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    Reconstruction and visualization of large-scale volumetric models of neocortical circuits for physically-plausible in silico optical studies.
    BMC Bioinformatics 2017 Sep 13;18(Suppl 10):402. Epub 2017 Sep 13.
    Blue Brain Project (BBP), École Polytechnique Fédérale de Lausanne (EPFL), Biotech Campus, Chemin des Mines 9, Geneva, 1202, Switzerland.
    Background: We present a software workflow capable of building large scale, highly detailed and realistic volumetric models of neocortical circuits from the morphological skeletons of their digitally reconstructed neurons. The limitations of the existing approaches for creating those models are explained, and then, a multi-stage pipeline is discussed to overcome those limitations. Starting from the neuronal morphologies, we create smooth piecewise watertight polygonal models that can be efficiently utilized to synthesize continuous and plausible volumetric models of the neurons with solid voxelization. Read More

    Vermont: a multi-perspective visual interactive platform for mutational analysis.
    BMC Bioinformatics 2017 Sep 13;18(Suppl 10):403. Epub 2017 Sep 13.
    Department of Computer Science, Universidade Federal de Viçosa, Peter Henry Rolfs avenue, Campus Universitário, Viçosa, 36570-900, Brazil.
    Background: A huge amount of data about genomes and sequence variation is available and continues to grow on a large scale, which makes experimentally characterizing these mutations infeasible regarding disease association and effects on protein structure and function. Therefore, reliable computational approaches are needed to support the understanding of mutations and their impacts. Here, we present VERMONT 2. Read More

    MediSyn: uncertainty-aware visualization of multiple biomedical datasets to support drug treatment selection.
    BMC Bioinformatics 2017 Sep 13;18(Suppl 10):393. Epub 2017 Sep 13.
    Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Gustaf Hällströmin katu 2b, Helsinki, 00560, Finland.
    Background: Dispersed biomedical databases limit user exploration to generate structured knowledge. Linked Data unifies data structures and makes the dispersed data easy to search across resources, but it lacks supporting human cognition to achieve insights. In addition, potential errors in the data are difficult to detect in their free formats. Read More

    Bayesian Unidimensional Scaling for visualizing uncertainty in high dimensional datasets with latent ordering of observations.
    BMC Bioinformatics 2017 Sep 13;18(Suppl 10):394. Epub 2017 Sep 13.
    Department of Statistics, Stanford University, Stanford, 94305, USA.
    Background: Detecting patterns in high-dimensional multivariate datasets is non-trivial. Clustering and dimensionality reduction techniques often help in discerning inherent structures. In biological datasets such as microbial community composition or gene expression data, observations can be generated from a continuous process, often unknown. Read More

    CellNetVis: a web tool for visualization of biological networks using force-directed layout constrained by cellular components.
    BMC Bioinformatics 2017 Sep 13;18(Suppl 10):395. Epub 2017 Sep 13.
    University of São Paulo, Instituto de Ciências Matemáticas e de Computação, Av. Trabalhador São-carlense, 400, São Carlos-SP, Brazil.
    Background: The advent of "omics" science has brought new perspectives in contemporary biology through the high-throughput analyses of molecular interactions, providing new clues in protein/gene function and in the organization of biological pathways. Biomolecular interaction networks, or graphs, are simple abstract representations where the components of a cell (e.g. Read More

    C-State: an interactive web app for simultaneous multi-gene visualization and comparative epigenetic pattern search.
    BMC Bioinformatics 2017 Sep 13;18(Suppl 10):392. Epub 2017 Sep 13.
    CSIR- Centre for Cellular and Molecular Biology, Hyderabad, India.
    Background: Comparative epigenomic analysis across multiple genes presents a bottleneck for bench biologists working with NGS data. Despite the development of standardized peak analysis algorithms, the identification of novel epigenetic patterns and their visualization across gene subsets remains a challenge.

