bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identifying Transcription Regulatory Elements in the Human and Mouse Genomes Using Tissue-specific Gene Expression Profiles
Authors: Karmaker Amitava , Yoon Kihoon , Doderer Mark , Kruzelock Russell , Kwek Stephen 
Published: December 2017

Summary Revealing the complex interaction between trans- and cis-regulatory elements and identifying these potential binding sites are fundamental problems in understanding gene expression. The progresses in ChIP-chip technology facilitate identifying DNA sequences that are recognized by a specific transcription factor. However, protein-DNA binding is a necessary, but not sufficient, condition for transcription regulation. We need to demonstrate that their gene expression levels are correlated to further confirm regulatory relationship. Here, instead of using a linear correlation coefficient, we used a non-linear function that seems to better capture possible regulatory relationships. By analyzing tissue-specific gene expression profiles of human and mouse, we delineate a list of pairs of transcription factor and gene with highly correlated expression levels, which may have regulatory relationships. Using two closely-related species (human and mouse), we perform comparative genome analysis to cross-validate the quality of our prediction. Our findings are confirmed by matching publicly available TFBS databases (like TRANFAC and ConSite) and by reviewing biological literature. For example, according to our analysis, 80% and 85.71% of the targets genes associated with E2F5 and RELB transcription factors have the corresponding known binding sites. We also substantiated our results on some oncogenes with the biomedical literature. Moreover, we performed further analysis on them and found that BCR and DEK may be regulated by some common transcription factors. Similar results for BTG1, FCGR2B and LCK genes were also reported.
Defining Mapping Mashups with BioXMash
Authors: Hunt Ela , Jakubowska Joanna , Bösinger Caroline , Norrie Moira C. 
Published: December 2017

Summary We present a novel approach to XML data integration which allows a biologist to select data from a large XML file repository, add it to a genome map, and produce a mapping mashup showing integrated data in map context. This approach can be used to produce contextual views of arbitrary XML data which relates to objects shown on a map. A biologist using BioXMash searches in XML tags, and is guided by XML path data availability, shown as the number of values reachable via a path, in both global, genome-wide, and local, per-gene, context. Then she examines sample values in an area of interest on the map. If required, the resulting data is dumped to files, for subsequent analysis.This is a lightweight integration approach, and differs significantly from other known methods. It assumes that data integration can be performed on a lab computer with limited memory, with no database installation or programming knowledge. It is different from BioMarts which predefine possible data selections, in that arbitrary data sources related to map items can be used. BioXMash offers full textual search in XML paths, shows path statistics, and supports visual verification of data values. Repeated scanning of all XML files at query time is avoided by the use of a high level indexing technique. Our prototype demonstrates this new approach. It efficiently supports data browsing on 2 GB od data from GeneCards with an index size of 40 MB.
Stochastic effects in a compartmental model for mitotic checkpoint regulation
Authors: Ibrahim Bashar , Dittrich Peter , Diekmann Stephan , Schmitt Eberhard 
Published: December 2017

Summary The proper segregation of sister chromatids at onset of anaphase is surveyed by the mitotic spindle assembly checkpoint. The concentration dynamics of the complexes APC:Cdc20 and MCC:APC determine exit from metaphase to anaphase. We have developed a model based on 14 proteins and complexes to describe concentration dynamics by ordinary differential equations in three compartments coupled by diffusion. One kinetochore in each compartment determines the attachment status to the spindle pole. Here, we focus on the role of noise in the segregation surveillance process. The deterministic differential equations are enriched by a stochastic term adding white noise of different amplitudes. Obviously, for the known physiological parameter ranges, noise does not disturb the checkpoint function. On the other hand, there is a connection between diffusion and noise, that could become important when considering a larger number of chromosomes.
Data Linkage Graph: computation, querying and knowledge discovery of life science database networks
Authors: Lange Matthias , Himmelbach Axel , Schweizer Patrick , Scholz Uwe 
Published: December 2017

