bluuebunny/crossref_metadata_2025 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

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

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

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

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

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

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

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

We 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

The 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

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

Background: 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

The 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

A 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

Despite 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

A 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

Gene 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.
Enhancing Data Integration with Text Analysis to Find Proteins Implicated in Plant Stress Response
Authors: Hassani-Pak Keywan , Legaie Roxane , Canevet Catherine , Berg Hugo A. van den , Moore Jonathan D. , Rawlings Christopher J.
Published: December 2017

High throughput genomic studies can identify large numbers of potential candidate genes, which must be interpreted and filtered by investigators to select the best ones for further analysis. Prioritization is generally based on evidence that supports the role of a gene product in the biological process being investigated. The two most important bodies of information providing such evidence are bioinformatics databases and the scientific literature. In this paper we present an extension to the Ondex data integration framework that uses text mining techniques over Medline abstracts as a method for accessing both these bodies of evidence in a consistent way. In an example use case, we apply our method to create a knowledge base of Arabidopsis proteins implicated in plant stress response and use various scoring metrics to identify key protein-stress associations. In conclusion, we show that the additional text mining features are able to highlight proteins using the scientific literature that would not have been seen using data integration alone. Ondex is an open-source software project and can be downloaded, together with the text mining features described here, from www.ondex.org.
A Model of Phospholipid Biosynthesis in Tumor in Response to an Anticancer Agent in Vivo
Authors: Behzadi Mahsa , Demidem Aicha , Morvan Daniel , Schwartz Laurent , Stepien Georges , Steyaert Jean-Marc
Published: December 2017

We study, in this paper, a model for the core of the system of the Glycerophospholipid metabolism in the murine cells. It comprises the simple and enzymatic reactions of PhosphatidylEthanolamine and the PhosphatidylCholine . The model’s general structure is taken from a number of books and articles. We translate this model into a set of ordinary differential equations (ODEs), to propose a quantitative explanation of the experimental experiences and the observed results. In order to make it usable as a basis for simulations and mathematical analysis we need to make precise the various constants present in the equations but which are usually not directly accessible in the literature. In a first step we considered experimental data of rat’s liver cells obtained by NMR spectroscopy: given the values of metabolite concentrations we find appropriate parameter values which allow us to describe the system with ODEs. We have then performed several analyses using the developed model such as stability analysis. A first interesting result is the global stability of the system which was observed by simulation and then proved by mathematical arguments. A second important result is that we observe on the diagrams that the steady state for normal cells is precisely a singular point of order two, whereas tumoral cells present different characteristics; this fact has been proved for PhosphatidylEthanolamine N-Methyl transferase (PEMT), an enzyme which seems to be identified for the first time as a crucial element in the tumoral process. In a second step we applied our model to experimental data of proton HRMAS NMR spectroscopy for solid B16 melanoma and Lewis lung (3LL) 3LL carcinoma cells treated by Chloroethyl Nitrosourea (CENU). We performed a complete comparative analysis of parameters in order to learn the predictive statements to explain increases and decreases which one can observe in concentrations.
Clustering of gene expression profiles: creating initialization-independent clusterings by eliminating unstable genes
Authors: De Mulder Wim , Kuiper Martin , Boel René
Published: December 2017

Clustering is an important approach in the analysis of biological data, and often a first step to identify interesting patterns of coexpression in gene expression data. Because of the high complexity and diversity of gene expression data, many genes cannot be easily assigned to a cluster, but even if the dissimilarity of these genes with all other gene groups is large, they will finally be forced to become member of a cluster. In this paper we show how to detect such elements, called unstable elements. We have developed an approach for iterative clustering algorithms in which unstable elements are deleted, making the iterative algorithm less dependent on initial centers. Although the approach is unsupervised, it is less likely that the clusters into which the reduced data set is subdivided contain false positives. This clustering yields a more differentiated approach for biological data, since the cluster analysis is divided into two parts: the pruned data set is divided into highly consistent clusters in an unsupervised way and the removed, unstable elements for which no meaningful cluster exists in unsupervised terms can be given a cluster with the use of biological knowledge and information about the likelihood of cluster membership. We illustrate our framework on both an artificial and real biological data set.
A flexible statistics web processing service - Added value for information systems for experiment data
Authors: Heimann Dennis , Nieschulze Jens , König-Ries Birgitta
Published: December 2017

Data management in the life sciences has evolved from simple storage of data to complex information systems providing additional functionalities like analysis and visualization capabilities, demanding the integration of statistical tools.

In many cases the used statistical tools are hard-coded within the system. That leads to an expensive integration, substitution, or extension of tools because all changes have to be done in program code.

Other systems are using generic solutions for tool integration but adapting them to another system is mostly rather extensive work.

