bluuebunny/crossref_metadata_2025_split huggingface dataset:
Construction of coffee transcriptome networks based on gene annotation semantics
Authors: Castillo Luis F. , Galeano Narmer , Isaza Gustavo A. , Gaitan Alvaro 
Published: December 2017

Summary Gene annotation is a process that encompasses multiple approaches on the analysis of nucleic acids or protein sequences in order to assign structural and functional characteristics to gene models. When thousands of gene models are being described in an organism genome, construction and visualization of gene networks impose novel challenges in the understanding of complex expression patterns and the generation of new knowledge in genomics research. In order to take advantage of accumulated text data after conventional gene sequence analysis, this work applied semantics in combination with visualization tools to build transcriptome networks from a set of coffee gene annotations. A set of selected coffee transcriptome sequences, chosen by the quality of the sequence comparison reported by Basic Local Alignment Search Tool (BLAST) and Interproscan, were filtered out by coverage, identity, length of the query, and e-values. Meanwhile, term descriptors for molecular biology and biochemistry were obtained along the Wordnet dictionary in order to construct a Resource Description Framework (RDF) using Ruby scripts and Methontology to find associations between concepts. Relationships between sequence annotations and semantic concepts were graphically represented through a total of 6845 oriented vectors, which were reduced to 745 non-redundant associations. A large gene network connecting transcripts by way of relational concepts was created where detailed connections remain to be validated for biological significance based on current biochemical and genetics frameworks. Besides reusing text information in the generation of gene connections and for data mining purposes, this tool development opens the possibility to visualize complex and abundant transcriptome data, and triggers the formulation of new hypotheses in metabolic pathways analysis.
Towards a Classification Approach using Meta-Biclustering: Impact of Discretization in the Analysis of Expression Time Series
Authors: Carreiro André V. , Ferreira Artur J. , Figueiredo Mário A. T. , Madeira Sara C. 
Published: December 2017

Summary Biclustering has been recognized as a remarkably effective method for discovering local temporal expression patterns and unraveling potential regulatory mechanisms, essential to understanding complex biomedical processes, such as disease progression and drug response. In this work, we propose a classification approach based on meta-biclusters (a set of similar biclusters) applied to prognostic prediction. We use real clinical expression time series to predict the response of patients with multiple sclerosis to treatment with Interferon-β. As compared to previous approaches, the main advantages of this strategy are the interpretability of the results and the reduction of data dimensionality, due to biclustering. This would allow the identification of the genes and time points which are most promising for explaining different types of response profiles, according to clinical knowledge. We assess the impact of different unsupervised and supervised discretization techniques on the classification accuracy. The experimental results show that, in many cases, the use of these discretization methods improves the classification accuracy, as compared to the use of the original features.
Predicting breast cancer chemotherapeutic response using a novel tool for microarray data analysis
Authors: Cheng Jie , Greshock Joel , Painter Jeffery , Lin Xiwu , Lee Kwan , Zheng Shu , Menius Alan 
Published: December 2017

Summary We developed a novel tool for microarray data analysis that can parsimoniously discover highly predictive genes by finding the optimal trade off between fold change and t-test p value through rigorous cross validation. In addition to find a small set of highly predictive genes, the tool also has a procedure that recursively discovers and removes predictive genes from the dataset until no such genes can be found. We applied our tool to a public breast cancer dataset with the goal to discover genes that can predict patient’s response to a preoperative chemotherapy. The results show that estrogen receptor (ER) gene is the most important gene to predict chemotherapeutic response and no gene signatures can add much clinical benefit for the whole patient population. We further identified a clinically homogenous subgroup of patients (ER-negative, PR-negative and HER2-negative) whose response to the chemotherapy can be reasonably predicted. Many of the discovered predictive markers for this subgroup of patients were successfully validated using a blinded validation set.
Predicting Genes Involved in Human Cancer Using Network Contextual Information
Authors: Rahmani Hossein , Blockeel Hendrik , Bender Andreas 
Published: December 2017

Summary Protein-Protein Interaction (PPI) networks have been widely used for the task of predicting proteins involved in cancer. Previous research has shown that functional information about the protein for which a prediction is made, proximity to specific other proteins in the PPI network, as well as local network structure are informative features in this respect. In this work, we introduce two new types of input features, reflecting additional information: (1) Functional Context: the functions of proteins interacting with the target protein (rather than the protein itself); and (2) Structural Context: the relative position of the target protein with respect to specific other proteins selected according to a novel ANOVA (analysis of variance) based measure. We also introduce a selection strategy to pinpoint the most informative features. Results show that the proposed feature types and feature selection strategy yield informative features. A standard machine learning method (Naive Bayes) that uses the features proposed here outperforms the current state-of-the-art methods by more than 5% with respect to F-measure. In addition, manual inspection confirms the biological relevance of the top-ranked features.
Can MiRBase Provide Positive Data for Machine Learning for the Detection of MiRNA Hairpins?
Authors: Saçar Müşerref Duygu , Hamzeiy Hamid , Allmer Jens 
Published: December 2017

