Gene ARMADA: an integrated multi-analysis platform for microarray data implemented in MATLAB

<p>Abstract</p> <p>Background</p> <p>The microarray data analysis realm is ever growing through the development of various tools, open source and commercial. However there is absence of predefined rational algorithmic analysis workflows or batch standardized processing to incorporate all steps, from raw data import up to the derivation of significantly differentially expressed gene lists. This absence obfuscates the analytical procedure and obstructs the massive comparative processing of genomic microarray datasets. Moreover, the solutions provided, heavily depend on the programming skills of the user, whereas in the case of GUI embedded solutions, they do not provide direct support of various raw image analysis formats or a versatile and simultaneously flexible combination of signal processing methods.</p> <p>Results</p> <p>We describe here Gene ARMADA (Automated Robust MicroArray Data Analysis), a MATLAB implemented platform with a Graphical User Interface. This suite integrates all steps of microarray data analysis including automated data import, noise correction and filtering, normalization, statistical selection of differentially expressed genes, clustering, classification and annotation. In its current version, Gene ARMADA fully supports 2 coloured cDNA and Affymetrix oligonucleotide arrays, plus custom arrays for which experimental details are given in tabular form (Excel spreadsheet, comma separated values, tab-delimited text formats). It also supports the analysis of already processed results through its versatile import editor. Besides being fully automated, Gene ARMADA incorporates numerous functionalities of the Statistics and Bioinformatics Toolboxes of MATLAB. In addition, it provides numerous visualization and exploration tools plus customizable export data formats for seamless integration by other analysis tools or MATLAB, for further processing. Gene ARMADA requires MATLAB 7.4 (R2007a) or higher and is also distributed as a stand-alone application with MATLAB Component Runtime.</p> <p>Conclusion</p> <p>Gene ARMADA provides a highly adaptable, integrative, yet flexible tool which can be used for automated quality control, analysis, annotation and visualization of microarray data, constituting a starting point for further data interpretation and integration with numerous other tools.</p

KEGGconverter: a tool for the in-silico modelling of metabolic networks of the KEGG Pathways database

<p>Abstract</p> <p>Background</p> <p>The KEGG Pathway database is a valuable collection of metabolic pathway maps. Nevertheless, the production of simulation capable metabolic networks from KEGG Pathway data is a challenging complicated work, regardless the already developed tools for this scope. Originally used for illustration purposes, KEGG Pathways through KGML (KEGG Markup Language) files, can provide complete reaction sets and introduce species versioning, which offers advantages for the scope of cellular metabolism simulation modelling. In this project, KEGGconverter is described, implemented also as a web-based application, which uses as source KGML files, in order to construct integrated pathway SBML models fully functional for simulation purposes.</p> <p>Results</p> <p>A case study of the integration of six human metabolic pathways from KEGG depicts the ability of KEGGconverter to automatically produce merged and converted to SBML fully functional pathway models, enhanced with default kinetics. The suitability of the developed tool is demonstrated through a comparison with other state-of-the art relevant software tools for the same data fusion and conversion tasks, thus illustrating the problems and the relevant workflows. Moreover, KEGGconverter permits the inclusion of additional reactions in the resulting model which represent flux cross-talk with neighbouring pathways, providing in this way improved simulative accuracy. These additional reactions are introduced by exploiting relevant semantic information for the elements of the KEGG Pathways database. The architecture and functionalities of the web-based application are presented.</p> <p>Conclusion</p> <p>KEGGconverter is capable of producing integrated analogues of metabolic pathways appropriate for simulation tasks, by inputting only KGML files. The web application acts as a user friendly shell which transparently enables the automated biochemically correct pathway merging, conversion to SBML format, proper renaming of the species, and insertion of default kinetic properties for the pertaining reactions. The tool is available at: <url>http://www.grissom.gr/keggconverter</url></p

KEGGconverter: a tool for the in-silico modelling of metabolic networks of the KEGG Pathways database

