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                        "title": "MultiLoc2: Integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction",
                        "abstract": "Background: Knowledge of subcellular localization of proteins is crucial to proteomics, drug target discovery and systems biology since localization and biological function are highly correlated. In recent years, numerous computational prediction methods have been developed. Nevertheless, there is still a need for prediction methods that show more robustness and higher accuracy. Results: We extended our previous MultiLoc predictor by incorporating phylogenetic profiles and Gene Ontology terms. Two different datasets were used for training the system, resulting in two versions of this high-accuracy prediction method. One version is specialized for globular proteins and predicts up to five localizations, whereas a second version covers all eleven main eukaryotic subcellular localizations. In a benchmark study with five localizations, MultiLoc2 performs considerably better than other methods for animal and plant proteins and comparably for fungal proteins. Furthermore, MultiLoc2 performs clearly better when using a second dataset that extends the benchmark study to all eleven main eukaryotic subcellular localizations. Conclusion: MultiLoc2 is an extensive high-performance subcellular protein localization prediction system. By incorporating phylogenetic profiles and Gene Ontology terms MultiLoc2 yields higher accuracies compared to its previous version. Moreover, it outperforms other prediction systems in two benchmarks studies. MultiLoc2 is available as user-friendly and free web-service, available at: http://www-bs.informatik.uni-tuebingen.de/Services/MultiLoc2. © 2009 Blum et al; licensee BioMed Central Ltd.",
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                                "name": "Blum T."
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                        "abstract": "© The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.High-throughput data production technologies, particularly 'next-generation' DNA sequencing, have ushered in widespread and disruptive changes to biomedical research. Making sense of the large datasets produced by these technologies requires sophisticated statistical and computational methods, as well as substantial computational power. This has led to an acute crisis in life sciences, as researchers without informatics training attempt to perform computation-dependent analyses. Since 2005, the Galaxy project has worked to address this problem by providing a framework that makes advanced computational tools usable by non experts. Galaxy seeks to make data-intensive research more accessible, transparent and reproducible by providing a Web-based environment in which users can perform computational analyses and have all of the details automatically tracked for later inspection, publication, or reuse. In this report we highlight recently added features enabling biomedical analyses on a large scale.",
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                        "title": "Identifying structural variation in haploid microbial genomes from short-read resequencing data using breseq",
                        "abstract": "© 2014 Barrick et al. Background: Mutations that alter chromosomal structure play critical roles in evolution and disease, including in the origin of new lifestyles and pathogenic traits in microbes. Large-scale rearrangements in genomes are often mediated by recombination events involving new or existing copies of mobile genetic elements, recently duplicated genes, or other repetitive sequences. Most current software programs for predicting structural variation from short-read DNA resequencing data are intended primarily for use on human genomes. They typically disregard information in reads mapping to repeat sequences, and significant post-processing and manual examination of their output is often required to rule out false-positive predictions and precisely describe mutational events. Results: We have implemented an algorithm for identifying structural variation from DNA resequencing data as part of the breseq computational pipeline for predicting mutations in haploid microbial genomes. Our method evaluates the support for new sequence junctions present in a clonal sample from split-read alignments to a reference genome, including matches to repeat sequences. Then, it uses a statistical model of read coverage evenness to accept or reject these predictions. Finally, breseq combines predictions of new junctions and deleted chromosomal regions to output biologically relevant descriptions of mutations and their effects on genes. We demonstrate the performance of breseq on simulated Escherichia coli genomes with deletions generating unique breakpoint sequences, new insertions of mobile genetic elements, and deletions mediated by mobile elements. Then, we reanalyze data from an E. coli K-12 mutation accumulation evolution experiment in which structural variation was not previously identified. Transposon insertions and large-scale chromosomal changes detected by breseq account for ~25% of spontaneous mutations in this strain. In all cases, we find that breseq is able to reliably predict structural variation with modest read-depth coverage of the reference genome (>40-fold). Conclusions: Using breseq to predict structural variation should be useful for studies of microbial epidemiology, experimental evolution, synthetic biology, and genetics when a reference genome for a closely related strain is available. In these cases, breseq can discover mutations that may be responsible for important or unintended changes in genomes that might otherwise go undetected.",
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                            {
                                "name": "Colburn G."