    Results: We developed a fast and interactive web app, C-State (Chromatin-State), to query and plot chromatin landscapes across multiple loci and cell types. Read More

    biospear: an R package for biomarker selection in penalized Cox regression.
    Bioinformatics 2017 Sep 12. Epub 2017 Sep 12.
    Gustave Roussy, Université Paris-Saclay, Service de biostatistique et d'épidémiologie, Villejuif, 94805, France.
    Summary: The R package biospear allows selecting the biomarkers with the strongest impact on survival and on the treatment effect in high-dimensional Cox models, and estimating expected survival probabilities. Most of the implemented approaches are based on penalized regression techniques.

    Availability: The package is available on the CRAN. Read More

    Methods for discovering genomic loci exhibiting complex patterns of differential methylation.
    BMC Bioinformatics 2017 Sep 18;18(1):416. Epub 2017 Sep 18.
    Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge, CB2 3EA, UK.
    Background: Cytosine methylation is widespread in most eukaryotic genomes and is known to play a substantial role in various regulatory pathways. Unmethylated cytosines may be converted to uracil through the addition of sodium bisulphite, allowing genome-wide quantification of cytosine methylation via high-throughput sequencing. The data thus acquired allows the discovery of methylation 'loci'; contiguous regions of methylation consistently methylated across biological replicates. Read More

    Cleaning by clustering: methodology for addressing data quality issues in biomedical metadata.
    BMC Bioinformatics 2017 Sep 18;18(1):415. Epub 2017 Sep 18.
    Institute of Data Science, Maastricht University, Maastricht, 6200, MD, The Netherlands.
    Background: The ability to efficiently search and filter datasets depends on access to high quality metadata. While most biomedical repositories require data submitters to provide a minimal set of metadata, some such as the Gene Expression Omnibus (GEO) allows users to specify additional metadata in the form of textual key-value pairs (e.g. Read More

    Deep learning methods for protein torsion angle prediction.
    BMC Bioinformatics 2017 Sep 18;18(1):417. Epub 2017 Sep 18.
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA.
    Background: Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. Read More

    Segmentation and classification of two-channel C. elegans nucleus-labeled fluorescence images.
    BMC Bioinformatics 2017 Sep 15;18(1):412. Epub 2017 Sep 15.
    Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Yiheyuan Road, Beijing, 100871, China.
    Background: Aging is characterized by a gradual breakdown of cellular structures. Nuclear abnormality is a hallmark of progeria in human. Analysis of age-dependent nuclear morphological changes in Caenorhabditis elegans is of great value to aging research, and this calls for an automatic image processing method that is suitable for both normal and abnormal structures. Read More

    mmquant: how to count multi-mapping reads?
    BMC Bioinformatics 2017 Sep 15;18(1):411. Epub 2017 Sep 15.
    MIAT, Toulouse INRA, BP 52627, Castanet-Tolosan cedex, 31326, France.
    Background: RNA-Seq is currently used routinely, and it provides accurate information on gene transcription. However, the method cannot accurately estimate duplicated genes expression. Several strategies have been previously used (drop duplicated genes, distribute uniformly the reads, or estimate expression), but all of them provide biased results. Read More

    Spliceman2: a computational web server that predicts defects in pre-mRNA splicing.
    Bioinformatics 2017 Sep;33(18):2943-2945
    Department of Molecular Biology, Cell Biology, and Biochemistry, Brown University, Providence, RI 02903, USA.
    Summary: Most pre-mRNA transcripts in eukaryotic cells must undergo splicing to remove introns and join exons, and splicing elements present a large mutational target for disease-causing mutations. Splicing elements are strongly position dependent with respect to the transcript annotations. In 2012, we presented Spliceman, an online tool that used positional dependence to predict how likely distant mutations around annotated splice sites were to disrupt splicing. Read More

    POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles.
    Bioinformatics 2017 Sep;33(17):2756-2758
    Biomedicine Discovery Institute, Monash University, VIC 3800, Australia.
    Summary: Evolutionary information in the form of a Position-Specific Scoring Matrix (PSSM) is a widely used and highly informative representation of protein sequences. Accordingly, PSSM-based feature descriptors have been successfully applied to improve the performance of various predictors of protein attributes. Even though a number of algorithms have been proposed in previous studies, there is currently no universal web server or toolkit available for generating this wide variety of descriptors. Read More

