bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of integrated biomolecular networks using a generic network analysis suite
Authors: Oesterheld Matthias , Mewes Hans Werner , Stümpflen Volker
Published: December 2017

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

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

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

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

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

Summar

y 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 facilitate

The 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 in

ReMatch 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

With 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

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

We 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

Metabolic 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.
The LAILAPS Search Engine: Relevance Ranking in Life Science Databases
Authors: Lange Matthias , Spies Karl , Bargsten Joachim , Haberhauer Gregor , Klapperstück Matthias , Leps Michael , Weinel Christian , Wünschiers Röbbe , Weißbach Mandy , Stein Jens , Scholz Uwe
Published: December 2017

Search engines and retrieval systems are popular tools at a life science desktop. The manual inspection of hundreds of database entries, that reflect a life science concept or fact, is a time intensive daily work. Hereby, not the number of query results matters, but the relevance does. In this paper, we present the LAILAPS search engine for life science databases. The concept is to combine a novel feature model for relevance ranking, a machine learning approach to model user relevance profiles, ranking improvement by user feedback tracking and an intuitive and slim web user interface, that estimates relevance rank by tracking user interactions. Queries are formulated as simple keyword lists and will be expanded by synonyms. Supporting a flexible text index and a simple data import format, LAILAPS can easily be used both as search engine for comprehensive integrated life science databases and for small in-house project databases.

With a set of features, extracted from each database hit in combination with user relevance preferences, a neural network predicts user specific relevance scores. Using expert knowledge as training data for a predefined neural network or using users own relevance training sets, a reliable relevance ranking of database hits has been implemented.

In this paper, we present the LAILAPS system, the concepts, benchmarks and use cases. LAILAPS is public available for SWISSPROT data at http://lailaps.ipk-gatersleben.de
Efficient Online Transcription Factor Binding Site Adjustment by Integrating Transitive Graph Projection with MoRAine 2.0
Authors: Wittkop Tobias , Rahmann Sven , Baumbach Jan
Published: December 2017

We investigated the problem of imprecisely determined prokaryotic transcription factor (TF) binding sites (TFBSs). We found that the identification and reinvestigation of questionable binding motifs may result in improved models of these motifs. Subsequent modelbased predictions of gene regulatory interactions may be performed with increased accuracy when the TFBSs annotation underlying these models has been re-adjusted.

We present MoRAine 2.0, a significantly improved version of MoRAine. It can automatically identify cases of unfavorable TFBS strand annotations and imprecisely determined TFBS positions. With release 2.0, we close the gap between reasonable running time and high accuracy. Furthermore, it requires only minimal input from the user: (1) the input TFBS sequences and (2) the length of the flanking sequences.

Conclusions: MoRAine 2.0 is an easy-to-use, integrated, and publicly available web tool for the re-annotation of questionable TFBSs. It can be used online or downloaded as a stand-alone version from http://moraine.cebitec.uni-bielefeld.de.
MEMOPS: Data modelling and automatic code generation
Authors: Fogh Rasmus H. , Boucher Wayne , Ionides John M.C. , Vranken Wim F. , Stevens Tim J. , Laue Ernest D.
Published: December 2017

In recent years the amount of biological data has exploded to the point where much useful information can only be extracted by complex computational analyses. Such analyses are greatly facilitated by metadata standards, both in terms of the ability to compare data originating from different sources, and in terms of exchanging data in standard forms, e.g. when running processes on a distributed computing infrastructure. However, standards thrive on stability whereas science tends to constantly move, with new methods being developed and old ones modified. Therefore maintaining both metadata standards, and all the code that is required to make them useful, is a non-trivial problem. Memops is a framework that uses an abstract definition of the metadata (described in UML) to generate internal data structures and subroutine libraries for data access (application programming interfaces - APIs - currently in Python, C and Java) and data storage (in XML files or databases). For the individual project these libraries obviate the need for writing code for input parsing, validity checking or output. Memops also ensures that the code is always internally consistent, massively reducing the need for code reorganisation. Across a scientific domain a Memops-supported data model makes it easier to support complex standards that can capture all the data produced in a scientific area, share them among all programs in a complex software pipeline, and carry them forward to deposition in an archive. The principles behind the Memops generation code will be presented, along with example applications in Nuclear Magnetic Resonance (NMR) spectroscopy and structural biology.
Novel Developments of the MetaCrop Information System for Facilitating Systems Biological Approaches
Authors: Hippe Klaus , Colmsee Christian , Czauderna Tobias , Grafahrend-Belau Eva , Junker Björn H. , Klukas Christian , Scholz Uwe , Schreiber Falk , Weise Stephan
Published: December 2017

