25,940 results match your criteria Bioinformatics[Journal]


Predicting mechanism of action of cellular perturbations with pathway activity signatures.

Bioinformatics 2020 Jul 11. Epub 2020 Jul 11.

Division of Biostatistics and Bioinformatics, Department of Environmental Health, University of Cincinnati, 160 Panzeca Way, Cincinnati, OH 45267-0056, USA.

Motivation: Misregulation of signaling pathway activity is etiologic for many human diseases, and modulating activity of signaling pathways is often the preferred therapeutic strategy. Understanding the mechanism of action (MOA) of bioactive chemicals in terms of targeted signaling pathways is the essential first step in evaluating their therapeutic potential. Changes in signaling pathway activity are often not reflected in changes in expression of pathway genes which makes MOA inferences from transcriptional signatures a difficult problem. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btaa590DOI Listing

Adaptive Multi-Orientation Resolution Analysis of Complex Filamentous Network Images.

Bioinformatics 2020 Jul 11. Epub 2020 Jul 11.

Department of Biophysics, UT Southwestern Medical Center, Dallas, TX 75390.

Motivation: Microscopy images of cytoskeletal, nucleoskeletal, and other structures contain complex junctions of overlapping filaments with arbitrary geometry. Yet state-of-the-art algorithms generally perform single orientation analysis to segment these structures, resulting in gaps near junctions, or assume particular junction geometries to detect them.

Results: We developed a fully automated image analysis approach to address the challenge of determining the number of orientations and their values at each point in space in order to detect both lines and their junctions. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btaa627DOI Listing

RecBic: a fast and accurate algorithm recognizing trend-preserving biclusters.

Bioinformatics 2020 Jul 11. Epub 2020 Jul 11.

Research Center for Mathematics and Interdisciplinary Sciences.

Motivation: Biclustering has emerged as a powerful approach to identifying functional patterns in complex biological data. However, existing tools are limited by their accuracy and efficiency to recognize various kinds of complex biclusters submerged in ever large datasets. We introduce a novel fast and highly accurate algorithm RecBic to identify various forms of complex biclusters in gene expression datasets. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btaa630DOI Listing

RTExtract: Time-series NMR spectra quantification based on 3D surface ridge tracking.

Bioinformatics 2020 Jul 12. Epub 2020 Jul 12.

Institute of Bioinformatics, University of Georgia, Athens, GA, USA.

Motivation: Time-series NMR has advanced our knowledge about metabolic dynamics. Before analyzing compounds through modeling or statistical methods, chemical features need to be tracked and quantified. However, because of peak overlap and peak shifting, the available protocols are time consuming at best or even impossible for some regions in NMR spectra. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btaa631DOI Listing

Automatic classification of single-molecule force spectroscopy traces from heterogeneous samples.

Bioinformatics 2020 Jul 11. Epub 2020 Jul 11.

Scuola Internazionale Superiore di Studi Avanzati (SISSA), Trieste, 34136, Italy.

Motivation: Single-molecule force spectroscopy (SMFS) experiments pose the challenge of analyzing protein unfolding data (traces) coming from preparations with heterogeneous composition (e.g. where different proteins are present in the sample). Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btaa626DOI Listing

Keras R-CNN: library for cell detection in biological images using deep neural networks.

BMC Bioinformatics 2020 Jul 11;21(1):300. Epub 2020 Jul 11.

The Broad Institute, Cambridge, MA, USA.

Background: A common yet still manual task in basic biology research, high-throughput drug screening and digital pathology is identifying the number, location, and type of individual cells in images. Object detection methods can be useful for identifying individual cells as well as their phenotype in one step. State-of-the-art deep learning for object detection is poised to improve the accuracy and efficiency of biological image analysis. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03635-xDOI Listing

Escher-Trace: a web application for pathway-based visualization of stable isotope tracing data.

BMC Bioinformatics 2020 Jul 10;21(1):297. Epub 2020 Jul 10.

Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.

Background: Stable isotope tracing has become an invaluable tool for probing the metabolism of biological systems. However, data analysis and visualization from metabolic tracing studies often involve multiple software packages and lack pathway architecture. A deep understanding of the metabolic contexts from such datasets is required for biological interpretation. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03632-0DOI Listing

Leveraging Bayesian networks and information theory to learn risk factors for breast cancer metastasis.

