled by a novel approach for dealing with communication needs between relocated modules and with inhomogeneities found in commonly used FPGAs. Our method is based on dynamic relocation of module positions during runtime, with only very little reconfiguration overhead; the objective is to maximize the length of contiguous free space that is available for new modules. We describe a number of algorithmic aspects of good defragmentation, and present an optimization method based on tabu search. Experimental results indicate that we can improve the quality of module layout by roughly 50% over static layout. Among other benefits, this improvement avoids unnecessary rejection of modules.

How efficiently can we search an unknown environment for a goal in an unknown position? How much would it help if the environment were known? We answer these questions for simple polygons and for undirected graphs by providing online search strategies that are as good as the best offline search algorithms, up to a constant factor. For other settings we prove that no such online algorithms exist. We introduce a natural measure which gives reasonable results and is more realistic than pure pessimistic competitive analysis.