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Berlin Brandenburg

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  • 1
    In: BMC Bioinformatics, 2011, Vol.12, p.145-145
    Description: Background Flow Cytometry is a process by which cells, and other microscopic particles, can be identified, counted, and sorted mechanically through the use of hydrodynamic pressure and laser-activated fluorescence labeling. As immunostained cells pass individually through the flow chamber of the instrument, laser pulses cause fluorescence emissions that are recorded digitally for later analysis as multidimensional vectors. Current, widely adopted analysis software limits users to manual separation of events based on viewing two or three simultaneous dimensions. While this may be adequate for experiments using four or fewer colors, advances have lead to laser flow cytometers capable of recording 20 different colors simultaneously. In addition, mass-spectrometry based machines capable of recording at least 100 separate channels are being developed. Analysis of such high-dimensional data by visual exploration alone can be error-prone and susceptible to unnecessary bias. Fortunately, the field of Data Mining provides many tools for automated group classification of multi-dimensional data, and many algorithms have been adapted or created for flow cytometry. However, the majority of this research has not been made available to users through analysis software packages and, as such, are not in wide use. Results We have developed a new software application for analysis of multi-color flow cytometry data. The main goals of this effort were to provide a user-friendly tool for automated gating (classification) of multi-color data as well as a platform for development and dissemination of new analysis tools. With this software, users can easily load single or multiple data sets, perform automated event classification, and graphically compare results within and between experiments. We also make available a simple plugin system that enables researchers to implement and share their data analysis and classification/population discovery algorithms. Conclusions The FIND (Flow Investigation using N-Dimensions) platform presented here provides a powerful, user-friendly environment for analysis of Flow Cytometry data as well as providing a common platform for implementation and distribution of new automated analysis techniques to users around the world.
    Keywords: Software
    E-ISSN: 1471-2105
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  • 2
    Language: English
    In: Journal of Bacteriology, May, 2011, Vol.193(9-10), p.2527(9)
    Description: Bacterial strategies of innate immune evasion and essential metabolic functions are critical for commensalhost homeostasis. Previously, we showed that Sap translocator function is necessary for nontypeable Haemophilus influenzae (NTHI) behaviors that mediate diseases of the human airway. Antimicrobial peptide (AP) lethality is limited by binding mediated by the Sap complex. SapA shares homology with the dipeptide-binding protein (DppA) and the heme-binding lipoprotein (HbpA), both of which have previously been shown to bind the iron-containing compound heme, whose acquisition is essential for Haemophilus survival. Computational modeling revealed conserved SapA residues, similarly modeled to mediate heme binding in HbpA. Here, we directly demonstrate that SapA bound heme and was essential for heme utilization by iron-starved NTHI. Further, the Sap translocator permease mediated heme transport into the bacterial cytoplasm, thus defining a heretofore unknown mechanism of intracytoplasmic membrane heme transport in Haemophilus. Since we demonstrate multiple ligand specificity for the SapA-binding protein, we tested whether APs would compete with heme for SapA binding. We showed that human [beta]-defensins 2 and 3, human cathelicidin LL-37, human neutrophil protein 1, and melittin displaced heme bound to SapA, thus supporting a hierarchy wherein immune evasion supercedes even the needed iron acquisition functions of the Sap system. doi: 10.1128/JB.01313-10
    Keywords: Haemophilus Influenzae -- Physiological Aspects ; Haemophilus Influenzae -- Genetic Aspects ; Heme -- Physiological Aspects ; Heme -- Genetic Aspects
    ISSN: 0021-9193
    Source: Cengage Learning, Inc.
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  • 3
    Language: English
    In: Advances in experimental medicine and biology, 2011, Vol.696, pp.101-11
    Description: Microarray technology is a primary tool for elucidating the differences between similar populations of nucleic acid molecules. It is frequently used to detect mRNA population shifts that result from perturbations such as environmental changes, host-pathogen interactions, or the shift from therapeutic to toxic drug doses. Unfortunately, current microarray analysis methods provide only undirected discovery tools and cannot be directed to a particular hypothesis. To address this issue, we demonstrate that biologically relevant aspects of expression profiles may be captured by precision-reduced descriptions that are fully human readable, and that biologically relevant relationships may be captured by applying familiar pattern searching techniques to these precision-reduced descriptions. Even expression profiles that are reduced to only a single bit of precision, retain the surprising ability to reproduce the clustering results from the full 16-bit precision original data. We also illustrate that simple verbal descriptions ("my gene's expression went up briefly at timepoint 10") of expression profiles are quantitative entities fully compatible with clustering and searching within the precision reduced data.
