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  • 1.
    Fredriksson, Nils Johan
    et al.
    Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Hermansson, Malte
    Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden.
    Wilén, Britt-Marie
    Department of Civil and Environmental Engineering, Water Environment Technology, Chalmers University of Technology, Gothenburg, Sweden.
    Impact of T-RFLP data analysis choices on assessments of microbial community structure and dynamics2014In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 15, article id 360Article in journal (Refereed)
    Abstract [en]

    Background: Terminal restriction fragment length polymorphism (T-RFLP) analysis is a common DNA-fingerprinting technique used for comparisons of complex microbial communities. Although the technique is well established there is no consensus on how to treat T-RFLP data to achieve the highest possible accuracy and reproducibility. This study focused on two critical steps in the T-RFLP data treatment: the alignment of the terminal restriction fragments (T-RFs), which enables comparisons of samples, and the normalization of T-RF profiles, which adjusts for differences in signal strength, total fluorescence, between samples.

    Results: Variations in the estimation of T-RF sizes were observed and these variations were found to affect the alignment of the T-RFs. A novel method was developed which improved the alignment by adjusting for systematic shifts in the T-RF size estimations between the T-RF profiles. Differences in total fluorescence were shown to be caused by differences in sample concentration and by the gel loading. Five normalization methods were evaluated and the total fluorescence normalization procedure based on peak height data was found to increase the similarity between replicate profiles the most. A high peak detection threshold, alignment correction, normalization and the use of consensus profiles instead of single profiles increased the similarity of replicate T-RF profiles, i.e. lead to an increased reproducibility. The impact of different treatment methods on the outcome of subsequent analyses of T-RFLP data was evaluated using a dataset from a longitudinal study of the bacterial community in an activated sludge wastewater treatment plant. Whether the alignment was corrected or not and if and how the T-RF profiles were normalized had a substantial impact on ordination analyses, assessments of bacterial dynamics and analyses of correlations with environmental parameters.

    Conclusions: A novel method for the evaluation and correction of the alignment of T-RF profiles was shown to reduce the uncertainty and ambiguity in alignments of T-RF profiles. Large differences in the outcome of assessments of bacterial community structure and dynamics were observed between different alignment and normalization methods. The results of this study can therefore be of value when considering what methods to use in the analysis of T-RFLP data.

  • 2.
    Fredriksson, Nils Johan
    et al.
    Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Hermansson, Malte
    Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden.
    Wilén, Britt-Marie
    Department of Civil and Environmental Engineering, Water Environment Technology, Chalmers University of Technology, Gothenburg, Sweden.
    Tools for T-RFLP data analysis using Excel2014In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 15, article id 361Article in journal (Refereed)
    Abstract [en]

    Background: Terminal restriction fragment length polymorphism (T-RFLP) analysis is a DNA-fingerprinting method that can be used for comparisons of the microbial community composition in a large number of samples. There is no consensus on how T-RFLP data should be treated and analyzed before comparisons between samples are made, and several different approaches have been proposed in the literature. The analysis of T-RFLP data can be cumbersome and time-consuming, and for large datasets manual data analysis is not feasible. The currently available tools for automated T-RFLP analysis, although valuable, offer little flexibility, and few, if any, options regarding what methods to use. To enable comparisons and combinations of different data treatment methods an analysis template and an extensive collection of macros for T-RFLP data analysis using Microsoft Excel were developed.

    Results: The Tools for T-RFLP data analysis template provides procedures for the analysis of large T-RFLP datasets including application of a noise baseline threshold and setting of the analysis range, normalization and alignment of replicate profiles, generation of consensus profiles, normalization and alignment of consensus profiles and final analysis of the samples including calculation of association coefficients and diversity index. The procedures are designed so that in all analysis steps, from the initial preparation of the data to the final comparison of the samples, there are various different options available. The parameters regarding analysis range, noise baseline, T-RF alignment and generation of consensus profiles are all given by the user and several different methods are available for normalization of the T-RF profiles. In each step, the user can also choose to base the calculations on either peak height data or peak area data.

    Conclusions: The Tools for T-RFLP data analysis template enables an objective and flexible analysis of large T-RFLP datasets in a widely used spreadsheet application.

