bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Graphical Notation to Describe the Logical Interactions of Biological Pathways
Authors: Moodie Stuart L. , Sorokin Anatoly , Goryanin Igor , Ghazal Peter 
Published: December 2017

Summary The modelling of biological intra-cellular pathways requires the systematic capture and representation of interactions between components that are biologically correct and computationally rigorous. The challenge is two fold, to verify and extend our understanding of such pathways by comparing in silico models to experiments; and to ensure that such models are understandable by biologists and for checking biological validity. In this report we present a graphical notation, the Edinburgh Pathway Notation (EPN), which satisfies the central biologically driven requirements while providing a strict formal framework for analysis. The EPN emphasises the use of a logical representation of pathways, which is particularly suited to pathways were some mechanisms are not known in detail.
Functional diversity within protein superfamilies
Authors: Casbon James , Saqi Mansoor 
Published: December 2017

Summary Structural genomics projects are leading to the discovery of relationships between proteins that would not have been anticipated from consideration of sequence alone. However the assignment of function via structure remains difficult as some structures are compatible with a variety of functions. In this study we explore the relationships between structural diversity and functional diversity within distantly related members of SCOP superfamilies. We use the Gene Ontology functional classification scheme and Greens path entropy to measure functional diversity. We observe a negative correlation between the functional entropy of a superfamily and the size of the conserved core.
The ABC (Analysing Biomolecular Contacts)-database
Authors: Walter Peter , Ansari Sam , Helms Volkhard 
Published: December 2017

Abstract As protein-protein interactions are one of the basic mechanisms in most cellular processes, it is desirable to understand the molecular details of protein-protein contacts and ultimately be able to predict which proteins interact. Interface areas on a protein surface that are involved in protein interactions exhibit certain characteristics. Therefore, several attempts were made to distinguish protein interactions from each other and to categorize them. One way of classification are the groups of transient and permanent interactions. Previously two of the authors analysed several properties for transient complexes such as the amino acid and secondary structure element composition and pairing preferences. Certainly, interfaces can be characterized by many more possible attributes and this is a subject of intense ongoing research. Although several freely available online databases exist that illuminate various aspects of protein-protein interactions, we decided to construct a new database collecting all desired interface features allowing for facile selection of subsets of complexes. As database-server we applied MySQL and the program logic was written in JAVA. Furthermore several class extensions and tools such as JMOL were included to visualize the interfaces and JfreeChart for the representation of diagrams and statistics. The contact data is automatically generated from standard PDB files by a tcl/tk-script running through the molecular visualization package VMD. Currently the database contains 536 interfaces extracted from 479 PDB files and it can be queried by various types of parameters. Here, we describe the database design and demonstrate its usefulness with a number of selected features. Availability: The ABC-database can be accessed on http://service.bioinformatik.unisaarland.de/abc.
Flexible web-based integration of distributed large-scale human protein interaction maps
Authors: Chaurasia Gautam , Iqbal Yasir , Hänig Christian , Herzel Hanspeter , Wanker Erich E. , Futschik Matthias E. 
Published: December 2017

Summary Protein-protein interactions constitute the backbone of many molecular processes. This has motivated the recent construction of several large-scale human protein-protein interaction maps [1-10]. Although these maps clearly offer a wealth of information, their use is challenging: complexity, rapid growth, and fragmentation of interaction data hamper their usability. To overcome these hurdles, we have developed a publicly accessible database termed UniHI (Unified Human Interactome) for integration of human protein-protein interaction data. This database is designed to provide biomedical researchers a common platform for exploring previously disconnected human interaction maps. UniHI offers researchers flexible integrated tools for accessing comprehensive information about the human interactome. Several features included in the UniHI allow users to perform various types of network-oriented and functional analysis. At present, UniHI contains over 160,000 distinct interactions between 17,000 unique proteins from ten major interaction maps derived by both computational and experimental approaches [1-10]. Here we describe the details of the implementation and maintenance of UniHI and discuss the challenges that have to be addressed for a successful integration of interaction data.
Duplicate detection of 2D-NMR Spectra
Authors: Hinneburg Alexander , Egert Björn , Porzel Andrea 
Published: December 2017