Summary To support the interpretation of measured molecular facts, like gene expression experiments or EST sequencing, the functional or the system biological context has to be considered. Doing so, the relationship to existing biological knowledge has to be discovered. In general, biological knowledge is worldwide represented in a network of databases. In this paper we present a method for knowledge extraction in life science databases, which prevents the scientists from screen scraping and web clicking approaches.We developed a method for extraction of knowledge networks from distributed, heterogeneous life science databases. To meet the requirement of the very large data volume, the method used is based on the concept of data linkage graphs (DLG).We present an efficient software which enables the joining of millions of data points over hundreds of databases. In order to motivate possible applications, we computed networks of protein knowledge, which interconnect metabolic, disease, enzyme and gene function data.The computed networks enabled a holistic relationship among measured experimental facts and the combined biological knowledge. This was successfully applied for a high throughput functional classification of barley EST and gene expression experiments with the perspective of an automated pipeline for the provisioning of controlled annotation of plant gene arrays and chips.Availability: The data linkage graphs (XML or TGF format), the schema integrated database schema (GML or GRAPH-ML) and the graph computation software may be downloaded from the following URL: http://pgrc.ipk-gatersleben.de/dlg/
Community-based Linking of Biological Network Resources: Databases, Formats and Tools
Authors: Telgkamp Michael , Koschützki Dirk , Schwöbbermeyer Henning , Schreiber Falk 
Published: December 2017

Summary The analysis of biological networks is increasingly important in the life sciences and in particular in systems biology. Computer-based analysis tools are exploited for the investigation of these networks. However, to find relevant data sources can be a time-consuming task, access to information changes, frequently it is not obvious to a user which tools can be used in combination with specific data sources, and network data is often not available in a format directly usable by analysis tools.To assist in collecting information about biological network data sources, and to help in investigating possible combinations of data sources and analysis software, we created BiNCo-wiki. BiNCo-wiki is a wiki system that stores information related to biological networks and allows all users to add or modify this information in an easy way. The collaborative character of a wiki system supports easy sharing of information and allows the community-based extension of the information already stored in BiNCo-wiki. We describe the general structure and functionality of the system and its initial content. BiNCo-wiki is accessible, free of charge, at http://pgrc.ipk-gatersleben.de/BiNCO-wiki.
Analysis of integrated biomolecular networks using a generic network analysis suite
Authors: Oesterheld Matthias , Mewes Hans Werner , Stümpflen Volker 
Published: December 2017

Abstract The informative value of biomolecular networks has shifted from being solely information resources for possible cellular partners (whether these embody proteins, (ribo)nucleic acids or small molecules) towards becoming models for the functional connectivity within a cell. These models are increasingly exploited to make quantitative predictions about the cell’s functional organization as well as about the functionality of individual elements in the network.A large number of concepts and methods have been proposed in order to interpret experimental data mapped to cellular networks these systems and to make use of the rich source of information they represent.We will present a system for the Comprehensive Analysis of Biomolecular Networks (CABiNet), capable of integrating available network analysis methods. Integration is done by classifying each method into one of four separate categories using standardized interfaces that encapsulate the functionality of the method in a distinct component with standardized in- and output. These components can be accessed individually or in an integrated form using a processing pipeline for semi-automatic analyses.Additionally, the system can be used to query both biomolecular networks as well as the derived results of network analysis methods, such as clustering algorithms, in order to provide a service for researchers who are focused towards the functional context of any particular cellular entity.CABiNet is designed in an easy-to-use and easy-to-extend software framework that allows a straightforward integration of novel components. We will demonstrate the capabilities of the system by introducing several use cases.The CABiNet suite can be accessed at http://mips.gsf.de/genre/proj/CABiNet. Source code including additional components that can be accessed using the API is available upon request.
Supervised classification of combined copy number and gene expression data
Authors: Riccadonna S. , Jurman G. , Merler S. , Paoli S. , Quattrone A. , Furlanello C. 
Published: December 2017

Summary In this paper we apply a predictive profiling method to genome copy number aberrations (CNA) in combination with gene expression and clinical data to identify molecular patterns of cancer pathophysiology. Predictive models and optimal feature lists for the platforms are developed by a complete validation SVM-based machine learning system. Ranked list of genome CNA sites (assessed by comparative genomic hybridization arrays – aCGH) and of differentially expressed genes (assessed by microarray profiling with Affy HG-U133A chips) are computed and combined on a breast cancer dataset for the discrimination of Luminal/ ER+ (Lum/ER+) and Basal-like/ER- classes. Different encodings are developed and applied to the CNA data, and predictive variable selection is discussed. We analyze the combination of profiling information between the platforms, also considering the pathophysiological data. A specific subset of patients is identified that has a different response to classification by chromosomal gains and losses and by differentially expressed genes, corroborating the idea that genomic CNA can represent an independent source for tumor classification.
Integration of constraints documented in SBML, SBO, and the SBML Manual facilitates validation of biological models
Authors: Lister Allyson L. , Pocock Matthew , Wipat Anil 
Published: December 2017