BMC Bioinformatics 2020 Jul 10;21(1):298. Epub 2020 Jul 10.

Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.

Background: Even though we have established a few risk factors for metastatic breast cancer (MBC) through epidemiologic studies, these risk factors have not proven to be effective in predicting an individual's risk of developing metastasis. Therefore, identifying critical risk factors for MBC continues to be a major research imperative, and one which can lead to advances in breast cancer clinical care. The objective of this research is to leverage Bayesian Networks (BN) and information theory to identify key risk factors for breast cancer metastasis from data. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03638-8DOI Listing

DNA Chisel, a versatile sequence optimizer.

Bioinformatics 2020 Jul 9. Epub 2020 Jul 9.

Edinburgh Genome Foundry, SynthSys, School of Biological Sciences, University of Edinburgh, EH93BF Edinburgh, UK.

Motivation: Accounting for biological and practical requirements in DNA sequence design often results in challenging optimization problems. Current software solutions are problem-specific and hard to combine.

Results: DNA Chisel is an easy-to-use, easy-to-extend sequence optimization framework allowing to freely define and combine optimization specifications via Python scripts or Genbank annotations. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btaa558DOI Listing

Exploring generative deep learning for omics data by using log-linear models.

Bioinformatics 2020 Jul 9. Epub 2020 Jul 9.

Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Stefan-Meier-Str. 26, 79104 Freiburg, Germany.

Motivation: Following many successful applications to image data, deep learning is now also increasingly considered for omics data. In particular, generative deep learning not only provides competitive prediction performance, but also allows for uncovering structure by generating synthetic samples. However, exploration and visualization is not as straightforward as with image applications. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btaa623DOI Listing

Efficient implied alignment.

BMC Bioinformatics 2020 Jul 9;21(1):296. Epub 2020 Jul 9.

Division of Invertebrate Zoology, American Museum of Natural History, 200 Central Park West, New York, 10024-5192, NY, USA.

Background: Given a binary tree [Formula: see text] of n leaves, each leaf labeled by a string of length at most k, and a binary string alignment function ⊗, an implied alignment can be generated to describe the alignment of a dynamic homology for [Formula: see text]. This is done by first decorating each node of [Formula: see text] with an alignment context using ⊗, in a post-order traversal, then, during a subsequent pre-order traversal, inferring on which edges insertion and deletion events occurred using those internal node decorations.

Results: Previous descriptions of the implied alignment algorithm suggest a technique of "back-propagation" with time complexity [Formula: see text]. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03595-2DOI Listing

Bioinformatics recipes: creating, executing and distributing reproducible data analysis workflows.

BMC Bioinformatics 2020 Jul 8;21(1):292. Epub 2020 Jul 8.

Department of Biochemistry and Molecular Biology, Pennsylvania State University, 201 Old Main, University Park, PA, 16802, USA.

Background: Bioinformaticians collaborating with life scientists need software that allows them to involve their collaborators in the process of data analysis.

Results: We have developed a web application that allows researchers to publish and execute data analysis scripts. Within the platform bioinformaticians are able to deploy data analysis workflows (recipes) that their collaborators can execute via point and click interfaces. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03602-6DOI Listing

HEnRY: a DZIF LIMS tool for the collection and documentation of biomaterials in multicentre studies.

BMC Bioinformatics 2020 Jul 8;21(1):290. Epub 2020 Jul 8.

Faculty of Medicine and University Hospital Cologne, Department I for Internal Medicine, University of Cologne, Herderstr. 52-54, 50931, Cologne, Germany.

Background: Well-characterized biomaterials of high quality have great potential for acceleration and quality improvement in translational biomedical research. To improve accessibility of local sample collections, efforts have been made to create central biomaterial banks and catalogues. Available technical solutions for creating professional local sample catalogues and connecting them to central systems are cost intensive and/or technically complex to implement. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03596-1DOI Listing

Critique of the pairwise method for estimating qPCR amplification efficiency: beware of correlated data!