Summary Experimental detection and validation of miRNAs is a tedious, time-consuming, and expensive process. Computational methods for miRNA gene detection are being developed so that the number of candidates that need experimental validation can be reduced to a manageable amount. Computational methods involve homology-based and ab inito algorithms. Both approaches are dependent on positive and negative training examples. Positive examples are usually derived from miRBase, the main resource for experimentally validated miRNAs. We encountered some problems with miRBase which we would like to report here. Some problems, among others, we encountered are that folds presented in miRBase are not always the fold with the minimum free energy; some entries do not seem to conform to expectations of miRNAs, and some external accession numbers are not valid. In addition, we compared the prediction accuracy for the same negative dataset when the positive data came from miRBase or miRTarBase and found that the latter led to more precise prediction models. We suggest that miRBase should introduce some automated facilities for ensuring data quality to overcome these problems.
Excemplify: A Flexible Template Based Solution, Parsing and Managing Data in Spreadsheets for Experimentalists
Authors: Shi Lei , Jong Lenneke , Wittig Ulrike , Lucarelli Philippe , Stepath Markus , Mueller Stephanie , D‘Alessandro Lorenza Alice , Klingmüller Ursula , Müller Wolfgang 
Published: December 2017

Summary In systems biology, quantitative experimental data is the basis of building mathematical models. In most of the cases, they are stored in Excel files and hosted locally. To have a public database for collecting, retrieving and citing experimental raw data as well as experimental conditions is important for both experimentalists and modelers. However, the great effort needed in the data handling procedure and in the data submission procedure becomes the crucial limitation for experimentalists to contribute to a database, thereby impeding the database to deliver its benefit. Moreover, manual copy and paste operations which are commonly used in those procedures increase the chance of making mistakes. Excemplify, a web-based application, proposes a flexible and adaptable template-based solution to solve these problems. Comparing to the normal template based uploading approach, which is supported by some public databases, rather than predefining a format that is potentiall impractical, Excemplify allows users to create their own experiment-specific content templates in different experiment stages and to build corresponding knowledge bases for parsing. Utilizing the embedded knowledge of used templates, Excemplify is able to parse experimental data from the initial setup stage and generate following stages spreadsheets automatically. The proposed solution standardizes the flows of data traveling according to the standard procedures of applying the experiment, cuts down the amount of manual effort and reduces the chance of mistakes caused by manual data handling. In addition, it maintains the context of meta-data from the initial preparation manuscript and improves the data consistency. It interoperates and complements RightField and SEEK as well.
Discovery of miR-mRNA interactions via simultaneous Bayesian inference of gene networks and clusters using sequence-based predictions and expression data
Authors: Godsey Brian 
Published: December 2017

Summary MicroRNAs (miRs) are known to interfere with mRNA expression, and much work has been put into predicting and inferring miR-mRNA interactions. Both sequence-based interaction predictions as well as interaction inference based on expression data have been proven somewhat successful; furthermore, models that combine the two methods have had even more success. In this paper, I further refine and enrich the methods of miR-mRNA interaction discovery by integrating a Bayesian clustering algorithm into a model of prediction-enhanced miR-mRNA target inference, creating an algorithm called PEACOAT, which is written in the R language. I show that PEACOAT improves the inference of miR-mRNA target interactions using both simulated data and a data set of microarrays from samples of multiple myeloma patients. In simulated networks of 25 miRs and mRNAs, our methods using clustering can improve inference in roughly two-thirds of cases, and in the multiple myeloma data set, KEGG pathway enrichment was found to be more significant with clustering than without. Our findings are consistent with previous work in clustering of non-miR genetic networks and indicate that there could be a significant advantage to clustering of miR and mRNA expression data as a part of interaction inference.
Classification of Breast Cancer Subtypes by combining Gene Expression and DNA Methylation Data
Authors: List Markus , Hauschild Anne-Christin , Tan Qihua , Kruse Torben A. , Baumbach Jan , Batra Richa 
Published: December 2017