Journal URL: http://www.biomedcentral.com/bmcbioinformatics

ANASTASIA: An Automated Metagenomic Analysis Pipeline for Novel Enzyme Discovery Exploiting Next Generation Sequencing Data

Metagenomic analysis of environmental samples provides deep insight into the enzymatic mixture of the corresponding niches, capable of revealing peptide sequences with novel functional properties exploiting the high performance of next-generation sequencing (NGS) technologies. At the same time due to their ever increasing complexity, there is a compelling need for ever larger computational configurations to ensure proper bioinformatic analysis, and fine annotation. With the aiming to address the challenges of such an endeavor, we have developed a novel web-based application named ANASTASIA (automated nucleotide aminoacid sequences translational plAtform for systemic interpretation and analysis). ANASTASIA provides a rich environment of bioinformatic tools, either publicly available or novel, proprietary algorithms, integrated within numerous automated algorithmic workflows, and which enables versatile data processing tasks for (meta)genomic sequence datasets. ANASTASIA was initially developed in the framework of the European FP7 project HotZyme, whose aim was to perform exhaustive analysis of metagenomes derived from thermal springs around the globe and to discover new enzymes of industrial interest. ANASTASIA has evolved to become a stable and extensible environment for diversified, metagenomic, functional analyses for a range of applications overarching industrial biotechnology to biomedicine, within the frames of the ELIXIR-GR project. As a showcase, we report the successful in silico mining of a novel thermostable esterase termed “EstDZ4” from a metagenomic sample collected from a hot spring located in Krisuvik, Iceland

EstDZ3:a new esterolytic enzyme exhibiting remarkable thermostability

Lipolytic enzymes that retain high levels of catalytic activity when exposed to a variety of denaturing conditions are of high importance for a number of biotechnological applications. In this study, we aimed to identify new lipolytic enzymes, which are highly resistant to prolonged exposure at elevated temperatures. To achieve this, we searched for genes encoding for such proteins in the genomes of a microbial consortium residing in a hot spring located in China. After performing a functional genomic screening on a bacterium of the genus Dictyoglomus, which was isolated from this hot spring after in situ enrichment, we identified a new esterolytic enzyme, termed EstDZ3. Detailed biochemical characterization of the recombinant enzyme, revealed that it constitutes a slightly alkalophilic and highly active esterase against esters of fatty acids with short to medium chain lengths. Importantly, EstDZ3 exhibits remarkable thermostability, as it retained high levels of catalytic activity after exposure to temperatures as high as 95 oC for several hours. Interestingly, EstDZ3 was found to have very little similarity to previously characterized esterolytic enzymes. Computational modelling of the three-dimensional structure of this new enzyme predicted that it exhibits a typical α/β hydrolase fold, which seems to include a subdomain insertion. This insertion is similar to the one present in its closest homologue of known function and structure, the cinnamoyl esterase Lj0536 from Lactobacillus johnsonii. As it was found in the case of Lj0536, this structural feature is expected to be an important determinant of the catalytic properties of EstDZ3. The high levels of esterolytic activity of EstDZ3, combined with its remarkable thermostability and good stability against a wide range of metal ions, organic solvents, and other denaturing agents, render this new enzyme a candidate biocatalyst for high-temperature biotechnological applications

