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Journal of Integrative Bioinformatics

Ruiquan Ge, Guoqin Mai, Ruochi Zhang, Xundong Wu, Qing Wu, Fengfeng Zhou
Background Miniature inverted repeat transposable element (MITE) is a short transposable element, carrying no protein-coding regions. However, its high proliferation rate and sequence-specific insertion preference renders it as a good genetic tool for both natural evolution and experimental insertion mutagenesis. Recently active MITE copies are those with clear signals of Terminal Inverted Repeats (TIRs) and Direct Repeats (DRs), and are recently translocated into their current sites. Their proliferation ability renders them good candidates for the investigation of genomic evolution...
August 10, 2017: Journal of Integrative Bioinformatics
Lijun Wang, Md Asif Ahsan, Ming Chen
Several methods for identifying relationships among pairs of genes have been developed. In this article, we present a generalized approach for measuring relationships between any pairs of genes, which is based on statistical prediction. We derive two particular versions of the generalized approach, least squares estimation (LSE) and nearest neighbors prediction (NNP). According to mathematical proof, LSE is equivalent to the methods based on correlation; and NNP is approximate to one popular method called the maximal information coefficient (MIC) according to the performances in simulations and real dataset...
July 21, 2017: Journal of Integrative Bioinformatics
Shyamasree Ghosh
Planaria is a member of the Phylum Platyhelminthes including flatworms. Planarians possess the unique ability of regeneration from adult stem cells or neoblasts and finds importance as a model organism for regeneration and developmental studies. Although research is being actively carried out globally through conventional methods to understand the process of regeneration from neoblasts, biology of development, neurobiology and immunology of Planaria, there are many thought provoking questions related to stem cell plasticity, and uniqueness of regenerative potential in Planarians amongst other members of Phylum Platyhelminthes...
July 6, 2017: Journal of Integrative Bioinformatics
Gökhan Karakülah
Novel transcript discovery through RNA sequencing has substantially improved our understanding of the transcriptome dynamics of biological systems. Endogenous target mimicry (eTM) transcripts, a novel class of regulatory molecules, bind to their target microRNAs (miRNAs) by base pairing and block their biological activity. The objective of this study was to provide a computational analysis framework for the prediction of putative eTM sequences in plants, and as an example, to discover previously un-annotated eTMs in Prunus persica (peach) transcriptome...
June 28, 2017: Journal of Integrative Bioinformatics
Ralf Hofestädt, Falk Schreiber, Björn Sommer, Jens Allmer
No abstract text is available yet for this article.
June 13, 2017: Journal of Integrative Bioinformatics
Hamid Hamzeiy, Rabia Suluyayla, Christoph Brinkrolf, Sebastian Jan Janowski, Ralf Hofestaedt, Jens Allmer
MicroRNAs (miRNAs) are small RNA molecules which are known to take part in post-transcriptional regulation of gene expression. Here, VANESA, an existing platform for reconstructing, visualizing, and analysis of large biological networks, has been further expanded to include all experimentally validated human miRNAs available within miRBase, TarBase and miRTarBase. This is done by integrating a custom hybrid miRNA database to DAWIS-M.D., VANESA's main data source, enabling the visualization and analysis of miRNAs within large biological pathways such as those found within the Kyoto Encyclopedia of Genes and Genomes (KEGG)...
June 13, 2017: Journal of Integrative Bioinformatics
Keywan Hassani-Pak, Christopher Rawlings
Genetics and "omics" studies designed to uncover genotype to phenotype relationships often identify large numbers of potential candidate genes, among which the causal genes are hidden. Scientists generally lack the time and technical expertise to review all relevant information available from the literature, from key model species and from a potentially wide range of related biological databases in a variety of data formats with variable quality and coverage. Computational tools are needed for the integration and evaluation of heterogeneous information in order to prioritise candidate genes and components of interaction networks that, if perturbed through potential interventions, have a positive impact on the biological outcome in the whole organism without producing negative side effects...
