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BMC Bioinformatics

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https://www.readbyqxmd.com/read/28549446/-gnparser-a-powerful-parser-for-scientific-names-based-on-parsing-expression-grammar
#1
Dmitry Y Mozzherin, Alexander A Myltsev, David J Patterson
BACKGROUND: Scientific names in biology act as universal links. They allow us to cross-reference information about organisms globally. However variations in spelling of scientific names greatly diminish their ability to interconnect data. Such variations may include abbreviations, annotations, misspellings, etc. Authorship is a part of a scientific name and may also differ significantly. To match all possible variations of a name we need to divide them into their elements and classify each element according to its role...
May 26, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28549411/fluorender-joint-freehand-segmentation-and-visualization-for-many-channel-fluorescence-data-analysis
#2
Yong Wan, Hideo Otsuna, Holly A Holman, Brig Bagley, Masayoshi Ito, A Kelsey Lewis, Mary Colasanto, Gabrielle Kardon, Kei Ito, Charles Hansen
BACKGROUND: Image segmentation and registration techniques have enabled biologists to place large amounts of volume data from fluorescence microscopy, morphed three-dimensionally, onto a common spatial frame. Existing tools built on volume visualization pipelines for single channel or red-green-blue (RGB) channels have become inadequate for the new challenges of fluorescence microscopy. For a three-dimensional atlas of the insect nervous system, hundreds of volume channels are rendered simultaneously, whereas fluorescence intensity values from each channel need to be preserved for versatile adjustment and analysis...
May 26, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28549410/large-scale-tissue-histopathology-image-classification-segmentation-and-visualization-via-deep-convolutional-activation-features
#3
Yan Xu, Zhipeng Jia, Liang-Bo Wang, Yuqing Ai, Fang Zhang, Maode Lai, Eric I-Chao Chang
BACKGROUND: Histopathology image analysis is a gold standard for cancer recognition and diagnosis. Automatic analysis of histopathology images can help pathologists diagnose tumor and cancer subtypes, alleviating the workload of pathologists. There are two basic types of tasks in digital histopathology image analysis: image classification and image segmentation. Typical problems with histopathology images that hamper automatic analysis include complex clinical representations, limited quantities of training images in a dataset, and the extremely large size of singular images (usually up to gigapixels)...
May 26, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28545524/an-unsupervised-learning-approach-for-tracking-mice-in-an-enclosed-area
#4
Jakob Unger, Mike Mansour, Marcin Kopaczka, Nina Gronloh, Marc Spehr, Dorit Merhof
BACKGROUND: In neuroscience research, mouse models are valuable tools to understand the genetic mechanisms that advance evidence-based discovery. In this context, large-scale studies emphasize the need for automated high-throughput systems providing a reproducible behavioral assessment of mutant mice with only a minimum level of manual intervention. Basic element of such systems is a robust tracking algorithm. However, common tracking algorithms are either limited by too specific model assumptions or have to be trained in an elaborate preprocessing step, which drastically limits their applicability for behavioral analysis...
May 25, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28545462/sequence-based-prediction-of-protein-protein-interaction-using-a-deep-learning-algorithm
#5
Tanlin Sun, Bo Zhou, Luhua Lai, Jianfeng Pei
BACKGROUND: Protein-protein interactions (PPIs) are critical for many biological processes. It is therefore important to develop accurate high-throughput methods for identifying PPI to better understand protein function, disease occurrence, and therapy design. Though various computational methods for predicting PPI have been developed, their robustness for prediction with external datasets is unknown. Deep-learning algorithms have achieved successful results in diverse areas, but their effectiveness for PPI prediction has not been tested...
May 25, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28545448/limitations-of-a-metabolic-network-based-reverse-ecology-method-for-inferring-host-pathogen-interactions
#6
Kazuhiro Takemoto, Kazuki Aie
BACKGROUND: Host-pathogen interactions are important in a wide range of research fields. Given the importance of metabolic crosstalk between hosts and pathogens, a metabolic network-based reverse ecology method was proposed to infer these interactions. However, the validity of this method remains unclear because of the various explanations presented and the influence of potentially confounding factors that have thus far been neglected. RESULTS: We re-evaluated the importance of the reverse ecology method for evaluating host-pathogen interactions while statistically controlling for confounding effects using oxygen requirement, genome, metabolic network, and phylogeny data...
May 25, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28545397/jed-a-java-essential-dynamics-program-for-comparative-analysis-of-protein-trajectories
#7
Charles C David, Ettayapuram Ramaprasad Azhagiya Singam, Donald J Jacobs
BACKGROUND: Essential Dynamics (ED) is a common application of principal component analysis (PCA) to extract biologically relevant motions from atomic trajectories of proteins. Covariance and correlation based PCA are two common approaches to determine PCA modes (eigenvectors) and their eigenvalues. Protein dynamics can be characterized in terms of Cartesian coordinates or internal distance pairs. In understanding protein dynamics, a comparison of trajectories taken from a set of proteins for similarity assessment provides insight into conserved mechanisms...
