Curated Data Set Research Articles

Comprehensive assessment and performance improvement of effector protein predictors for bacterial secretion systems III, IV and VI.

Bacterial effector proteins secreted by various protein secretion systems play crucial roles in host-pathogen interactions. In this context, computational tools capable of accurately predicting effector proteins of the various types of bacterial secretion systems are highly desirable. Existing computational approaches use different machine learning (ML) techniques and heterogeneous features derived from protein sequences and/or structural information. These predictors differ not only in terms of the used ML methods but also with respect to the used curated data sets, the features selection and their prediction performance. Here, we provide a comprehensive survey and benchmarking of currently available tools for the prediction of effector proteins of bacterial types III, IV and VI secretion systems (T3SS, T4SS and T6SS, respectively). We review core algorithms, feature selection techniques, tool availability and applicability and evaluate the prediction performance based on carefully curated independent test data sets. In an effort to improve predictive performance, we constructed three ensemble models based on ML algorithms by integrating the output of all individual predictors reviewed. Our benchmarks demonstrate that these ensemble models outperform all the reviewed tools for the prediction of effector proteins of T3SS and T4SS. The webserver of the proposed ensemble methods for T3SS and T4SS effector protein prediction is freely available at http://tbooster.erc.monash.edu/index.jsp. We anticipate that this survey will serve as a useful guide for interested users and that the new ensemble predictors will stimulate research into host-pathogen relationships and inspiration for the development of new bioinformatics tools for predicting effector proteins of T3SS, T4SS and T6SS.

Lessons from collecting a million biometric samples

Over the past decade, independent evaluations have become commonplace in many areas of experimental computer science, including face and gesture recognition. A key attribute of many successful independent evaluations is a curated data set. Desired aspects associated with these data sets include appropriateness to the experimental design, a corpus size large enough to allow statistically rigorous characterization of results, and the availability of comprehensive metadata that allow inferences to be made on various data set attributes. In this paper, we review a ten-year biometric sampling effort that enabled the creation of several key biometrics challenge problems. We summarize the design and execution of data collections, identify key challenges, and convey some lessons learned.

RDF graph alignment with bisimulation

We investigate the problem of aligning two RDF databases, an essential problem in understanding the evolution of ontologies. Our approaches address three fundamental challenges: 1) the use of "blank" (null) names, 2) ontology changes in which different names are used to identify the same entity, and 3) small changes in the data values as well as small changes in the graph structure of the RDF database. We propose approaches inspired by the classical notion of graph bisimulation and extend them to capture the natural metrics of edit distance on the data values and the graph structure. We evaluate our methods on three evolving curated data sets. Overall, our results show that the proposed methods perform well and are scalable.

Analysis of ToxCast Data—In Vitroand Physiochemical Properties—In the Accurate Classification of Chemicals That Induce HepatocarcinogenesisIn Vivo

Abstract In vitro and in silico methods continue to be evaluated for their potential to inform chemical toxicology evaluation. The research arm of the Environmental Protection Agency has been one of many research bodies evaluating the potential of such methods as part of their ToxCast initiative. We set out to advance the ongoing discussion of improving toxicity testing by exploring whether or not ToxCast physiochemical properties and high-throughput assay data could be used as covariates in predictive models to accurately classify chemicals that either do not or do induce hepatocarcinogenesis in vivo. ToxCast physiochemical and high-throughput assay data were evaluated against known chemicals and in vivo endpoints from the ToxRef curated data set. Hepatocarcinogen-causing chemicals were found to be larger and more lipophilic and complex in shape than control group chemicals. Adjusted logistic regression models using physiochemical properties as covariates accurately classified 71% of the chemicals into t...

BloodSpot: a database of gene expression profiles and transcriptional programs for healthy and malignant haematopoiesis

Research on human and murine haematopoiesis has resulted in a vast number of gene-expression data sets that can potentially answer questions regarding normal and aberrant blood formation. To researchers and clinicians with limited bioinformatics experience, these data have remained available, yet largely inaccessible. Current databases provide information about gene-expression but fail to answer key questions regarding co-regulation, genetic programs or effect on patient survival. To address these shortcomings, we present BloodSpot (www.bloodspot.eu), which includes and greatly extends our previously released database HemaExplorer, a database of gene expression profiles from FACS sorted healthy and malignant haematopoietic cells. A revised interactive interface simultaneously provides a plot of gene expression along with a Kaplan–Meier analysis and a hierarchical tree depicting the relationship between different cell types in the database. The database now includes 23 high-quality curated data sets relevant to normal and malignant blood formation and, in addition, we have assembled and built a unique integrated data set, BloodPool. Bloodpool contains more than 2000 samples assembled from six independent studies on acute myeloid leukemia. Furthermore, we have devised a robust sample integration procedure that allows for sensitive comparison of user-supplied patient samples in a well-defined haematopoietic cellular space.