  • 3.
    Kashani, Zahra RM
    et al.
    Institute of Biochemistry and Biophysics, University of Tehran.
    Ahrabian, Hayedeh
    School of Mathematics and Computer Science, University of Tehran.
    Elahi, Elahe
    School of Biology, University of Tehran.
    Nowzari-Dalini, Abbas
    School of Mathematics and Computer Science, University of Tehran.
    Ansari, Elnaz S
    School of Mathematics and Computer Science, University of Tehran.
    Asadi, Sahar
    Örebro University, School of Science and Technology.
    Mohammadi, Shahin
    School of Mathematics and Computer Science, University of Tehran.
    Schreiber, Falk
    Institute for Computer Science, Martin-Luther-University Halle-Wittenberg.
    Masoudi-Nejad, Ali
    Institute of Biochemistry and Biophysics, University of Tehran.
    Kavosh: a new algorithm for finding network motifs2009In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 10, no 318Article in journal (Refereed)
    Abstract [en]

    Background

    Complex networks are studied across many fields of science and are particularly important to understand biological processes. Motifs in networks are small connected sub-graphs that occur significantly in higher frequencies than in random networks. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Existing algorithms for finding network motifs are extremely costly in CPU time and memory consumption and have practically restrictions on the size of motifs.

    Results

    We present a new algorithm (Kavosh), for finding k-size network motifs with less memory and CPU time in comparison to other existing algorithms. Our algorithm is based on counting all k-size sub-graphs of a given graph (directed or undirected). We evaluated our algorithm on biological networks of E. coli and S. cereviciae, and also on non-biological networks: a social and an electronic network.

    Conclusion

    The efficiency of our algorithm is demonstrated by comparing the obtained results with three well-known motif finding tools. For comparison, the CPU time, memory usage and the similarities of obtained motifs are considered. Besides, Kavosh can be employed for finding motifs of size greater than eight, while most of the other algorithms have restriction on motifs with size greater than eight. The Kavosh source code and help files are freely available at: http://Lbb.ut.ac.ir/Download/LBBsoft/Kavosh/.

  • 4.
    Katajamaa, Mikko
    et al.
    1Turku Centre for Biotechnology, Turku, Finland.
    Oresic, Matej
    2VTT Biotechnology, Espoo, Finland.
    Processing methods for differential analysis of LC/MS profile data2005In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 6, article id 179Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Liquid chromatography coupled to mass spectrometry (LC/MS) has been widely used in proteomics and metabolomics research. In this context, the technology has been increasingly used for differential profiling, i.e. broad screening of biomolecular components across multiple samples in order to elucidate the observed phenotypes and discover biomarkers. One of the major challenges in this domain remains development of better solutions for processing of LC/MS data.

    RESULTS: We present a software package MZmine that enables differential LC/MS analysis of metabolomics data. This software is a toolbox containing methods for all data processing stages preceding differential analysis: spectral filtering, peak detection, alignment and normalization. Specifically, we developed and implemented a new recursive peak search algorithm and a secondary peak picking method for improving already aligned results, as well as a normalization tool that uses multiple internal standards. Visualization tools enable comparative viewing of data across multiple samples. Peak lists can be exported into other data analysis programs. The toolbox has already been utilized in a wide range of applications. We demonstrate its utility on an example of metabolic profiling of Catharanthus roseus cell cultures.

    CONCLUSION: The software is freely available under the GNU General Public License and it can be obtained from the project web page at: http://mzmine.sourceforge.net/.

  • 5.
    Pluskal, Tomás
    et al.
    G0 Cell Unit, Okinawa Institute of Science and Technology (OIST), Onna Okinawa, Japan.
    Castillo, Sandra
    Quantitative Biology and Bioinformatics, VTT Technical Research Centre of Finland, Espoo, Finland.
    Villar-Briones, Alejandro
    G0 Cell Unit, Okinawa Institute of Science and Technology (OIST), Onna Okinawa, Japan.
    Oresic, Matej
    Quantitative Biology and Bioinformatics, VTT Technical Research Centre of Finland, Espoo, Finland.
    MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data2010In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 11, article id 395Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Mass spectrometry (MS) coupled with online separation methods is commonly applied for differential and quantitative profiling of biological samples in metabolomic as well as proteomic research. Such approaches are used for systems biology, functional genomics, and biomarker discovery, among others. An ongoing challenge of these molecular profiling approaches, however, is the development of better data processing methods. Here we introduce a new generation of a popular open-source data processing toolbox, MZmine 2.