    Interactive visual exploration and refinement of cluster assignments.
    BMC Bioinformatics 2017 Sep 12;18(1):406. Epub 2017 Sep 12.
    Scientific Computing and Imaging Institute, University of Utah, 72 Sout Central Campus Drive, Salt Lake City, 84112, USA.
    Background: With ever-increasing amounts of data produced in biology research, scientists are in need of efficient data analysis methods. Cluster analysis, combined with visualization of the results, is one such method that can be used to make sense of large data volumes. At the same time, cluster analysis is known to be imperfect and depends on the choice of algorithms, parameters, and distance measures. Read More

    Statistical classifiers for diagnosing disease from immune repertoires: a case study using multiple sclerosis.
    BMC Bioinformatics 2017 Sep 7;18(1):401. Epub 2017 Sep 7.
    Department of Clinical Sciences, UT Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, TX, 75390-9066, USA.
    Background: Deep sequencing of lymphocyte receptor repertoires has made it possible to comprehensively profile the clonal composition of lymphocyte populations. This opens the door for novel approaches to diagnose and prognosticate diseases with a driving immune component by identifying repertoire sequence patterns associated with clinical phenotypes. Indeed, recent studies support the feasibility of this, demonstrating an association between repertoire-level summary statistics (e. Read More

    CATS (Coordinates of Atoms by Taylor Series): protein design with backbone flexibility in all locally feasible directions.
    Bioinformatics 2017 Jul;33(14):i5-i12
    Department of Computer Science, Duke University, Durham, NC 27708, USA.
    Motivation: When proteins mutate or bind to ligands, their backbones often move significantly, especially in loop regions. Computational protein design algorithms must model these motions in order to accurately optimize protein stability and binding affinity. However, methods for backbone conformational search in design have been much more limited than for sidechain conformational search. Read More

    When loss-of-function is loss of function: assessing mutational signatures and impact of loss-of-function genetic variants.
    Bioinformatics 2017 Jul;33(14):i389-i398
    Department of Computer Science and Informatics, Indiana University, Bloomington, IN, USA.
    Motivation: Loss-of-function genetic variants are frequently associated with severe clinical phenotypes, yet many are present in the genomes of healthy individuals. The available methods to assess the impact of these variants rely primarily upon evolutionary conservation with little to no consideration of the structural and functional implications for the protein. They further do not provide information to the user regarding specific molecular alterations potentially causative of disease. Read More

    miniMDS: 3D structural inference from high-resolution Hi-C data.
    Bioinformatics 2017 Jul;33(14):i261-i266
    Department of Biochemistry and Molecular Biology and Center for Eukaryotic Gene Regulation, The Pennsylvania State University, University Park, PA 16802, USA.
    Motivation: Recent experiments have provided Hi-C data at resolution as high as 1 kbp. However, 3D structural inference from high-resolution Hi-C datasets is often computationally unfeasible using existing methods.

    Results: We have developed miniMDS, an approximation of multidimensional scaling (MDS) that partitions a Hi-C dataset, performs high-resolution MDS separately on each partition, and then reassembles the partitions using low-resolution MDS. Read More

    Tumor phylogeny inference using tree-constrained importance sampling.
    Bioinformatics 2017 Jul;33(14):i152-i160
    Department of Computer Science, Princeton University, Princeton, NJ, 08544 USA.
    Motivation: A tumor arises from an evolutionary process that can be modeled as a phylogenetic tree. However, reconstructing this tree is challenging as most cancer sequencing uses bulk tumor tissue containing heterogeneous mixtures of cells.