Towards Better BBB Passage Prediction Using an Extensive and Curated Data Set.

In the present report, the challenging task of drug delivery across the blood-brain barrier (BBB) is addressed via a computational approach. The BBB passage was modeled using classification and regression schemes on a novel extensive and curated data set (the largest to the best of our knowledge) in terms of log BB. Prior to the model development, steps of data analysis that comprise chemical data curation, structural, cutoff and cluster analysis (CA) were conducted. Linear Discriminant Analysis (LDA) and Multiple Linear Regression (MLR) were used to fit classification and correlation functions. The best LDA-based model showed overall accuracies over 85 % and 83 % for the training and test sets, respectively. Also a MLR-based model with acceptable explanation of more than 69 % of the variance in the experimental log BB was developed. A brief and general interpretation of proposed models allowed the estimation on how 'near' our computational approach is to the factors that determine the passage of molecules through the BBB. In a final effort some popular and powerful Machine Learning methods were considered. Comparable or similar performance was observed respect to the simpler linear techniques. Most of the compounds with anomalous behavior were put aside into a set denoted as controversial set and discussion regarding to these compounds is provided. Finally, our results were compared with methodologies previously reported in the literature showing comparable to better results. The results could represent useful tools available and reproducible by all scientific community in the early stages of neuropharmaceutical drug discovery/development projects.

Using support vector machines to improve elemental ion identification in macromolecular crystal structures.

In the process of macromolecular model building, crystallographers must examine electron density for isolated atoms and differentiate sites containing structured solvent molecules from those containing elemental ions. This task requires specific knowledge of metal-binding chemistry and scattering properties and is prone to error. A method has previously been described to identify ions based on manually chosen criteria for a number of elements. Here, the use of support vector machines (SVMs) to automatically classify isolated atoms as either solvent or one of various ions is described. Two data sets of protein crystal structures, one containing manually curated structures deposited with anomalous diffraction data and another with automatically filtered, high-resolution structures, were constructed. On the manually curated data set, an SVM classifier was able to distinguish calcium from manganese, zinc, iron and nickel, as well as all five of these ions from water molecules, with a high degree of accuracy. Additionally, SVMs trained on the automatically curated set of high-resolution structures were able to successfully classify most common elemental ions in an independent validation test set. This method is readily extensible to other elemental ions and can also be used in conjunction with previous methods based on a priori expectations of the chemical environment and X-ray scattering.

High-Quality Thermochemistry Data on Polycyclic Aromatic Hydrocarbons via Quantum Chemistry

In this article, recent work on calculating high-quality enthalpies of formation for polycyclic aromatic hydrocarbons (PAHs) based on both density functional theory (DFT) and Gaussian-3 (G3) model chemistry methods is discussed. It is shown that through the use of an empirical correction model, the systematic errors in low-level DFT calculations can be controlled to produce reliable thermochemistry. It is further shown that the G3 results have a more regular systematic error and as a consequence a simpler model may be used to correct for systematic deviations. It is seen that the resulting enthalpy of formation values are in good agreement with the available experimental data, and that the predictions are sufficiently robust to point out certain errors in experimental determinations. This work was done as part of a larger effort to create a curated data set of property data for a large set of PAH molecules. In furtherance of this goal, UV/Vis spectra have been computed using time-dependent DFT. The collection of PAH data is being made publicly available through a web-based database that is briefly described.

Local dynamics of proteins and DNA evaluated from crystallographic B factors.

The dynamics of protein and nucleic acid structures is as important as their average static picture. The local molecular dynamics concealed in diffraction images is expressed as so-called B factors. To find out how the crystal-derived B factors represent the dynamic behaviour of atoms and residues of proteins and DNA in their complexes, the distributions of scaled B factors from a carefully curated data set of over 700 protein-DNA crystal structures were analyzed [Schneider et al. (2014), Nucleic Acids Res. 42, 3381-3394]. Amino acids and nucleotides were categorized based on their molecular neighbourhood as solvent-accessible, solvent-inaccessible (i.e. forming the protein core) or lying at protein-protein or protein-DNA interfaces; the backbone and side-chain atoms were analyzed separately. The B factors of two types of crystal-ordered water molecules were also analyzed. The analysis confirmed several expected features of protein and DNA dynamics, but also revealed surprising facts. Solvent-accessible amino acids have B factors that are larger than those of residues at the biomolecular interfaces, and core-forming amino acids are the most restricted in their movement. A unique feature of the latter group is that their side-chain and backbone atoms are restricted in their movement to the same extent; in all other amino-acid groups the side chains are more floppy than the backbone. The low values of the B factors of water molecules bridging proteins with DNA and the very large fluctuations of DNA phosphates are surprising. The features discriminating different types of residues are less pronounced in structures with lower crystallographic resolution. Some of the observed trends are likely to be the consequence of improper refinement protocols that may need to be rectified.