    RESULTS: A key concept of the MZmine 2 software design is the strict separation of core functionality and data processing modules, with emphasis on easy usability and support for high-resolution spectra processing. Data processing modules take advantage of embedded visualization tools, allowing for immediate previews of parameter settings. Newly introduced functionality includes the identification of peaks using online databases, MSn data support, improved isotope pattern support, scatter plot visualization, and a new method for peak list alignment based on the random sample consensus (RANSAC) algorithm. The performance of the RANSAC alignment was evaluated using synthetic datasets as well as actual experimental data, and the results were compared to those obtained using other alignment algorithms.

    CONCLUSIONS: MZmine 2 is freely available under a GNU GPL license and can be obtained from the project website at: http://mzmine.sourceforge.net/. The current version of MZmine 2 is suitable for processing large batches of data and has been applied to both targeted and non-targeted metabolomic analyses.

  • 6.
    Repsilber, Dirk
    et al.
    Department of Genetics and Biometry, Research Institute for the Biology of Farm Animals, Dummerstorf, Germany.
    Kern, Sabine
    Bioinformatics Chair, Institute for Biochemistry and Biology at the University of Potsdam, Potsdam-Golm, Germany.
    Telaar, Anna
    Department of Genetics and Biometry, Research Institute for the Biology of Farm Animals, Dummerstorf, Germany .
    Walzl, Gerhard
    Molecular Biology and Human Genetics, University of Stellenbosch, Tygerberg, Cape Town, South Africa .
    Black, Gillian F
    Molecular Biology and Human Genetics, University of Stellenbosch, Tygerberg, Cape Town, South Africa .
    Selbig, Joachim
    Bioinformatics Chair, Institute for Biochemistry and Biology at the University of Potsdam, Potsdam-Golm, Germany .
    Parida, Shreemanta K
    Department of Immunology, Max-Planck-Institute for Infection Biology, Berlin, Germany.
    Kaufmann, Stefan H E
    Department of Immunology, Max-Planck-Institute for Infection Biology, Berlin, Germany.
    Jacobsen, Marc
    Department of Immunology, Max-Planck-Institute for Infection Biology, Berlin, Germany; Department of Immunology, Bernhard-Nocht-Institute for Tropical Medicine, Hamburg, Germany.
    Biomarker discovery in heterogeneous tissue samples: taking the in-silico deconfounding approach2010In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 11, article id 27Article in journal (Refereed)
    Abstract [en]

    Background: For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or in-silico deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues.

    Results: Experimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data are optimal. We demonstrate the feasibility of predicting proportions of constituting cell types from gene expression data of single samples, as a prerequisite for a deconfounding-based classification approach.Classification cross-validation errors with and without using deconfounding results are reported as well as sample-size dependencies. Implementation of the algorithm, simulation and analysis scripts are available.

    Conclusions: The deconfounding algorithm without decorrelation using quantile normalization on non-log data is proposed for biomarkers that are difficult to detect, and for cases where confounding by varying proportions of cell types is the suspected reason. In this case, a deconfounding ranking approach can be used as a powerful alternative to, or complement of, other statistical learning approaches to define candidate biomarkers for molecular diagnosis and prediction in biomedicine, in realistically noisy conditions and with moderate sample sizes.

  • 7.
    Rush, Stephen
    et al.
    Örebro University, School of Medical Sciences.
    Repsilber, Dirk
    Örebro University, School of Medical Sciences.
    Capturing context-specific regulation in molecular interaction networks2018In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 19, no 1, article id 539Article in journal (Refereed)
    Abstract [en]

    Background: Molecular profiles change in response to perturbations. These changes are coordinated into functional modules via regulatory interactions. The genes and their products within a functional module are expected to be differentially expressed in a manner coherent with their regulatory network. This perspective presents a promising approach to increase precision in detecting differential signals as well as for describing differential regulatory signals within the framework of a priori knowledge about the underlying network, and so from a mechanistic point of view.

