23,423 results match your criteria Bioinformatics [Journal]


Parameter estimation in models of biological oscillators: an automated regularised estimation approach.

BMC Bioinformatics 2019 Feb 15;20(1):82. Epub 2019 Feb 15.

(Bio)Process Engineering Group, IIM-CSIC, Eduardo Cabello 6, Vigo, 36208, Spain.

Background: Dynamic modelling is a core element in the systems biology approach to understanding complex biosystems. Here, we consider the problem of parameter estimation in models of biological oscillators described by deterministic nonlinear differential equations. These problems can be extremely challenging due to several common pitfalls: (i) a lack of prior knowledge about parameters (i. Read More

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http://dx.doi.org/10.1186/s12859-019-2630-yDOI Listing
February 2019

Functional Heatmap: an automated and interactive pattern recognition tool to integrate time with multi-omics assays.

BMC Bioinformatics 2019 Feb 15;20(1):81. Epub 2019 Feb 15.

Integrative Systems Biology Program, US Army Center for Environmental Health Research, Fort Detrick, Frederick, MD, 21702-5010, USA.

Background: Life science research is moving quickly towards large-scale experimental designs that are comprised of multiple tissues, time points, and samples. Omic time-series experiments offer answers to three big questions: what collective patterns do most analytes follow, which analytes follow an identical pattern or synchronize across multiple cohorts, and how do biological functions evolve over time. Existing tools fall short of robustly answering and visualizing all three questions in a unified interface. Read More

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http://dx.doi.org/10.1186/s12859-019-2657-0DOI Listing
February 2019

SUBSTRA: Supervised Bayesian Patient Stratification.

Bioinformatics 2019 Feb 15. Epub 2019 Feb 15.

Computational Systems Immunology, Pfizer Worldwide R&D, Berlin, Germany.

Motivation: Patient stratification methods are key to the vision of precision medicine. Here, we consider transcriptional data to segment the patient population into subsets relevant to a given phenotype. Whereas most existing patient stratification methods focus either on predictive performance or interpretable features, we developed a method striking a balance between these two important goals. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz112DOI Listing
February 2019

Dhaka: Variational Autoencoder for Unmasking Tumor Heterogeneity from Single Cell Genomic Data.

Bioinformatics 2019 Feb 15. Epub 2019 Feb 15.

Microsoft Research, Redmond, USA.

Motivation: Intra-tumor heterogeneity is one of the key confounding factors in deciphering tumor evolution. Malignant cells exhibit variations in their gene expression, copy numbers, and mutation even when originating from a single progenitor cell. Single cell sequencing of tumor cells has recently emerged as a viable option for unmasking the underlying tumor heterogeneity. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz095DOI Listing
February 2019

BioKEEN: A library for learning and evaluating biological knowledge graph embeddings.

Bioinformatics 2019 Feb 15. Epub 2019 Feb 15.

Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany.

Summary: Knowledge graph embeddings (KGEs) have received significant attention in other domains due to their ability to predict links and create dense representations for graphs' nodes and edges. However, the software ecosystem for their application to bioinformatics remains limited and inaccessible for users without expertise in programming and machine learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs) and PyKEEN (Python KnowlEdge EmbeddiNgs) to facilitate their easy use through an interactive command line interface. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz117DOI Listing
February 2019

DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks.

Bioinformatics 2019 Feb 2. Epub 2019 Feb 2.

Department of Electrical and Computer Engineering, College Station, USA.

Motivation: Drug discovery demands rapid quantification of compound-protein interaction (CPI). However, there is a lack of methods that can predict compound-protein affinity from sequences alone with high applicability, accuracy, and interpretability.

Results: We present a seamless integration of domain knowledges and learning-based approaches. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz111DOI Listing
February 2019

Explore, edit and leverage genomic annotations using Python GTF toolkit.

Bioinformatics 2019 Feb 15. Epub 2019 Feb 15.

Aix Marseille Univ, INSERM, UMR U1090, TAGC, Marseille, France.

Motivation: While Python has become very popular in bioinformatics, a limited number of libraries exist for fast manipulation of gene coordinates in Ensembl GTF format.

