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  • 1
    Language: English
    In: Journal of inherited metabolic disease, 2018-04-05, Vol.41 (3), p.533-539
    Description: Significant improvements in automated image analysis have been achieved in recent years and tools are now increasingly being used in computer-assisted syndromology. However, the ability to recognize a syndromic facial gestalt might depend on the syndrome and may also be confounded by severity of phenotype, size of available training sets, ethnicity, age, and sex. Therefore, benchmarking and comparing the performance of deep-learned classification processes is inherently difficult. For a systematic analysis of these influencing factors we chose the lysosomal storage diseases mucolipidosis as well as mucopolysaccharidosis type I and II that are known for their wide and overlapping phenotypic spectra. For a dysmorphic comparison we used Smith-Lemli-Opitz syndrome as another inborn error of metabolism and Nicolaides-Baraitser syndrome as another disorder that is also characterized by coarse facies. A classifier that was trained on these five cohorts, comprising 289 patients in total, achieved a mean accuracy of 62%. We also developed a simulation framework to analyze the effect of potential confounders, such as cohort size, age, sex, or ethnic background on the distinguishability of phenotypes. We found that the true positive rate increases for all analyzed disorders for growing cohorts (n = [10...40]) while ethnicity and sex have no significant influence. The dynamics of the accuracies strongly suggest that the maximum distinguishability is a phenotype-specific value, which has not been reached yet for any of the studied disorders. This should also be a motivation to further intensify data sharing efforts, as computer-assisted syndrome classification can still be improved by enlarging the available training sets.
    Subject(s): Analysis ; Biochemistry ; Biochemistry, general ; general ; Human Genetics ; Internal Medicine ; Medical genetics ; Medical research ; Medicine ; Medicine & Public Health ; Medicine, Experimental ; Metabolic Diseases ; Metabolism, Inborn errors of ; Pediatrics ; Phenomics ; Physiological aspects
    ISSN: 0141-8955
    E-ISSN: 1573-2665
    Source: Wiley Online Library All Journals
    Source: Get It Now
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  • 2
    Language: English
    In: PloS one, 2013, Vol.8 (8), p.e70151-e70151
    Description: The identification of disease-causing mutations in next-generation sequencing (NGS) data requires efficient filtering techniques. In patients with rare recessive diseases, compound heterozygosity of pathogenic mutations is the most likely inheritance model if the parents are non-consanguineous. We developed a web-based compound heterozygous filter that is suited for data from NGS projects and that is easy to use for non-bioinformaticians. We analyzed the power of compound heterozygous mutation filtering by deriving background distributions for healthy individuals from different ethnicities and studied the effectiveness in trios as well as more complex pedigree structures. While usually more then 30 genes harbor potential compound heterozygotes in single exomes, this number can be markedly reduced with every additional member of the pedigree that is included in the analysis. In a real data set with exomes of four family members, two sisters affected by Mabry syndrome and their healthy parents, the disease-causing gene PIGO, which harbors the pathogenic compound heterozygous variants, could be readily identified. Compound heterozygous filtering is an efficient means to reduce the number of candidate mutations in studies aiming at identifying recessive disease genes in non-consanguineous families. A web-server is provided to make this filtering strategy available at www.gene-talk.de.
    Subject(s): Acids ; Analysis ; Bioinformatics ; Biology ; Computational Biology - methods ; Computer Science ; Disease ; Exome - genetics ; Families & family life ; Filtration ; Genes ; Genetic aspects ; Genetics ; Genomes ; Heredity ; Heterozygote ; Heterozygotes ; High-Throughput Nucleotide Sequencing ; Humans ; Medicine ; Mutation ; Parameter estimation ; Parents ; Parents & parenting ; Pedigree
    ISSN: 1932-6203
    E-ISSN: 1932-6203
    Source: Academic Search Ultimate
    Source: PubMed Central
    Source: DOAJ Directory of Open Access Journals - Not for CDI Discovery
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  • 3
    Language: English
    In: SIAM journal on computing, 2008-01, Vol.38 (3), p.881-898
    Description: 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.