BMC Bioinformatics 2020 Jul 8;21(1):291. Epub 2020 Jul 8.

Department of Chemistry, Vanderbilt University, Nashville, TN, 37235, USA.

Background: A recently proposed method for estimating qPCR amplification efficiency E analyzes fluorescence intensity ratios from pairs of points deemed to lie in the exponential growth region on the amplification curves for all reactions in a dilution series. This method suffers from a serious problem: The resulting ratios are highly correlated, as they involve multiple use of the raw data, for example, yielding ~ 250 E estimates from ~ 25 intensity readings. The resulting statistics for such estimates are falsely optimistic in their assessment of the estimation precision. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03604-4DOI Listing

SOAPTyping: an open-source and cross-platform tool for sequence-based typing for HLA class I and II alleles.

BMC Bioinformatics 2020 Jul 8;21(1):295. Epub 2020 Jul 8.

BGI-Shenzhen, Shenzhen, 518083, China.

Background: The human leukocyte antigen (HLA) gene family plays a key role in the immune response and thus is crucial in many biomedical and clinical settings. Utilizing Sanger sequencing, the golden standard technology for HLA typing enables accurate identification of HLA alleles in high-resolution. However, only the commercial software, such as uTYPE, SBT-Assign, and SBTEngine, and very few open-source tools could be applied to perform HLA typing based on Sanger sequencing. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03624-0DOI Listing

SLFinder, a pipeline for the novel identification of splice-leader sequences: a good enough solution for a complex problem.

BMC Bioinformatics 2020 Jul 8;21(1):293. Epub 2020 Jul 8.

Laboratorio de Biología Computacional, Departamento de Desarrollo Biotecnológico, Instituto de Higiene, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay.

Background: Spliced Leader trans-splicing is an important mechanism for the maturation of mRNAs in several lineages of eukaryotes, including several groups of parasites of great medical and economic importance. Nevertheless, its study across the tree of life is severely hindered by the problem of identifying the SL sequences that are being trans-spliced.

Results: In this paper we present SLFinder, a four-step pipeline meant to identify de novo candidate SL sequences making very few assumptions regarding the SL sequence properties. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03610-6DOI Listing

Galgo: A bi-objective evolutionary meta-heuristic identifies robust transcriptomic classifiers associated with patient outcome across multiple cancer types.

Bioinformatics 2020 Jul 8. Epub 2020 Jul 8.

Laboratory of Oncology, Institute of Medicine and Experimental Biology of Cuyo (IMBECU), National Scientific and Technical Research Council (CONICET), Mendoza, 5500, Argentina.

Motivation: Statistical and machine learning analyses of tumor transcriptomic profiles offer a powerful resource to gain deeper understanding of tumor subtypes and disease prognosis. Currently prognostic gene expression signatures do not exist for all cancer types, and most developed to date have been optimized for individual tumor types. In Galgo we implement a bi-objective optimization approach that prioritizes gene signature cohesiveness and patient survival in parallel which provides greater power to identify tumor transcriptomic phenotypes strongly associated with patient survival. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btaa619DOI Listing

ProVision: A web based platform for rapid analysis of proteomics data processed by MaxQuant.

Bioinformatics 2020 Jul 8. Epub 2020 Jul 8.

Section Molecular Microbiology, Amsterdam Institute for Molecules, Medicines and Systems, Vrije Universiteit Amsterdam 1081 HZ, Amsterdam, The Netherlands.

Summary: Proteomics is a powerful tool for protein expression analysis and is becoming more readily available to researchers through core facilities or specialised collaborations. However, one major bottleneck for routine implementation and accessibility of this technology to the wider scientific community is the complexity of data analysis. To this end, we have created ProVision, a free open-source web-based analytics platform that allows users to analyse data from two common proteomics relative quantification workflows, namely label-free and tandem mass tag-based experiments. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btaa620DOI Listing

The Minimum Information about a Molecular Interaction Causal Statement (MI2CAST).

Bioinformatics 2020 Jul 8. Epub 2020 Jul 8.

Department of Biology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.

