MOTIVATION: The functional mechanisms underlying disease association remain unknown for Genome-wide Association Studies (GWAS) susceptibility variants located outside coding regions. Synthesis of effects from multiple surrounding functional variants has been suggested as an explanation of hard-to-interpret findings. We define filter criteria based on linkage disequilibrium measures and allele frequencies which reflect expected properties of synthesizing variant sets. For eligible candidate sets, we search for haplotype markers that are highly correlated with associated variants. RESULTS: Via simulations we assess the performance of our approach and suggest parameter settings which guarantee 95% sensitivity at 20-fold reduced computational cost. We apply our method to 1000 Genomes data and confirmed Crohn's Disease (CD) and Type 2 Diabetes (T2D) variants. A proportion of 36.9% allowed explanation by three-variant-haplotypes carrying at least two functional variants, as compared to 16.4% for random variants ([Formula: see text]). Association could be explained by missense variants for MUC19, PER3 (CD) and HMG20A (T2D). In a CD GWAS-imputed using haplotype reference consortium data (64 976 haplotypes)-we could confirm the syntheses of MUC19 and PER3 and identified synthesis by missense variants for 6 further genes (ZGPAZ, GPR65, CLN3/NPIPB8, LOC102723878, rs2872507, GCKR). In all instances, the odds ratios of the synthesizing haplotypes were virtually identical to that of the index SNP. In summary, we demonstrate the potential of synthesis analysis to guide functional follow-up of GWAS findings. AVAILABILITY AND IMPLEMENTATION: All methods are implemented in the C/C ++ toolkit GetSynth, available at http://sourceforge.net/projects/getsynth/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Protocols to generate strand-specific transcriptomes with next-generation sequencing platforms have been used by the scientific community roughly since 2008. Strand-specific reads allow for detection of antisense events and a higher resolution of expression profiles enabling extension of current transcript annotations. However, applications making use of this strandedness information are still scarce. RESULTS: Here we present a tool (Janus), which focuses on the identification of transcriptional active regions in antisense orientation to known and novel transcribed elements of the genome. Janus can compare the antisense events of multiple samples and assigns scores to identify mutual expression of either transcript in a sense/antisense pair, which could hint to regulatory mechanisms. Janus is able to make use of single-nucleotide variant (SNV) and methylation data, if available, and reports the sense to antisense ratio of regions in the vicinity of the identified genetic and epigenetic variation. Janus interrogates positions of heterozygous SNVs to identify strand-specific allelic imbalance. AVAILABILITY: Janus is written in C/C++ and freely available at http://www.ikmb.uni-kiel.de/janus/janus.html under terms of GNU General Public License, for both, Linux and Windows 64×. Although the binaries will work without additional downloads, the software depends on bamtools (https://github.com/pezmaster31/bamtools) for compilation. A detailed tutorial section is included in the first section of the supplemental material and included as brief readme.txt in the tutorial archive. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Next generation sequencing (NGS) technologies allow a rapid and cost-effective compilation of large RNA sequence datasets in model and non-model organisms. However, the storage and analysis of transcriptome information from different NGS platforms is still a significant bottleneck, leading to a delay in data dissemination and subsequent biological understanding. Especially database interfaces with transcriptome analysis modules going beyond mere read counts are missing. Here, we present the Transcriptome Analysis and Comparison Explorer (T-ACE), a tool designed for the organization and analysis of large sequence datasets, and especially suited for transcriptome projects of non-model organisms with little or no a priori sequence information. T-ACE offers a TCL-based interface, which accesses a PostgreSQL database via a php-script. Within T-ACE, information belonging to single sequences or contigs, such as annotation or read coverage, is linked to the respective sequence and immediately accessible. Sequences and assigned information can be searched via keyword- or BLAST-search. Additionally, T-ACE provides within and between transcriptome analysis modules on the level of expression, GO terms, KEGG pathways and protein domains. Results are visualized and can be easily exported for external analysis. We developed T-ACE for laboratory environments, which have only a limited amount of bioinformatics support, and for collaborative projects in which different partners work on the same dataset from different locations or platforms (Windows/Linux/MacOS). For laboratories with some experience in bioinformatics and programming, the low complexity of the database structure and open-source code provides a framework that can be customized according to the different needs of the user and transcriptome project.

MOTIVATION: Copy number variation (CNV), a major contributor to human genetic variation, comprises >/= 1 kb genomic deletions and insertions. Yet, the identification of CNVs from microarray data is still hampered by high false negative and positive prediction rates due to the noisy nature of the raw data. Here, we present CNVineta, an R package for rapid data mining and visualization of CNVs in large case-control datasets genotyped with single nucleotide polymorphism oligonucleotide arrays. CNVineta is compatible with various established CNV prediction algorithms, can be used for genome-wide association analysis of rare and common CNVs and enables rapid and serial display of log(2) of raw data ratios as well as B-allele frequencies for visual quality inspection. In summary, CNVineta aides in the interpretation of large-scale CNV datasets and prioritization of target regions for follow-up experiments. AVAILABILITY AND IMPLEMENTATION: CNVineta is available as an R package and can be downloaded from http://www.ikmb.uni-kiel.de/CNVineta/; the package contains a tutorial outlining a typical workflow. The CNVineta compatible HapMap dataset can also be downloaded from the link above.