    Keywords: Gene Expression Profiling -- Statistics & Numerical Data ; Oligonucleotide Array Sequence Analysis -- Statistics & Numerical Data
    ISBN: 9781441970459
    ISSN: 0065-2598
    Source: MEDLINE/PubMed (U.S. National Library of Medicine)
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  • 4
    Language: English
    In: The Journal of Bacteriology, 2011, Vol. 193(10), p.2527
    Description: Bacterial strategies of innate immune evasion and essential metabolic functions are critical for commensal-host homeostasis. Previously, we showed that Sap translocator function is necessary for nontypeable Haemophilus influenzae (NTHI) behaviors that mediate diseases of the human airway. Antimicrobial peptide (AP) lethality is limited by binding mediated by the Sap complex. SapA shares homology with the dipeptide-binding protein (DppA) and the heme-binding lipoprotein (HbpA), both of which have previously been shown to bind the iron-containing compound heme, whose acquisition is essential for Haemophilus survival. Computational modeling revealed conserved SapA residues, similarly modeled to mediate heme binding in HbpA. Here, we directly demonstrate that SapA bound heme and was essential for heme utilization by iron-starved NTHI. Further, the Sap translocator permease mediated heme transport into the bacterial cytoplasm, thus defining a heretofore unknown mechanism of intracytoplasmic membrane heme transport in Haemophilus. Since we demonstrate multiple ligand specificity for the SapA-binding protein, we tested whether APs would compete with heme for SapA binding. We showed that human β-defensins 2 and 3, human cathelicidin LL-37, human neutrophil protein 1, and melittin displaced heme bound to SapA, thus supporting a hierarchy wherein immune evasion supercedes even the needed iron acquisition functions of the Sap system.
    Keywords: Bacterial Proteins -- Metabolism ; Haemophilus Influenzae -- Metabolism ; Heme -- Metabolism ; Membrane Transport Proteins -- Metabolism ; Virulence Factors -- Metabolism;
    ISSN: 0021-9193
    ISSN: 00219193
    E-ISSN: 10985530
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  • 5
    Language: English
    In: 2012, Vol.7(10), p.e48349
    Description: Uropathogenic Escherichia coli (UPEC) utilizes a complex community-based developmental pathway for growth within superficial epithelial cells of the bladder during cystitis. Extracellular DNA (eDNA) is a common matrix component of organized bacterial communities. Integration host factor (IHF) is a heterodimeric protein that binds to double-stranded DNA and produces a hairpin bend. IHF-dependent DNA architectural changes act both intrabacterially and extrabacterially to regulate gene expression and community stability, respectively. We demonstrate that both IHF subunits are required for efficient colonization of the bladder, but are dispensable for early colonization of the kidney. The community architecture of the intracellular bacterial communities (IBCs) is quantitatively different in the absence of either IhfA or IhfB in the murine model for human urinary tract infection (UTI). Restoration of Type 1 pili by ectopic production does not restore colonization in the absence of IhfA, but partially compensates in the absence of IhfB. Furthermore, we describe a binding site for IHF that is upstream of the operon that encodes for the P-pilus. Taken together, these data suggest that both IHF and its constituent subunits (independent of the heterodimer), are able to participate in multiple aspects of the UPEC pathogenic lifestyle, and may have utility as a target for treatment of bacterial cystitis.
    Keywords: Research Article ; Biology ; Medicine ; Infectious Diseases ; Microbiology ; Biochemistry
    E-ISSN: 1932-6203
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  • 6
    Language: English
    In: Nucleic acids research, 01 July 2005, Vol.33(Web Server issue), pp.W315-9
    Description: A fundamental problem with applying Consensus, Weight-Matrix or hidden Markov models as search tools for biosequences is that there is no way to know, from the model, if the modeled sequences display any dependencies between positional identities. In some instances, these dependencies are crucial in correctly accepting or rejecting other sequences as members of the family. MAVL (multiple alignment variation linker) and StickWRLD provide a web-based method to visually survey the model-training sequences to discover and characterize possible dependencies. Initially introduced for nucleic acid sequences, with MAVL/StickWRLD, it is easy to distinguish typical DNA or RNA structural dependencies in input families, identify mixed populations of distinct subfamilies, or discover novel dependencies that result from binding interactions or other selective pressures [W. Ray (2004) Nucleic Acids Res., 32, W59-W63]. Since the announcement of MAVL/StickWRLD for nucleic acids, one of the most requested new features has been the extension of this visualization method to support protein alignments. We are pleased to report that this extension has been successful, that the basic visualization has been augmented in several ways to enhance protein viewing, and that the results with protein alignments are even more dramatic than with NA alignments. MAVL/StickWRLD can be accessed at http://www.microbial-pathogenesis.org/stickwrld/.