    Results: We present Coherent Network Expression (CoNE), an effective procedure for identifying differentially activated functional modules in molecular interaction networks. Differential gene expression is chosen as example, and differential signals coherent with the regulatory nature of the network are identified. We apply our procedure to systematically simulated data, comparing its performance to alternative methods. We then take the example case of a transcription regulatory network in the context of particle-induced pulmonary inflammation, recapitulating and proposing additional candidates to previously obtained results. CoNE is conveniently implemented in an R-package along with simulation utilities.

    Conclusion: Combining coherent interactions with error control on differential gene expression results in uniformly greater specificity in inference than error control alone, ensuring that captured functional modules constitute real findings.

  • 8.
    Rögnvaldsson, Thorsteinn
    et al.
    Örebro University, School of Science and Technology.
    Etchells, Terence A.
    You, Liwen
    Garwicz, Daniel
    Jarman, Ian
    Lisboa, Paulo J. G.
    How to find simple and accurate rules for viral protease cleavage specificities2009In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 10, p. 149-Article in journal (Refereed)
    Abstract [en]

    Background: Proteases of human pathogens are becoming increasingly important drug targets, hence it is necessary to understand their substrate specificity and to interpret this knowledge in practically useful ways. New methods are being developed that produce large amounts of cleavage information for individual proteases and some have been applied to extract cleavage rules from data. However, the hitherto proposed methods for extracting rules have been neither easy to understand nor very accurate. To be practically useful, cleavage rules should be accurate, compact, and expressed in an easily understandable way. Results: A new method is presented for producing cleavage rules for viral proteases with seemingly complex cleavage profiles. The method is based on orthogonal search-based rule extraction (OSRE) combined with spectral clustering. It is demonstrated on substrate data sets for human immunodeficiency virus type 1 (HIV-1) protease and hepatitis C (HCV) NS3/4A protease, showing excellent prediction performance for both HIV-1 cleavage and HCV NS3/4A cleavage, agreeing with observed HCV genotype differences. New cleavage rules (consensus sequences) are suggested for HIV-1 and HCV NS3/4A cleavages. The practical usability of the method is also demonstrated by using it to predict the location of an internal cleavage site in the HCV NS3 protease and to correct the location of a previously reported internal cleavage site in the HCV NS3 protease. The method is fast to converge and yields accurate rules, on par with previous results for HIV-1 protease and better than previous state-of-the-art for HCV NS3/4A protease. Moreover, the rules are fewer and simpler than previously obtained with rule extraction methods. Conclusion: A rule extraction methodology by searching for multivariate low-order predicates yields results that significantly outperform existing rule bases on out-of-sample data, but are more transparent to expert users. The approach yields rules that are easy to use and useful for interpreting experimental data.

  • 9.
    Scheubert, Lena
    et al.
    Institute of Computer Science, University of Osnabrück, Osnabrück, Germany; Institute for Biostatistics and Informatics in Medicine and Ageing Research, University of Rostock, Rostock, Germany.
    Luštrek, Mitja
    Department of Intelligent Systems, Jozef Stefan Institute, Ljubljana, Slovenia.
    Schmidt, Rainer
    Institute for Biostatistics and Informatics in Medicine and Ageing Research, University of Rostock, Rostock, Germany.
    Repsilber, Dirk
    Leibniz Institute for Farm Animal Biology (FBN Dummersdorf), Dummerstorf, Germany.
    Fuellen, Georg
    Institute for Biostatistics and Informatics in Medicine and Ageing Research, University of Rostock, Rostock, Germany; German Center for Neroudegenerative Disorders (DZNE), Rostock, Germany.
    Tissue-based Alzheimer gene expression markers-comparison of multiple machine learning approaches and investigation of redundancy in small biomarker sets2012In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 13, no 1, article id 266Article in journal (Refereed)
    Abstract [en]

    Background: Alzheimer's disease has been known for more than 100 years and the underlying molecular mechanisms are not yet completely understood. The identification of genes involved in the processes in Alzheimer affected brain is an important step towards such an understanding. Genes differentially expressed in diseased and healthy brains are promising candidates.