Motivation: A large variety of molecular interactions occurs between biomolecular components in cells. When a molecular interaction results in a regulatory effect, exerted by one component onto a downstream component, a so-called 'causal interaction' takes place. Causal interactions constitute the building blocks in our understanding of larger regulatory networks in cells. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btaa622DOI Listing
July 2020
4.981 Impact Factor

TreeSAPP: The Tree-based Sensitive and Accurate Phylogenetic Profiler.

Bioinformatics 2020 Jul 8. Epub 2020 Jul 8.

Graduate program in Bioinformatics, University of British Columbia, Vancouver, V5Z 4S6, Canada.

Motivation: Microbial communities drive matter and energy transformations integral to global biogeochemical cycles, yet many taxonomic groups facilitating these processes remain poorly represented in biological sequence databases. Due to this missing information, taxonomic assignment of sequences from environmental genomes remains inaccurate.

Results: We present the Tree-based Sensitive and Accurate Phylogenetic Profiler (TreeSAPP) software for functionally and taxonomically classifying genes, reactions and pathways from genomes of cultivated and uncultivated microorganisms using reference packages representing coding sequences mediating multiple globally-relevant biogeochemical cycles. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btaa588DOI Listing

DNA Features Viewer, a sequence annotation formatting and plotting library for Python.

Bioinformatics 2020 Jul 8. Epub 2020 Jul 8.

Edinburgh Genome Foundry, SynthSys, School of Biological Sciences, University of Edinburgh, EH9 3BF Edinburgh, UK.

Motivation: While the Python programming language counts many Bioinformatics and Computational Biology libraries, none offers customizable sequence annotation visualizations with layout optimization.

Results: DNA Features Viewer is a sequence annotation plotting library which optimizes plot readability while letting users tailor other visual aspects (colors, labels, highlights, etc.) to their particular use case. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btaa213DOI Listing

matFR: a matlab toolbox for feature ranking.

Bioinformatics 2020 Jul 8. Epub 2020 Jul 8.

Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, GD 518055, China.

Summary: Nowadays, it is feasible to collect massive features for quantitative representation and precision medicine, and thus, automatic ranking to figure out the most informative and discriminative ones becomes increasingly important. To address this issue, 42 feature ranking (FR) methods are integrated to form a MATLAB toolbox (matFR). The methods apply mutual information, statistical analysis, structure clustering and other principles to estimate the relative importance of features in specific measure spaces. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btaa621DOI Listing

SCEBE: an efficient and scalable algorithm for genome-wide association studies on longitudinal outcomes with mixed-effects modeling.

Brief Bioinform 2020 Jul 7. Epub 2020 Jul 7.

Janssen Research and Development LLC, Raritan, NJ, USA.

Genome-wide association studies (GWAS) using longitudinal phenotypes collected over time is appealing due to the improvement of power. However, computation burden has been a challenge because of the complex algorithms for modeling the longitudinal data. Approximation methods based on empirical Bayesian estimates (EBEs) from mixed-effects modeling have been developed to expedite the analysis. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1093/bib/bbaa130DOI Listing

Predicting microRNA-disease associations from lncRNA-microRNA interactions via Multiview Multitask Learning.

Brief Bioinform 2020 Jul 7. Epub 2020 Jul 7.

City University of Hong Kong.

Motivation: Identifying microRNAs that are associated with different diseases as biomarkers is a problem of great medical significance. Existing computational methods for uncovering such microRNA-diseases associations (MDAs) are mostly developed under the assumption that similar microRNAs tend to associate with similar diseases. Since such an assumption is not always valid, these methods may not always be applicable to all kinds of MDAs. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1093/bib/bbaa133DOI Listing

Normal tissue content impact on the GBM molecular classification.

Brief Bioinform 2020 Jul 7. Epub 2020 Jul 7.

Brain Tumour Laboratory, Scientific Director at Vithas Hospitals, Managing Director at Fundación Vithas and Professor at the Medial School of Francisco de Vitoria University.

Molecular classification of glioblastoma has enabled a deeper understanding of the disease. The four-subtype model (including Proneural, Classical, Mesenchymal and Neural) has been replaced by a model that discards the Neural subtype, found to be associated with samples with a high content of normal tissue. These samples can be misclassified preventing biological and clinical insights into the different tumor subtypes from coming to light. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1093/bib/bbaa129DOI Listing

Testing cell-type-specific mediation effects in genome-wide epigenetic studies.

