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BMC Bioinformatics

Samuel J Yang, Marc Berndl, D Michael Ando, Mariya Barch, Arunachalam Narayanaswamy, Eric Christiansen, Stephan Hoyer, Chris Roat, Jane Hung, Curtis T Rueden, Asim Shankar, Steven Finkbeiner, Philip Nelson
BACKGROUND: Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with high accuracy is important for obtaining a clean, unbiased image dataset. Complicating this task is the fact that image focus quality is only well-defined in foreground regions of images, and as a result, most previous approaches only enable a computation of the relative difference in quality between two or more images, rather than an absolute measure of quality...
March 15, 2018: BMC Bioinformatics
Michael Kluge, Caroline C Friedel
BACKGROUND: The development of high-throughput experimental technologies, such as next-generation sequencing, have led to new challenges for handling, analyzing and integrating the resulting large and diverse datasets. Bioinformatical analysis of these data commonly requires a number of mutually dependent steps applied to numerous samples for multiple conditions and replicates. To support these analyses, a number of workflow management systems (WMSs) have been developed to allow automated execution of corresponding analysis workflows...
March 13, 2018: BMC Bioinformatics
Shu Yang, Junwen Wang, Raymond T Ng
BACKGROUND: Characterizing the binding preference of RNA-binding proteins (RBP) is essential for us to understand the interaction between an RBP and its RNA targets, and to decipher the mechanism of post-transcriptional regulation. Experimental methods have been used to generate protein-RNA binding data for a number of RBPs in vivo and in vitro. Utilizing the binding data, a couple of computational methods have been developed to detect the RNA sequence or structure preferences of the RBPs...
March 12, 2018: BMC Bioinformatics
Rajiv Gandhi Govindaraj, Michal Brylinski
BACKGROUND: Detecting similar ligand-binding sites in globally unrelated proteins has a wide range of applications in modern drug discovery, including drug repurposing, the prediction of side effects, and drug-target interactions. Although a number of techniques to compare binding pockets have been developed, this problem still poses significant challenges. RESULTS: We evaluate the performance of three algorithms to calculate similarities between ligand-binding sites, APoc, SiteEngine, and G-LoSA...
March 9, 2018: BMC Bioinformatics
Haowen Zhang, Yuandong Chan, Kaichao Fan, Bertil Schmidt, Weiguo Liu
BACKGROUND: Various indexing techniques have been applied by next generation sequencing read mapping tools. The choice of a particular data structure is a trade-off between memory consumption, mapping throughput, and construction time. RESULTS: We present the succinct hash index - a novel data structure for read mapping which is a variant of the classical q-gram index with a particularly small memory footprint occupying between 3.5 and 5.3 GB for a human reference genome for typical parameter settings...
March 9, 2018: BMC Bioinformatics
Gen Li, Dereje Jima, Fred A Wright, Andrew B Nobel
BACKGROUND: Expression quantitative trait loci (eQTL) analysis identifies genetic markers associated with the expression of a gene. Most existing eQTL analyses and methods investigate association in a single, readily available tissue, such as blood. Joint analysis of eQTL in multiple tissues has the potential to improve, and expand the scope of, single-tissue analyses. Large-scale collaborative efforts such as the Genotype-Tissue Expression (GTEx) program are currently generating high quality data in a large number of tissues...
March 9, 2018: BMC Bioinformatics
Jesse M Zhang, Jue Fan, H Christina Fan, David Rosenfeld, David N Tse
BACKGROUND: With the recent proliferation of single-cell RNA-Seq experiments, several methods have been developed for unsupervised analysis of the resulting datasets. These methods often rely on unintuitive hyperparameters and do not explicitly address the subjectivity associated with clustering. RESULTS: In this work, we present DendroSplit, an interpretable framework for analyzing single-cell RNA-Seq datasets that addresses both the clustering interpretability and clustering subjectivity issues...
March 9, 2018: BMC Bioinformatics
H-M Müller, K M Van Auken, Y Li, P W Sternberg
BACKGROUND: The biomedical literature continues to grow at a rapid pace, making the challenge of knowledge retrieval and extraction ever greater. Tools that provide a means to search and mine the full text of literature thus represent an important way by which the efficiency of these processes can be improved. RESULTS: We describe the next generation of the Textpresso information retrieval system, Textpresso Central (TPC). TPC builds on the strengths of the original system by expanding the full text corpus to include the PubMed Central Open Access Subset (PMC OA), as well as the WormBase C...
March 9, 2018: BMC Bioinformatics
Kumar Parijat Tripathi, Marina Piccirillo, Mario Rosario Guarracino
BACKGROUND: The endomembrane system, known as secretory pathway, is responsible for the synthesis and transport of protein molecules in cells. Therefore, genes involved in the secretory pathway are essential for the cellular development and function. Recent scientific investigations show that ER and Golgi apparatus may provide a convenient drug target for cancer therapy. On the other hand, it is known that abundantly expressed genes in different cellular organelles share interconnected pathways and co-regulate each other activities...
March 8, 2018: BMC Bioinformatics
Lokmane Chebouba, Bertrand Miannay, Dalila Boughaci, Carito Guziolowski
BACKGROUND: During the last years, several approaches were applied on biomedical data to detect disease specific proteins and genes in order to better target drugs. It was shown that statistical and machine learning based methods use mainly clinical data and improve later their results by adding omics data. This work proposes a new method to discriminate the response of Acute Myeloid Leukemia (AML) patients to treatment. The proposed approach uses proteomics data and prior regulatory knowledge in the form of networks to predict cancer treatment outcomes by finding out the different Boolean networks specific to each type of response to drugs...