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                            {
                                "name": "Deatherage D.E."
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                            {
                                "name": "Traverse C.C."
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                            {
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                            {
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                                "name": "Meyer A.G."
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                        "date": "2014-11-29T00:00:00Z",
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                            {
                                "name": "Hao X."
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                    "metadata": {
                        "title": "The variant call format and VCFtools",
                        "abstract": "Summary: The variant call format (VCF) is a generic format for storing DNA polymorphism data such as SNPs, insertions, deletions and structural variants, together with rich annotations. VCF is usually stored in a compressed manner and can be indexed for fast data retrieval of variants from a range of positions on the reference genome. The format was developed for the 1000 Genomes Project, and has also been adopted by other projects such as UK10K, dbSNP and the NHLBI Exome Project. VCFtools is a software suite that implements various utilities for processing VCF files, including validation, merging, comparing and also provides a general Perl API. © The Author(s) 2011. Published by Oxford University Press.",
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                        "authors": [
                            {
                                "name": "Danecek P."
                            },
                            {
                                "name": "Auton A."
                            },
                            {
                                "name": "Abecasis G."
                            },
                            {
                                "name": "Albers C.A."
                            },
                            {
                                "name": "Banks E."
                            },
                            {
                                "name": "DePristo M.A."
                            },
                            {
                                "name": "Handsaker R.E."
                            },
                            {
                                "name": "Lunter G."
                            },
                            {
                                "name": "Marth G.T."
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                            {
                                "name": "Sherry S.T."
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                            {
                                "name": "McVean G."
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                            {
                                "name": "Durbin R."
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                            {
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                            {
                                "name": "Eberhard C."
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                            {
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                        "title": "Noisy: Identification of problematic columns in multiple sequence alignments",
                        "abstract": "Motivation: Sequence-based methods for phylogenetic reconstruction from (nucleic acid) sequence data are notoriously plagued by two effects: homoplasies and alignment errors. Large evolutionary distances imply a large number of homoplastic sites. As most protein-coding genes show dramatic variations in substitution rates that are not uncorrelated across the sequence, this often leads to a patchwork pattern of (i) phylogenetically informative and (ii) effectively randomized regions. In highly variable regions, furthermore, alignment errors accumulate resulting in sometimes misleading signals in phylogenetic reconstruction. Results: We present here a method that, based on assessing the distribution of character states along a cyclic ordering of the taxa, allows the identification of phylogenetically uninformative homoplastic sites in a multiple sequence alignment. Removal of these sites appears to improve the performance of phylogenetic reconstruction algorithms as measured by various indices of \"tree quality\". In particular, we obtain more stable trees due to the exclusion of phylogenetically incompatible sites that most likely represent strongly randomized characters. Software: The computer program noisy implements this approach. It can be employed to improving phylogenetic reconstruction capability with quite a considerable success rate whenever (1) the average bootstrap support obtained from the original alignment is low, and (2) there are sufficiently many taxa in the data set - at least, say, 12 to 15 taxa. The software can be obtained under the GNU Public License from http://www.bioinf.uni-leipzig.de/Software/noisy/. © 2008 Dress et al; licensee BioMed Central Ltd.",
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                        "authors": [
                            {
                                "name": "Dress A.W.M."
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                            {
                                "name": "Flamm C."
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                            {
                                "name": "Fritzsch G."
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                            {
                                "name": "Grunewald S."
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                            {
                                "name": "Kruspe M."
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                            {
                                "name": "Prohaska S.J."
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                            {
                                "name": "Stadler P.F."
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                        ],
                        "date": "2008-06-24T00:00:00Z",
                        "journal": "Algorithms for Molecular Biology"
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