Crop plants play a major role in human and animal nutrition and increasingly contribute to chemical or pharmaceutical industry and renewable resources. In order to achieve important goals, such as the improvement of growth or yield, it is indispensable to understand biological processes on a detailed level. Therefore, the well-structured management of fine-grained information about metabolic pathways is of high interest. Thus, we developed the MetaCrop information system, a manually curated repository of high quality information concerning the metabolism of crop plants. However, the data access to and flexible export of information of MetaCrop in standard exchange formats had to be improved. To automate and accelerate the data access we designed a set of web services to be integrated into external software. These web services have already been used by an add-on for the visualisation toolkit VANTED. Furthermore, we developed an export feature for the MetaCrop web interface, thus enabling the user to compose individual metabolic models using SBML.
Prediction of thioredoxin and glutaredoxin target proteins by identifying reversibly oxidized cysteinyl residues
Authors: Lee Hang-mao , Dietz Karl Josef , Hofestädt Ralf
Published: December 2017

A significant part of cellular proteins undergo reversible thiol-dependent redox transitions which often control or switch protein functions. Thioredoxins and glutaredoxins constitute two key players in this redox regulatory protein network. Both interact with various categories of proteins containing reversibly oxidized cysteinyl residues. The identification of thioredoxin/glutaredoxin target proteins is a critical step in constructing the redox regulatory network of cells or subcellular compartments. Due to the scarcity of thioredoxin/glutaredoxin target protein records in the public database, a tool called Reversibly Oxidized Cysteine Detector (ROCD) is implemented here to identify potential thioredoxin/glutaredoxin target proteins computationally, so that the in silico construction of redox regulatory network may become feasible. ROCD was tested on 46 thioredoxin target proteins in plant mitochondrion, and the recall rate was 66.7% when 50% sequence identity was chosen for structural model selection. ROCD will be used to predict the thioredoxin/glutaredoxin target proteins in human liver mitochondrion for our redox regulatory network construction project. The ROCD will be developed further to provide prediction with more reliability and incorporated into biological network visualization tools as a node prediction component. This work will advance the capability of traditional database- or text mining-based method in the network construction.
Study of the triplet periodicity phase shifts in genes
Authors: Korotkov Eugene V. , Korotkova Maria A.
Published: December 2017

The definition of a phase shift of triplet periodicity (TP) is introduced. The mathematical algorithm for detection of TP phase shift of nucleotide sequences has been developed. Gene sequences from Kegg-46 data bank were analyzed with a purpose of searching genes with a phase shift of TP. The presence of a phase shift of triplet periodicity has been shown for 318329 genes (~10% from the number of genes in Kegg-46). We suppose that shifts of the TP phase may indicate the shifts of reading frame (RF) in genes. A relationship between the phase shifts of TP and the frame shifts in genes is discussed.
Comparison of different algorithms for simultaneous estimation of multiple parameters in kinetic metabolic models
Authors: Baker Syed Murtuza , Schallau Kai , Junker Björn H.
Published: December 2017

Computational models in systems biology are usually characterized by a lack of reliable parameter values. This is especially true for kinetic metabolic models. Experimental data can be used to estimate these missing parameters. Different optimization techniques have been explored to solve this challenging task but none has proved to be superior to the other. In this paper we review the problem of parameter estimation in kinetic models. We focus on the suitability of four commonly used optimization techniques of parameter estimation in biochemical pathways and make a comparison between those methods. The suitability of each technique is evaluated based on the ability of converging to a solution within a reasonable amount of time. As most local optimization methods fail to arrive at a satisfactory solution we only considered the global optimization techniques. A case study of the upper part of Glycolysis consisting 15 parameters is taken as the benchmark model for evaluating these methods.
Kernel based machine learning algorithm for the efficient prediction of type III polyketide synthase family of proteins
Authors: Mallika V , Sivakumar Kc , Jaichand S , Soniya Ev
Published: December 2017