Results: We have developed the GTF toolkit Python package (pygtftk), which aims at providing easy and powerful manipulation of gene coordinates in GTF format. For optimal performances, the core engine of pygtftk is a C dynamic library (libgtftk) while the Python API provides usability and readability for developing scripts. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz116DOI Listing
February 2019

Driver Network as a Biomarker: Systematic integration and network modeling of multi-omics data to derive driver signaling pathways for drug combination prediction.

Bioinformatics 2019 Feb 15. Epub 2019 Feb 15.

Department of Systems Medicine and Bioengineering, Houston Methodist Research Institute; Weill Cornell Medicine of Cornell University, Houston, TX, USA.

Motivation: Drug combinations that simultaneously suppress multiple cancer driver signaling pathways increase therapeutic options and may reduce drug resistance. We have developed a computational systems biology tool, DrugComboExplorer, to identify driver signaling pathways and predict synergistic drug combinations by integrating the knowledge embedded in vast amounts of available pharmacogenomics and omics data.

Results: This tool generates driver signaling networks by processing DNA sequencing, gene copy number, DNA methylation, and RNA-seq data from individual cancer patients using an integrated pipeline of algorithms, including bootstrap aggregating-based Markov random field, weighted co-expression network analysis, and supervised regulatory network learning. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz109DOI Listing
February 2019
3 Reads
4.981 Impact Factor

3DBIONOTES v3.0: Crossing molecular and structural biology data with genomic variations.

Bioinformatics 2019 Feb 15. Epub 2019 Feb 15.

Spanish National Institute for Bioinformatics (INB ELIXIR-ES) and Biocomputing Unit, National Centre of Biotechnology (CSIC)/Instruct Image Processing Centre, C/ Darwin n° 3, Campus of Cantoblanco, Madrid, Spain.

Motivation: Many diseases are associated to single nucleotide polymorphisms that affect critical regions of proteins as binding sites or post translational modifications. Therefore, analysing genomic variants with structural and molecular biology data is a powerful framework in order to elucidate the potential causes of such diseases.

Results: A new version of our web framework 3DBIONOTES is presented. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz118DOI Listing
February 2019

Iterative unsupervised domain adaptation for generalized cell detection from brightfield z-stacks.

BMC Bioinformatics 2019 Feb 15;20(1):80. Epub 2019 Feb 15.

Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.

Background: Cell counting from cell cultures is required in multiple biological and biomedical research applications. Especially, accurate brightfield-based cell counting methods are needed for cell growth analysis. With deep learning, cells can be detected with high accuracy, but manually annotated training data is required. Read More

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http://dx.doi.org/10.1186/s12859-019-2605-zDOI Listing
February 2019

BioVR: a platform for virtual reality assisted biological data integration and visualization.

BMC Bioinformatics 2019 Feb 15;20(1):78. Epub 2019 Feb 15.

Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, One Lomb Memorial Drive, Rochester, NY, 14623, USA.

Background: Functional characterization of single nucleotide variants (SNVs) involves two steps, the first step is to convert DNA to protein and the second step is to visualize protein sequences with their structures. As massively parallel sequencing has emerged as a leading technology in genomics, resulting in a significant increase in data volume, direct visualization of SNVs together with associated protein sequences/structures in a new user interface (UI) would be a more effective way to assess their potential effects on protein function.

Results: We have developed BioVR, an easy-to-use interactive, virtual reality (VR)-assisted platform for integrated visual analysis of DNA/RNA/protein sequences and protein structures using Unity3D and the C# programming language. Read More

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http://dx.doi.org/10.1186/s12859-019-2666-zDOI Listing
February 2019

FunMappOne: a tool to hierarchically organize and visually navigate functional gene annotations in multiple experiments.

BMC Bioinformatics 2019 Feb 15;20(1):79. Epub 2019 Feb 15.

Faculty of Medicine and Life Sciences, University of Tampere, Arvo Ylpön katu 34 - Arvo building, Tampere, FI-33014, Finland.