Abstract 2D-Nuclear magnetic resonance (NMR) spectra are used in the (structural) analysis of small molecules. In contrast to 1D-NMR spectra, 2D-NMR spectra correlate the chemical shifts of 1H and 13C at the same time. A spectrum consists of several peaks in a two--dimensional space. The most important information of a peak is the location of its center, which captures the bonding relationships of hydrogen and carbon atoms. A spectrum contains much information about the chemical structure of a product, but in most cases the structure cannot be read off in a simple and straightforward manner. Structure elucidation involves a considerable amount (manual) efforts. Using high-field NMR spectrometers, many 2D-NMR spectra can be recorded in short time. So the common situation is that a lab or company has a repository of 2D-NMR spectra, partially annotated with the structural information. For the remaining spectra the structure in unknown. In case two research labs are collaborating, the repositories will be merged and annotations shared. We reduce that problem to the task of finding duplicates in a given set of 2D-NMR spectra. Therefore, we propose a simple but robust definition of 2D-NMR duplicates, which allows for small measurement errors. We give a quadratic algorithm for the problem, which can be implemented in SQL. Further, we analyze a more abstract class of heuristics, which are based on selecting particular peaks. Such a heuristic works as a filter step on the pairs of possible duplicates and allows false positives. We compare all methods with respect to their run time. Finally we discuss the effectiveness of the duplicate definition on real data.
MetHouse: Raw and Preprocessed Mass Spectrometry Data
Authors: Gaida Antje , Neumann Steffen 
Published: December 2017

Abstract We are developing a vendor-independent archive and on top of that a data warehouse for mass spectrometry metabolomics data. The archive schema resembles the communitydeveloped object model, the Java implementation of the model classes, and an editor (for both mzData XML files and the database) have been generated using the Eclipse Modeling Framework. Persistence is handled by the JDO2 -compliant framework JPOX. The main content of the Data Warehouse are the results of the signal processing and peak-picking tasks, carried out using the XCMS package from Bioconductor, putative identification and mass decomposition are added to the warehouse afterwards. We present the system architecture, current content, performance observations and describe the analysis tools on top of the warehouse. Availability: http://msbi.ipb-halle.de/
A Reliable and Distributed LIMS for Efficient Management of the Microarray Experiment Environment
Authors: Jin Hee-Jeong , Lee Jeong-Won , Cho Hwan-Gue 
Published: December 2017

Summary A microarray is a principal technology in molecular biology. It generates thousands of expressions of genotypes at once. Typically, a microarray experiment contains many kinds of information, such as gene names, sequences, expression profiles, scanned images, and annotation. So, the organization and analysis of vast amounts of data are required. Microarray LIMS (Laboratory Information Management System) provides data management, search, and basic analysis. Recently, microarray joint researches, such as the skeletal system disease and anti-cancer medicine have been widely conducted. This research requires data sharing among laboratories within the joint research group. In this paper, we introduce a web based microarray LIMS, SMILE (Small and solid MIcroarray Lims for Experimenters), especially for shared data management. The data sharing function of SMILE is based on Friend-to-Friend (F2F), which is based on anonymous P2P (Peer-to-Peer), in which people connect directly with their “friends”. It only allows its friends to exchange data directly using IP addresses or digital signatures you trust. In SMILE, there are two types of friends: “service provider”, which provides data, and “client”, which is provided with data. So, the service provider provides shared data only to its clients. SMILE provides useful functions for microarray experiments, such as variant data management, image analysis, normalization, system management, project schedule management, and shared data management. Moreover, it connections with two systems: ArrayMall for analyzing microarray images and GENAW for constructing a genetic network. SMILE is available on http://neobio.cs.pusan.ac.kr:8080/smile.
IMS2 – An integrated medical software system for early lung cancer detection using ion mobility spectrometry data of human breath
Authors: Baumbach Jan , Bunkowski Alexander , Lange Sita , Oberwahrenbrock Timm , Kleinbölting Nils , Rahmann Sven , Baumbach Jörg Ingo 
Published: December 2017

Abstract IMS2 is an Integrated Medical Software system for the analysis of Ion Mobility Spectrometry (IMS) data. It assists medical staff with the following IMS data processing steps: acquisition, visualization, classification, and annotation. IMS2 provides data analysis and interpretation features on the one hand, and also helps to improve the classification by increasing the number of the pre-classified datasets on the other hand. It is designed to facilitate early detection of lung cancer, one of the most common cancer types with one million deaths each year around the world.After reviewing the IMS technology, we first describe the software architecture of IMS2 and then the integrated classification module, including necessary pre-processing steps and different classification methods. The Lung Hospital Hemer (Germany) provided IMS data of 35 patients suffering from lung cancer and 72 samples of healthy persons. IMS2 correctly classifies 99% of the samples, evaluated using 10-fold cross-validation.
GeneBrowser: an approach for integration and functional classification of genomic data
Authors: Arrais Joel , Santos Bruno , Fernandes João , Carreto Laura , Santos Manuel A.S. , Oliveira José Luis 
Published: December 2017