March 8, 2018: BMC Bioinformatics
Maurizio Giordano, Kumar Parijat Tripathi, Mario Rosario Guarracino
BACKGROUND: System toxicology aims at understanding the mechanisms used by biological systems to respond to toxicants. Such understanding can be leveraged to assess the risk of chemicals, drugs, and consumer products in living organisms. In system toxicology, machine learning techniques and methodologies are applied to develop prediction models for classification of toxicant exposure of biological systems. Gene expression data (RNA/DNA microarray) are often used to develop such prediction models...
March 8, 2018: BMC Bioinformatics
Diego Fioravanti, Ylenia Giarratano, Valerio Maggio, Claudio Agostinelli, Marco Chierici, Giuseppe Jurman, Cesare Furlanello
BACKGROUND: Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data based on the Convolutional Neural Networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure. The patristic distance between variables is used together with a sparsified version of MultiDimensional Scaling to embed the phylogenetic tree in a Euclidean space...
March 8, 2018: BMC Bioinformatics
Garrett Jenkinson, Jordi Abante, Andrew P Feinberg, John Goutsias
BACKGROUND: DNA methylation is a stable form of epigenetic memory used by cells to control gene expression. Whole genome bisulfite sequencing (WGBS) has emerged as a gold-standard experimental technique for studying DNA methylation by producing high resolution genome-wide methylation profiles. Statistical modeling and analysis is employed to computationally extract and quantify information from these profiles in an effort to identify regions of the genome that demonstrate crucial or aberrant epigenetic behavior...
March 7, 2018: BMC Bioinformatics
Bin Zou, Victor H F Lee, Hong Yan
BACKGROUND: Non-small cell lung cancer (NSCLC) with activating EGFR mutations, especially exon 19 deletions and the L858R point mutation, is particularly responsive to gefitinib and erlotinib. However, the sensitivity varies for less common and rare EGFR mutations. There are various explanations for the low sensitivity of EGFR exon 20 insertions and the exon 20 T790 M point mutation to gefitinib/erlotinib. However, few studies discuss, from a structural perspective, why less common mutations, like G719X and L861Q, have moderate sensitivity to gefitinib/erlotinib...
March 7, 2018: BMC Bioinformatics
Shuxiang Ruan, Gary D Stormo
BACKGROUND: Transcription factor (TF) binding site specificity is commonly represented by some form of matrix model in which the positions in the binding site are assumed to contribute independently to the site's activity. The independence assumption is known to be an approximation, often a good one but sometimes poor. Alternative approaches have been developed that use k-mers (DNA "words" of length k) to account for the non-independence, and more recently DNA structural parameters have been incorporated into the models...
March 6, 2018: BMC Bioinformatics
Crhisllane Rafaele Dos Santos Vasconcelos, Túlio de Lima Campos, Antonio Mauro Rezende
BACKGROUND: Systematic analysis of a parasite interactome is a key approach to understand different biological processes. It makes possible to elucidate disease mechanisms, to predict protein functions and to select promising targets for drug development. Currently, several approaches for protein interaction prediction for non-model species incorporate only small fractions of the entire proteomes and their interactions. Based on this perspective, this study presents an integration of computational methodologies, protein network predictions and comparative analysis of the protozoan species Leishmania braziliensis and Leishmania infantum...
March 6, 2018: BMC Bioinformatics
Jalal K Siddiqui, Elizabeth Baskin, Mingrui Liu, Carmen Z Cantemir-Stone, Bofei Zhang, Russell Bonneville, Joseph P McElroy, Kevin R Coombes, Ewy A Mathé
BACKGROUND: Integration of transcriptomic and metabolomic data improves functional interpretation of disease-related metabolomic phenotypes, and facilitates discovery of putative metabolite biomarkers and gene targets. For this reason, these data are increasingly collected in large (> 100 participants) cohorts, thereby driving a need for the development of user-friendly and open-source methods/tools for their integration. Of note, clinical/translational studies typically provide snapshot (e...
March 5, 2018: BMC Bioinformatics
Armen Abnousi, Shira L Broschat, Ananth Kalyanaraman
BACKGROUND: Clustering of protein sequences is of key importance in predicting the structure and function of newly sequenced proteins and is also of use for their annotation. With the advent of multiple high-throughput sequencing technologies, new protein sequences are becoming available at an extraordinary rate. The rapid growth rate has impeded deployment of existing protein clustering/annotation tools which depend largely on pairwise sequence alignment. RESULTS: In this paper, we propose an alignment-free clustering approach, coreClust, for annotating protein sequences using detected conserved regions...
March 5, 2018: BMC Bioinformatics
Skylar W Marvel, Kimberly To, Fabian A Grimm, Fred A Wright, Ivan Rusyn, David M Reif
BACKGROUND: Drawing integrated conclusions from diverse source data requires synthesis across multiple types of information. The ToxPi (Toxicological Prioritization Index) is an analytical framework that was developed to enable integration of multiple sources of evidence by transforming data into integrated, visual profiles. Methodological improvements have advanced ToxPi and expanded its applicability, necessitating a new, consolidated software platform to provide functionality, while preserving flexibility for future updates...
March 5, 2018: BMC Bioinformatics
Luan Lin, Wilson H McKerrow, Bryce Richards, Chukiat Phonsom, Charles E Lawrence
BACKGROUND: The nearest neighbor model and associated dynamic programming algorithms allow for the efficient estimation of the RNA secondary structure Boltzmann ensemble. However because a given RNA secondary structure only contains a fraction of the possible helices that could form from a given sequence, the Boltzmann ensemble is multimodal. Several methods exist for clustering structures and finding those modes. However less focus is given to exploring the underlying reasons for this multimodality: the presence of conflicting basepairs...
March 5, 2018: BMC Bioinformatics
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