Summary Selecting the most promising treatment strategy for breast cancer crucially depends on determining the correct subtype. In recent years, gene expression profiling has been investigated as an alternative to histochemical methods. Since databases like TCGA provide easy and unrestricted access to gene expression data for hundreds of patients, the challenge is to extract a minimal optimal set of genes with good prognostic properties from a large bulk of genes making a moderate contribution to classification. Several studies have successfully applied machine learning algorithms to solve this so-called gene selection problem. However, more diverse data from other OMICS technologies are available, including methylation. We hypothesize that combining methylation and gene expression data could already lead to a largely improved classification model, since the resulting model will reflect differences not only on the transcriptomic, but also on an epigenetic level. We compared so-called random forest derived classification models based on gene expression and methylation data alone, to a model based on the combined features and to a model based on the gold standard PAM50. We obtained bootstrap errors of 10-20% and classification error of 1-50%, depending on breast cancer subtype and model. The gene expression model was clearly superior to the methylation model, which was also reflected in the combined model, which mainly selected features from gene expression data. However, the methylation model was able to identify unique features not considered as relevant by the gene expression model, which might provide deeper insights into breast cancer subtype differentiation on an epigenetic level.
VANESA - A Software Application for the Visualization and Analysis of Networks in Systems Biology Applications
Authors: Brinkrolf Christoph , Janowski Sebastian Jan , Kormeier Benjamin , Lewinski Martin , Hippe Klaus , Borck Daniela , Hofestädt Ralf 
Published: December 2017

Summary VANESA is a modeling software for the automatic reconstruction and analysis of biological networks based on life-science database information. Using VANESA, scientists are able to model any kind of biological processes and systems as biological networks. It is now possible for scientists to automatically reconstruct important molecular systems with information from the databases KEGG, MINT, IntAct, HPRD, and BRENDA. Additionally, experimental results can be expanded with database information to better analyze the investigated elements and processes in an overall context. Users also have the possibility to use graph theoretical approaches in VANESA to identify regulatory structures and significant actors within the modeled systems. These structures can then be further investigated in the Petri net environment of VANESA. It is platform-independent, free-of-charge, and available at http://vanesa.sf.net.
Integrating Sequence Analysis with Biophysical Modelling for Accurate Transcription Start Site Prediction
Authors: Djordjevic Marko 
Published: December 2017

Summary Promoter prediction in bacteria is a classical bioinformatics problem, where available methods for regulatory element detection exhibit a very high number of false positives. We here argue that accurate transcription start site (TSS) prediction is a complex problem, where available methods for sequence motif discovery are not in itself well adopted for solving the problem. We here instead propose that the problem requires integration of quantitative understanding of transcription initiation with careful description of promoter sequence specificity. We review evidence for this viewpoint, and discuss a current progress on these issues on the example of sigma70 transcription start sites in E. coli.
A distributed computational search strategy for the identification of diagnostics targets: Application to finding aptamer targets for methicillin-resistant staphylococci
Authors: Flanagan Keith , Cockell Simon , Harwood Colin , Hallinan Jennifer , Nakjang Sirintra , Lawry Beth , Wipat Anil 
Published: December 2017

Summary The rapid and cost-effective identification of bacterial species is crucial, especially for clinical diagnosis and treatment. Peptide aptamers have been shown to be valuable for use as a component of novel, direct detection methods. These small peptides have a number of advantages over antibodies, including greater specificity and longer shelf life. These properties facilitate their use as the detector components of biosensor devices. However, the identification of suitable aptamer targets for particular groups of organisms is challenging. We present a semi-automated processing pipeline for the identification of candidate aptamer targets from whole bacterial genome sequences. The pipeline can be configured to search for protein sequence fragments that uniquely identify a set of strains of interest. The system is also capable of identifying additional organisms that may be of interest due to their possession of protein fragments in common with the initial set. Through the use of Cloud computing technology and distributed databases, our system is capable of scaling with the rapidly growing genome repositories, and consequently of keeping the resulting data sets up-to-date. The system described is also more generically applicable to the discovery of specific targets for other diagnostic approaches such as DNA probes, PCR primers and antibodies.
A harmonised vocabulary for communicating and interchanging Biofilms experimental results
Authors: Sousa Ana Margarida , Pereira Maria Olívia , Azevedo Nuno F. , Lourenço Anália 
Published: December 2017