June 13, 2017: Journal of Integrative Bioinformatics
Müşerref Duygu Saçar Demirci, Jens Allmer
MicroRNAs (miRNAs) are involved in the post-transcriptional regulation of protein abundance and thus have a great impact on the resulting phenotype. It is, therefore, no wonder that they have been implicated in many diseases ranging from virus infections to cancer. This impact on the phenotype leads to a great interest in establishing the miRNAs of an organism. Experimental methods are complicated which led to the development of computational methods for pre-miRNA detection. Such methods generally employ machine learning to establish models for the discrimination between miRNAs and other sequences...
July 28, 2017: Journal of Integrative Bioinformatics
Jennifer Maier, Marianne Black, Serena Bonaretti, Bastian Bier, Bjoern Eskofier, Jang-Hwan Choi, Marc Levenston, Garry Gold, Rebecca Fahrig, Andreas Maier
Osteoarthritis is a degenerative disease affecting bones and cartilage especially in the human knee. In this context, cartilage thickness is an indicator for knee cartilage health. Thickness measurements are performed on medical images acquired in-vivo. Currently, there is no standard method agreed upon that defines a distance measure in articular cartilage. In this work, we present a comparison of different methods commonly used in literature. These methods are based on nearest neighbors, surface normal vectors, local thickness and potential field lines...
July 28, 2017: Journal of Integrative Bioinformatics
Simon J Larsen, Jan Baumbach
Comparative analysis of biological networks is a major problem in computational integrative systems biology. By computing the maximum common edge subgraph between a set of networks, one is able to detect conserved substructures between them and quantify their topological similarity. To aid such analyses we have developed CytoMCS, a Cytoscape app for computing inexact solutions to the maximum common edge subgraph problem for two or more graphs. Our algorithm uses an iterative local search heuristic for computing conserved subgraphs, optimizing a squared edge conservation score that is able to detect not only fully conserved edges but also partially conserved edges...
July 21, 2017: Journal of Integrative Bioinformatics
Nora K Speicher, Nico Pfeifer
Personalized treatment of patients based on tissue-specific cancer subtypes has strongly increased the efficacy of the chosen therapies. Even though the amount of data measured for cancer patients has increased over the last years, most cancer subtypes are still diagnosed based on individual data sources (e.g. gene expression data). We propose an unsupervised data integration method based on kernel principal component analysis. Principal component analysis is one of the most widely used techniques in data analysis...
July 8, 2017: Journal of Integrative Bioinformatics
Dan Søndergaard, Svend Nielsen, Christian N S Pedersen, Søren Besenbacher
A cancer of unknown primary (CUP) is a metastatic cancer for which standard diagnostic tests fail to identify the location of the primary tumor. CUPs account for 3-5% of cancer cases. Using molecular data to determine the location of the primary tumor in such cases can help doctors make the right treatment choice and thus improve the clinical outcome. In this paper, we present a new method for predicting the location of the primary tumor using gene expression data: locating cancers of unknown primary (LoCUP)...
July 7, 2017: Journal of Integrative Bioinformatics
Eudes Barbosa, Richard Röttger, Anne-Christin Hauschild, Siomar de Castro Soares, Sebastian Böcker, Vasco Azevedo, Jan Baumbach
Distinct bacteria are able to cope with highly diverse lifestyles; for instance, they can be free living or host-associated. Thus, these organisms must possess a large and varied genomic arsenal to withstand different environmental conditions. To facilitate the identification of genomic features that might influence bacterial adaptation to a specific niche, we introduce LifeStyle-Specific-Islands (LiSSI). LiSSI combines evolutionary sequence analysis with statistical learning (Random Forest with feature selection, model tuning and robustness analysis)...