Abstract The creation of quantitative, simulatable, Systems Biology Markup Language (SBML) models that accurately simulate the system under study is a time-intensive manual process that requires careful checking. Currently, the rules and constraints of model creation, curation, and annotation are distributed over at least three separate documents: the SBML schema document (XSD), the Systems Biology Ontology (SBO), and the “Structures and Facilities for Model Definition” document. The latter document contains the richest set of constraints on models, and yet it is not amenable to computational processing. We have developed a Web Ontology Language (OWL) knowledge base that integrates these three structure documents, and that contains a representative sample of the information contained within them. This Model Format OWL (MFO) performs both structural and constraint integration and can be reasoned over and validated. SBML Models are represented as individuals of OWL classes, resulting in a single computationally amenable resource for model checking. Knowledge that was only accessible to humans is now explicitly and directly available for computational approaches. The integration of all structural knowledge for SBML models into a single resource creates a new style of model development and checking.
A Tool for Evaluating Strategies for Grouping of Biological Data
Authors: Jakonienė Vaida , Lambrix Patrick 
Published: December 2017

Summary During the last decade an enormous amount of biological data has been generated and techniques and tools to analyze this data have been developed. Many of these tools use some form of grouping and are used in, for instance, data integration, data cleaning, prediction of protein functionality, and correlation of genes based on microarray data. A number of aspects influence the quality of the grouping results: the data sources, the grouping attributes and the algorithms implementing the grouping procedure. Many methods exist, but it is often not clear which methods perform best for which grouping tasks. The study of the properties, and the evaluation and the comparison of the different aspects that influence the quality of the grouping results, would give us valuable insight in how the grouping procedures could be used in the best way. It would also lead to recommendations on how to improve the current procedures and develop new procedures. To be able to perform such studies and evaluations we need environments that allow us to compare and evaluate different grouping strategies. In this paper we present a framework, KitEGA1, for such an environment, and present its current prototype implementation. We illustrate its use by comparing grouping strategies for classifying proteins regarding biological function and isozymes.
Frequency Analysis of the Splice Site Regions in Different Organisms
Authors: Rekha T. Shashi , Mitra Chanchal K 
Published: December 2017

Summary We have carried out a comparative analysis of the sub-sequences of size six| ten at the (donor| acceptor) splice site regions of five different organisms. The frequency analysis of the unique sub-sequences at the donor and acceptor regions suggests that the distribution of their occurrence is approximately exponential. We have observed that the number of unique sub-sequences (occurring with different frequencies) at the donor region are less than at the acceptor, suggesting that the sub-sequences at the acceptor region are more variable. The sub-sequences with high percentage of occurrence (uniqueness) are considered to be highly involved in splicing. Our analysis suggests that sub-sequences of length ~6-8 nucleotides (nt) at the splice sites – with six bases in intron (including the two central, conserved dinucleotides) and two bases in exon are optimal for the efficient assembly and binding of the spliceosomal complex during the process of splicing. The score pattern obtained by the alignment of the nucleotides at the donor region with the acceptor and vice-versa also suggests that a single sub-sequence at the donor region have different degree of similarity with sub-sequences at the acceptor thus determining that the donor sub-sequences are more crucial in pairing with the corresponding acceptor sub-sequences during the process of splicing.
ReMatch: a web-based tool to construct, store and share stoichiometric metabolic models with carbon maps for metabolic flux analysis
Authors: Pitkänen Esa , Åkerlund Arto , Rantanen Ari , Jouhten Paula , Ukkonen Esko 
Published: December 2017

Summary ReMatch is a web-based, user-friendly tool that constructs stoichiometric network models for metabolic flux analysis, integrating user-developed models into a database collected from several comprehensive metabolic data resources, including KEGG, MetaCyc and CheBI. Particularly, ReMatch augments the metabolic reactions of the model with carbon mappings to facilitateThe construction of a network model consisting of biochemical reactions is the first step in most metabolic modelling tasks. This model construction can be a tedious task as the required information is usually scattered to many separate databases whose interoperability is suboptimal, due to the heterogeneous naming conventions of metabolites in different databases. Another, particularly severe data integration problem is faced inReMatch has been developed to solve the above data integration problems. First, ReMatch matches the imported user-developed model against the internal ReMatch database while considering a comprehensive metabolite name thesaurus. This, together with wild card support, allows the user to specify the model quickly without having to look the names up manually. Second, ReMatch is able to augment reactions of the model with carbon mappings, obtained either from the internal database or given by the user with an easy-touse tool.The constructed models can be exported into 13C-FLUX and SBML file formats. Further, a stoichiometric matrix and visualizations of the network model can be generated. The constructed models of metabolic networks can be optionally made available to the other users of ReMatch. Thus, ReMatch provides a common repository for metabolic network models with carbon mappings for the needs of metabolic flux analysis community. ReMatch is freely available for academic use at http://www.cs.helsinki.fi/group/sysfys/software/rematch/.
Mining Protein Databases using Machine Learning Techniques
Authors: Camargo Renata da Silva , Niranjan Mahesan 
Published: December 2017