Type III Polyketide synthases (PKS) are family of proteins considered to have significant role in the biosynthesis of various polyketides in plants, fungi and bacteria. As these proteins show positive effects to human health, more researches are going on regarding this particular protein. Developing a tool to identify the probability of sequence, being a type III polyketide synthase will minimize the time consumption and manpower efforts. In this approach, we have designed and implemented PKSIIIpred, a high performance prediction server for type III PKS where the classifier is Support Vector Machine (SVM). Based on the limited training dataset, the tool efficiently predicts the type III PKS superfamily of proteins with high sensitivity and specificity. PKSIIIpred is available at http://type3pks.in/prediction/. We expect that this tool may serve as a useful resource for type III PKS researchers. Currently work is being progressed for further betterment of prediction accuracy by including more sequence features in the training dataset.
Key2Ann: a tool to process sequence sets by replacing database identifiers with a human-readable annotation
Authors: Pürzer Andreas , Grassmann Felix , Birzer Dietmar , Merkl Rainer
Published: December 2017

Deducing common properties or degrees of phylogenetic relationship by analyzing a grouping or clustering of sequence sets is a frequently used technique in computational biology. If interpreted by means of visual inspection, the conclusions depend for many of these applications on meaningful names for the input data. In accordance with the aim of the analysis, the sequences should be provided with names indicating the function of the genes or gene-products, the phylogenetic position or other properties characterizing the contributing species. However, sequences extracted from databases are most often annotated with identifiers which only implicitly contain the desired information. To solve this problem, we have designed and implemented a tool named Key2Ann, which replaces in multiple fasta files the database keys with short terms indicating the taxonomic position or other features like the gene name or the EC-number. In addition, properties like habitat, growth temperature or the degree of pathogenicity can be coded for microbial species. To allow for highest flexibility, the user can control the composition of the names by means of command line parameters. Key2Ann is written in Java and can be downloaded via http://www-bioinf.uni-regensburg.de/downl/Key2Ann.zip. We demonstrate the usage of Key2Ann by discussing three typical examples of phylogenetic analysis.
WIBL: Workbench for Integrative Biological Learning
Authors: Lesk Victor , Taubert Jan , Rawlings Chris , Dunbar Stuart , Muggleton Stephen
Published: December 2017

The construction of integrated datasets from potentially hundreds of sources with bespoke formats, and their subsequent visualization and analysis, is a recurring challenge in systems biology. We present WIBL, a visualization and model development environment initially geared towards logic-based modelling of biological systems using integrated datasets. WIBL combines data integration, visualisation and modelling in a single portalbased workbench providing a comprehensive solution for interdisciplinary systems biology projects.
Database supported candidate search for Metabolite identification
Authors: Hildebrandt Christian , Wolf Sebastian , Neumann Steffen
Published: December 2017

Mass spectrometry is an important analytical technology for the identification of metabolites and small compounds by their exact mass. But dozens or hundreds of different compounds may have a similar mass or even the same molecule formula. Further elucidation requires tandem mass spectrometry, which provides the masses of compound fragments, but in silico fragmentation programs require substantial computational resources if applied to large numbers of candidate structures.

We present and evaluate an approach to obtain candidates from a relational database which contains 28 million compounds from PubChem.

A training phase associates tandem-MS peaks with corresponding fragment structures. For the candidate search, the peaks in a query spectrum are translated to fragment structures, and the candidates are retrieved and sorted by the number of matching fragment structures. In the cross validation the evaluation of the relative ranking positions (RRP) using different sizes of training sets confirms that a larger coverage of training data improves the average RRP from 0.65 to 0.72. Our approach allows downstream algorithms to process candidates in order of importance.
Integrative Visualization of Temporally Varying Medical Image Patterns
Authors: Soh Jung , Xiao Mei , Do Thao , Meruvia-Pastor Oscar , Sensen Christoph W.
Published: December 2017