    Results: We introduce P robabilistic A lgorithm for S omatic Tr ee I nference (PASTRI), a new algorithm for bulk-tumor sequencing data that clusters somatic mutations into clones and infers a phylogenetic tree that describes the evolutionary history of the tumor. Read More

    Efficient approximations of RNA kinetics landscape using non-redundant sampling.
    Bioinformatics 2017 Jul;33(14):i283-i292
    AMIB project, Inria Saclay, 91120 Palaiseau, France.
    Motivation: Kinetics is key to understand many phenomena involving RNAs, such as co-transcriptional folding and riboswitches. Exact out-of-equilibrium studies induce extreme computational demands, leading state-of-the-art methods to rely on approximated kinetics landscapes, obtained using sampling strategies that strive to generate the key landmarks of the landscape topology. However, such methods are impeded by a large level of redundancy within sampled sets. Read More

    Integrative deep models for alternative splicing.
    Bioinformatics 2017 Jul;33(14):i274-i282
    Department of Computer and Information Science, School of Engineering.
    Motivation: Advancements in sequencing technologies have highlighted the role of alternative splicing (AS) in increasing transcriptome complexity. This role of AS, combined with the relation of aberrant splicing to malignant states, motivated two streams of research, experimental and computational. The first involves a myriad of techniques such as RNA-Seq and CLIP-Seq to identify splicing regulators and their putative targets. Read More

    DeepBound: accurate identification of transcript boundaries via deep convolutional neural fields.
    Bioinformatics 2017 Jul;33(14):i267-i273
    Computational Bioscience Research Center (CBRC), Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
    Motivation: Reconstructing the full-length expressed transcripts ( a.k.a. Read More

    Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression.
    Bioinformatics 2017 Jul;33(14):i359-i368
    Institute for Molecular Medicine Finland FIMM, University of Helsinki, 00014 Helsinki, Finland.
    Motivation: A prime challenge in precision cancer medicine is to identify genomic and molecular features that are predictive of drug treatment responses in cancer cells. Although there are several computational models for accurate drug response prediction, these often lack the ability to infer which feature combinations are the most predictive, particularly for high-dimensional molecular datasets. As increasing amounts of diverse genome-wide data sources are becoming available, there is a need to build new computational models that can effectively combine these data sources and identify maximally predictive feature combinations. Read More

    Predicting phenotypes from microarrays using amplified, initially marginal, eigenvector regression.
    Bioinformatics 2017 Jul;33(14):i350-i358
    Department of Statistics, Indiana University, Bloomington, IN 47405, USA.
    Motivation: The discovery of relationships between gene expression measurements and phenotypic responses is hampered by both computational and statistical impediments. Conventional statistical methods are less than ideal because they either fail to select relevant genes, predict poorly, ignore the unknown interaction structure between genes, or are computationally intractable. Thus, the creation of new methods which can handle many expression measurements on relatively small numbers of patients while also uncovering gene-gene relationships and predicting well is desirable. Read More

    Improved data-driven likelihood factorizations for transcript abundance estimation.
    Bioinformatics 2017 Jul;33(14):i142-i151
    Department of Computer Science, Stony Brook University, Stony Brook, NY 11790, USA.
    Motivation: Many methods for transcript-level abundance estimation reduce the computational burden associated with the iterative algorithms they use by adopting an approximate factorization of the likelihood function they optimize. This leads to considerably faster convergence of the optimization procedure, since each round of e.g. Read More

    deBGR: an efficient and near-exact representation of the weighted de Bruijn graph.
    Bioinformatics 2017 Jul;33(14):i133-i141
    Department of Computer Science, Stony Brook University, Stony Brook, NY 11790, USA.
    Motivation: Almost all de novo short-read genome and transcriptome assemblers start by building a representation of the de Bruijn Graph of the reads they are given as input. Even when other approaches are used for subsequent assembly (e.g. Read More

    Multiple network-constrained regressions expand insights into influenza vaccination responses.
    Bioinformatics 2017 Jul;33(14):i208-i216
    Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA.
    Motivation: Systems immunology leverages recent technological advancements that enable broad profiling of the immune system to better understand the response to infection and vaccination, as well as the dysregulation that occurs in disease. An increasingly common approach to gain insights from these large-scale profiling experiments involves the application of statistical learning methods to predict disease states or the immune response to perturbations. However, the goal of many systems studies is not to maximize accuracy, but rather to gain biological insights. Read More