    Subject(s): Algorithms ; Approximation ; Competition ; Computer science ; Grants ; Polygons ; Ratios ; Search strategies
    ISSN: 0097-5397
    E-ISSN: 1095-7111
    Source: Business Source Ultimate
    Source: Academic Search Ultimate
    Source: Alma/SFX Local Collection
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  • 4
    Language: English
    In: Bioinformatics (Oxford, England), 2012, Vol.28 (19), p.2515-2516
    Description: 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. GeneTalk is available at www.gene-talk.de. Users can login without registering in a demo account. peter.krawitz@gene-talk.de.
    Subject(s): Applications Notes ; Biological and medical sciences ; Computational Biology - methods ; Exome ; Fundamental and applied biological sciences. Psychology ; General aspects ; Genome, Human ; Humans ; Information Dissemination - methods ; Internet ; Knowledge Management ; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) ; Molecular Sequence Annotation ; Polymorphism, Genetic ; Precision Medicine ; Sequence Analysis, DNA - methods ; Software ; User-Computer Interface
    ISSN: 1367-4803
    E-ISSN: 1367-4811
    E-ISSN: 1460-2059
    Source: Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
    Source: Alma/SFX Local Collection
    Source: Oxford Journals 2016 Current and Archive A-Z Collection
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  • 5
    Language: English
    In: Bioinformatics (Oxford, England), 2017-01-01, Vol.33 (1), p.72-78
    Description: 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. 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. 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: heinrich@molgen.mpg.deSupplementary information: Supplementary data are available at Bioinformatics online.
    Subject(s): Algorithms ; Exome ; Female ; Genetic Linkage ; Genome, Human ; Genomics - methods ; High-Throughput Nucleotide Sequencing - methods ; Humans ; Male ; Mutation ; Original Papers ; Pedigree ; Sequence Analysis, DNA - methods ; Software
    ISSN: 1367-4803
    E-ISSN: 1367-4811
    E-ISSN: 1460-2059
    Source: Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
    Source: Alma/SFX Local Collection
    Source: Oxford Journals 2016 Current and Archive A-Z Collection
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  • 6
    Language: English
    In: Bioinformatics (Oxford, England), 2015-11-15, Vol.31 (22), p.3577-3583
    Description: 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. 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. Datasets used in our analysis are available at ftp://ftp.1000genomes.ebi.ac.uk./vol1/ftp/ : peter.krawitz@charite.de Supplementary data are available at Bioinformatics online.
    Subject(s): Case-Control Studies ; Data Accuracy ; Disease - genetics ; Ethnic Groups - genetics ; Genetic Association Studies ; Genetic Variation ; Humans ; ROC Curve ; Sequence Analysis, DNA
    ISSN: 1367-4803
    E-ISSN: 1367-4811
    E-ISSN: 1460-2059
    Source: Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
    Source: Alma/SFX Local Collection
    Source: Oxford Journals 2016 Current and Archive A-Z Collection
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  • 7
    Article
    Article
    2012
    ISSN: 0178-4617 
    Language: English
    In: Algorithmica, 2012-11-16, Vol.68 (4), p.1019-1044
    Description: We analyze the problem of packing squares in an online fashion: Given a semi-infinite strip of width 1 and an unknown sequence of squares of side length in [0,1] that arrive from above, one at a time. The objective is to pack these items as they arrive, minimizing the resulting height. Just like in the classical game of Tetris, each square must be moved along a collision-free path to its final destination. In addition, we account for gravity in both motion (squares must never move up) and position (any final destination must be supported from below). A similar problem has been considered before; the best previous result is by Azar and Epstein, who gave a 4-competitive algorithm in a setting without gravity (i.e., with the possibility of letting squares “hang in the air”) based on ideas of shelf packing: Squares are assigned to different horizontal levels, allowing an analysis that is reminiscent of some bin-packing arguments. We apply a geometric analysis to establish a competitive factor of 3.5 for the bottom-left heuristic and present a -competitive algorithm.