Summary The achievements coming from genome analysis depend greatly on the quality of computational and processing methods. Tools for functional mRNA profiling and for gene information integration have become essential to this task. We have developed GeneBrowser as a novel approach that combines the advantages of mRNA profiling tools, at genome-scale experiments, with the features provided by data integration systems. For a given set of genes, GeneBrowser integrates bibliography information with functional annotations, using Gene Ontology, Entrez Gene, KEGG Orthology and KEGG Pathways. The result is a comprehensive and easy to use web application that helps researchers to extract knowledge from large data sets and to speed up the discovery process.Availability: GeneBrowser is freely available at http://bioinformatics.ua.pt/genebrowser
Converting Biomolecular Modelling Data Based on an XML Representation
Authors: Sun Yudong , McKeever Steve 
Published: December 2017

SummaryBiomolecular modelling has provided computational simulation based methods for investigating biological processes from quantum chemical to cellular levels. Modelling such microscopic processes requires atomic description of a biological system and conducts in fine timesteps. Consequently the simulations are extremely computationally demanding. To tackle this limitation, different biomolecular models have to be integrated in order to achieve high-performance simulations. The integration of diverse biomolecular models needs to convert molecular data between different data representations of different models. This data conversion is often non-trivial, requires extensive human input and is inevitably error prone. In this paper we present an automated data conversion method for biomolecular simulations between molecular dynamics and quantum mechanics/molecular mechanics models. Our approach is developed around an XML data representation called BioSimML (Biomolecular Simulation Markup Language). BioSimML provides a domain specific data representation for biomolecular modelling which can effciently support data interoperability between different biomolecular simulation models and data formats.
Widespread existence of uncorrelated probe intensities from within the same probeset on Affymetrix GeneChips
Authors: Sanchez-Graillet Olivia , Rowsell Joanna , Langdon William B. , Stalteri Maria , Arteaga-Salas Jose M. , Upton Graham J. G. , Harrison Andrew P. 
Published: December 2017

SummaryWe have developed a computational pipeline to analyse large surveys of A ymetrix GeneChips, for example NCBI’s Gene Expression Omnibus. GEO samples data for many organisms, tissues and phenotypes. Because of this experimental diversity, any observed correlations between probe intensities can be associated either with biology that is robust, such as common co-expression, or with systematic biases associated with the GeneChip technology.Our bioinformatics pipeline integrates the mapping of probes to exons, quality control checks on each GeneChip which identifies flaws in hybridization quality, and the mining of correlations in intensities between groups of probes. The output from our pipeline has enabled us to identify systematic biases in GeneChip data. We are also able to use the pipeline as a discovery tool for biology.We have discovered that in the majority of cases, A ymetrix probesets on Human GeneChips do not measure one unique block of transcription. Instead we see numerous examples of outlier probes. Our study has also identified that in a number of probesets the mismatch probes are an informative diagnostic of expression, rather than providing a measure of background contamination. We report evidence for systematic biases in GeneChip technology associated with probe-probe interactions. We also see signatures associated with post-transcriptional processing of RNA, such as alternative polyadenylation.
Integrative Systems Biology Visualization with MAYDAY
Authors: Symonsy Stephan , Zipplies Christian , Battke Florian , Nieselt Kay 
Published: December 2017

SummaryVisualization is pivotal for gaining insight in systems biology data. As the size and complexity of datasets and supplemental information increases, an efficient, integrated framework for general and specialized views is necessary. MAYDAY is an application for analysis and visualization of general ‘omics’ data. It follows a trifold approach for data visualization, consisting of flexible data preprocessing, highly customizable data perspective plots for general purpose visualization and systems based plots. Here, we introduce two new systems biology visualization tools for MAYDAY. Efficiently implemented genomic viewers allow the display of variables associated with genomic locations. Multiple variables can be viewed using our new track-based ChromeTracks tool. A functional perspective is provided by visualizing metabolic pathways either in KEGG or BioPax format. Multiple options of displaying pathway components are available, including Systems Biology Graphical Notation (SBGN) glyphs. Furthermore, pathways can be viewed together with gene expression data either as heatmaps or profiles.We apply our tools to two ‘omics’ datasets of Pseudomonas aeruginosa. The general analysis and visualization tools of MAYDAY as well as our ChromeTracks viewer are applied to a transcriptome dataset. We furthermore integrate this dataset with a metabolome dataset and compare the activity of amino acid degradation pathways between these two datasets, by visually enhancing the pathway diagrams produced by MAYDAY.
The LAILAPS Search Engine: A Feature Model for Relevance Ranking in Life Science Databases
Authors: Lange Matthias , Spies Karl , Colmsee Christian , Flemming Steffen , Klapperstück Matthias , Scholz Uwe 
Published: December 2017