Brief Bioinform 2020 Jul 7. Epub 2020 Jul 7.

Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong SAR, China.

Epigenome-wide mediation analysis aims to identify DNA methylation CpG sites that mediate the causal effects of genetic/environmental exposures on health outcomes. However, DNA methylations in the peripheral blood tissues are usually measured at the bulk level based on a heterogeneous population of white blood cells. Using the bulk level DNA methylation data in mediation analysis might cause confounding bias and reduce study power. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1093/bib/bbaa131DOI Listing

MMiRNA-Viewer, a bioinformatics tool for visualizing functional annotation for MiRNA and MRNA pairs in a network.

BMC Bioinformatics 2020 Jul 6;21(Suppl 4):247. Epub 2020 Jul 6.

USF Genomics, College of Public Health, University of South Florida, Tampa, FL, 33620, USA.

Background: Although there are many studies on the characteristics of miRNA-mRNA interactions using miRNA and mRNA sequencing data, the complexity of the change of the correlation coefficients and expression values of the miRNA-mRNA pairs between tumor and normal samples is still not resolved, and this hinders the potential clinical applications. There is an urgent need to develop innovative methodologies and tools that can characterize and visualize functional consequences of cancer risk gene and miRNA pairs while analyzing the tumor and normal samples simultaneously.

Results: We developed an innovative bioinformatics tool for visualizing functional annotation of miRNA-mRNA pairs in a network, known as MMiRNA-Viewer. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-3436-7DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7336395PMC

Toward a more holistic method of genome assembly assessment.

BMC Bioinformatics 2020 Jul 6;21(Suppl 4):249. Epub 2020 Jul 6.

Department of Computer Science and Engineering, Mississippi State University, Mississippi State, MS, USA.

Background: A key use of high throughput sequencing technology is the sequencing and assembly of full genome sequences. These genome assemblies are commonly assessed using statistics relating to contiguity of the assembly. Measures of contiguity are not strongly correlated with information about the biological completion or correctness of the assembly, and a commonly reported metric, N50, can be misleading. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-3382-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7336394PMC

MASS: predict the global qualities of individual protein models using random forests and novel statistical potentials.

Authors:
Tong Liu Zheng Wang

BMC Bioinformatics 2020 Jul 6;21(Suppl 4):246. Epub 2020 Jul 6.

Department of Computer Science, University of Miami, 1365 Memorial Drive, P.O. Box 248154, Coral Gables, FL, 33124, USA.

Background: Protein model quality assessment (QA) is an essential procedure in protein structure prediction. QA methods can predict the qualities of protein models and identify good models from decoys. Clustering-based methods need a certain number of models as input. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-3383-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7336608PMC

Drug-target interaction prediction using semi-bipartite graph model and deep learning.

BMC Bioinformatics 2020 Jul 6;21(Suppl 4):248. Epub 2020 Jul 6.

Department of Electrical and Computer Engineering, The University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX, 75080, USA.

Background: Identifying drug-target interaction is a key element in drug discovery. In silico prediction of drug-target interaction can speed up the process of identifying unknown interactions between drugs and target proteins. In recent studies, handcrafted features, similarity metrics and machine learning methods have been proposed for predicting drug-target interactions. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-3518-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7336396PMC

PDXGEM: patient-derived tumor xenograft-based gene expression model for predicting clinical response to anticancer therapy in cancer patients.

BMC Bioinformatics 2020 Jul 6;21(1):288. Epub 2020 Jul 6.

Department of Cell Biology, Microbiology and Molecular Biology, University of South Florida, Tampa, FL, 33620, USA.

Background: Cancer is a highly heterogeneous disease with varying responses to anti-cancer drugs. Although several attempts have been made to predict the anti-cancer therapeutic responses, there remains a great need to develop highly accurate prediction models of response to the anti-cancer drugs for clinical applications toward a personalized medicine. Patient derived xenografts (PDXs) are preclinical cancer models in which the tissue or cells from a patient's tumor are implanted into an immunodeficient or humanized mouse. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03633-zDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7336455PMC

Optical map guided genome assembly.

