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  • Eils, Roland  (17)
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  • 1
    Language: English
    In: BMC Bioinformatics, Dec 20, 2011, Vol.12, p.485
    Description: Background High-content, high-throughput RNA interference (RNAi) offers unprecedented possibilities to elucidate gene function and involvement in biological processes. Microscopy based screening allows phenotypic observations at the level of individual cells. It was recently shown that a cell's population context significantly influences results. However, standard analysis methods for cellular screens do not currently take individual cell data into account unless this is important for the phenotype of interest, i.e. when studying cell morphology. Results We present a method that normalizes and statistically scores microscopy based RNAi screens, exploiting individual cell information of hundreds of cells per knockdown. Each cell's individual population context is employed in normalization. We present results on two infection screens for hepatitis C and dengue virus, both showing considerable effects on observed phenotypes due to population context. In addition, we show on a non-virus screen that these effects can be found also in RNAi data in the absence of any virus. Using our approach to normalize against these effects we achieve improved performance in comparison to an analysis without this normalization and hit scoring strategy. Furthermore, our approach results in the identification of considerably more significantly enriched pathways in hepatitis C virus replication than using a standard analysis approach. Conclusions Using a cell-based analysis and normalization for population context, we achieve improved sensitivity and specificity not only on a individual protein level, but especially also on a pathway level. This leads to the identification of new host dependency factors of the hepatitis C and dengue viruses and higher reproducibility of results.
    Keywords: Genes -- Identification And Classification ; Genetic Testing -- Methods ; Genetic Testing -- Research ; Rna Interference -- Physiological Aspects ; Rna Interference -- Usage
    ISSN: 1471-2105
    Source: Cengage Learning, Inc.
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  • 2
    Language: English
    In: PLoS ONE, 01 January 2012, Vol.7(12), p.e52555
    Description: miRNA cluster miR-17-92 is known as oncomir-1 due to its potent oncogenic function. miR-17-92 is a polycistronic cluster that encodes 6 miRNAs, and can both facilitate and inhibit cell proliferation. Known targets of miRNAs encoded by this cluster are largely regulators of cell cycle progression and apoptosis. Here, we show that miRNAs encoded by this cluster and sharing the seed sequence of miR-17 exert their influence on one of the most essential cellular processes - endocytic trafficking. By mRNA expression analysis we identified that regulation of endocytic trafficking by miR-17 can potentially be achieved by targeting of a number of trafficking regulators. We have thoroughly validated TBC1D2/Armus, a GAP of Rab7 GTPase, as a novel target of miR-17. Our study reveals regulation of endocytic trafficking as a novel function of miR-17, which might act cooperatively with other functions of miR-17 and related miRNAs in health and disease.
    Keywords: Sciences (General)
    E-ISSN: 1932-6203
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  • 3
    Language: English
    In: BMC Bioinformatics, Dec 20, 2011, Vol.12, p.485
    Description: Background High-content, high-throughput RNA interference (RNAi) offers unprecedented possibilities to elucidate gene function and involvement in biological processes. Microscopy based screening allows phenotypic observations at the level of individual cells. It was recently shown that a cell's population context significantly influences results. However, standard analysis methods for cellular screens do not currently take individual cell data into account unless this is important for the phenotype of interest, i.e. when studying cell morphology. Results We present a method that normalizes and statistically scores microscopy based RNAi screens, exploiting individual cell information of hundreds of cells per knockdown. Each cell's individual population context is employed in normalization. We present results on two infection screens for hepatitis C and dengue virus, both showing considerable effects on observed phenotypes due to population context. In addition, we show on a non-virus screen that these effects can be found also in RNAi data in the absence of any virus. Using our approach to normalize against these effects we achieve improved performance in comparison to an analysis without this normalization and hit scoring strategy. Furthermore, our approach results in the identification of considerably more significantly enriched pathways in hepatitis C virus replication than using a standard analysis approach. Conclusions Using a cell-based analysis and normalization for population context, we achieve improved sensitivity and specificity not only on a individual protein level, but especially also on a pathway level. This leads to the identification of new host dependency factors of the hepatitis C and dengue viruses and higher reproducibility of results.
