bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysing Microarray Data using the Multi-functional Immune Ontologiser
Authors: Khalid Sabah , Khan Mohsin , Wang Ping , Liu Xiaohui , Li Suling 
Published: December 2017

Abstract Gene expression microarrays are a prominent experimental tool in functional genomics allowing researchers to gain a deeper understanding of biological processes. To date, no such tool has been developed to allow researchers with a specialised biological research interest to distinctively identify those genes and gene functionalities associated more strongly with the research area. Based on this functional analysis capability we present a specialised multi-functional Immune Ontologiser – a software, specialised for immunologists to annotate multiple genes from microarray datasets within two new ontologies: a newly structured Immune Ontology focussed at immunology and haematology and a uniquely curated ImmunoArray-PubOntology. The Immune Ontology functionally annotates genes identifying immunology related functions enriched with upregulated or downregulated genes of interest. The ImmunoArray-PubOntology compares and contrasts gene functionality of microarray datasets, comparing genes of interest with the differential gene expression matrices published amongst immunologyrelated microarray literature. This aspect facilitates literature mining by extracting publications containing gene sets of interest in a well-structured immunological context where the literature has been categorised according to disease types. The software consists of a query-optimised database of two parts – the ImmunoGene-database and a unique Database of Immunological Microarray Publications (DIMP) to provide the user with a more detailed insight into other studies involving their genes and research groups investigating similar research areas. Using our Immune Ontologiser software to analyse tolerance array data we identify 70 interesting up-regulated genes in terms of their functionality within tolerance. Furthermore, from these 70 genes we identify 15 genes to have immunology-related functions. More interestingly, the remaining 55 genes were not previously known to be directly involved within the immunology related condition and hence we have identified target genes for future investigation. Among the 70 genes, 21 have been identified by our software to be studied within various immunology-related diseases via microarray experiments performed by other laboratories.The software and database schema is freely available at ftp://ftp.brunel.ac.uk/cspgssk. Additional material is available online at http://www.brunel.ac.uk/about/acad/health/healthres/researchareas/mi/publications/supplementary. A detailed microarray protocol is available at http://www.ebi.ac.uk/arrayexpress under the Accession Number: E-MEXP-283.
BN++ – A Biological Information System
Authors: Küntzer Jan , Blum Torsten , Gerasch Andreas , Backes Christina , Hildebrandt Andreas , Kaufmann Michael , Kohlbacher Oliver , Lenhof Hans-Peter 
Published: December 2017

Summary Recent years have seen an explosive growth in the amount of biochemical data available. Numerous databases have been established and are being used as an essential resource by biologists around the world. The sheer amount and heterogeneity of these data poses a major challenge: data integration and, based thereupon, the integrative analysis of these data. We present BN++, the biochemical network library, a powerful software package for integrating, analyzing, and visualizing biochemical data in the context of networks. BN++ is based on a comprehensive and extensible object model (BioCore), which has been implemented as a C++ framework, a Java class library, and a relational database. The C++ framework is used to efficiently import, integrate, and analyze the data, which is stored in a data warehouse. The Java-based viewer (BiNA) provides a powerful platform-independent visualization of the data using sophisticated graph layout algorithms. Currently, the data warehouse imports and integrates data from about a dozen important databases including, among others, sequence data, metabolic and regulatory networks, and protein interaction data. We illustrate the usefulness of BN++ with a few select example applications.Availability: BN++ is open source software available from our website at www.bnplusplus.org.
Data Cleaning and Semantic Improvement in Biological Databases
Authors: Apiletti Daniele , Bruno Giulia , Ficarra Elisa , Baralis Elena 
Published: December 2017

Summary Public genomic and proteomic databases can be affected by a variety of errors. These errors may involve either the description or the meaning of data (namely, syntactic or semantic errors). We focus our analysis on the detection of semantic errors, in order to verify the accuracy of the stored information. In particular, we address the issue of data constraints and functional dependencies among attributes in a given relational database. Constraints and dependencies show semantics among attributes in a database schema and their knowledge may be exploited to improve data quality and integration in database design, and to perform query optimization and dimensional reduction. We propose a method to discover data constraints and functional dependencies by means of association rule mining. Association rules are extracted among attribute values and allow us to find causality relationships among them. Then, by analyzing the support and confidence of each rule, (probabilistic) data constraints and functional dependencies may be detected. With our method we can both show the presence of erroneous data and highlight novel semantic information. Moreover, our method is database-independent because it infers rules from data. In this paper, we report the application of our techniques to the SCOP (Structural Classification of Proteins) and CATH Protein Structure Classification databases.
MILAN – A medical information-system linking agents to metabolic networks
Authors: Prins B. , Hofestädt R. 
Published: December 2017