We have developed a tool for the visualization of temporal changes of disease patterns, using stacks of medical images collected in time-series experiments. With this tool, users can generate 3D surface models representing disease patterns and observe changes over time in size, shape, and location of clinically significant image patterns. Statistical measurements of the volume of the observed disease patterns can be performed simultaneously. Spatial data integration occurs through the combination of 2D slices of an image stack into a 3D surface model. Temporal integration occurs through the sequential visualization of the 3D models from different time points. Visual integration enables the tool to show 2D images, 3D models and statistical data simultaneously. As an example, the tool has been used to visualize brain MRI scans of several multiple sclerosis patients. It has been developed in Java™, to ensure portability and platform independence, with a user-friendly interface and can be downloaded free of charge for academic users.
DBE2 – Management of experimental data for the VANTED system
Authors: Mehlhorn Hendrik , Schreiber Falk
Published: December 2017

DBE2 is an information system for the management of biological experiment data from different data domains in a unified and simple way. It provides persistent data storage, worldwide accessibility of the data and the opportunity to load, save, modify, and annotate the data. It is seamlessly integrated in the VANTED system as an add-on, thereby extending the VANTED platform towards data management. DBE2 also utilizes controlled vocabulary from the Ontology Lookup Service to allow the management of terms such as substance names, species names, and measurement units, aiming at an eased data integration.
Data partitioning enables the use of standard SOAP Web Services in genome-scale workflows
Authors: Sztromwasser Paweł , Petersen Kjell , Puntervoll Pál
Published: December 2017

Biological databases and computational biology tools are provided by research groups around the world, and made accessible on the Web. Combining these resources is a common practice in bioinformatics, but integration of heterogeneous and often distributed tools and datasets can be challenging. To date, this challenge has been commonly addressed in a pragmatic way, by tedious and error-prone scripting. Recently however a more reliable technique has been identified and proposed as the platform that would tie together bioinformatics resources, namely Web Services. In the last decade the Web Services have spread wide in bioinformatics, and earned the title of recommended technology. However, in the era of high-throughput experimentation, a major concern regarding Web Services is their ability to handle large-scale data traffic. We propose a stream-like communication pattern for standard SOAP Web Services, that enables efficient flow of large data traffic between a workflow orchestrator and Web Services. We evaluated the data-partitioning strategy by comparing it with typical communication patterns on an example pipeline for genomic sequence annotation. The results show that data-partitioning lowers resource demands of services and increases their throughput, which in consequence allows to execute in-silico experiments on genome-scale, using standard SOAP Web Services and workflows. As a proof-of-principle we annotated an RNA-seq dataset using a plain BPEL workflow engine.
Putting Encyclopaedia Knowledge into Structural Form: Finite State Transducers Approach
Authors: Pajić Vesna
Published: December 2017

In biology and functional genomics in particular, understanding the dependence and interplay between different genome and ecological characteristics of organisms is a very challenging problem. There are some public databases which combine this kind of information, but there is still much more information about microbes and other organisms that reside in unstructured and semi-structured documents, such as encyclopaedias. In this paper we present a method for extracting information from semi-structured resources, such as encyclopaedias, based on finite state transducers, consisting of two clearly distinguished phases. The first phase strongly relies on the analysis of the document structure and it is used for locating records of data in the text. The second phase is based on the finite state transducers created for extracting the data, which can be modified so as to achieve the preferred efficiency and it is used for extracting the particular characteristic from the text. We show how the two phase method is applied to the text of the encyclopaedia “Systematic Bacteriology”. A fully structured database with genotype and phenotype characteristics of organisms has been created from the encyclopaedia unstructured descriptions.
Bio-Search Computing: Integration and global ranking of bioinformatics search results
Authors: Masseroli Marco , Ghisalberti Giorgio , Ceri Stefano
Published: December 2017

In the Life Sciences, numerous questions can be addressed only by comprehensively searching different types of data that are inherently ordered, or are associated with ranked confidence values. We previously proposed Search Computing to support the integration of the results of search engines with other data and computational resources. This paper presents how well known bioinformatics resources can be described as search services in the search computing framework and integrated analyses over such services can be carried out. An initial set of bioinformatics services has been described and registered in the search computing framework and a bioinformatics search computing (Bio-SeCo) application using these services has been created. This current prototype application, the available services that it uses, the queries that are supported, the kind of interaction that is therefore made available to the users, and the future scenarios are here described and discussed.
Inter-dinucleotide distances in the human genome: an analysis of the whole-genome and protein-coding distributions
Authors: Bastos Carlos A. C. , Afreixo Vera , Pinho Armando J. , Garcia Sara P. , Rodrigues João M. O. S. , Ferreira Paulo J. S. G.
Published: December 2017