    Keywords: Computer Graphics ; Models, Molecular ; Protein Conformation ; Software ; Sequence Alignment -- Methods ; Sequence Analysis, Protein -- Methods
    ISSN: 03051048
    E-ISSN: 1362-4962
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  • 7
    Language: English
    In: Journal of visualized experiments : JoVE, 14 July 2015(101), pp.e52878
    Description: Protein alignments are commonly used to evaluate the similarity of protein residues, and the derived consensus sequence used for identifying functional units (e.g., domains). Traditional consensus-building models fail to account for interpositional dependencies - functionally required covariation of residues that tend to appear simultaneously throughout evolution and across the phylogentic tree. These relationships can reveal important clues about the processes of protein folding, thermostability, and the formation of functional sites, which in turn can be used to inform the engineering of synthetic proteins. Unfortunately, these relationships essentially form sub-motifs which cannot be predicted by simple "majority rule" or even HMM-based consensus models, and the result can be a biologically invalid "consensus" which is not only never seen in nature but is less viable than any extant protein. We have developed a visual analytics tool, StickWRLD, which creates an interactive 3D representation of a protein alignment and clearly displays covarying residues. The user has the ability to pan and zoom, as well as dynamically change the statistical threshold underlying the identification of covariants. StickWRLD has previously been successfully used to identify functionally-required covarying residues in proteins such as Adenylate Kinase and in DNA sequences such as endonuclease target sites.
    Keywords: Proteins -- Chemistry ; Sequence Alignment -- Methods
    E-ISSN: 1940-087X
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  • 8
    Language: English
    In: Cancer Informatics, January 2014, Vol.13s3
    Description: As datasets increase in complexity, the time required for analysis (both computational and human domain-expert) increases. One of the significant impediments introduced by such burgeoning data is the difficulty in knowing what features to include or exclude from statistical models. Simple tables of summary statistics rarely provide an adequate picture of the patterns and details of the dataset to enable researchers to make well-informed decisions about the adequacy of the models they are constructing. We have developed a tool, StickWRLD, which allows the user to visually browse through their data, displaying all possible correlations. By allowing the user to dynamically modify the retention parameters (both P and the residual, r), StickWRLD allows the user to identify significant correlations and disregard potential correlations that do not meet those same criteria – effectively filtering through all possible correlations quickly and identifying possible relationships of interest for further analysis. In this study, we applied StickWRLD to a semi-synthetic dataset constructed from two published human datasets. In addition to detecting high-probability correlations in this dataset, we were able to quickly identify gene–SNP correlations that would have gone undetected using more traditional approaches due to issues of low penetrance.
    Keywords: Visual Analytics ; Eqtl ; Gene–Snp Correlation ; Medicine
    E-ISSN: 1176-9351
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  • 9
    Language: English
    In: Advances in experimental medicine and biology, 2011, Vol.696, pp.425-32
    Keywords: Algorithms ; Image Interpretation, Computer-Assisted -- Methods
    ISBN: 9781441970459
    ISSN: 0065-2598
    Source: MEDLINE/PubMed (U.S. National Library of Medicine)
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  • 10
    Language: English
    In: BMC Bioinformatics, May 10, 2011, Vol.12, p.145
    Description: Background Flow Cytometry is a process by which cells, and other microscopic particles, can be identified, counted, and sorted mechanically through the use of hydrodynamic pressure and laser-activated fluorescence labeling. As immunostained cells pass individually through the flow chamber of the instrument, laser pulses cause fluorescence emissions that are recorded digitally for later analysis as multidimensional vectors. Current, widely adopted analysis software limits users to manual separation of events based on viewing two or three simultaneous dimensions. While this may be adequate for experiments using four or fewer colors, advances have lead to laser flow cytometers capable of recording 20 different colors simultaneously. In addition, mass-spectrometry based machines capable of recording at least 100 separate channels are being developed. Analysis of such high-dimensional data by visual exploration alone can be error-prone and susceptible to unnecessary bias. Fortunately, the field of Data Mining provides many tools for automated group classification of multi-dimensional data, and many algorithms have been adapted or created for flow cytometry. However, the majority of this research has not been made available to users through analysis software packages and, as such, are not in wide use. Results We have developed a new software application for analysis of multi-color flow cytometry data. The main goals of this effort were to provide a user-friendly tool for automated gating (classification) of multi-color data as well as a platform for development and dissemination of new analysis tools. With this software, users can easily load single or multiple data sets, perform automated event classification, and graphically compare results within and between experiments. We also make available a simple plugin system that enables researchers to implement and share their data analysis and classification/population discovery algorithms. Conclusions The FIND (Flow Investigation using N-Dimensions) platform presented here provides a powerful, user-friendly environment for analysis of Flow Cytometry data as well as providing a common platform for implementation and distribution of new automated analysis techniques to users around the world.
    Keywords: Flow Cytometry -- Usage ; Algorithms -- Analysis ; Data Mining -- Methods
    ISSN: 1471-2105
    Source: Cengage Learning, Inc.
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