This paper shows a way to provide statistical functionality over a statistics web service, which can be easily integrated in any information system and set up using XML configuration files. The statistical functionality is extendable by simply adding the description of a new application to a configuration file. The service architecture as well as the data exchange process between client and service and the adding of analysis applications to the underlying service provider are described. Furthermore a practical example demonstrates the functionality of the service.
PathJam: a new service for integrating biological pathway information
Authors: Glez-Peña Daniel , Reboiro-Jato Miguel , Domínguez Rubén , Gómez-López Gonzalo , Pisano David G. , Fdez-Riverola Florentino
Published: December 2017

Biological pathways are crucial to much of the scientific research today including the study of specific biological processes related with human diseases. PathJam is a new comprehensive and freely accessible web-server application integrating scattered human pathway annotation from several public sources. The tool has been designed for both (i) being intuitive for wet-lab users providing statistical enrichment analysis of pathway annotations and (ii) giving support to the development of new integrative pathway applications. PathJam’s unique features and advantages include interactive graphs linking pathways and genes of interest, downloadable results in fully compatible formats, GSEA compatible output files and a standardized RESTful API.
Visualization and Analysis of a Cardio Vascular Diseaseand MUPP1-related Biological Network combining Text Mining and Data Warehouse Approaches
Authors: Sommer Björn , Tiys Evgeny S. , Kormeier Benjamin , Hippe Klaus , Janowski Sebastian J. , Ivanisenko Timofey V. , Bragin Anatoly O. , Arrigo Patrizio , Demenkov Pavel S. , Kochetov Alexey V. , Ivanisenko Vladimir A. , Kolchanov Nikolay A. , Hofestädt Ralf
Published: December 2017

Detailed investigation of socially important diseases with modern experimental methods has resulted in the generation of large volume of valuable data. However, analysis and interpretation of this data needs application of efficient computational techniques and systems biology approaches. In particular, the techniques allowing the reconstruction of associative networks of various biological objects and events can be useful. In this publication, the combination of different techniques to create such a network associated with an abstract cell environment is discussed in order to gain insights into the functional as well as spatial interrelationships. It is shown that experimentally gained knowledge enriched with data warehouse content and text mining data can be used for the reconstruction and localization of a cardiovascular disease developing network beginning with MUPP1/MPDZ (multi-PDZ domain protein).
Web2.0 paves new ways for collaborative and exploratory analysis of Chemical Compounds in Spectrometry Data
Authors: Loyek Christian , Bunkowski Alexander , Vautz Wolfgang , Nattkemper Tim W.
Published: December 2017

In nowadays life science projects, sharing data and data interpretation is becoming increasingly important. This considerably calls for novel information technology approaches, which enable the integration of expert knowledge from different disciplines in combination with advanced data analysis facilities in a collaborative manner. Since the recent development of web technologies offers scientific communities new ways for cooperation and communication, we propose a fully web-based software approach for the collaborative analysis of bioimage data and demonstrate the applicability of Web2.0 techniques to ion mobility spectrometry image data. Our approach allows collaborating experts to easily share, explore and discuss complex image data without any installation of software packages. Scientists only need a username and a password to get access to our system and can directly start exploring and analyzing their data.
Life Sciences Data and Application Integration with B-Fabric
Authors: Türker Can , Akal Fuat , Schlapbach Ralph
Published: December 2017

In this demo paper, we sketch B-Fabric, an all-in-one solution for management of life sciences data. B-Fabric has two major purposes. First, it is a system for the integrated management of experimental data and scientific annotations. Second, it is a system infrastructure supporting on-the fly coupling of user applications, and thus serving as extensible platform for fast-paced, cutting-edge, collaborative research.
GMB: An Efficient Query Processor for Biological Data
Authors: Taha Kamal , Elmasri Ramez
Published: December 2017

Bioinformatics applications manage complex biological data stored into distributed and often heterogeneous databases and require large computing power. These databases are too big and complicated to be rapidly queried every time a user submits a query, due to the overhead involved in decomposing the queries, sending the decomposed queries to remote databases, and composing the results. There is also considerable communication costs involved. This study addresses the mentioned problems in Grid-based environment for bioinformatics. We propose a Grid middleware called GMB that alleviates these problems by caching the results of Frequently Used Queries (FUQ). Queries are classified based on their types and frequencies. FUQ are answered from the middleware, which improves their response time. GMB acts as a gateway to TeraGrid Grid: it resides between users’ applications and TeraGrid Grid. We evaluate GMB experimentally.
Modelling Proteolytic Enzymes With Support Vector Machines
Authors: Morgado Lionel , Pereira Carlos , Veríssimo Paula , Dourado António
Published: December 2017