Background: Functional annotation of genes is an essential step in omics data analysis. Multiple databases and methods are currently available to summarize the functions of sets of genes into higher level representations, such as ontologies and molecular pathways. Annotating results from omics experiments into functional categories is essential not only to understand the underlying regulatory dynamics but also to compare multiple experimental conditions at a higher level of abstraction. Read More

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http://dx.doi.org/10.1186/s12859-019-2639-2DOI Listing
February 2019

MultiDomainBenchmark: a multi-domain query and subject database suite.

BMC Bioinformatics 2019 Feb 14;20(1):77. Epub 2019 Feb 14.

National Center for Biotechnology Information, Bethesda, National Institutes of Health, 8600 Rockville Pike, Bethesda, 20894, MD, USA.

Background: Genetic sequence database retrieval benchmarks play an essential role in evaluating the performance of sequence searching tools. To date, all phylogenetically diverse benchmarks known to the authors include only query sequences with single protein domains. Domains are the primary building blocks of protein structure and function. Read More

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http://dx.doi.org/10.1186/s12859-019-2660-5DOI Listing
February 2019

ADS-HCSpark: A scalable HaplotypeCaller leveraging adaptive data segmentation to accelerate variant calling on Spark.

BMC Bioinformatics 2019 Feb 14;20(1):76. Epub 2019 Feb 14.

Communication & Computer Network Lab of Guangdong, School of Computer Science & Engineering, South China University of Technology, Wushan Road, Guangzhou, 510641, China.

Background: The advance of next generation sequencing enables higher throughput with lower price, and as the basic of high-throughput sequencing data analysis, variant calling is widely used in disease research, clinical treatment and medicine research. However, current mainstream variant caller tools have a serious problem of computation bottlenecks, resulting in some long tail tasks when performing on large datasets. This prevents high scalability on clusters of multi-node and multi-core, and leads to long runtime and inefficient usage of computing resources. Read More

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http://dx.doi.org/10.1186/s12859-019-2665-0DOI Listing
February 2019

WGDdetector: a pipeline for detecting whole genome duplication events using the genome or transcriptome annotations.

BMC Bioinformatics 2019 Feb 13;20(1):75. Epub 2019 Feb 13.

CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, 666303, Yunnan, China.

Background: With the availability of well-assembled genomes of a growing number of organisms, identifying the bioinformatic basis of whole genome duplication (WGD) is a growing field of genomics. The most extant software for detecting footprints of WGDs has been restricted to a well-assembled genome. However, the massive poor quality genomes and the more accessible transcriptomes have been largely ignored, and in theoretically they are also likely to contribute to detect WGD using dS based method. Read More

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http://dx.doi.org/10.1186/s12859-019-2670-3DOI Listing
February 2019

Disease Pathway Cut for Multi-Target drugs.

BMC Bioinformatics 2019 Feb 13;20(1):74. Epub 2019 Feb 13.

Department of Industrial Engineering, Ajou University, 206, World cup-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16499, Republic of Korea.

Background: Biomarker discovery studies have been moving the focus from a single target gene to a set of target genes. However, the number of target genes in a drug should be minimum to avoid drug side-effect or toxicity. But still, the set of target genes should effectively block all possible paths of disease progression. Read More

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http://dx.doi.org/10.1186/s12859-019-2638-3DOI Listing
February 2019

ST-Steiner: A Spatio-Temporal Gene Discovery Algorithm.

Bioinformatics 2019 Feb 13. Epub 2019 Feb 13.

Computer Engineering Department, Bilkent University, Ankara, Turkey.

Motivation: Whole exome sequencing (WES) studies for Autism Spectrum Disorder (ASD) could identify only around six dozen risk genes to date because the genetic architecture of the disorder is highly complex. To speed the gene discovery process up, a few network-based ASD gene discovery algorithms were proposed. Although these methods use static gene interaction networks, functional clustering of genes is bound to evolve during neurodevelopment and disruptions are likely to have a cascading effect on the future associations. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz110DOI Listing
February 2019

Biomolecular Reaction & Interaction Dynamics Global Environment (BRIDGE).

Bioinformatics 2019 Feb 13. Epub 2019 Feb 13.

Scientific Computing Research Unit and Department of Chemistry, University of Cape Town, Rondebosch, South Africa.