July 5, 2017: Journal of Integrative Bioinformatics
Sepideh Sadegh, Maryam Nazarieh, Christian Spaniol, Volkhard Helms
Gene-regulatory networks are an abstract way of capturing the regulatory connectivity between transcription factors, microRNAs, and target genes in biological cells. Here, we address the problem of identifying enriched co-regulatory three-node motifs that are found significantly more often in real network than in randomized networks. First, we compare two randomization strategies, that either only conserve the degree distribution of the nodes' in- and out-links, or that also conserve the degree distributions of different regulatory edge types...
July 4, 2017: Journal of Integrative Bioinformatics
Kavita Goswami, Anita Tripathi, Neeti Sanan-Mishra
Increase in soil salt causes osmotic and ionic stress to plants, which inhibits their growth and productivity. Rice production is also hampered by salinity and the effect of salt is most severe at the seedling and reproductive stages. Salainity tolerance is a quantitative property controlled by multiple genes coding for signaling molecules, ion transporters, metabolic enzymes and transcription regulators. MicroRNAs are key modulators of gene-expression that act at the post-transcriptional level by translation repression or transcript cleavage...
June 22, 2017: Journal of Integrative Bioinformatics
Maximilian Miller, Chengsheng Zhu, Yana Bromberg
With the advent of modern day high-throughput technologies, the bottleneck in biological discovery has shifted from the cost of doing experiments to that of analyzing results. clubber is our automated cluster-load balancing system developed for optimizing these "big data" analyses. Its plug-and-play framework encourages re-use of existing solutions for bioinformatics problems. clubber's goals are to reduce computation times and to facilitate use of cluster computing. The first goal is achieved by automating the balance of parallel submissions across available high performance computing (HPC) resources...
June 13, 2017: Journal of Integrative Bioinformatics
Noël Malod-Dognin, Nataša Pržulj
Mapping the complete functional layout of a cell and understanding the cross-talk between different processes are fundamental challenges. They elude us because of the incompleteness and noisiness of molecular data and because of the computational intractability of finding the exact answer. We perform a simple integration of three types of baker's yeast omics data to elucidate the functional organization and lines of cross-functional communication. We examine protein-protein interaction (PPI), co-expression (COEX) and genetic interaction (GI) data, and explore their relationship with the gold standard of functional organization, the Gene Ontology (GO)...
June 10, 2017: Journal of Integrative Bioinformatics
Markus List
Docker virtualization allows for software tools to be executed in an isolated and controlled environment referred to as a container. In Docker containers, dependencies are provided exactly as intended by the developer and, consequently, they simplify the distribution of scientific software and foster reproducible research. The Docker paradigm is that each container encapsulates one particular software tool. However, to analyze complex biomedical data sets, it is often necessary to combine several software tools into elaborate workflows...
June 10, 2017: Journal of Integrative Bioinformatics
Qihua Tan, Mads Thomassen, Mark Burton, Kristian Fredløv Mose, Klaus Ejner Andersen, Jacob Hjelmborg, Torben Kruse
Modeling complex time-course patterns is a challenging issue in microarray study due to complex gene expression patterns in response to the time-course experiment. We introduce the generalized correlation coefficient and propose a combinatory approach for detecting, testing and clustering the heterogeneous time-course gene expression patterns. Application of the method identified nonlinear time-course patterns in high agreement with parametric analysis. We conclude that the non-parametric nature in the generalized correlation analysis could be an useful and efficient tool for analyzing microarray time-course data and for exploring the complex relationships in the omics data for studying their association with disease and health...
June 6, 2017: Journal of Integrative Bioinformatics
Ali M Yazbeck, Kifah R Tout, Peter F Stadler, Jana Hertel
The miRBase currently reports more than 25,000 microRNAs in several hundred genomes that belong to more than 1000 families of homologous sequences. Quantitative investigations of miRNA gene evolution requires the construction of data sets that are consistent in their coverage and include those genomes that are of interest in a given study. Given the size and structure of data, this can be achieved only with the help of a fully automatic pipeline that improves the available seed alignments, extends the set of available sequences by homology search, and reliably identifies true positive homology search results...
June 5, 2017: Journal of Integrative Bioinformatics
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