The strong activity felt in proteomics during the last decade created huge amounts of data, for which the knowledge is limited. Retrieving information from these proteins is the next step. For that, computational techniques are indispensable. Although there is not yet a silver bullet approach to solve the problem of enzyme detection and classification, machine learning formulations such as the state-of-the-art Support Vector Machine (SVM) appear among the most reliable options. A SVM based framework for peptidase analysis, that recognizes the hierarchies demarked in the MEROPS database is presented. Feature selection with SVM-RFE is used to improve the discriminative models and build classifiers computationally more efficient than alignment based techniques.
Prognostic Prediction through Biclustering-Based Classification of Clinical Gene Expression Time Series
Authors: Carreiro André V. , Anunciação Orlando , Carriço João A. , Madeira Sara C.
Published: December 2017

The constant drive towards a more personalized medicine led to an increasing interest in temporal gene expression analyzes. It is now broadly accepted that considering a temporal perspective represents a great advantage to better understand disease progression and treatment results at a molecular level. In this context, biclustering algorithms emerged as an important tool to discover local expression patterns in biomedical applications, and CCC-Biclustering arose as an efficient algorithm relying on the temporal nature of data to identify all maximal temporal patterns in gene expression time series. In this work, CCC-Biclustering was integrated in new biclustering-based classifiers for prognostic prediction. As case study we analyzed multiple gene expression time series in order to classify the response of Multiple Sclerosis patients to the standard treatment with Interferon-β, to which nearly half of the patients reveal a negative response. In this scenario, using an effective predictive model of a patient’s response would avoid useless and possibly harmful therapies for the non-responder group. The results revealed interesting potentialities to be further explored in classification problems involving other (clinical) time series.
Classification methods for finding articles describing protein-protein interactions in PubMed
Authors: Matos Sérgio , Oliveira José Luis
Published: December 2017

With the rapid expansion in the number of published papers in the biomedical field, finding relevant articles has become a demanding task for researchers. This has led to increasing interest in the use of text mining tools that help search the literature and identify the most relevant documents or information. One specific topic of interest is related to the identification of articles that might be used for extracting protein-protein interactions. Using the BioCreative III Article Classification Task dataset, composed of PubMed abstracts classified as relevant or non-relevant for describing protein-protein interactions, we compare different classification methods with different sets of features. The best results - area under the interpolated precision-recall curve of 0.654 - indicate that the proposed classification strategy could be incorporated in the database curation workflows in order to prioritize articles for extraction of protein-protein interactions. Furthermore, we also analysed the use of this method for ranking documents resulting from general PubMed queries, and propose that this approach could be useful for general researchers looking for publications describing protein-protein interactions within a particular topic of interest.
Tracking T and B cells from two-photon microscopy imaging using constrained SMC clusters
Authors: Olivieri D. , Faro J. , Gomez-Conde I. , Tadokoro C. E.
Published: December 2017

This paper describes a novel software algorithm, called constrained Sequential Monte Carlo (SMC) clusters, for tracking a large collection of individual cells from intra-vital two-photon microscopy image sequences. We show how our method and software tool, implemented in python, is useful for quantifying the motility of T and B lymphocytes involved in an immune response vs lymphocytes under non immune conditions. We describe the theory behind our algorithm and briefly discuss the architecture of our software. Finally, we demonstrate both the functionality and utility of software by applying it to two practical examples from videos displaying lymphocyte motility in B cell zones (follicles) and T cell zones of lymph nodes.
A Study of the Short and Long-term Regulation of E. coli Metabolic Pathways
Authors: Lourenço Anália , Carneiro Sónia , Pinto José P. , Rocha Miguel , Ferreira Eugénio C. , Rocha Isabel
Published: December 2017

The present study addresses the regulatory network of Escherichia coli and offers a global view of the short- and long-term regulation of its metabolic pathways. The regulatory mechanisms responsible for key metabolic activities and the structure behind such mechanisms are detailed. Most metabolic functions are dependent on the activity of transcriptional regulators over gene expression - the so-called long-term regulation. However, enzymatic regulation - the so-called short-term regulation - often overlays transcriptional regulation and even, in particular metabolic pathways, enzymatic regulation may prevail. As such, understanding the balance between these two types of regulation is necessary to be able to predict and control cell responses, specifically cell responses to the various environmental stresses.
IDPredictor: predict database links in biomedical database
Authors: Mehlhorn Hendrik , Lange Matthias , Scholz Uwe , Schreiber Falk
Published: December 2017

Knowledge found in biomedical databases, in particular in Web information systems, is a major bioinformatics resource. In general, this biological knowledge is worldwide represented in a network of databases. These data is spread among thousands of databases, which overlap in content, but differ substantially with respect to content detail, interface, formats and data structure.