SummaryEfficient and effective information retrieval in life sciences is one of the most pressing challenge in bioinformatics. The incredible growth of life science databases to a vast network of interconnected information systems is to the same extent a big challenge and a great chance for life science research. The knowledge found in the Web, in particular in life-science databases, are a valuable major resource. In order to bring it to the scientist desktop, it is essential to have well performing search engines. Thereby, not the response time nor the number of results is important. The most crucial factor for millions of query results is the relevance ranking.In this paper, we present a feature model for relevance ranking in life science databases and its implementation in the LAILAPS search engine. Motivated by the observation of user behavior during their inspection of search engine result, we condensed a set of 9 relevance discriminating features. These features are intuitively used by scientists, who briefly screen database entries for potential relevance. The features are both sufficient to estimate the potential relevance, and efficiently quantifiable.The derivation of a relevance prediction function that computes the relevance from this features constitutes a regression problem. To solve this problem, we used artificial neural networks that have been trained with a reference set of relevant database entries for 19 protein queries.Supporting a flexible text index and a simple data import format, this concepts are implemented in the LAILAPS search engine. It can easily be used both as search engine for comprehensive integrated life science databases and for small in-house project databases. LAILAPS is publicly available for SWISSPROT data at http://lailaps.ipk-gatersleben.de
An integrative approach to modeling biological networks
Authors: Memišević Vesna , Milenković Tijana , Pržulj Nataša 
Published: December 2017

SummaryNetworks are used to model real-world phenomena in various domains, including systems biology. Since proteins carry out biological processes by interacting with other proteins, it is expected that cellular functions are reflected in the structure of protein-protein interaction (PPI) networks. Similarly, the topology of residue interaction graphs (RIGs) that model proteins’ 3-dimensional structure might provide insights into protein folding, stability, and function. An important step towards understanding these networks is finding an adequate network model, since models can be exploited algorithmically as well as used for predicting missing data. Evaluating the fit of a model network to the data is a formidable challenge, since network comparisons are computationally infeasible and thus have to rely on heuristics, or “network properties.” We show that it is difficult to assess the reliability of the fit of a model using any network property alone. Thus, we present an integrative approach that feeds a variety of network properties into five machine learning classifiers to predict the best-fitting network model for PPI networks and RIGs. We confirm that geometric random graphs (GEO) are the best-fitting model for RIGs. Since GEO networks model spatial relationships between objects and are thus expected to replicate well the underlying structure of spatially packed residues in a protein, the good fit of GEO to RIGs validates our approach. Additionally, we apply our approach to PPI networks and confirm that the structure of merged data sets containing both binary and co-complex data that are of high coverage and confidence is also consistent with the structure of GEO, while the structure of less complete and lower confidence data is not. Since PPI data are noisy, we test the robustness of the five classifiers to noise and show that their robustness levels differ. We demonstrate that none of the classifiers predicts noisy scale-free (SF) networks as GEO, whereas noisy GEOs can be classified as SF. Thus, it is unlikely that our approach would predict a real-world network as GEO if it had a noisy SF structure. However, it could classify the data as SF if it had a noisy GEO structure. Therefore, the structure of the PPI networks is the most consistent with the structure of a noisy GEO.
ExPlanes: Exploring Planes in Triplet Data
Authors: Schwenk Bruno , Selbig Joachim , Ben-Zion Yehuda , Holschneider Matthias 
Published: December 2017

Summary Many methods for the analysis of gene expression-, protein- or metabolite-data focus on the investigation of binary relationships, while the underlying biological processes creating this data may generate relations of higher than bivariate complexity. We give a novel method ExPlanes that helps to explore certain types of ternary relationships in a statistically robust, Bayesian framework. To arrive at an characterization of the data structure contained in triplet data we investigate 2-dimensional planes being the only linear structures that cannot be inferred from projections of the data. The key part of our methodology is the definition of a robust, Bayesian plane posterior under the assumption of an invariant prior and a Gaussian error model. A numerical representation of the plane posterior can be explored interactively. Beyond this purely Bayesian approach we can use the plane posterior to construct a family of posterior-based test statistics that allow testing the data for different plane related hypotheses. To demonstrate practicability we queried triplets of metabolic data from a plant crossing experiment for the presence of plane-, line- and point-structures by using posterior-based test statistics and were able to show their distinctiveness.