Summary The genome, the protein-biosynthesis and the metabolism constitute a complex, strongly linked system. The complete functionality of this system is not entirely understood yet. Especially pharmacologists and medical doctors are interested to know how the system works regarding the influence of medications (Drug-Pointing). In the last thirty years biochemical research delivered a huge amount of relevant data for this field. The problem is, that all this data is highly distributed in thousands of databases which are just partly interconnected. Gathering and interconnecting this large amount of distributed data by hand significantly slows down the research process. Hence it is vitally important to create tools in terms of information-systems, which support the process of understanding through automatic data-integration and analysis. In this paper we would like to introduce our approach of a corresponding information-system. MILAN is an extensible datawarehouse framework, which has already integrated about 7.7 million medical publication-abstracts as well as metabolic pathways and information about enzymes, ligands, agents and pharmaceuticals from a collection of major biochemical databases. The system provides a set of functionalities to show where the metabolism is affected by a certain drug and instantly displays the most relevant publications for the particular case. Future development will focus on the integration of more databases and the development of additional functions like detection of side-effects and drug-interactions.
Integration of Transcriptome and Proteome Data from Human-Pathogenic Fungi by Using a Data Warehouse
Authors: Albrecht Daniela , Kniemeyer Olaf , Brakhage Axel A. , Berth Matthias , Guthke Reinhard 
Published: December 2017

Summary A data warehouse for the integrated storage and visualisation of genome and experimental transcriptome and proteome data of human-pathogenic fungi was established. It provides tools for uploading images and corresponding data from microarray experiments, two-dimensional (2D) gel experiments and mass spectrometry (MS) analyses. All data are cross-linked. A user can find out, on which gels in the database an interesting protein was detected. Additionally, he can see on which microarrays the corresponding mRNA had been spotted and whether these spots show interesting intensity values. So the data warehouse enables an integrated analysis of both transcriptome and proteome data. Some of the uploaded data were transcriptome and proteome time series data of temperature shift experiments obtained from Aspergillus fumigatus. Several proteins were differentially regulated at different times after the temperature shift. For a couple of them also the respective transcripts were found to be differentially expressed. For even more of those proteins the transcripts did not show differential regulation and vice versa. So both kinds of data clearly complement each other and should be analysed together.
Deriving biological applications from domain specific process models
Authors: Jablonski Stefan , Faerber Matthias , Volz Bernhard , Genthner Stefanie 
Published: December 2017

Abstract In this paper we present how the process modeling and execution tools iPM and iPE can be used to model and execute biological processes. The main focus of this paper is on the flexibility of iPM and iPE with respect to the customization to the biological application domain. We will demonstrate the flexibility of our modeling methodology by giving two examples: Modeling the invocation semantics of web services used in the biological application domain and the processing of streamed data.
Using Data Warehouse Technology in Crop Plant Bioinformatics
Authors: Kuenne Christian , Grosse Ivo , Matthies Inge , Scholz Uwe , Sretenovic-Rajicic Tatjana , Stein Nils , Stephanik Andreas , Steuernagel Burkhard , Weise Stephan 
Published: December 2017

Summary Plant-specific data is managed in heterogeneous formats and is dispersed geographically. Based on this data, efficient analyses require a materialised integration, often realised with data warehouse technology today. We describe the requirements, problems and solution strategies for domain-crossing integration as the fundament for analysing plant biological data based on three current case studies. First, we introduce a system for retrieval of markers and mapping positions based on clustering of ESTs. The second case study illustrates the steps for diversity studies after genotyping a collection of about 3,000 ryegrass accessions (Lolium spp.), whereas in the third example data of approximately 250 barley cultivars (Hordeum vulgare) were used for associating haplotype- and SNP-patterns with malting parameters. For all case studies, we integrate data from different domains - sequence and marker data as well as IPK Genebank data including passport and phenotypic information. Specific problems associated with plant biological data and possible solution strategies are shown.
Subgraph Queries by Context-free Grammars
Authors: Sevon Petteri , Eronen Lauri 
Published: December 2017

SummaryWe describe a method for querying vertex- and edge-labeled graphs using context-free grammars to specify the class of interesting paths. We introduce a novel problem, finding the connection subgraph induced by the set of matching paths between given two vertices or two sets of vertices. Such a subgraph provides a concise summary of the relationship between the vertices. We also present novel algorithms for parsing subgraphs directly without enumerating all the individual paths. We evaluate experimentally the presented parsing algorithms on a set of real graphs derived from publicly available biomedical databases and on randomly generated graphs. The results indicate that parsing the connection subgraph directly is much more effective than parsing individual paths separately. Furthermore, we show that using a bidirectional parsing algorithm, in most cases, allows for searching twice as long paths as using a unidirectional search strategy.
GOblet: Annotation of anonymous sequence data with Gene Ontology and Pathway terms
Authors: Groth Detlef , Hartmann Stefanie , Panopoulou Georgia , Poustka Albert J. , Hennig Steffen 
Published: December 2017