    Genomes as documents of evolutionary history: a probabilistic macrosynteny model for the reconstruction of ancestral genomes.
    Bioinformatics 2017 Jul;33(14):i369-i378
    Department of Genetics, Smurfit Institute of Genetics, Trinity College Dublin, University of Dublin, Dublin 2, Ireland.
    Motivation: It has been argued that whole-genome duplication (WGD) exerted a profound influence on the course of evolution. For the purpose of fully understanding the impact of WGD, several formal algorithms have been developed for reconstructing pre-WGD gene order in yeast and plant. However, to the best of our knowledge, those algorithms have never been successfully applied to WGD events in teleost and vertebrate, impeded by extensive gene shuffling and gene losses. Read More

    Image-based spatiotemporal causality inference for protein signaling networks.
    Bioinformatics 2017 Jul;33(14):i217-i224
    Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
    Motivation: Efforts to model how signaling and regulatory networks work in cells have largely either not considered spatial organization or have used compartmental models with minimal spatial resolution. Fluorescence microscopy provides the ability to monitor the spatiotemporal distribution of many molecules during signaling events, but as of yet no methods have been described for large scale image analysis to learn a complex protein regulatory network. Here we present and evaluate methods for identifying how changes in concentration in one cell region influence concentration of other proteins in other regions. Read More

    Exploiting sequence-based features for predicting enhancer-promoter interactions.
    Bioinformatics 2017 Jul;33(14):i252-i260
    Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
    Motivation: A large number of distal enhancers and proximal promoters form enhancer-promoter interactions to regulate target genes in the human genome. Although recent high-throughput genome-wide mapping approaches have allowed us to more comprehensively recognize potential enhancer-promoter interactions, it is still largely unknown whether sequence-based features alone are sufficient to predict such interactions.

    Results: Here, we develop a new computational method (named PEP) to predict enhancer-promoter interactions based on sequence-based features only, when the locations of putative enhancers and promoters in a particular cell type are given. Read More

    A new method to study the change of miRNA-mRNA interactions due to environmental exposures.
    Bioinformatics 2017 Jul;33(14):i199-i207
    Department of Genetics and Genomic Sciences.
    Motivation: Integrative approaches characterizing the interactions among different types of biological molecules have been demonstrated to be useful for revealing informative biological mechanisms. One such example is the interaction between microRNA (miRNA) and messenger RNA (mRNA), whose deregulation may be sensitive to environmental insult leading to altered phenotypes. The goal of this work is to develop an effective data integration method to characterize deregulation between miRNA and mRNA due to environmental toxicant exposures. Read More

    Direct AUC optimization of regulatory motifs.
    Bioinformatics 2017 Jul;33(14):i243-i251
    Institute of Machine Learning and Systems Biology, Department of College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.
    Motivation: The discovery of transcription factor binding site (TFBS) motifs is essential for untangling the complex mechanism of genetic variation under different developmental and environmental conditions. Among the huge amount of computational approaches for de novo identification of TFBS motifs, discriminative motif learning (DML) methods have been proven to be promising for harnessing the discovery power of accumulated huge amount of high-throughput binding data. However, they have to sacrifice accuracy for speed and could fail to fully utilize the information of the input sequences. Read More

    Discovery and genotyping of novel sequence insertions in many sequenced individuals.
    Bioinformatics 2017 Jul;33(14):i161-i169
    School of Computing Science, Simon Fraser University, Burnaby, V5A 1S6, Canada.
    Motivation: Despite recent advances in algorithms design to characterize structural variation using high-throughput short read sequencing (HTS) data, characterization of novel sequence insertions longer than the average read length remains a challenging task. This is mainly due to both computational difficulties and the complexities imposed by genomic repeats in generating reliable assemblies to accurately detect both the sequence content and the exact location of such insertions. Additionally, de novo genome assembly algorithms typically require a very high depth of coverage, which may be a limiting factor for most genome studies. Read More