    Results: Based on microarray data we identify potential biomarkers as well as biomarker combinations using three feature selection methods: information gain, mean decrease accuracy of random forest and a wrapper of genetic algorithm and support vector machine (GA/SVM). Information gain and random forest are two commonly used methods. We compare their output to the results obtained from GA/SVM. GA/SVM is rarely used for the analysis of microarray data, but it is able to identify genes capable of classifying tissues into different classes at least as well as the two reference methods.

    Conclusion: Compared to the other methods, GA/SVM has the advantage of finding small, less redundant sets of genes that, in combination, show superior classification characteristics. The biological significance of the genes and gene pairs is discussed.

  • 10.
    Snipen, Lars
    et al.
    Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, Norway.
    Repsilber, Dirk
    Department of Biology and Biochemistry/Bioinformatics, University of Potsdam, Germany.
    Nyquist, Ludvig
    Microbial Gene Technology, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, Norway.
    Ziegler, Andreas
    Institute of Medical Biometry and Statistics, University at Lübeck, Germany.
    Aakra, Agot
    Microbial Gene Technology, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, Norway.
    Aastveit, Are
    Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, Norway.
    Detection of divergent genes in microbial aCGH experiments2006In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 7, article id 181Article in journal (Refereed)
    Abstract [en]

    Background: Array-based comparative genome hybridization (aCGH) is a tool for rapid comparison of genomes from different bacterial strains. The purpose of such analysis is to detect highly divergent or absent genes in a sample strain compared to an index strain. Development of methods for analyzing aCGH data has primarily focused on copy number abberations in cancer research. In microbial aCGH analyses, genes are typically ranked by log-ratios, and classification into divergent or present is done by choosing a cutoff log-ratio, either manually or by statistics calculated from the log-ratio distribution. As experimental settings vary considerably, it is not possible to develop a classical discriminant or statistical learning approach.

    Methods: We introduce a more efficient method for analyzing microbial aCGH data using a finite mixture model and a data rotation scheme. Using the average posterior probabilities from the model fitted to log-ratios before and after rotation, we get a score for each gene, and demonstrate its advantages for ranking and detecting divergent genes with enlarged specificity and sensitivity.

    Results: The procedure is tested and compared to other approaches on simulated data sets, as well as on four experimental validation data sets for aCGH analysis on fully sequenced strains of Staphylococcus aureus and Streptococcus pneumoniae.

    Conclusion: When tested on simulated data as well as on four different experimental validation data sets from experiments with only fully sequenced strains, our procedure out-competes the standard procedures of using a simple log-ratio cutoff for classification into present and divergent genes.

  • 11.
    Sysi-Aho, Marko
    et al.
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Katajamaa, Mikko
    Turku Centre for Biotechnology, Turku, Finland.
    Yetukuri, Laxman
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Oresic, Matej
    VTT Technical Research Centre of Finland, Espoo, Finland.
    Normalization method for metabolomics data using optimal selection of multiple internal standards2007In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 8, article id 93Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Success of metabolomics as the phenotyping platform largely depends on its ability to detect various sources of biological variability. Removal of platform-specific sources of variability such as systematic error is therefore one of the foremost priorities in data preprocessing. However, chemical diversity of molecular species included in typical metabolic profiling experiments leads to different responses to variations in experimental conditions, making normalization a very demanding task.

    RESULTS: With the aim to remove unwanted systematic variation, we present an approach that utilizes variability information from multiple internal standard compounds to find optimal normalization factor for each individual molecular species detected by metabolomics approach (NOMIS). We demonstrate the method on mouse liver lipidomic profiles using Ultra Performance Liquid Chromatography coupled to high resolution mass spectrometry, and compare its performance to two commonly utilized normalization methods: normalization by l2 norm and by retention time region specific standard compound profiles. The NOMIS method proved superior in its ability to reduce the effect of systematic error across the full spectrum of metabolite peaks. We also demonstrate that the method can be used to select best combinations of standard compounds for normalization.

    CONCLUSION: Depending on experiment design and biological matrix, the NOMIS method is applicable either as a one-step normalization method or as a two-step method where the normalization parameters, influenced by variabilities of internal standard compounds and their correlation to metabolites, are first calculated from a study conducted in repeatability conditions. The method can also be used in analytical development of metabolomics methods by helping to select best combinations of standard compounds for a particular biological matrix and analytical platform.

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