SummaryThe functional annotation of genomic data has become a major task for the ever-growing number of sequencing projects. In order to address this challenge, we recently developed GOblet, a free web service for the annotation of anonymous sequences with Gene Ontology (GO) terms. However, to overcome limitations of the GO terminology, and to aid in understanding not only single components but as well systemic interactions between the individual components, we have now extended the GOblet web service to integrate also pathway annotations. Furthermore, we extended and upgraded the data analysis pipeline with improved summaries, and added term enrichment and clustering algorithms. Finally, we are now making GOblet available as a stand-alone application for high-throughput processing on local machines. The advantages of this frequently requested feature is that a) the user can avoid restrictions of our web service for uploading and processing large amounts of data, and that b) confidential data can be analysed without insecure transfer to a public web server. The stand-alone version of the web service has been implemented using platform independent Tcl-scripts, which can be run with just a single runtime file utilizing the Starkit technology. The GOblet web service and the stand-alone application are freely available at http://goblet.molgen.mpg.de.
2.5D Visualisation of Overlapping Biological Networks
Authors: Fung David C.Y. , Hong Seok-Hee , Koschützki Dirk , Schreiber Falk , Xu Kai 
Published: December 2017

Abstract Biological data is often structured in the form of complex interconnected networks such as protein interaction and metabolic networks. In this paper, we investigate a new problem of visualising such overlapping biological networks. Two networks overlap if they share some nodes and edges. We present an approach for constructing visualisations of two overlapping networks, based on a restricted three dimensional representation. More specifically, we use three parallel two dimensional planes placed in three dimensions to represent overlapping networks: one for each network (the top and the bottom planes) and one for the overlapping part (in the middle plane).Our method aims to achieve both drawing aesthetics (or conventions) for each individual network, and highlighting the intersection part by them. Using three biological datasets, we evaluate our visualisation design with the aim to test whether overlapping networks can support the visual analysis of heterogeneous and yet interconnected networks.
MoRAine - A web server for fast computational transcription factor binding motif re-annotation
Authors: Baumbach Jan , Wittkop Tobias , Weile Jochen , Kohl Thomas , Rahmann Sven 
Published: December 2017

SummaryBackground: A precise experimental identification of transcription factor binding motifs (TFBMs), accurate to a single base pair, is time-consuming and difficult. For several databases, TFBM annotations are extracted from the literature and stored 5ʹ → 3ʹ relative to the target gene. Mixing the two possible orientations of a motif results in poor information content of subsequently computed position frequency matrices (PFMs) and sequence logos. Since these PFMs are used to predict further TFBMs, we address the question if the TFBMs underlying a PFM can be re-annotated automatically to improve both the information content of the PFM and subsequent classification performance.Results: We present MoRAine, an algorithm that re-annotates transcription factor binding motifs. Each motif with experimental evidence underlying a PFM is compared against each other such motif. The goal is to re-annotate TFBMs by possibly switching their strands and shifting them a few positions in order to maximize the information content of the resulting adjusted PFM. We present two heuristic strategies to perform this optimization and subsequently show that MoRAine significantly improves the corresponding sequence logos. Furthermore, we justify the method by evaluating specificity, sensitivity, true positive, and false positive rates of PFM-based TFBM predictions for E. coli using the original database motifs and the MoRAine-adjusted motifs. The classification performance is considerably increased if MoRAine is used as a preprocessing step.Conclusions: MoRAine is integrated into a publicly available web server and can be used online or downloaded as a stand-alone version from http://moraine.cebitec.uni-bielefeld.de.
Graph-based sequence annotation using a data integration approach
Authors: Pesch Robert , Lysenko Artem , Hindle Matthew , Hassani-Pak Keywan , Thiele Ralf , Rawlings Christopher , Köhler Jacob , Taubert Jan 
Published: December 2017

SummaryThe automated annotation of data from high throughput sequencing and genomics experiments is a significant challenge for bioinformatics. Most current approaches rely on sequential pipelines of gene finding and gene function prediction methods that annotate a gene with information from different reference data sources. Each function prediction method contributes evidence supporting a functional assignment. Such approaches generally ignore the links between the information in the reference datasets. These links, however, are valuable for assessing the plausibility of a function assignment and can be used to evaluate the confidence in a prediction. We are working towards a novel annotation system that uses the network of information supporting the function assignment to enrich the annotation process for use by expert curators and predicting the function of previously unannotated genes. In this paper we describe our success in the first stages of this development. We present the data integration steps that are needed to create the core database of integrated reference databases (UniProt, PFAM, PDB, GO and the pathway database Ara- Cyc) which has been established in the ONDEX data integration system. We also present a comparison between different methods for integration of GO terms as part of the function assignment pipeline and discuss the consequences of this analysis for improving the accuracy of gene function annotation.The methods and algorithms presented in this publication are an integral part of the ONDEX system which is freely available from http://ondex.sf.net/.
Effectiveness of Closure of Public Places with Time Delay in Disease Control
Authors: Wang Zhenggang , Szeto Kwok Yip , Chi-Ching Leung Frederick 
Published: December 2017