    Efficient simulation of intrinsic, extrinsic and external noise in biochemical systems.
    Bioinformatics 2017 Jul;33(14):i319-i324
    Max Planck Institute for Dynamics of Complex Technical Systems, Process Systems Engineering, 39106 Magdeburg, Germany.
    Motivation: Biological cells operate in a noisy regime influenced by intrinsic, extrinsic and external noise, which leads to large differences of individual cell states. Stochastic effects must be taken into account to characterize biochemical kinetics accurately. Since the exact solution of the chemical master equation, which governs the underlying stochastic process, cannot be derived for most biochemical systems, approximate methods are used to obtain a solution. Read More

    Predicting multicellular function through multi-layer tissue networks.
    Bioinformatics 2017 Jul;33(14):i190-i198
    Department of Computer Science, Stanford University, Stanford, CA 94305, USA.
    Motivation: Understanding functions of proteins in specific human tissues is essential for insights into disease diagnostics and therapeutics, yet prediction of tissue-specific cellular function remains a critical challenge for biomedicine.

    Results: Here, we present OhmNet , a hierarchy-aware unsupervised node feature learning approach for multi-layer networks. We build a multi-layer network, where each layer represents molecular interactions in a different human tissue. Read More

    popFBA: tackling intratumour heterogeneity with Flux Balance Analysis.
    Bioinformatics 2017 Jul;33(14):i311-i318
    SYSBIO Centre of Systems Biology, 20126 Milan, Italy.
    Motivation: Intratumour heterogeneity poses many challenges to the treatment of cancer. Unfortunately, the transcriptional and metabolic information retrieved by currently available computational and experimental techniques portrays the average behaviour of intermixed and heterogeneous cell subpopulations within a given tumour. Emerging single-cell genomic analyses are nonetheless unable to characterize the interactions among cancer subpopulations. Read More

    Estimation of time-varying growth, uptake and excretion rates from dynamic metabolomics data.
    Bioinformatics 2017 Jul;33(14):i301-i310
    Inria, Centre de Recherche Grenoble - Rhône-Alpes, Montbonnot, France.
    Motivation: Technological advances in metabolomics have made it possible to monitor the concentration of extracellular metabolites over time. From these data, it is possible to compute the rates of uptake and excretion of the metabolites by a growing cell population, providing precious information on the functioning of intracellular metabolism. The computation of the rate of these exchange reactions, however, is difficult to achieve in practice for a number of reasons, notably noisy measurements, correlations between the concentration profiles of the different extracellular metabolites, and discontinuties in the profiles due to sudden changes in metabolic regime. Read More

    A scalable moment-closure approximation for large-scale biochemical reaction networks.
    Bioinformatics 2017 Jul;33(14):i293-i300
    Institute of Computational Biology, Helmholtz Zentrum München - German Research Center for Environmental Health, 85764 Neuherberg, Germany.
    Motivation: Stochastic molecular processes are a leading cause of cell-to-cell variability. Their dynamics are often described by continuous-time discrete-state Markov chains and simulated using stochastic simulation algorithms. As these stochastic simulations are computationally demanding, ordinary differential equation models for the dynamics of the statistical moments have been developed. Read More

    Association testing of bisulfite-sequencing methylation data via a Laplace approximation.
    Bioinformatics 2017 Jul;33(14):i325-i332
    Computer Science Department, University of California Los Angeles, Los Angeles, CA 90095, USA.
    Motivation: Epigenome-wide association studies can provide novel insights into the regulation of genes involved in traits and diseases. The rapid emergence of bisulfite-sequencing technologies enables performing such genome-wide studies at the resolution of single nucleotides. However, analysis of data produced by bisulfite-sequencing poses statistical challenges owing to low and uneven sequencing depth, as well as the presence of confounding factors. Read More

    TITER: predicting translation initiation sites by deep learning.
    Bioinformatics 2017 Jul;33(14):i234-i242
    Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing 100084, China.
    Motivation: Translation initiation is a key step in the regulation of gene expression. In addition to the annotated translation initiation sites (TISs), the translation process may also start at multiple alternative TISs (including both AUG and non-AUG codons), which makes it challenging to predict TISs and study the underlying regulatory mechanisms. Meanwhile, the advent of several high-throughput sequencing techniques for profiling initiating ribosomes at single-nucleotide resolution, e. Read More