EstDZ3: A New Esterolytic Enzyme Exhibiting Remarkable Thermostability

Lipolytic enzymes that retain high levels of catalytic activity when exposed to a variety of denaturing conditions are of high importance for a number of biotechnological applications. In this study, we aimed to identify new lipolytic enzymes, which are highly resistant to prolonged exposure at elevated temperatures. To achieve this, we searched for genes encoding for such proteins in the genomes of a microbial consortium residing in a hot spring located in China. After performing a functional genomic screening on a bacterium of the genus Dictyoglomus, which was isolated from this hot spring after in situ enrichment, we identified a new esterolytic enzyme, termed EstDZ3. Detailed biochemical characterization of the recombinant enzyme, revealed that it constitutes a slightly alkalophilic and highly active esterase against esters of fatty acids with short to medium chain lengths. Importantly, EstDZ3 exhibits remarkable thermostability, as it retained high levels of catalytic activity after exposure to temperatures as high as 95 oC for several hours. Interestingly, EstDZ3 was found to have very little similarity to previously characterized esterolytic enzymes. Computational modelling of the three-dimensional structure of this new enzyme predicted that it exhibits a typical α/β hydrolase fold, which seems to include a subdomain insertion. This insertion is similar to the one present in its closest homologue of known function and structure, the cinnamoyl esterase Lj0536 from Lactobacillus johnsonii. As it was found in the case of Lj0536, this structural feature is expected to be an important determinant of the catalytic properties of EstDZ3. The high levels of esterolytic activity of EstDZ3, combined with its remarkable thermostability and good stability against a wide range of metal ions, organic solvents, and other denaturing agents, render this new enzyme a candidate biocatalyst for high-temperature biotechnological applications

Discovery and characterization of a thermostable and highly halotolerant GH5 cellulase from an Icelandic hot spring isolate

Journal ArticleCopyright: © 2016 Zarafeta et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.With the ultimate goal of identifying robust cellulases for industrial biocatalytic conversions, we have isolated and characterized a new thermostable and very halotolerant GH5 cellulase. This new enzyme, termed CelDZ1, was identified by bioinformatic analysis from the genome of a polysaccharide-enrichment culture isolate, initiated from material collected from an Icelandic hot spring. Biochemical characterization of CelDZ1 revealed that it is a glycoside hydrolase with optimal activity at 70°C and pH 5.0 that exhibits good thermostability, high halotolerance at near-saturating salt concentrations, and resistance towards metal ions and other denaturing agents. X-ray crystallography of the new enzyme showed that CelDZ1 is the first reported cellulase structure that lacks the defined sugar-binding 2 subsite and revealed structural features which provide potential explanations of its biochemical characteristics.This work has been carried out in the framework of the HotZyme Project (http://hotzyme.com, grant agreement no. 265933) financed by the European Union 7th Framework Programme FP7/2007-2013, an EU FP7 Collaborative programme

Escherichia coli genome-wide promoter analysis: Identification of additional AtoC binding target elements

<p>Abstract</p> <p>Background</p> <p>Studies on bacterial signal transduction systems have revealed complex networks of functional interactions, where the response regulators play a pivotal role. The AtoSC system of <it>E. coli </it>activates the expression of <it>atoDAEB </it>operon genes, and the subsequent catabolism of short-chain fatty acids, upon acetoacetate induction. Transcriptome and phenotypic analyses suggested that <it>atoSC </it>is also involved in several other cellular activities, although we have recently reported a palindromic repeat within the <it>atoDAEB </it>promoter as the single, <it>cis</it>-regulatory binding site of the AtoC response regulator. In this work, we used a computational approach to explore the presence of yet unidentified AtoC binding sites within other parts of the <it>E. coli </it>genome.</p> <p>Results</p> <p>Through the implementation of a computational <it>de novo </it>motif detection workflow, a set of candidate motifs was generated, representing putative AtoC binding targets within the <it>E. coli </it>genome. In order to assess the biological relevance of the motifs and to select for experimental validation of those sequences related robustly with distinct cellular functions, we implemented a novel approach that applies Gene Ontology Term Analysis to the motif hits and selected those that were qualified through this procedure. The computational results were validated using Chromatin Immunoprecipitation assays to assess the <it>in vivo </it>binding of AtoC to the predicted sites. This process verified twenty-two additional AtoC binding sites, located not only within intergenic regions, but also within gene-encoding sequences.</p> <p>Conclusions</p> <p>This study, by tracing a number of putative AtoC binding sites, has indicated an AtoC-related cross-regulatory function. This highlights the significance of computational genome-wide approaches in elucidating complex patterns of bacterial cell regulation.</p