We study the inter-dinucleotide distance distributions in the human genome, both in the whole-genome and protein-coding regions. The inter-dinucleotide distance is defined as the distance to the next occurrence of the same dinucleotide. We consider the 16 sequences of inter-dinucleotide distances and two reading frames. Our results show a period-3 oscillation in the protein-coding inter-dinucleotide distance distributions that is absent from the whole-genome distributions. We also compare the distance distribution of each dinucleotide to a reference distribution, that of a random sequence generated with the same dinucleotide abundances, revealing the CG dinucleotide as the one with the highest cumulative relative error for the first 60 distances. Moreover, the distance distribution of each dinucleotide is compared to the distance distribution of all other dinucleotides using the Kullback-Leibler divergence. We find that the distance distribution of a dinucleotide and that of its reversed complement are very similar, hence, the divergence between them is very small. This is an interesting finding that may give evidence of a stronger parity rule than Chargaff’s second parity rule.
Bioinformatic tools help molecular characterization of Perkinsus olseni differentially expressed genes
Authors: Ascenso Rita M. T.
Published: December 2017

In the 80ies, in Southern Europe and in particular in Ria Formosa there was an episode of heavy mortality of the economically relevant clam Ruditapes (R.) decussatus associated with a debilitating disease (Perkinsosis) caused by Perkinsus olseni. This protozoan parasite was poorly known concerning its’ differential transcriptome in response to its host, R. decussatus. This laboratory available protozoan system was used to identify parasite genes related to host interaction. Beyond the application of molecular biology technologies and methodologies, only the help of Bioinformatics tools allowed to analyze the results of the study. The strategy started with SSH technique, allowing the identification of parasite up-regulated genes in response to its natural host, then a macroarray was constructed and hybridized to characterize the parasite genes expression when exposed to bivalves hemolymph from permissive host (R. decussatus), resistant host (R. philippinarum) and non permissive bivalve (Donax trunculus) that cohabit in the same or adjacent habitats in Southern Portugal. Genes and respective peptides full molecular characterization depended on several Bioinformatic tools application. Also a new Bioinformatic tool was developed.
A Hierarchical Approach to Protein Fold Prediction
Authors: Mohammad Tabrez Anwar Shamim , Nagarajaram Hampapathalu Adimurthy
Published: December 2017

Fold recognition, assigning novel proteins to known structures, forms an important component of the overall protein structure discovery process. The available methods for protein fold recognition are limited by the low fold-coverage and/or low prediction accuracies. We describe here a new Support Vector Machine (SVM)-based method for protein fold prediction with high prediction accuracy and high fold-coverage. The new method of fold prediction with high fold-coverage was developed by training and testing on a large number of folds in order to make the method suitable for large scale fold predictions. However, presence of large number of folds in the training set made the classification task difficult as a consequence of increased complexity involved in binary classifications of SVMs. In order to overcome this complexity we adopted a hierarchical approach where fold-prediction is made in two steps. At the first step structural class of the query is predicted and at the second step fold is predicted within the predicted structural class. This decreased the complexity of the classification problem and also improved the overall fold prediction accuracy. To the best of our knowledge this is the first taxonomic fold recognition method to cover over 700 protein-folds and gives prediction accuracy of around 70% on a benchmark dataset. Since the new method gives rise to state of the art prediction performance and hence can be very useful for structural characterization of proteins discovered in various genomes.
UBioLab: a web-LABoratory for Ubiquitous in-silico experiments
Authors: Bartocci E. , Di Berardini M. R. , Merelli E. , Vito L.
Published: December 2017

The huge and dynamic amount of bioinformatic resources (e.g., data and tools) available nowadays in Internet represents a big challenge for biologists -for what concerns their management and visualization- and for bioinformaticians -for what concerns the possibility of rapidly creating and executing in-silico experiments involving resources and activities spread over the WWW hyperspace. Any framework aiming at integrating such resources as in a physical laboratory has imperatively to tackle -and possibly to handle in a transparent and uniform way- aspects concerning physical distribution, semantic heterogeneity, co-existence of different computational paradigms and, as a consequence, of different invocation interfaces (i.e., OGSA for Grid nodes, SOAP for Web Services, Java RMI for Java objects, etc.). The framework UBioLab has been just designed and developed as a prototype following the above objective. Several architectural features -as those ones of being fully Web-based and of combining domain ontologies, Semantic Web and workflow techniques- give evidence of an effort in such a direction.

