CopywriteR: DNA copy number detection from off-target sequence data.

Current methods for detection of copy number variants (CNV) and aberrations (CNA) from targeted sequencing data are based on the depth of coverage of captured exons. Accurate CNA determination is complicated by uneven genomic distribution and non-uniform capture efficiency of targeted exons. Here we present CopywriteR, which eludes these problems by exploiting 'off-target' sequence reads. CopywriteR allows for extracting uniformly distributed copy number information, can be used without reference, and can be applied to sequencing data obtained from various techniques including chromatin immunoprecipitation and target enrichment on small gene panels. CopywriteR outperforms existing methods and constitutes a widely applicable alternative to available tools.

Index Tracking Using Data-Mining Techniques and Mixed-Binary Linear Programming

Index tracking has become one of the most common strategies in asset management. The index-tracking problem consists of constructing a portfolio that replicates the future performance of an index by including only a subset of the index constituents in the portfolio. Finding the most representative subset is challenging when the number of stocks in the index is large. We introduce a new three-stage approach that at first identifies promising subsets by employing data-mining techniques, then determines the stock weights in the subsets using mixed-binary linear programming, and finally evaluates the subsets based on cross validation. The best subset is returned as the tracking portfolio. Our approach outperforms state-of-the-art methods in terms of out-of-sample performance and running times.

A second generation radiation hybrid map to aid the assembly of the bovine genome sequence

BACKGROUND: Several approaches can be used to determine the order of loci on chromosomes and hence develop maps of the genome. However, all mapping approaches are prone to errors either arising from technical deficiencies or lack of statistical support to distinguish between alternative orders of loci. The accuracy of the genome maps could be improved, in principle, if information from different sources was combined to produce integrated maps. The publicly available bovine genomic sequence assembly with 6x coverage (Btau_2.0) is based on whole genome shotgun sequence data and limited mapping data however, it is recognised that this assembly is a draft that contains errors. Correcting the sequence assembly requires extensive additional mapping information to improve the reliability of the ordering of sequence scaffolds on chromosomes. The radiation hybrid (RH) map described here has been contributed to the international sequencing project to aid this process. RESULTS: An RH map for the 30 bovine chromosomes is presented. The map was built using the Roslin 3000-rad RH panel (BovGen RH map) and contains 3966 markers including 2473 new loci in addition to 262 amplified fragment-length polymorphisms (AFLP) and 1231 markers previously published with the first generation RH map. Sequences of the mapped loci were aligned with published bovine genome maps to identify inconsistencies. In addition to differences in the order of loci, several cases were observed where the chromosomal assignment of loci differed between maps. All the chromosome maps were aligned with the current 6x bovine assembly (Btau_2.0) and 2898 loci were unambiguously located in the bovine sequence. The order of loci on the RH map for BTA 5, 7, 16, 22, 25 and 29 differed substantially from the assembled bovine sequence. From the 2898 loci unambiguously identified in the bovine sequence assembly, 131 mapped to different chromosomes in the BovGen RH map. CONCLUSION: Alignment of the BovGen RH map with other published RH and genetic maps showed higher consistency in marker order and chromosome assignment than with the current 6x sequence assembly. This suggests that the bovine sequence assembly could be significantly improved by incorporating additional independent mapping information.

Complete Genome Sequence of Bovine Pestivirus Strain PG-2, a Second Member of the Tentative Pestivirus Species Giraffe

We report the complete genome sequence of bovine pestivirus strain PG-2. The sequence data from this virus showed that PG-2 is closely related to the giraffe pestivirus strain H138. PG-2 and H138 belong to one pestivirus species that should be considered an approved member of the genus Pestivirus.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

Imputation of sequence level genotypes in the Franches-Montagnes horse breed

BACKGROUND A cost-effective strategy to increase the density of available markers within a population is to sequence a small proportion of the population and impute whole-genome sequence data for the remaining population. Increased densities of typed markers are advantageous for genome-wide association studies (GWAS) and genomic predictions. METHODS We obtained genotypes for 54 602 SNPs (single nucleotide polymorphisms) in 1077 Franches-Montagnes (FM) horses and Illumina paired-end whole-genome sequencing data for 30 FM horses and 14 Warmblood horses. After variant calling, the sequence-derived SNP genotypes (~13 million SNPs) were used for genotype imputation with the software programs Beagle, Impute2 and FImpute. RESULTS The mean imputation accuracy of FM horses using Impute2 was 92.0%. Imputation accuracy using Beagle and FImpute was 74.3% and 77.2%, respectively. In addition, for Impute2 we determined the imputation accuracy of all individual horses in the validation population, which ranged from 85.7% to 99.8%. The subsequent inclusion of Warmblood sequence data further increased the correlation between true and imputed genotypes for most horses, especially for horses with a high level of admixture. The final imputation accuracy of the horses ranged from 91.2% to 99.5%. CONCLUSIONS Using Impute2, the imputation accuracy was higher than 91% for all horses in the validation population, which indicates that direct imputation of 50k SNP-chip data to sequence level genotypes is feasible in the FM population. The individual imputation accuracy depended mainly on the applied software and the level of admixture.