Motivation: The pathway from genomics through proteomics and onto a molecular description of biochemical processes make the discovery of drugs and biomaterials possible. A research framework common to genomics and proteomics is needed to conduct biomolecular simulations that will connect biological data to the dynamic molecular mechanisms of enzymes and proteins. Novice biomolecular modelers are faced with the daunting task of complex setups and a myriad of possible choices preventing their use of molecular simulations and their ability to conduct reliable and reproducible computations that can be shared with collaborators and verified for procedural accuracy. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz107DOI Listing
February 2019

Gsmodutils: A python based framework for test-driven genome scale metabolic model development.

Bioinformatics 2019 Feb 13. Epub 2019 Feb 13.

School of Computer Science, University of Nottingham, Nottingham, NG8 1BB, United Kingdom.

Motivation: Genome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for their continued management. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz088DOI Listing
February 2019

Ensuring privacy and security of genomic data and functionalities.

Brief Bioinform 2019 Feb 12. Epub 2019 Feb 12.

Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia.

In recent times, the reduced cost of DNA sequencing has resulted in a plethora of genomic data that is being used to advance biomedical research and improve clinical procedures and healthcare delivery. These advances are revolutionizing areas in genome-wide association studies (GWASs), diagnostic testing, personalized medicine and drug discovery. This, however, comes with security and privacy challenges as the human genome is sensitive in nature and uniquely identifies an individual. Read More

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http://dx.doi.org/10.1093/bib/bbz013DOI Listing
February 2019

Finding enzyme cofactors in Protein Data Bank.

Bioinformatics 2019 Feb 13. Epub 2019 Feb 13.

European Molecular Biology Laboratory (EMBL), European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.

Motivation: Cofactors are essential for many enzyme reactions. The Protein Data Bank (PDB) contains >67,000 entries containing enzyme structures, many with bound cofactor or cofactor-like molecules. This work aims to identify and categorise these small molecules in the PDB and make it easier to find them. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz115DOI Listing
February 2019

Comparison of Six Breast Cancer Classifiers using qPCR.

Bioinformatics 2019 Feb 13. Epub 2019 Feb 13.

Department of Gynecology, Institute of Clincal Epidemiology, Martin-Luther-Universität, Ernst-Grube-Straße 40, Halle an der Saale, Germany.

Motivation: Several gene expression based risk scores and subtype classifiers for breast cancer were developed to distinguish high and low risk patients. Evaluating the performance of these classifiers helps to decide which classifiers should be used in clinical practice for personal therapeutic recommendations. So far, studies that compared multiple classifiers in large independent patient cohorts mostly used microarray measurements. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz103DOI Listing
February 2019

Identification of Caveolin-1 Domain Signatures via Graphlet Analysis of Single Molecule Super-Resolution Data.

Bioinformatics 2019 Feb 13. Epub 2019 Feb 13.

Medical Image Analysis Lab, School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada.

Motivation: Network analysis and unsupervised machine learning processing of single molecule localization microscopy (SMLM) of caveolin-1 (Cav1) antibody labeling of prostate cancer cells identified biosignatures and structures for caveolae and three distinct non-caveolar scaffolds (S1A, S1B and S2) (Khater et al., 2018). To obtain further insight into low-level molecular interactions within these different structural domains, we now introduce graphlet decomposition over a range of proximity thresholds and show that frequency of different subgraph (k = 4 nodes) patterns for machine learning approaches (classification, identification, automatic labeling, etc. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz113DOI Listing
February 2019

rMETL: sensitive mobile element insertion detection with long read realignment.

Bioinformatics 2019 Feb 13. Epub 2019 Feb 13.

Center for Bioinformatics, School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, China.

Summary: Mobile element insertion (MEI) is a major category of structure variations (SVs). The rapid development of long read sequencing technologies provides the opportunity to detect MEIs sensitively. However, the signals of MEI implied by noisy long reads are highly complex due to the repetitiveness of mobile elements as well as the high sequencing error rates. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz106DOI Listing
February 2019

Temporal network alignment via GoT-WAVE.

Bioinformatics 2019 Feb 13. Epub 2019 Feb 13.

CRACS & INESC-TEC, Faculdade de Ciências, Universidade do Porto, R. Campo Alegre, Porto, Portugal.