SummaryWith a large amount of information relating to proteins accumulating in databases widely available online, it is of interest to apply machine learning techniques that, by extracting underlying statistical regularities in the data, make predictions about the functional and evolutionary characteristics of unseen proteins. Such predictions can help in achieving a reduction in the space over which experiment designers need to search in order to improve our understanding of the biochemical properties. Previously it has been suggested that an integration of features computable by comparing a pair of proteins can be achieved by an artificial neural network, hence predicting the degree to which they may be evolutionary related and homologous.We compiled two datasets of pairs of proteins, each pair being characterised by seven distinct features. We performed an exhaustive search through all possible combinations of features, for the problem of separating remote homologous from analogous pairs, we note that significant performance gain was obtained by the inclusion of sequence and structure information. We find that the use of a linear classifier was enough to discriminate a protein pair at the family level. However, at the superfamily level, to detect remote homologous pairs was a relatively harder problem. We find that the use of nonlinear classifiers achieve significantly higher accuracies.In this paper, we compare three different pattern classification methods on two problems formulated as detecting evolutionary and functional relationships between pairs of proteins, and from extensive cross validation and feature selection based studies quantify the average limits and uncertainties with which such predictions may be made. Feature selection points to a “knowledge gap” in currently available functional annotations. We demonstrate how the scheme may be employed in a framework to associate an individual protein with an existing family of evolutionarily related proteins.
Towards the integration of computational systems biology and high-throughput data: supporting differential analysis of microarray gene expression data
Authors: Segata Nicola , Blanzieri Enrico , Priami Corrado 
Published: December 2017

Summary The paradigmatic shift occurred in biology that led first to high-throughput experimental techniques and later to computational systems biology must be applied also to the analysis paradigm of the relation between local models and data to obtain an effective prediction tool. In this work we introduce a unifying notational framework for systems biology models and high-throughput data in order to allow new integrations on the systemic scale like the use of in silico predictions to support the mining of gene expression datasets. Using the framework, we propose two applications concerning the use of system level models to support the differential analysis of microarray expression data. We tested the potentialities of the approach with a specific microarray experiment on the phosphate system in Saccharomyces cerevisiae and a computational model of the PHO pathway that supports the systems biology concepts.
A Bayes Random Fields Approach for Integrative Large-Scale Regulatory Network Analysis
Authors: Yuan Yinyin , Li Chang-Tsun 
Published: December 2017

SummaryWe present a Bayes-Random Fields framework which is capable of integrating unlimited data sources for discovering relevant network architecture of large-scale networks. The random field potential function is designed to impose a cluster constraint, teamed with a full Bayesian approach for incorporating heterogenous data sets. The probabilistic nature of our framework facilitates robust analysis in order to minimize the influence of noise inherent in the data on the inferred structure in a seamless and coherent manner. This is later proved in its applications to both large-scale synthetic data sets and Saccharomyces Cerevisiae data sets. The analytical and experimental results reveal the varied characteristic of different types of data and reflect their discriminative ability in terms of identifying direct gene interactions.
An approach to pathway reconstruction using whole genome metabolic models and sensitive sequence searching
Authors: Saqi Mansoor , Dobson Richard Jb. , Kraben Preben , Hodgson David A. , Wild David L. 
Published: December 2017

SummaryMetabolic models have the potential to impact on genome annotation and on the interpretation of gene expression and other high throughput genome data. The genome of Streptomyces coelicolor genome has been sequenced and some 30% of the open reading frames (ORFs) lack any functional annotation. A recently constructed metabolic network model for S. coelicolor highlights biochemical functions which should exist to make the metabolic model complete and consistent. These include 205 reactions for which no ORF is associated. Here we combine protein functional predictions for the unannotated open reading frames in the genome with ‘missing but expected’ functions inferred from the metabolic model. The approach allows function predictions to be evaluated in the context of the biochemical pathway reconstruction, and feed back iteratively into the metabolic model. We describe the approach and discuss a few illustrative examples.