May 25, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28545394/agennt-annotation-of-enzyme-families-by-means-of-refined-neighborhood-networks
#8
Florian Kandlinger, Maximilian G Plach, Rainer Merkl
BACKGROUND: Large enzyme families may contain functionally diverse members that give rise to clusters in a sequence similarity network (SSN). In prokaryotes, the genome neighborhood of a gene-product is indicative of its function and thus, a genome neighborhood network (GNN) deduced for an SSN provides strong clues to the specific function of enzymes constituting the different clusters. The Enzyme Function Initiative ( http://enzymefunction.org/ ) offers services that compute SSNs and GNNs...
May 25, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28545393/quantgenius-implementation-of-a-decision-support-system-for-qpcr-based-gene-quantification
#9
Špela Baebler, Miha Svalina, Marko Petek, Katja Stare, Ana Rotter, Maruša Pompe-Novak, Kristina Gruden
BACKGROUND: Quantitative molecular biology remains a challenge for researchers due to inconsistent approaches for control of errors in the final results. Due to several factors that can influence the final result, quantitative analysis and interpretation of qPCR data are still not trivial. Together with the development of high-throughput qPCR platforms, there is a need for a tool allowing for robust, reliable and fast nucleic acid quantification. RESULTS: We have developed "quantGenius" ( http://quantgenius...
May 25, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28545391/a-random-effects-model-for-the-identification-of-differential-splicing-reids-using-exon-and-hta-arrays
#10
Marijke Van Moerbeke, Adetayo Kasim, Willem Talloen, Joke Reumers, Hinrick W H Göhlmann, Ziv Shkedy
BACKGROUND: Alternative gene splicing is a common phenomenon in which a single gene gives rise to multiple transcript isoforms. The process is strictly guided and involves a multitude of proteins and regulatory complexes. Unfortunately, aberrant splicing events do occur which have been linked to genetic disorders, such as several types of cancer and neurodegenerative diseases (Fan et al., Theor Biol Med Model 3:19, 2006). Therefore, understanding the mechanism of alternative splicing and identifying the difference in splicing events between diseased and healthy tissue is crucial in biomedical research with the potential of applications in personalized medicine as well as in drug development...
May 25, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28545390/mqaprank-improved-global-protein-model-quality-assessment-by-learning-to-rank
#11
Xiaoyang Jing, Qiwen Dong
BACKGROUND: Protein structure prediction has achieved a lot of progress during the last few decades and a greater number of models for a certain sequence can be predicted. Consequently, assessing the qualities of predicted protein models in perspective is one of the key components of successful protein structure prediction. Over the past years, a number of methods have been developed to address this issue, which could be roughly divided into three categories: single methods, quasi-single methods and clustering (or consensus) methods...
May 25, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28535748/dtwscore-differential-expression-and-cell-clustering-analysis-for-time-series-single-cell-rna-seq-data
#12
Zhuo Wang, Shuilin Jin, Guiyou Liu, Xiurui Zhang, Nan Wang, Deliang Wu, Yang Hu, Chiping Zhang, Qinghua Jiang, Li Xu, Yadong Wang
BACKGROUND: The development of single-cell RNA sequencing has enabled profound discoveries in biology, ranging from the dissection of the composition of complex tissues to the identification of novel cell types and dynamics in some specialized cellular environments. However, the large-scale generation of single-cell RNA-seq (scRNA-seq) data collected at multiple time points remains a challenge to effective measurement gene expression patterns in transcriptome analysis. RESULTS: We present an algorithm based on the Dynamic Time Warping score (DTWscore) combined with time-series data, that enables the detection of gene expression changes across scRNA-seq samples and recovery of potential cell types from complex mixtures of multiple cell types...
May 23, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28532442/an-integrated-enhancement-and-reconstruction-strategy-for-the-quantitative-extraction-of-actin-stress-fibers-from-fluorescence-micrographs
#13
Zhen Zhang, Shumin Xia, Pakorn Kanchanawong
BACKGROUND: The stress fibers are prominent organization of actin filaments that perform important functions in cellular processes such as migration, polarization, and traction force generation, and whose collective organization reflects the physiological and mechanical activities of the cells. Easily visualized by fluorescence microscopy, the stress fibers are widely used as qualitative descriptors of cell phenotypes. However, due to the complexity of the stress fibers and the presence of other actin-containing cellular features, images of stress fibers are relatively challenging to quantitatively analyze using previously developed approaches, requiring significant user intervention...