    Subject(s): Algorithm Analysis and Problem Complexity ; Algorithmics. Computability. Computer arithmetics ; Algorithms ; Analysis ; Applied sciences ; Article ; Computer Science ; Computer science; control theory; systems ; Computer Systems Organization and Communication Networks ; Data Structures and Information Theory ; Data Structures, Cryptology and Information Theory ; Exact sciences and technology ; Gravity ; Mathematics of Computing ; Online packing ; Squares ; Strip packing ; Tetris ; Theoretical computing ; Theory of Computation
    ISSN: 0178-4617
    E-ISSN: 1432-0541
    Source: Alma/SFX Local Collection
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  • 8
    Language: English
    In: Genome medicine, 2013, Vol.5 (7), p.69-69
    Description: With exome sequencing becoming a tool for mutation detection in routine diagnostics there is an increasing need for platform-independent methods of quality control. We present a genotype-weighted metric that allows comparison of all the variant calls of an exome to a high-quality reference dataset of an ethnically matched population. The exome-wide genotyping accuracy is estimated from the distance to this reference set, and does not require any further knowledge about data generation or the bioinformatics involved. The distances of our metric are visualized by non-metric multidimensional scaling and serve as an intuitive, standardizable score for the quality assessment of exome data.
    Subject(s): Comparative analysis ; Models ; Quality control
    ISSN: 1756-994X
    E-ISSN: 1756-994X
    Source: BioMedCentral Open Access
    Source: Academic Search Ultimate
    Source: PubMed Central
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  • 9
    Language: English
    In: NAR Genomics and Bioinformatics, 2021-06-23, Vol.3 (3), p.lqab078-lqab078
    Description: Abstract Many rare syndromes can be well described and delineated from other disorders by a combination of characteristic symptoms. These phenotypic features are best documented with terms of the Human Phenotype Ontology (HPO), which are increasingly used in electronic health records (EHRs), too. Many algorithms that perform HPO-based gene prioritization have also been developed; however, the performance of many such tools suffers from an over-representation of atypical cases in the medical literature. This is certainly the case if the algorithm cannot handle features that occur with reduced frequency in a disorder. With Cada, we built a knowledge graph based on both case annotations and disorder annotations. Using network representation learning, we achieve gene prioritization by link prediction. Our results suggest that Cada exhibits superior performance particularly for patients that present with the pathognomonic findings of a disease. Additionally, information about the frequency of occurrence of a feature can readily be incorporated, when available. Crucial in the design of our approach is the use of the growing amount of phenotype–genotype information that diagnostic labs deposit in databases such as ClinVar. By this means, Cada is an ideal reference tool for differential diagnostics in rare disorders that can also be updated regularly.
    ISSN: 2631-9268
    E-ISSN: 2631-9268
    Source: Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
    Source: PubMed Central
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  • 10
    Language: English
    In: Integrative biology (Cambridge), 2011-11, Vol.3 (11), p.1071-1086
    Description: The bioreaction database established by Ma and Zeng (Bioinformatics, 2003, 19, 270-277) for in silico reconstruction of genome-scale metabolic networks has been widely used. Based on more recent information in the reference databases KEGG LIGAND and Brenda, we upgrade the bioreaction database in this work by almost doubling the number of reactions from 3565 to 6851. Over 70% of the reactions have been manually updated/revised in terms of reversibility, reactant pairs, currency metabolites and error correction. For the first time, 41 spontaneous sugar mutarotation reactions are introduced into the biochemical database. The upgrade significantly improves the reconstruction of genome scale metabolic networks. Many gaps or missing biochemical links can be recovered, as exemplified with three model organisms Homo sapiens, Aspergillus niger, and Escherichia coli. The topological parameters of the constructed networks were also largely affected, however, the overall network structure remains scale-free. Furthermore, we consider the problem of computing biologically feasible shortest paths in reconstructed metabolic networks. We show that these paths are hard to compute and present solutions to find such paths in networks of small and medium size.
    Subject(s): Algorithms ; Aspergillus niger - metabolism ; Computational Biology - methods ; Databases, Factual ; Databases, Genetic ; Escherichia coli - metabolism ; Genome - physiology ; Glucose - metabolism ; Humans ; Metabolic Networks and Pathways - physiology ; Models, Biological ; Software
    ISSN: 1757-9708
    E-ISSN: 1757-9708
    Source: Royal Society of Chemistry:BIBSAM:Gold incl Archive:2015-2017
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