Motivation: Network alignment (NA) finds conserved regions between two networks. NA methods optimize node conservation (NC) and edge conservation (EC). Dynamic graphlet degree vectors (DGDVs) are a state-of-the-art dynamic NC measure, used within the fastest and most accurante NA method for temporal networks: DynaWAVE. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz119DOI Listing
February 2019

refineD: Improved protein structure refinement using machine learning based restrained relaxation.

Bioinformatics 2019 Feb 13. Epub 2019 Feb 13.

Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA.

Motivation: Protein structure refinement aims to bring moderately accurate template-based protein models closer to the native state through conformational sampling. However, guiding the sampling towards the native state by effectively using restraints remains a major issue in structure refinement.

Results: Here, we develop a machine learning based restrained relaxation protocol that uses deep discriminative learning based binary classifiers to predict multi-resolution probabilistic restraints from the starting structure and subsequently converts these restraints to be integrated into Rosetta all-atom energy function as additional scoring terms during structure refinement. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz101DOI Listing
February 2019

From POS tagging to dependency parsing for biomedical event extraction.

BMC Bioinformatics 2019 Feb 12;20(1):72. Epub 2019 Feb 12.

School of Computing and Information Systems, The University of Melbourne, Melbourne, Australia.

Background: Given the importance of relation or event extraction from biomedical research publications to support knowledge capture and synthesis, and the strong dependency of approaches to this information extraction task on syntactic information, it is valuable to understand which approaches to syntactic processing of biomedical text have the highest performance.

Results: We perform an empirical study comparing state-of-the-art traditional feature-based and neural network-based models for two core natural language processing tasks of part-of-speech (POS) tagging and dependency parsing on two benchmark biomedical corpora, GENIA and CRAFT. To the best of our knowledge, there is no recent work making such comparisons in the biomedical context; specifically no detailed analysis of neural models on this data is available. Read More

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http://dx.doi.org/10.1186/s12859-019-2604-0DOI Listing
February 2019

IMMAN: an R/Bioconductor package for Interolog protein network reconstruction, mapping and mining analysis.

BMC Bioinformatics 2019 Feb 12;20(1):73. Epub 2019 Feb 12.

School of Biological Science, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.

Background: Reconstruction of protein-protein interaction networks (PPIN) has been riddled with controversy for decades. Particularly, false-negative and -positive interactions make this progress even more complicated. Also, lack of a standard PPIN limits us in the comparison studies and results in the incompatible outcomes. Read More

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http://dx.doi.org/10.1186/s12859-019-2659-yDOI Listing
February 2019

In silico drug repositioning based on drug-miRNA associations.

Brief Bioinform 2019 Feb 11. Epub 2019 Feb 11.

Department of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, P. R. China.

Drug repositioning has become a prevailing tactic as this strategy is efficient, economical and low risk for drug discovery. Meanwhile, recent studies have confirmed that small-molecule drugs can modulate the expression of disease-related miRNAs, which indicates that miRNAs are promising therapeutic targets for complex diseases. In this study, we put forward and verified the hypothesis that drugs with similar miRNA profiles may share similar therapeutic properties. Read More

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http://dx.doi.org/10.1093/bib/bbz012DOI Listing
February 2019

Gag-protease coevolution shapes the outcome of Lopinavir-inclusive treatment regimens in chronically infected HIV-1 subtype C patients.

Authors:
V Marie M Gordon

Bioinformatics 2019 Feb 12. Epub 2019 Feb 12.

KwaZulu-Natal Research Innovation and Sequencing Platform, University of KwaZulu-Natal, Durban, South Africa.

Motivation: Commonly, protease inhibitor failure is characterized by the development of multiple protease resistance mutations (PRMs). While the impact of PRMs on therapy failure are understood, the introduction of Gag mutations with protease remains largely unclear.

Results: Here, we utilized phylogenetic analyses and Bayesian network learning as tools to understand Gag-protease coevolution and elucidate the pathways leading to Lopinavir failure in HIV-1 subtype C infected patients. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz076DOI Listing
February 2019
1 Read

mCSEA: Detecting subtle differentially methylated regions.