BMC Bioinformatics 2020 Jul 6;21(1):285. Epub 2020 Jul 6.

Department of Computer Science, Helsinki Institute for Information Technology, University of Helsinki, Pietari Kalmin katu 5, Helsinki, Finland.

Background: The long reads produced by third generation sequencing technologies have significantly boosted the results of genome assembly but still, genome-wide assemblies solely based on read data cannot be produced. Thus, for example, optical mapping data has been used to further improve genome assemblies but it has mostly been applied in a post-processing stage after contig assembly.

Results: We propose OPTICALKERMIT which directly integrates genome wide optical maps into contig assembly. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03623-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7336458PMC

HPG-DHunter: an ultrafast, friendly tool for DMR detection and visualization.

BMC Bioinformatics 2020 Jul 6;21(1):287. Epub 2020 Jul 6.

I2SysBio, CSIC-Universidad de Valencia, Cat. Agustín Escardino, Paterna (Valencia), Spain.

Background: Software tools for analyzing DNA methylation do not provide graphical results which can be easily identified, but huge text files containing the alignment of the samples and their methylation status at a resolution of base pairs. There have been proposed different tools and methods for finding Differentially Methylated Regions (DMRs) among different samples, but the execution time required by these tools is large, and the visualization of their results is far from being interactive. Additionally, these methods show more accurate results when identifying simulated DM regions that are long and have small within-group variation, but they have low concordance when used with real datasets, probably due to the different approaches they use for DMR identification. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03634-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7336418PMC

Proceedings of the 2019 MidSouth Computational Biology and Bioinformatics Society (MCBIOS) Conference.

BMC Bioinformatics 2020 Jul 6;21(Suppl 4):254. Epub 2020 Jul 6.

Foundational Medical Studies, Oakland University William Beaumont School of Medicine, Rochester, MI, 48309-4482, USA.

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03580-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7336605PMC

iPNHOT: a knowledge-based approach for identifying protein-nucleic acid interaction hot spots.

BMC Bioinformatics 2020 Jul 6;21(1):289. Epub 2020 Jul 6.

Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA.

Background: The interaction between proteins and nucleic acids plays pivotal roles in various biological processes such as transcription, translation, and gene regulation. Hot spots are a small set of residues that contribute most to the binding affinity of a protein-nucleic acid interaction. Compared to the extensive studies of the hot spots on protein-protein interfaces, the hot spot residues within protein-nucleic acids interfaces remain less well-studied, in part because mutagenesis data for protein-nucleic acids interaction are not as abundant as that for protein-protein interactions. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03636-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7336410PMC

Comparison of smartphone-based retinal imaging systems for diabetic retinopathy detection using deep learning.

BMC Bioinformatics 2020 Jul 6;21(Suppl 4):259. Epub 2020 Jul 6.

Dept. of Computer Science, University of Central Arkansas, 201 Donaghey Ave, Conway, AR, 72035, USA.

Background: Diabetic retinopathy (DR), the most common cause of vision loss, is caused by damage to the small blood vessels in the retina. If untreated, it may result in varying degrees of vision loss and even blindness. Since DR is a silent disease that may cause no symptoms or only mild vision problems, annual eye exams are crucial for early detection to improve chances of effective treatment where fundus cameras are used to capture retinal image. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03587-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7336606PMC

Diversity-guided Lamarckian random drift particle swarm optimization for flexible ligand docking.

BMC Bioinformatics 2020 Jul 6;21(1):286. Epub 2020 Jul 6.

Faculty of Engineering and Computing, Coventry University, Priory Street, Coventry, CV1 5FB, UK.

Background: Protein-ligand docking has emerged as a particularly important tool in drug design and development, and flexible ligand docking is a widely used method for docking simulations. Many docking software packages can simulate flexible ligand docking, and among them, Autodock is widely used. Focusing on the search algorithm used in Autodock, many new optimization approaches have been proposed over the last few decades. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03630-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7336682PMC

DUGMO: tool for the detection of unknown genetically modified organisms with high-throughput sequencing data for pure bacterial samples.