BACKGROUND: Genotyping of single-nucleotide polymorphisms (SNPs) is a fundamental technology in modern genetics. The SNPlex mid-throughput genotyping system (Applied Biosystems, Foster City, CA, USA) enables the multiplexed genotyping of up to 48 SNPs simultaneously in a single DNA sample. The high level of automation and the large amount of data produced in a high-throughput laboratory require advanced software tools for quality control and workflow management. RESULTS: We have developed two programs, which address two main aspects of quality control in a SNPlex genotyping environment: GMFilter improves the analysis of SNPlex plates by removing wells with a low overall signal intensity. It enables scientists to automatically process the raw data in a standardized way before analyzing a plate with the proprietary GeneMapper software from Applied Biosystems. SXTestPlate examines the genotype concordance of a SNPlex test plate, which was typed with a control SNP set. This program allows for regular quality control checks of a SNPlex genotyping platform. It is compatible to other genotyping methods as well. CONCLUSION: GMFilter and SXTestPlate provide a valuable tool set for laboratories engaged in genotyping based on the SNPlex system. The programs enhance the analysis of SNPlex plates with the GeneMapper software and enable scientists to evaluate the performance of their genotyping platform.

BACKGROUND: Normalization is the process of removing non-biological sources of variation between array experiments. Recent investigations of data in gene expression databases for varying organisms and tissues have shown that the majority of expressed genes exhibit a power-law distribution with an exponent close to -1 (i.e. obey Zipf's law). Based on the observation that our single channel and two channel microarray data sets also followed a power-law distribution, we were motivated to develop a normalization method based on this law, and examine how it compares with existing published techniques. A computationally simple and intuitively appealing technique based on this observation is presented. RESULTS: Using pairwise comparisons using MA plots (log ratio vs. log intensity), we compared this novel method to previously published normalization techniques, namely global normalization to the mean, the quantile method, and a variation on the loess normalization method designed specifically for boutique microarrays. Results indicated that, for single channel microarrays, the quantile method was superior with regard to eliminating intensity-dependent effects (banana curves), but Zipf's law normalization does minimize this effect by rotating the data distribution such that the maximal number of data points lie on the zero of the log ratio axis. For two channel boutique microarrays, the Zipf's law normalizations performed as well as, or better than existing techniques. CONCLUSION: Zipf's law normalization is a useful tool where the Quantile method cannot be applied, as is the case with microarrays containing functionally specific gene sets (boutique arrays).

Sequence variations in the gene products PYPAF1/CIAS1 and NOD2/CARD15 have been associated with several autoinflammatory diseases that, although clinically different, share a similar inflammatory pathophysiology. A multiple sequence alignment of homologous proteins demonstrates that some of the missense variants are located in highly conserved regions of the NTPase domain and possibly impair NTP-hydrolysis. Intriguingly, one of the variations, which is found identically in PYPAF1 and NOD2, is located at the same alignment position. Our findings suggest that evolutionary gene duplication can give rise to disease families because variants affect conserved sequence in a similar fashion.

UNLABELLED: We have developed an integrated laboratory information system that allows the flexible handling of pedigree, phenotype and genotype information. Specifically, it includes client applications for an integrated data import from TaqMan typing files, Mendel checking, data export, handling of pedigree and phenotype information and analysis features. AVAILABILITY: The SQL source code, sources and binaries of the client applications (NT and Windows95/98 platforms) and additional documentation are available at http://www.mucosa.de/.

MOTIVATION: The investigation of common disorders of polygenic inheritance using both population-based association designs and non-parametric linkage analysis within families is gaining increasing importance. We have created a program that allows for the flexible simulation of populations as a tool to investigate the properties of population-based mapping approaches. RESULTS: We have created a population simulation program, POPSIM, that (i) creates a virtual representation of every individual, (ii) makes no prior assumptions but the Mendelian rules and (iii) allows populations of several million individuals in size to be generated and to be followed over hundreds of generations. The parameters of the disease model, population structure and population expansion rate can be specified. Flexible sampling options exist that allow samples of families and individuals to be drawn at any given point during the population history. The program may be a useful tool in the study of the influence of genetic drift, recombination and admixture on the generation and maintenance of linkage disequilibrium in populations, as well as the evaluation of stochastic sampling characteristics of families and individuals conditional on a complex genetic phenotype from homogeneous and heterogeneous populations. AVAILABILITY: The source code as well as Sun and Windows NT4.0 console executables of the program are available under http://www.ukrv. de/ch/medgen/html/benutzer/j.hampe/popsim. html. CONTACT: J. [email protected]