    Alignment of dynamic networks.
    Bioinformatics 2017 Jul;33(14):i180-i189
    Department of Computer Science and Engineering, ECK Institute for Global Health, and Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN 46556, USA.
    Motivation: Network alignment (NA) aims to find a node mapping that conserves similar regions between compared networks. NA is applicable to many fields, including computational biology, where NA can guide the transfer of biological knowledge from well- to poorly-studied species across aligned network regions. Existing NA methods can only align static networks. Read More

    Identification of associations between genotypes and longitudinal phenotypes via temporally-constrained group sparse canonical correlation analysis.
    Bioinformatics 2017 Jul;33(14):i341-i349
    School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
    Motivation: Neuroimaging genetics identifies the relationships between genetic variants (i.e., the single nucleotide polymorphisms) and brain imaging data to reveal the associations from genotypes to phenotypes. Read More

    Incorporating interaction networks into the determination of functionally related hit genes in genomic experiments with Markov random fields.
    Bioinformatics 2017 Jul;33(14):i170-i179
    CEA, BIG, Biologie à Grande Echelle, F-38054 Grenoble, France.
    Motivation: Incorporating gene interaction data into the identification of 'hit' genes in genomic experiments is a well-established approach leveraging the 'guilt by association' assumption to obtain a network based hit list of functionally related genes. We aim to develop a method to allow for multivariate gene scores and multiple hit labels in order to extend the analysis of genomic screening data within such an approach.

    Results: We propose a Markov random field-based method to achieve our aim and show that the particular advantages of our method compared with those currently used lead to new insights in previously analysed data as well as for our own motivating data. Read More

    Denoising genome-wide histone ChIP-seq with convolutional neural networks.
    Bioinformatics 2017 Jul;33(14):i225-i233
    Department of Computer Science.
    Motivation: Chromatin immune-precipitation sequencing (ChIP-seq) experiments are commonly used to obtain genome-wide profiles of histone modifications associated with different types of functional genomic elements. However, the quality of histone ChIP-seq data is affected by many experimental parameters such as the amount of input DNA, antibody specificity, ChIP enrichment and sequencing depth. Making accurate inferences from chromatin profiling experiments that involve diverse experimental parameters is challenging. Read More

    Increasing the power of meta-analysis of genome-wide association studies to detect heterogeneous effects.
    Bioinformatics 2017 Jul;33(14):i379-i388
    Department of Convergence Medicine, University of Ulsan College of Medicine & Asan Institute for Life Sciences, Asan Medical Center, Songpa-gu, Seoul 138-736, Korea.
    Motivation: Meta-analysis is essential to combine the results of genome-wide association studies (GWASs). Recent large-scale meta-analyses have combined studies of different ethnicities, environments and even studies of different related phenotypes. These differences between studies can manifest as effect size heterogeneity. Read More

    Molecular signatures that can be transferred across different omics platforms.
    Bioinformatics 2017 Jul;33(14):i333-i340
    Statistical Bioinformatics.
    Motivation: Molecular signatures for treatment recommendations are well researched. Still it is challenging to apply them to data generated by different protocols or technical platforms.

    Results: We analyzed paired data for the same tumors (Burkitt lymphoma, diffuse large B-cell lymphoma) and features that had been generated by different experimental protocols and analytical platforms including the nanoString nCounter and Affymetrix Gene Chip transcriptomics as well as the SWATH and SRM proteomics platforms. Read More

    Large-scale structure prediction by improved contact predictions and model quality assessment.
    Bioinformatics 2017 Jul;33(14):i23-i29
    Science for Life Laboratory and Department of Biochemistry and Biophysics, Stockholm University, Box 1031, 17121 Solna, Sweden.
    Motivation: Accurate contact predictions can be used for predicting the structure of proteins. Until recently these methods were limited to very big protein families, decreasing their utility. However, recent progress by combining direct coupling analysis with machine learning methods has made it possible to predict accurate contact maps for smaller families. Read More

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