The integration of a semantic knowledge management system for distributed (bioinformatic) resources, a semantic-driven graphic environment for defining and monitoring ubiquitous workflows and an intelligent agent-based technology for their distributed execution allows UBioLab to be a semantic guide for bioinformaticians and biologists providing (i) a flexible environment for visualizing, organizing and inferring any (semantics and computational) “type” of domain knowledge (e.g., resources and activities, expressed in a declarative form), (ii) a powerful engine for defining and storing semantic-driven ubiquitous in-silico experiments on the domain hyperspace, as well as (iii) a transparent, automatic and distributed environment for correct experiment executions.
Transcription Network Analysis by A Sparse Binary Factor Analysis Algorithm
Authors: Tu Shikui , Chen Runsheng , Xu Lei
Published: December 2017

Transcription factor activities (TFAs), rather than expression levels, control gene expres- sion and provide valuable information for investigating TF-gene regulations. The underly- ing bimodal or switch-like patterns of TFAs may play important roles in gene regulation. Network Component Analysis (NCA) is a popular method to deduce TFAs and TF-gene control strengths from microarray data. However, it does not directly examine the bimodal- ity of TFAs and it needs the TF-gene connection topology to be a priori known. In this paper, we modify NCA to model gene expression regulation by Binary Factor Analysis (BFA), which directly captures switch-like patterns of TFAs. Moreover, sparse technique is employed on the mixing matrix of BFA, and thus the proposed sparse BYY-BFA al- gorithm, developed under Bayesian Ying-Yang (BYY) learning framework, can not only uncover the latent TFA profile’s switch-like patterns, but also be capable of automatically shutting off the unnecessary connections. Simulation study demonstrates the effectiveness of BYY-BFA, and a preliminary application to Saccharomyces cerevisiae cell cycle data and Escherichia coli carbon source transition data shows that the reconstructed binary pat- terns of TFAs by BYY-BFA are consistent with the ups and downs of TFAs by NCA, and that BYY-BFA also works well when the network topology is unknown.
A Rational Framework for Production Decision Making in Blood Establishments
Authors: Ramoa Augusto , Maia Salomé , Lourenço Anália
Published: December 2017

SAD_BaSe is a blood bank data analysis software, created to assist in the management of blood donations and the blood production chain in blood establishments. In particular, the system keeps track of several collection and production indicators, enables the definition of collection and production strategies, and the measurement of quality indicators required by the Quality Management System regulating the general operation of blood establishments.

This paper describes the general scenario of blood establishments and its main requirements in terms of data management and analysis. It presents the architecture of SAD_BaSe and identifies its main contributions. Specifically, it brings forward the generation of customized reports driven by decision making needs and the use of data mining techniques in the analysis of donor suspensions and donation discards.
Network Expansion and Pathway Enrichment Analysis towards Biologically Significant Findings from Microarrays
Authors: Wu Xiaogang , Reinhard Christoph , Li Shuyu D. , Huang Hui , Wei Tao , Pandey Ragini , Chen Jake Y.
Published: December 2017

In many cases, crucial genes show relatively slight changes between groups of samples (e.g. normal vs. disease), and many genes selected from microarray differential analysis by measuring the expression level statistically are also poorly annotated and lack of biological significance. In this paper, we present an innovative approach - network expansion and pathway enrichment analysis (NEPEA) for integrative microarray analysis. We assume that organized knowledge will help microarray data analysis in significant ways, and the organized knowledge could be represented as molecular interaction networks or biological pathways. Based on this hypothesis, we develop the NEPEA framework based on network expansion from the human annotated and predicted protein interaction (HAPPI) database, and pathway enrichment from the human pathway database (HPD). We use a recently-published microarray dataset (GSE24215) related to insulin resistance and type 2 diabetes (T2D) as case study, since this study provided a thorough experimental validation for both genes and pathways identified computationally from classical microarray analysis and pathway analysis. We perform our NEPEA analysis for this dataset based on the results from the classical microarray analysis to identify biologically significant genes and pathways. Our findings are not only consistent with the original findings mostly, but also obtained more supports from other literatures.