Bioinformatics 2019 Feb 12. Epub 2019 Feb 12.

Bioinformatics Unit. GENYO. Centre for Genomics and Oncological Research: Pfizer / University of Granada / Andalusian Regional Government, Granada, Spain.

Motivation: The identification of differentially methylated regions (DMRs) among phenotypes is one of the main goals of epigenetic analysis. Although there are several methods developed to detect DMRs, most of them are focused on detecting relatively large differences in methylation levels and fail to detect moderate, but consistent, methylation changes that might be associated to complex disorders.

Results: We present mCSEA, an R package that implements a Gene Set Enrichment Analysis method to identify differentially methylated regions from Illumina450K and EPIC array data. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz096DOI Listing
February 2019

PairedFB: a full hierarchical Bayesian model for paired RNA-seq data with heterogeneous treatment effects.

Bioinformatics 2018 Aug 23. Epub 2018 Aug 23.

Department of Applied Economics and Statistics, University of Delaware, Newark, DE, USA.

Motivation: Several methods have been proposed for the paired RNA-seq analysis. However, many of them do not consider the heterogeneity in treatment effect among pairs that can naturally arise in real data. In addition, it has been reported in literature that the false discovery rate (FDR) control of some popular methods has been problematic. Read More

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http://dx.doi.org/10.1093/bioinformatics/bty731DOI Listing

Modeling cell proliferation in human acute myeloid leukemia xenografts.

Bioinformatics 2019 Feb 7. Epub 2019 Feb 7.

Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.

Motivation: Acute myeloid leukemia is one of the most common hematological malignancies, characterized by high relapse and mortality rates. The inherent intra-tumor heterogeneity in acute myeloid leukemia is thought to play an important role in disease recurrence and resistance to chemotherapy. Although experimental protocols for cell proliferation studies are well established and widespread, they are not easily applicable to in vivo contexts, and the analysis of related time-series data is often complex to achieve. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz063DOI Listing
February 2019
1 Read

Two-Tier Mapper, an unbiased topology-based clustering method for enhanced global gene expression analysis.

Bioinformatics 2019 Feb 7. Epub 2019 Feb 7.

Swiss Institute for Experimental Cancer Research, Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland.

Motivation: Unbiased clustering methods are needed to analyze growing numbers of complex data sets. Currently available clustering methods often depend on parameters that are set by the user, they lack stability, and are not applicable to small data sets. To overcome these shortcomings we used topological data analysis, an emerging field of mathematics that can discerns additional feature and discover hidden insights on data sets and has a wide application range. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz052DOI Listing
February 2019

Computational methods for identifying the critical nodes in biological networks.

Brief Bioinform 2019 Feb 12. Epub 2019 Feb 12.

Department of Computer Science, Xiamen University, China.

A biological network is complex. A group of critical nodes determines the quality and state of such a network. Increasing studies have shown that diseases and biological networks are closely and mutually related and that certain diseases are often caused by errors occurring in certain nodes in biological networks. Read More

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https://academic.oup.com/bib/advance-article/doi/10.1093/bib
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http://dx.doi.org/10.1093/bib/bbz011DOI Listing
February 2019
6 Reads

A comparison of deterministic and stochastic approaches for sensitivity analysis in computational systems biology.

Brief Bioinform 2019 Feb 7. Epub 2019 Feb 7.

The Microsoft Research - University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, Rovereto (TN), Italy.

With the recent rising application of mathematical models in the field of computational systems biology, the interest in sensitivity analysis methods had increased. The stochastic approach, based on chemical master equations, and the deterministic approach, based on ordinary differential equations (ODEs), are the two main approaches for analyzing mathematical models of biochemical systems. In this work, the performance of these approaches to compute sensitivity coefficients is explored in situations where stochastic and deterministic simulation can potentially provide different results (systems with unstable steady states, oscillators with population extinction and bistable systems). Read More

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http://dx.doi.org/10.1093/bib/bbz014DOI Listing
February 2019

Biological Sequence Modeling with Convolutional Kernel Networks.

Bioinformatics 2019 Feb 7. Epub 2019 Feb 7.

Univ. Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France.

