@article{DandekarAhmedSamanetal.2013, author = {Dandekar, Thomas and Ahmed, Zeeshan and Saman, Zeeshan and Huber, Claudia and Hensel, Michael and Schomburg, Dietmar and M{\"u}nch, Richard and Eisenreich, Wolfgang}, title = {Software LS-MIDA for efficient mass isotopomer distribution analysis in metabolic modelling}, series = {BMC Bioinformatics}, journal = {BMC Bioinformatics}, doi = {10.1186/1471-2334-13-266}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-95882}, year = {2013}, abstract = {Background The knowledge of metabolic pathways and fluxes is important to understand the adaptation of organisms to their biotic and abiotic environment. The specific distribution of stable isotope labelled precursors into metabolic products can be taken as fingerprints of the metabolic events and dynamics through the metabolic networks. An open-source software is required that easily and rapidly calculates from mass spectra of labelled metabolites, derivatives and their fragments global isotope excess and isotopomer distribution. Results The open-source software "Least Square Mass Isotopomer Analyzer" (LS-MIDA) is presented that processes experimental mass spectrometry (MS) data on the basis of metabolite information such as the number of atoms in the compound, mass to charge ratio (m/e or m/z) values of the compounds and fragments under study, and the experimental relative MS intensities reflecting the enrichments of isotopomers in 13C- or 15 N-labelled compounds, in comparison to the natural abundances in the unlabelled molecules. The software uses Brauman's least square method of linear regression. As a result, global isotope enrichments of the metabolite or fragment under study and the molar abundances of each isotopomer are obtained and displayed. Conclusions The new software provides an open-source platform that easily and rapidly converts experimental MS patterns of labelled metabolites into isotopomer enrichments that are the basis for subsequent observation-driven analysis of pathways and fluxes, as well as for model-driven metabolic flux calculations.}, language = {en} } @article{AhmedZeeshanHuberetal.2014, author = {Ahmed, Zeeshan and Zeeshan, Saman and Huber, Claudia and Hensel, Michael and Schomburg, Dietmar and M{\"u}nch, Richard and Eylert, Eva and Eisenreich, Wolfgang and Dandekar, Thomas}, title = {'Isotopo' a database application for facile analysis and management of mass isotopomer data}, series = {Database}, volume = {2014}, journal = {Database}, number = {bau077}, doi = {10.1093/database/bau077}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-120102}, year = {2014}, abstract = {The composition of stable-isotope labelled isotopologues/isotopomers in metabolic products can be measured by mass spectrometry and supports the analysis of pathways and fluxes. As a prerequisite, the original mass spectra have to be processed, managed and stored to rapidly calculate, analyse and compare isotopomer enrichments to study, for instance, bacterial metabolism in infection. For such applications, we provide here the database application 'Isotopo'. This software package includes (i) a database to store and process isotopomer data, (ii) a parser to upload and translate different data formats for such data and (iii) an improved application to process and convert signal intensities from mass spectra of \(^{13}C\)-labelled metabolites such as tertbutyldimethylsilyl-derivatives of amino acids. Relative mass intensities and isotopomer distributions are calculated applying a partial least square method with iterative refinement for high precision data. The data output includes formats such as graphs for overall enrichments in amino acids. The package is user-friendly for easy and robust data management of multiple experiments.}, language = {en} } @article{AhmedZeeshanDandekar2016, author = {Ahmed, Zeeshan and Zeeshan, Saman and Dandekar, Thomas}, title = {Mining biomedical images towards valuable information retrieval in biomedical and life sciences}, series = {Database - The Journal of Biological Databases and Curation}, volume = {2016}, journal = {Database - The Journal of Biological Databases and Curation}, doi = {10.1093/database/baw118}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-162697}, pages = {baw118}, year = {2016}, abstract = {Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries.}, language = {en} }