BMC Bioinformatics 2020 Jul 6;21(1):284. Epub 2020 Jul 6.

ANSES, Laboratoire de Ploufragan, GVB unit, 22440, Ploufragan, France.

Background: The European Community has adopted very restrictive policies regarding the dissemination and use of genetically modified organisms (GMOs). In fact, a maximum threshold of 0.9% of contaminating GMOs is tolerated for a "GMO-free" label. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03611-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7336441PMC

Sparse reduced-rank regression for integrating omics data.

BMC Bioinformatics 2020 Jul 3;21(1):283. Epub 2020 Jul 3.

Division of Biostatistics, University of Minnesota, Minneapolis, 55455, MN, USA.

Background: The problem of assessing associations between multiple omics data including genomics and metabolomics data to identify biomarkers potentially predictive of complex diseases has garnered considerable research interest nowadays. A popular epidemiology approach is to consider an association of each of the predictors with each of the response using a univariate linear regression model, and to select predictors that meet a priori specified significance level. Although this approach is simple and intuitive, it tends to require larger sample size which is costly. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03606-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7333421PMC

Efficient weighted univariate clustering maps outstanding dysregulated genomic zones in human cancers.

Bioinformatics 2020 Jul 3. Epub 2020 Jul 3.

Department of Computer Science, New Mexico State University, Las Cruces, NM, USA.

Motivation: Chromosomal patterning of gene expression in cancer can arise from aneuploidy, genome disorganization, or abnormal DNA methylation. To map such patterns, we introduce a weighted univariate clustering algorithm to guarantee linear runtime, optimality, and reproducibility.

Results: We present the chromosome clustering method, establish its optimality and runtime, and evaluate its performance. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btaa613DOI Listing

Information theoretic Generalized Robinson-Foulds metrics for comparing phylogenetic trees.

Authors:
Martin R Smith

Bioinformatics 2020 Jul 3. Epub 2020 Jul 3.

Department of Earth Sciences, Lower Mountjoy, Durham University, Durham, DH1 3LE, UK.

Motivation: The Robinson-Foulds (RF) metric is widely used by biologists, linguists and chemists to quantify similarity between pairs of phylogenetic trees. The measure tallies the number of bipartition splits that occur in both trees-but this conservative approach ignores potential similarities between almost-identical splits, with undesirable consequences. 'Generalized' RF metrics address this shortcoming by pairing splits in one tree with similar splits in the other. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btaa614DOI Listing

Genome-wide Association Studies of Brain Imaging Data via Weighted Distance Correlation.

Bioinformatics 2020 Jul 3. Epub 2020 Jul 3.

Department of Statistical Science, School of Mathematics, Sun Yat-Sen University, Guangzhou, China.

Motivation: Imaging genetics is mainly used to reveal the pathogenesis of neuropsychiatric risk genes and understand the relationship between human brain structure, functional and individual differences. Increasingly, the brain-wide imaging phenotypes in voxels are available to test the association with genetic markers. A challenge with analyzing such data is their high dimensionality and complex relationships. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btaa612DOI Listing

Epiviz File Server: Query, Transform and Interactively Explore Data from Indexed Genomic Files.

Bioinformatics 2020 Jul 3. Epub 2020 Jul 3.

Center for Bioinformatics and Computational Biology, University of Maryland, College Park, Maryland.

Motivation: Genomic data repositories like The Cancer Genome Atlas (TCGA), Encyclopedia of DNA Elements (ENCODE), Bioconductor's AnnotationHub and ExperimentHub etc., provide public access to large amounts of genomic data as flat files. Researchers often download a subset of data files from these repositories to perform exploratory data analysis. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btaa591DOI Listing

Prediction of heart disease and classifiers' sensitivity analysis.

BMC Bioinformatics 2020 Jul 2;21(1):278. Epub 2020 Jul 2.

Department of Information Systems, College of Computer and Information Systems, Prince Sultan University, Riyadh, Kingdom of Saudi Arabia.