Motivation: The growing number of annotated biological sequences available makes it possible to learn genotype-phenotype relationships from data with increasingly high accuracy. When large quantities of labeled samples are available for training a model, convolutional neural networks can be used to predict the phenotype of unannotated sequences with good accuracy. Unfortunately, their performance with medium- or small-scale datasets is mitigated, which requires inventing new data-efficient approaches. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz094DOI Listing
February 2019

Variational Infinite Heterogeneous Mixture Model for Semi-supervised Clustering of Heart Enhancers.

Bioinformatics 2019 Feb 7. Epub 2019 Feb 7.

Department of Computer Science, University of Toronto, Toronto, Canada.

Motivation: Mammalian genomes can contain thousands of enhancers but only a subset are actively driving gene expression in a given cellular context. Integrated genomic datasets can be harnessed to predict active enhancers. One challenge in integration of large genomic datasets is the increasing heterogeneity: continuous, binary and discrete features may all be relevant. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz064DOI Listing
February 2019

Analyzing a co-occurrence gene-interaction network to identify disease-gene association.

BMC Bioinformatics 2019 Feb 8;20(1):70. Epub 2019 Feb 8.

Department of Physics, Khalifa University of Science and Technology, Abu Dhabi, P.O. Box 127788,, United Arab Emirates.

Background: Understanding the genetic networks and their role in chronic diseases (e.g., cancer) is one of the important objectives of biological researchers. Read More

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http://dx.doi.org/10.1186/s12859-019-2634-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6368766PMC
February 2019

Genomic prediction of tuberculosis drug-resistance: benchmarking existing databases and prediction algorithms.

BMC Bioinformatics 2019 Feb 8;20(1):68. Epub 2019 Feb 8.

NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, 119077, Singapore.

Background: It is possible to predict whether a tuberculosis (TB) patient will fail to respond to specific antibiotics by sequencing the genome of the infecting Mycobacterium tuberculosis (Mtb) and observing whether the pathogen carries specific mutations at drug-resistance sites. This advancement has led to the collation of TB databases such as PATRIC and ReSeqTB that possess both whole genome sequences and drug resistance phenotypes of infecting Mtb isolates. Bioinformatics tools have also been developed to predict drug resistance from whole genome sequencing (WGS) data. Read More

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http://dx.doi.org/10.1186/s12859-019-2658-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6368788PMC
February 2019

Predicting clinically promising therapeutic hypotheses using tensor factorization.

BMC Bioinformatics 2019 Feb 8;20(1):69. Epub 2019 Feb 8.

Computational Biology, GSK R&D, 1250 S. Collegeville Road, UP12-200, Collegeville, PA, USA.

Background: Determining which target to pursue is a challenging and error-prone first step in developing a therapeutic treatment for a disease, where missteps are potentially very costly given the long-time frames and high expenses of drug development. With current informatics technology and machine learning algorithms, it is now possible to computationally discover therapeutic hypotheses by predicting clinically promising drug targets based on the evidence associating drug targets with disease indications. We have collected this evidence from Open Targets and additional databases that covers 17 sources of evidence for target-indication association and represented the data as a tensor of 21,437 × 2211 × 17. Read More

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http://dx.doi.org/10.1186/s12859-019-2664-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6368709PMC
February 2019

Predicting protein functions by applying predicate logic to biomedical literature.

BMC Bioinformatics 2019 Feb 8;20(1):71. Epub 2019 Feb 8.

Department of Electrical and Computer Engineering, Khalifa University, Abu Dhabi, United Arab Emirates.

Background: A large number of computational methods have been proposed for predicting protein functions. The underlying techniques adopted by most of these methods revolve around predicting the functions of an unannotated protein p from already annotated proteins that have similar characteristics as p. Recent Information Extraction methods take advantage of the huge growth of biomedical literature to predict protein functions. Read More

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http://dx.doi.org/10.1186/s12859-019-2594-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6368809PMC
February 2019

CeModule: an integrative framework for discovering regulatory patterns from genomic data in cancer.

BMC Bioinformatics 2019 Feb 7;20(1):67. Epub 2019 Feb 7.

College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, China.