Summary Biofilm studies are at the crossroads of Biology, Chemistry, Medicine, Material Science and Engineering, among other fields. Data harmonisation in Biofilms is therefore crucial to allow for researchers to collaborate, interchange, understand, and replicate studies at an inter-laboratory and inter-domain scale. The international Minimum Information About a Biofilms Experiment initiative has prepared a set of guidelines for documenting biofilms experiments and data, namely the minimum information checklist. This paper goes a step forward and describes a new ontology for the broad description of biofilm experiments and data. In such an interdisciplinary context we chose to rely on a common integration framework provided by a foundational ontology that facilitates the addition and extension of various sub-domain modules, and the consistent integration of terminology extracted from several existing ontologies, e.g. EXPO and ChEBI. The community is participating actively in the production of this resource, and it is already used by public biofilms-centred databases, such as BiofOmics, and bioinformatics tools, such as the Biofilms Experiment Workbench. This practical validation serves the purpose of disseminating the controlled vocabulary among researchers and identifying current limitations, glitches, and inconsistencies. Information branches will be added, extended or refactored according to user feedback and group discussions.
High Performance Pattern Matching on Heterogeneous Platform
Authors: Soroushnia Shima , Daneshtalab Masoud , Plosila Juha , Pahikkala Tapio , Liljeberg Pasi 
Published: December 2017

Summary Pattern discovery is one of the fundamental tasks in bioinformatics and pattern recognition is a powerful technique for searching sequence patterns in the biological sequence databases. Fast and high performance algorithms are highly demanded in many applications in bioinformatics and computational molecular biology since the significant increase in the number of DNA and protein sequences expand the need for raising the performance of pattern matching algorithms. For this purpose, heterogeneous architectures can be a good choice due to their potential for high performance and energy efficiency. In this paper we present an efficient implementation of Aho-Corasick (AC) which is a well known exact pattern matching algorithm with linear complexity, and Parallel Failureless Aho-Corasick (PFAC) algorithm which is the massively parallelized version of AC algorithm without failure transitions, on a heterogeneous CPU/GPU architecture. We progressively redesigned the algorithms and data structures to fit on the GPU architecture. Our results on different protein sequence data sets show that the new implementation runs 15 times faster compared to the original implementation of the PFAC algorithm.
A web application for automatic prediction of gene translation elongation efficiency
Authors: Sokolov Vladimir S. , Zuraev Bulat S. , Lashin Sergei A. , Matushkin Yury G. 
Published: December 2017

Summary Expression efficiency is one of the major characteristics describing genes in various modern investigations. Expression efficiency of genes is regulated at various stages: transcription, translation, posttranslational protein modification and others. In this study, a special EloE (Elongation Efficiency) web application is described. The EloE sorts the organism’s genes in a descend order on their theoretical rate of the elongation stage of translation based on the analysis of their nucleotide sequences. Obtained theoretical data have a significant correlation with available experimental data of gene expression in various organisms. In addition, the program identifies preferential codons in organism’s genes and defines distribution of potential secondary structures energy in 5´ and 3´ regions of mRNA. The EloE can be useful in preliminary estimation of translation elongation efficiency for genes for which experimental data are not available yet. Some results can be used, for instance, in other programs modeling artificial genetic structures in genetically engineered experiments. The EloE web application is available at http://www-bionet.sscc.ru:7780/EloE.
Shared Bioinformatics Databases within the Unipro UGENE Platform
Authors: Protsyuk Ivan V. , Grekhov German A. , Tiunov Alexey V. , Fursov Mikhail Yu. 
Published: December 2017

Summary Unipro UGENE is an open-source bioinformatics toolkit that integrates popular tools along with original instruments for molecular biologists within a unified user interface. Nowadays, most bioinformatics desktop applications, including UGENE, make use of a local data model while processing different types of data. Such an approach causes an inconvenience for scientists working cooperatively and relying on the same data. This refers to the need of making multiple copies of certain files for every workplace and maintaining synchronization between them in case of modifications. Therefore, we focused on delivering a collaborative work into the UGENE user experience. Currently, several UGENE installations can be connected to a designated shared database and users can interact with it simultaneously. Such databases can be created by UGENE users and be used at their discretion. Objects of each data type, supported by UGENE such as sequences, annotations, multiple alignments, etc., can now be easily imported from or exported to a remote storage. One of the main advantages of this system, compared to existing ones, is the almost simultaneous access of client applications to shared data regardless of their volume. Moreover, the system is capable of storing millions of objects. The storage itself is a regular database server so even an inexpert user is able to deploy it. Thus, UGENE may provide access to shared data for users located, for example, in the same laboratory or institution. UGENE is available at: http://ugene.net/download.html.