    Keywords: Genes -- Identification And Classification ; Genetic Testing -- Methods ; Genetic Testing -- Research ; Rna Interference -- Physiological Aspects ; Rna Interference -- Usage
    ISSN: 1471-2105
    Source: Cengage Learning, Inc.
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  • 4
    Language: English
    In: BMC Bioinformatics, 01 December 2011, Vol.12(1), p.485
    Description: Abstract Background High-content, high-throughput RNA interference (RNAi) offers unprecedented possibilities to elucidate gene function and involvement in biological processes. Microscopy based screening allows phenotypic observations at the level of individual cells. It was recently shown that a cell's population context significantly influences results. However, standard analysis methods for cellular screens do not currently take individual cell data into account unless this is important for the phenotype of interest, i.e. when studying cell morphology. Results We present a method that normalizes and statistically scores microscopy based RNAi screens, exploiting individual cell information of hundreds of cells per knockdown. Each cell's individual population context is employed in normalization. We present results on two infection screens for hepatitis C and dengue virus, both showing considerable effects on observed phenotypes due to population context. In addition, we show on a non-virus screen that these effects can be found also in RNAi data in the absence of any virus. Using our approach to normalize against these effects we achieve improved performance in comparison to an analysis without this normalization and hit scoring strategy. Furthermore, our approach results in the identification of considerably more significantly enriched pathways in hepatitis C virus replication than using a standard analysis approach. Conclusions Using a cell-based analysis and normalization for population context, we achieve improved sensitivity and specificity not only on a individual protein level, but especially also on a pathway level. This leads to the identification of new host dependency factors of the hepatitis C and dengue viruses and higher reproducibility of results.
    Keywords: Biology
    ISSN: 1471-2105
    E-ISSN: 1471-2105
    Source: Directory of Open Access Journals (DOAJ)
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  • 5
  • 6
    Language: English
    In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, March 2013, Vol.10(2), pp.423-435
    Description: In this work, we model the problem of mining quasi-bicliques from weighted viral-host protein-protein interaction network as a biclustering problem for identifying strong interaction modules. In this regard, a multiobjective genetic algorithm-based biclustering technique is proposed that simultaneously optimizes three objective functions to obtain dense biclusters having high mean interaction strengths. The performance of the proposed technique has been compared with that of other existing biclustering methods on an artificial data. Subsequently, the proposed biclustering method is applied on the records of biologically validated and predicted interactions between a set of HIV-1 proteins and a set of human proteins to identify strong interaction modules. For this, the entire interaction information is realized as a bipartite graph. We have further investigated the biological significance of the obtained biclusters. The human proteins involved in the strong interaction module have been found to share common biological properties and they are identified as the gateways of viral infection leading to various diseases. These human proteins can be potential drug targets for developing anti-HIV drugs.