To support a functional annotation of lab data, such as protein sequences, metabolites or DNA sequences as well as a semi-automated data exploration in information retrieval environments, an integrated view to databases is essential. Search engines have the potential of assisting in data retrieval from these structured sources, but fall short of providing a comprehensive knowledge excerpt out of the interlinked databases. A prerequisit of supporting the concept of an integrated data view is to acquire insights into cross-references among database entities. This issue is being hampered by the fact, that only a fraction of all possible cross-references are explicitely tagged in the particular biomedical informations systems.

In this work, we investigate to what extend an automated construction of an integrated data network is possible. We propose a method that predicts and extracts cross-references from multiple life science databases and possible referenced data targets. We study the retrieval quality of our method and report on first, promising results. The method is implemented as the tool IDPredictor, which is published under the DOI 10.5447/IPK/2012/4 and is freely available using the URL: http://dx.doi.org/10.5447/IPK/2012/4.
Integrated simultaneous analysis of different biomedical data types with exact weighted bi-cluster editing
Authors: Sun Peng , Guo Jiong , Baumbach Jan
Published: December 2017

The explosion of biological data has largely influenced the focus of today’s biology research. Integrating and analysing large quantity of data to provide meaningful insights has become the main challenge to biologists and bioinformaticians. One major problem is the combined data analysis of data from different types, such as phenotypes and genotypes. This data is modelled as bi-partite graphs where nodes correspond to the different data points, mutations and diseases for instance, and weighted edges relate to associations between them. Bi-clustering is a special case of clustering designed for partitioning two different types of data simultaneously. We present a bi-clustering approach that solves the NP-hard weighted bi-cluster editing problem by transforming a given bi-partite graph into a disjoint union of bi-cliques. Here we contribute with an exact algorithm that is based on fixed-parameter tractability. We evaluated its performance on artificial graphs first. Afterwards we exemplarily applied our Java implementation to data of genome-wide association studies (GWAS) data aiming for discovering new, previously unobserved geno-to-pheno associations. We believe that our results will serve as guidelines for further wet lab investigations. Generally our software can be applied to any kind of data that can be modelled as bi-partite graphs. To our knowledge it is the fastest exact method for weighted bi-cluster editing problem.
ADOPS - Automatic Detection Of Positively Selected Sites
Authors: Reboiro-Jato David , Reboiro-Jato Miguel , Fdez-Riverola Florentino , Vieira Cristina P. , Fonseca Nuno A. , Vieira Jorge
Published: December 2017

Maximum-likelihood methods based on models of codon substitution have been widely used to infer positively selected amino acid sites that are responsible for adaptive changes. Nevertheless, in order to use such an approach, software applications are required to align protein and DNA sequences, infer a phylogenetic tree and run the maximum-likelihood models. Therefore, a significant effort is made in order to prepare input files for the different software applications and in the analysis of the output of every analysis. In this paper we present the ADOPS (Automatic Detection Of Positively Selected Sites) software. It was developed with the goal of providing an automatic and flexible tool for detecting positively selected sites given a set of unaligned nucleotide sequence data. An example of the usefulness of such a pipeline is given by showing, under different conditions, positively selected amino acid sites in a set of 54 Coffea putative S-RNase sequences. ADOPS software is freely available and can be downloaded from http://sing.ei.uvigo.es/ADOPS.
In silico strain optimization by adding reactions to metabolic models
Authors: Correia Sara , Rocha Miguel
Published: December 2017

Nowadays, the concerns about the environment and the needs to increase the productivity at low costs, demand for the search of new ways to produce compounds with industrial interest. Based on the increasing knowledge of biological processes, through genome sequencing projects, and high-throughput experimental techniques as well as the available computational tools, the use of microorganisms has been considered as an approach to produce desirable compounds. However, this usually requires to manipulate these organisms by genetic engineering and/ or changing the enviromental conditions to make the production of these compounds possible. In many cases, it is necessary to enrich the genetic material of those microbes with hereologous pathways from other species and consequently adding the potential to produce novel compounds.

This paper introduces a new plug-in for the OptFlux Metabolic Engineering platform, aimed at finding suitable sets of reactions to add to the genomes of selected microbes (wild type strain), as well as finding complementary sets of deletions, so that the mutant becomes able to overproduce compounds with industrial interest, while preserving their viability. The necessity of adding reactions to the metabolic model arises from existing gaps in the original model or motivated by the productions of new compounds by the organism. The optimization methods used are metaheuristics such as Evolutionary Algorithms and Simulated Annealing. The usefulness of this plug-in is demonstrated by a case study, regarding the production of vanillin by the bacterium E. coli.