May 22, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28532394/circularlogo-a-lightweight-web-application-to-visualize-intra-motif-dependencies
#14
Zhenqing Ye, Tao Ma, Michael T Kalmbach, Surendra Dasari, Jean-Pierre A Kocher, Liguo Wang
BACKGROUND: The sequence logo has been widely used to represent DNA or RNA motifs for more than three decades. Despite its intelligibility and intuitiveness, the traditional sequence logo is unable to display the intra-motif dependencies and therefore is insufficient to fully characterize nucleotide motifs. Many methods have been developed to quantify the intra-motif dependencies, but fewer tools are available for visualization. RESULT: We developed CircularLogo, a web-based interactive application, which is able to not only visualize the position-specific nucleotide consensus and diversity but also display the intra-motif dependencies...
May 22, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28532384/in-silico-approach-to-designing-rational-metagenomic-libraries-for-functional-studies
#15
Anna Kusnezowa, Lars I Leichert
BACKGROUND: With the development of Next Generation Sequencing technologies, the number of predicted proteins from entire (meta-) genomes has risen exponentially. While for some of these sequences protein functions can be inferred from homology, an experimental characterization is still a requirement for the determination of protein function. However, functional characterization of proteins cannot keep pace with our capabilities to generate more and more sequence data. RESULTS: Here, we present an approach to reduce the number of proteins from entire (meta-) genomes to a reasonably small number for further experimental characterization without loss of important information...
May 22, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28525968/survivalgwas_sv-software-for-the-analysis-of-genome-wide-association-studies-of-imputed-genotypes-with-time-to-event-outcomes
#16
Hamzah Syed, Andrea L Jorgensen, Andrew P Morris
BACKGROUND: Analysis of genome-wide association studies (GWAS) with "time to event" outcomes have become increasingly popular, predominantly in the context of pharmacogenetics, where the survival endpoint could be death, disease remission or the occurrence of an adverse drug reaction. However, methodology and software that can efficiently handle the scale and complexity of genetic data from GWAS with time to event outcomes has not been extensively developed. RESULTS: SurvivalGWAS_SV is an easy to use software implemented using C# and run on Linux, Mac OS X & Windows operating systems...
May 19, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28521770/coordinates-and-intervals-in-graph-based-reference-genomes
#17
Knut D Rand, Ivar Grytten, Alexander J Nederbragt, Geir O Storvik, Ingrid K Glad, Geir K Sandve
BACKGROUND: It has been proposed that future reference genomes should be graph structures in order to better represent the sequence diversity present in a species. However, there is currently no standard method to represent genomic intervals, such as the positions of genes or transcription factor binding sites, on graph-based reference genomes. RESULTS: We formalize offset-based coordinate systems on graph-based reference genomes and introduce methods for representing intervals on these reference structures...
May 18, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28521741/the-rainfall-plot-its-motivation-characteristics-and-pitfalls
#18
Diana Domanska, Daniel Vodák, Christin Lund-Andersen, Stefania Salvatore, Eivind Hovig, Geir Kjetil Sandve
BACKGROUND: A visualization referred to as rainfall plot has recently gained popularity in genome data analysis. The plot is mostly used for illustrating the distribution of somatic cancer mutations along a reference genome, typically aiming to identify mutation hotspots. In general terms, the rainfall plot can be seen as a scatter plot showing the location of events on the x-axis versus the distance between consecutive events on the y-axis. Despite its frequent use, the motivation for applying this particular visualization and the appropriateness of its usage have never been critically addressed in detail...
May 18, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28521733/nfpscanner-a-webtool-for-knowledge-based-deciphering-of-biomedical-networks
#19
Wenjian Xu, Yang Cao, Ziwei Xie, Haochen He, Song He, Hao Hong, Xiaochen Bo, Fei Li
BACKGROUND: Many biological pathways have been created to represent different types of knowledge, such as genetic interactions, metabolic reactions, and gene-regulating and physical-binding relationships. Biologists are using a wide range of omics data to elaborately construct various context-specific differential molecular networks. However, they cannot easily gain insight into unfamiliar gene networks with the tools that are currently available for pathways resource and network analysis...
May 18, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/28511665/clone-temporal-centrality-measures-for-incomplete-sequences-of-graph-snapshots
#20
Moritz Hanke, Ronja Foraita
BACKGROUND: Different phenomena like the spread of a disease, social interactions or the biological relation between genes can be thought of as dynamic networks. These can be represented as a sequence of static graphs (so called graph snapshots). Based on this graph sequences, classical vertex centrality measures like closeness and betweenness centrality have been extended to quantify the importance of single vertices within a dynamic network. An implicit assumption for the calculation of temporal centrality measures is that the graph sequence contains all information about the network dynamics over time...
May 16, 2017: BMC Bioinformatics
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