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                        "title": "IDBA - A practical iterative De Bruijn graph De Novo assembler",
                        "abstract": "The de Bruijn graph assembly approach breaks reads into k-mers before assembling them into contigs. The string graph approach forms contigs by connecting two reads with k or more overlapping nucleotides. Both approaches must deal with the following problems: false-positive vertices, due to erroneous reads; gap problem, due to non-uniform coverage; branching problem, due to erroneous reads and repeat regions. A proper choice of k is crucial but for single k there is always a trade-off: a small k favors the situation of erroneous reads and non-uniform coverage, and a large k favors short repeat regions. We propose an iterative de Bruijn graph approach iterating from small to large k exploring the advantages of the in between values. Our IDBA outperforms the existing algorithms by constructing longer contigs with similar accuracy and using less memory, both with real and simulated data. The running time of the algorithm is comparable to existing algorithms. © Springer-Verlag Berlin Heidelberg 2010.",
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                            {
                                "name": "Peng Y."
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                            {
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                    "term": "Genetic variation",
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                        "title": "A systematic, large-scale comparison of transcription factor binding site models",
                        "abstract": "© 2016 Hombach et al.Background: The modelling of gene regulation is a major challenge in biomedical research. This process is dominated by transcription factors (TFs) and mutations in their binding sites (TFBSs) may cause the misregulation of genes, eventually leading to disease. The consequences of DNA variants on TF binding are modelled in silico using binding matrices, but it remains unclear whether these are capable of accurately representing in vivo binding. In this study, we present a systematic comparison of binding models for 82 human TFs from three freely available sources: JASPAR matrices, HT-SELEX-generated models and matrices derived from protein binding microarrays (PBMs). We determined their ability to detect experimentally verified \"real\" in vivo TFBSs derived from ENCODE ChIP-seq data. As negative controls we chose random downstream exonic sequences, which are unlikely to harbour TFBS. All models were assessed by receiver operating characteristics (ROC) analysis. Results: While the area-under-curve was low for most of the tested models with only 47% reaching a score of 0.7 or higher, we noticed strong differences between the various position-specific scoring matrices with JASPAR and HT-SELEX models showing higher success rates than PBM-derived models. In addition, we found that while TFBS sequences showed a higher degree of conservation than randomly chosen sequences, there was a high variability between individual TFBSs. Conclusions: Our results show that only few of the matrix-based models used to predict potential TFBS are able to reliably detect experimentally confirmed TFBS. We compiled our findings in a freely accessible web application called ePOSSUM (http:/mutationtaster.charite.de/ePOSSUM/) which uses a Bayes classifier to assess the impact of genetic alterations on TF binding in user-defined sequences. Additionally, ePOSSUM provides information on the reliability of the prediction using our test set of experimentally confirmed binding sites.",
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                            {
                                "name": "Hombach D."
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                            {
                                "name": "Schwarz J.M."
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                            {
                                "name": "Robinson P.N."
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                            {
                                "name": "Schuelke M."
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                            {
                                "name": "Seelow D."
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                        "date": "2016-05-21T00:00:00Z",
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                        "abstract": "© 2015 Beat Wolf et al. Over recent years next generation sequencing (NGS) technologies evolved from costly tools used by very few, to a much more accessible and economically viable technology. Through this recently gained popularity, its use-cases expanded from research environments into clinical settings. But the technical know-how and infrastructure required to analyze the data remain an obstacle for a wider adoption of this technology, especially in smaller laboratories. We present GensearchNGS, a commercial DNAseq software suite distributed by Phenosystems SA. The focus of GensearchNGS is the optimal usage of already existing infrastructure, while keeping its use simple. This is achieved through the integration of existing tools in a comprehensive software environment, as well as custom algorithms developed with the restrictions of limited infrastructures in mind. This includes the possibility to connect multiple computers to speed up computing intensive parts of the analysis such as sequence alignments. We present a typical DNAseq workflow for NGS data analysis and the approach GensearchNGS takes to implement it. The presented workflow goes from raw data quality control to the final variant report. This includes features such as gene panels and the integration of online databases, like Ensembl for annotations or Cafe Variome for variant sharing.",
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                            {
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                        "title": "FusionMap: Detecting fusion genes from next-generation sequencing data at base-pair resolution",
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                                "name": "Ge H."
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                            {
                                "name": "Liu K."
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                            {
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                        "title": "Weighted neighbor joining: A likelihood-based approach to distance-based phylogeny reconstruction",
                        "abstract": "We introduce a distance-based phylogeny reconstruction method called 'weighted neighbor joining,' or 'Weighbor' for short. As in neighbor joining, two taxa are joined in each iteration; however, the Weighbor criterion for choosing a pair of taxa to join takes into account that errors in distance estimates are exponentially larger for longer distances. The criterion embodies a likelihood function on the distances, which are modeled as correlated Gaussian random variables with different means and variances, computed under a probabilistic model for sequence evolution. The Weighbor criterion consists of two terms, an additivity term and a positivity term, that quantify the implications of joining the pair. The first term evaluates deviations from additivity of the implied external branches, while the second term evaluates confidence that the implied internal branch has a positive branch length. Compared with maximum-likelihood phylogeny reconstruction, Weighbor is much faster, while building trees that are qualitatively and quantitatively similar. Weighbor appears to be relatively immune to the 'long branches attract' and 'long branch distracts' drawbacks observed with neighbor joining, BIONJ, and parsimony.",
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                                "name": "Bruno W.J."
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                            {
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                        "abstract": "The Mammalian Gene Collection (MGC) project is a new effort by the NIH to generate full-length complementary DNA (cDNA) resources. This project will provide publicly accessible resources to the full research community. The MGC project entails the production of libraries, sequencing, and database and repository development, as well as the support of library construction, sequencing, and analytic technologies dedicated to the goal of obtaining a full set of human and other mammalian full-length (open reading frame) sequences and clones of expressed genes.",
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