Background: Heart disease (HD) is one of the most common diseases nowadays, and an early diagnosis of such a disease is a crucial task for many health care providers to prevent their patients for such a disease and to save lives. In this paper, a comparative analysis of different classifiers was performed for the classification of the Heart Disease dataset in order to correctly classify and or predict HD cases with minimal attributes. The set contains 76 attributes including the class attribute, for 1025 patients collected from Cleveland, Hungary, Switzerland, and Long Beach, but in this paper, only a subset of 14 attributes are used, and each attribute has a given set value. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03626-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7331233PMC

USMPep: universal sequence models for major histocompatibility complex binding affinity prediction.

BMC Bioinformatics 2020 Jul 2;21(1):279. Epub 2020 Jul 2.

Fraunhofer Heinrich Hertz Institute, Einsteinufer 37, Berlin, 10587, Germany.

Background: Immunotherapy is a promising route towards personalized cancer treatment. A key algorithmic challenge in this process is to decide if a given peptide (neoepitope) binds with the major histocompatibility complex (MHC). This is an active area of research and there are many MHC binding prediction algorithms that can predict the MHC binding affinity for a given peptide to a high degree of accuracy. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03631-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7330990PMC

Accounting for grouped predictor variables or pathways in high-dimensional penalized Cox regression models.

BMC Bioinformatics 2020 Jul 2;21(1):277. Epub 2020 Jul 2.

Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, INSERM U1018 Oncostat, Villejuif, F-94805, France.

Background: The standard lasso penalty and its extensions are commonly used to develop a regularized regression model while selecting candidate predictor variables on a time-to-event outcome in high-dimensional data. However, these selection methods focus on a homogeneous set of variables and do not take into account the case of predictors belonging to functional groups; typically, genomic data can be grouped according to biological pathways or to different types of collected data. Another challenge is that the standard lasso penalisation is known to have a high false discovery rate. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03618-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7331150PMC

DiNeR: a Differential graphical model for analysis of co-regulation Network Rewiring.

BMC Bioinformatics 2020 Jul 2;21(1):281. Epub 2020 Jul 2.

Computational Biology and Bioinformatics Program, Yale University, New Haven, CT, 06520, USA.

Background: During transcription, numerous transcription factors (TFs) bind to targets in a highly coordinated manner to control the gene expression. Alterations in groups of TF-binding profiles (i.e. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03605-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7333332PMC

A cell-level quality control workflow for high-throughput image analysis.

BMC Bioinformatics 2020 Jul 2;21(1):280. Epub 2020 Jul 2.

Genomics Institute of the Novartis Research Foundation, 10675 John Jay Hopkins Drive, San Diego, California, 92121, USA.

Background: Image-based high throughput (HT) screening provides a rich source of information on dynamic cellular response to external perturbations. The large quantity of data generated necessitates computer-aided quality control (QC) methodologies to flag imaging and staining artifacts. Existing image- or patch-level QC methods require separate thresholds to be simultaneously tuned for each image quality metric used, and also struggle to distinguish between artifacts and valid cellular phenotypes. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1186/s12859-020-03603-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7333376PMC

Scirpy: A Scanpy extension for analyzing single-cell T-cell receptor sequencing data.

Bioinformatics 2020 Jul 2. Epub 2020 Jul 2.

Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria.

Summary: Advances in single-cell technologies have enabled the investigation of T cell phenotypes and repertoires at unprecedented resolution and scale. Bioinformatic methods for the efficient analysis of these large-scale datasets are instrumental for advancing our understanding of adaptive immune responses. However, while well-established solutions are accessible for the processing of single-cell transcriptomes, no streamlined pipelines are available for the comprehensive characterization of T cell receptors. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btaa611DOI Listing

TDAview: an online visualization tool for topological data analysis.

Bioinformatics 2020 Jul 2. Epub 2020 Jul 2.

Department of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, Australia.

Summary: TDAview is an online tool for topological data analysis and visualization. It implements the Mapper algorithm for topological data analysis and provides extensive graph visualization options. TDAview is a user-friendly tool that allows biologists and clinicians without programming knowledge to harness the power of topological data analysis. Read More

View Article

Download full-text PDF

Source
http://dx.doi.org/10.1093/bioinformatics/btaa600DOI Listing