    Keywords: Proteins ; Humans ; Bipartite Graph ; Biological Cells ; Optimization ; Linear Programming ; Bioinformatics ; Proteins ; Humans ; Bipartite Graph ; Biological Cells ; Optimization ; Linear Programming ; Bioinformatics ; Multiobjective Optimization ; Protein-Protein Interaction ; HIV-1 ; Biclustering ; Quasi-Biclique ; Biology
    ISSN: 1545-5963
    E-ISSN: 1557-9964
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  • 7
  • 8
    Language: English
    In: BMC bioinformatics, 20 December 2011, Vol.12, pp.485
    Description: High-content, high-throughput RNA interference (RNAi) offers unprecedented possibilities to elucidate gene function and involvement in biological processes. Microscopy based screening allows phenotypic observations at the level of individual cells. It was recently shown that a cell's population context significantly influences results. However, standard analysis methods for cellular screens do not currently take individual cell data into account unless this is important for the phenotype of interest, i.e. when studying cell morphology. We present a method that normalizes and statistically scores microscopy based RNAi screens, exploiting individual cell information of hundreds of cells per knockdown. Each cell's individual population context is employed in normalization. We present results on two infection screens for hepatitis C and dengue virus, both showing considerable effects on observed phenotypes due to population context. In addition, we show on a non-virus screen that these effects can be found also in RNAi data in the absence of any virus. Using our approach to normalize against these effects we achieve improved performance in comparison to an analysis without this normalization and hit scoring strategy. Furthermore, our approach results in the identification of considerably more significantly enriched pathways in hepatitis C virus replication than using a standard analysis approach. Using a cell-based analysis and normalization for population context, we achieve improved sensitivity and specificity not only on a individual protein level, but especially also on a pathway level. This leads to the identification of new host dependency factors of the hepatitis C and dengue viruses and higher reproducibility of results.
    Keywords: RNA Interference ; Dengue -- Genetics ; Hepatitis C -- Genetics ; Phosphotransferases -- Genetics ; Single-Cell Analysis -- Methods
    E-ISSN: 1471-2105
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  • 9
    In: American Journal of Gastroenterology, 2010, Vol.105(9), pp.2060-2071
    Description: OBJECTIVES:: METHODS:: RESULTS:: Expression profiling showed 272 upregulated genes, including those encoding for immunoglobulins, chemokines and their receptors, and 86 downregulated genes, including those for pancreatic proteases such as three trypsinogen isoforms. Protein profiling showed that the expression of trypsinogens and other pancreatic enzymes was greatly reduced. Immunohistochemistry showed a near-loss of trypsin-positive acinar cells, which was also confirmed by western blotting. The serum of AIP patients contained high titers of autoantibodies against the trypsinogens PRSS1 and PRSS2 but not against PRSS3. In addition, there were autoantibodies against the trypsin inhibitor PSTI (the product of the SPINK1 gene). In the pancreas of AIP animals, we found similar protein patterns and a reduction in trypsinogen. CONCLUSIONS::
    Keywords: Medicine;
    ISSN: 0002-9270
    E-ISSN: 15720241
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  • 10
    Language: English
    In: Bioinformatics (Oxford, England), 15 September 2010, Vol.26(18), pp.i653-8
    Description: Detecting human proteins that are involved in virus entry and replication is facilitated by modern high-throughput RNAi screening technology. However, hit lists from different laboratories have shown only little consistency. This may be caused by not only experimental discrepancies, but also not fully explored possibilities of the data analysis. We wanted to improve reliability of such screens by combining a population analysis of infected cells with an established dye intensity readout. Viral infection is mainly spread by cell-cell contacts and clustering of infected cells can be observed during spreading of the infection in situ and in vivo. We employed this clustering feature to define knockdowns which harm viral infection efficiency of human Hepatitis C Virus. Images of knocked down cells for 719 human kinase genes were analyzed with an established point pattern analysis method (Ripley's K-function) to detect knockdowns in which virally infected cells did not show any clustering and therefore were hindered to spread their infection to their neighboring cells. The results were compared with a statistical analysis using a common intensity readout of the GFP-expressing viruses and a luciferase-based secondary screen yielding five promising host factors which may suit as potential targets for drug therapy. We report of an alternative method for high-throughput imaging methods to detect host factors being relevant for the infection efficiency of viruses. The method is generic and has the potential to be used for a large variety of different viruses and treatments being screened by imaging techniques.
    Keywords: Image Processing, Computer-Assisted ; RNA Interference ; RNA, Small Interfering ; Virus Replication ; Biological Factors -- Analysis ; Hepacivirus -- Physiology
    ISSN: 13674803
    E-ISSN: 1367-4811
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