Background: Non-coding RNAs (ncRNAs) are emerging as key regulators and play critical roles in a wide range of tumorigenesis. Recent studies have suggested that long non-coding RNAs (lncRNAs) could interact with microRNAs (miRNAs) and indirectly regulate miRNA targets through competing interactions. Therefore, uncovering the competing endogenous RNA (ceRNA) regulatory mechanism of lncRNAs, miRNAs and mRNAs in post-transcriptional level will aid in deciphering the underlying pathogenesis of human polygenic diseases and may unveil new diagnostic and therapeutic opportunities. Read More

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http://dx.doi.org/10.1186/s12859-019-2654-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6367773PMC
February 2019
3 Reads

CERENKOV2: improved detection of functional noncoding SNPs using data-space geometric features.

BMC Bioinformatics 2019 Feb 6;20(1):63. Epub 2019 Feb 6.

School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, 97330, OR, USA.

Background: We previously reported on CERENKOV, an approach for identifying regulatory single nucleotide polymorphisms (rSNPs) that is based on 246 annotation features. CERENKOV uses the xgboost classifier and is designed to be used to find causal noncoding SNPs in loci identified by genome-wide association studies (GWAS). We reported that CERENKOV has state-of-the-art performance (by two traditional measures and a novel GWAS-oriented measure, AVGRANK) in a comparison to nine other tools for identifying functional noncoding SNPs, using a comprehensive reference SNP set (OSU17, 15,331 SNPs). Read More

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http://dx.doi.org/10.1186/s12859-019-2637-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6364436PMC
February 2019

Estimation of duplication history under a stochastic model for tandem repeats.

BMC Bioinformatics 2019 Feb 6;20(1):64. Epub 2019 Feb 6.

Department of Electrical Engineering, California Institute of Technology, Pasadena, USA.

Background: Tandem repeat sequences are common in the genomes of many organisms and are known to cause important phenomena such as gene silencing and rapid morphological changes. Due to the presence of multiple copies of the same pattern in tandem repeats and their high variability, they contain a wealth of information about the mutations that have led to their formation. The ability to extract this information can enhance our understanding of evolutionary mechanisms. Read More

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http://dx.doi.org/10.1186/s12859-019-2603-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6364452PMC
February 2019

Shambhala: a platform-agnostic data harmonizer for gene expression data.

BMC Bioinformatics 2019 Feb 6;20(1):66. Epub 2019 Feb 6.

I.M. Sechenov First Moscow State Medical University, Sechenov University, Moscow, 119991, Russia.

Background: Harmonization techniques make different gene expression profiles and their sets compatible and ready for comparisons. Here we present a new bioinformatic tool termed Shambhala for harmonization of multiple human gene expression datasets obtained using different experimental methods and platforms of microarray hybridization and RNA sequencing.

Results: Unlike previously published methods enabling good quality data harmonization for only two datasets, Shambhala allows conversion of multiple datasets into the universal form suitable for further comparisons. Read More

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http://dx.doi.org/10.1186/s12859-019-2641-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6366102PMC
February 2019

DeepPVP: phenotype-based prioritization of causative variants using deep learning.

BMC Bioinformatics 2019 Feb 6;20(1):65. Epub 2019 Feb 6.

Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology, 4700 KAUST, Thuwal, 23955-6900, Kingdom of Saudi Arabia.

Background: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient's phenotype. Read More

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http://dx.doi.org/10.1186/s12859-019-2633-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6364462PMC
February 2019
1 Read

Finding de novo methylated DNA motifs.

Bioinformatics 2019 Feb 6. Epub 2019 Feb 6.

Graduate Program of Bioinformatics and Systems Biology, University of California at San Diego, La Jolla, CA.

Motivation: Increasing evidence has shown that nucleotide modifications such as methylation and hydroxymethylation on cytosine would greatly impact the binding of transcription factors (TFs). However, there is a lack of motif finding algorithms with the function to search for motifs with modified bases. In this study, we expend on our previous motif finding pipeline Epigram to provide systematic de novo motif discovery and performance evaluation on methylated DNA motifs. Read More

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http://dx.doi.org/10.1093/bioinformatics/btz079DOI Listing
February 2019
4.981 Impact Factor