SummaryA theoretical basis for the evaluation of the effciency of quarantine measure is developed in a SIR model with time delay. In this model, the effectiveness of the closure of public places such as schools in disease control, modeled as a high degree node in a social network, is evaluated by considering the effect of the time delay in the identification of the infected. In the context of the SIR model, the relation between the number of infectious individuals who are identified with time delay and then quarantined and those who are not identified and continue spreading the virus are investigated numerically. The social network for the simulation is modeled by a scale free network. Closure measures are applied to those infected nodes with high degrees. The effectiveness of the measure can be controlled by the present value of the critical degree K
Goober: A fully integrated and user-friendly microarray data management and analysis solution for core labs and bench biologists
Authors: Luo Wen , Gudipati Murali , Jung Kevin , Chen Mao , Marschke Keith B. 
Published: December 2017

SummaryDespite the large number of software tools developed to address different areas of microarray data analysis, very few offer an all-in-one solution with little learning curve. For microarray core labs, there are even fewer software packages available to help with their routine but critical tasks, such as data quality control (QC) and inventory management. We have developed a simple-to-use web portal to allow bench biologists to analyze and query complicated microarray data and related biological pathways without prior training. Both experiment-based and gene-based analysis can be easily performed, even for the first-time user, through the intuitive multi-layer design and interactive graphic links. While being friendly to inexperienced users, most parameters in Goober can be easily adjusted via drop-down menus to allow advanced users to tailor their needs and perform more complicated analysis. Moreover, we have integrated graphic pathway analysis into the website to help users examine microarray data within the relevant biological content. Goober also contains features that cover most of the common tasks in microarray core labs, such as real time array QC, data loading, array usage and inventory tracking. Overall, Goober is a complete microarray solution to help biologists instantly discover valuable information from a microarray experiment and enhance the quality and productivity of microarray core labs. The whole package is freely available at http://sourceforge.net/projects/goober. A demo web server is available at http://www.goober-array.org.
Identifying the impact of G-Quadruplexes on Affymetrix 3′ Arrays using Cloud Computing
Authors: Memon Farhat N. , Owen Anne M. , Sanchez-Graillet Olivia , Upton Graham J.G. , Harrison Andrew P. 
Published: December 2017

SummaryA tetramer quadruplex structure is formed by four parallel strands of DNA/ RNA containing runs of guanine. These quadruplexes are able to form because guanine can Hoogsteen hydrogen bond to other guanines, and a tetrad of guanines can form a stable arrangement. Recently we have discovered that probes on Affymetrix GeneChips that contain runs of guanine do not measure gene expression reliably. We associate this finding with the likelihood that quadruplexes are forming on the surface of GeneChips.In order to cope with the rapidly expanding size of GeneChip array datasets in the public domain, we are exploring the use of cloud computing to replicate our experiments on 3′ arrays to look at the effect of the location of G-spots (runs of guanines). Cloud computing is a recently introduced high-performance solution that takes advantage of the computational infrastructure of large organisations such as Amazon and Google.We expect that cloud computing will become widely adopted because it enables bioinformaticians to avoid capital expenditure on expensive computing resources and to only pay a cloud computing provider for what is used. Moreover, as well as financial efficiency, cloud computing is an ecologically-friendly technology, it enables efficient data-sharing and we expect it to be faster for development purposes. Here we propose the advantageous use of cloud computing to perform a large data-mining analysis of public domain 3′ arrays.
A New Approach for Modelling Gene Regulatory Networks Using Fuzzy Petri Nets
Authors: Hamed Raed I. , Ahson S. I. , Parveen R. 
Published: December 2017

SummaryGene Regulatory Networks are models of genes and gene interactions at the expression level. The advent of microarray technology has challenged computer scientists to develop better algorithms for modeling the underlying regulatory relationship in between the genes. Fuzzy system has an ability to search microarray datasets for activator/repressor regulatory relationship. In this paper, we present a fuzzy reasoning model based on the Fuzzy Petri Net. The model considers the regulatory triplets by means of predicting changes in expression level of the target based on input expression level. This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. Through formalization of fuzzy reasoning, we propose an approach to construct a rule-based reasoning system. The experimental results show the proposed approach is feasible and acceptable to predict changes in expression level of the target gene.