Dynamic Response of a Model Levee on Sherman Island Peat: A Curated Data Set

A model levee resting atop soft compressible peaty organic soil in the Sacramento/San Joaquin Delta was shaken by forced vibration to study the seismic deformation potential of the underlying peat and measure dynamic levee–peat interaction. Forced vibration testing occurred over a frequency range of 0 Hz to 5 Hz and produced force amplitudes applied to the embankment crest that induced elastic to nonlinear levee-foundation responses. Available data include acceleration records from sensors mounted on the model levee and on the ground surface near the model levee, and acceleration and pore pressure measurements from sensors embedded in the underlying peat. A remote data acquisition system measured settlements and pore pressures over a span of more than a year, encompassing time before and after the dynamic testing. Small pore pressures were generated in the peat during testing although embankment settlements from cyclic loading were small.

Southern African Treatment Resistance Network (SATuRN) RegaDB HIV drug resistance and clinical management database: supporting patient management, surveillance and research in southern Africa

Substantial amounts of data have been generated from patient management and academic exercises designed to better understand the human immunodeficiency virus (HIV) epidemic and design interventions to control it. A number of specialized databases have been designed to manage huge data sets from HIV cohort, vaccine, host genomic and drug resistance studies. Besides databases from cohort studies, most of the online databases contain limited curated data and are thus sequence repositories. HIV drug resistance has been shown to have a great potential to derail the progress made thus far through antiretroviral therapy. Thus, a lot of resources have been invested in generating drug resistance data for patient management and surveillance purposes. Unfortunately, most of the data currently available relate to subtype B even though >60% of the epidemic is caused by HIV-1 subtype C. A consortium of clinicians, scientists, public health experts and policy markers working in southern Africa came together and formed a network, the Southern African Treatment and Resistance Network (SATuRN), with the aim of increasing curated HIV-1 subtype C and tuberculosis drug resistance data. This article describes the HIV-1 data curation process using the SATuRN Rega database. The data curation is a manual and time-consuming process done by clinical, laboratory and data curation specialists. Access to the highly curated data sets is through applications that are reviewed by the SATuRN executive committee. Examples of research outputs from the analysis of the curated data include trends in the level of transmitted drug resistance in South Africa, analysis of the levels of acquired resistance among patients failing therapy and factors associated with the absence of genotypic evidence of drug resistance among patients failing therapy. All these studies have been important for informing first- and second-line therapy. This database is a free password-protected open source database available on www.bioafrica.net. Database URL: http://www.bioafrica.net/regadb/

YStreX: yeast stress expression database.

Over the past decade genome-wide expression analyses have been often used to study how expression of genes changes in response to various environmental stresses. Many of these studies (such as effects of oxygen concentration, temperature stress, low pH stress, osmotic stress, depletion or limitation of nutrients, addition of different chemical compounds, etc.) have been conducted in the unicellular Eukaryal model, yeast Saccharomyces cerevisiae. However, the lack of a unifying or integrated, bioinformatics platform that would permit efficient and rapid use of all these existing data remain an important issue. To facilitate research by exploiting existing transcription data in the field of yeast physiology, we have developed the yStreX database. It is an online repository of analyzed gene expression data from curated data sets from different studies that capture genome-wide transcriptional changes in response to diverse environmental transitions. The first aim of this online database is to facilitate comparison of cross-platform and cross-laboratory gene expression data. Additionally, we performed different expression analyses, meta-analyses and gene set enrichment analyses; and the results are also deposited in this database. Lastly, we constructed a user-friendly Web interface with interactive visualization to provide intuitive access and to display the queried data for users with no background in bioinformatics.Database URL: http://www.ystrexdb.com

GINI: From ISH Images to Gene Interaction Networks

Accurate inference of molecular and functional interactions among genes, especially in multicellular organisms such as Drosophila, often requires statistical analysis of correlations not only between the magnitudes of gene expressions, but also between their temporal-spatial patterns. The ISH (in-situ-hybridization)-based gene expression micro-imaging technology offers an effective approach to perform large-scale spatial-temporal profiling of whole-body mRNA abundance. However, analytical tools for discovering gene interactions from such data remain an open challenge due to various reasons, including difficulties in extracting canonical representations of gene activities from images, and in inference of statistically meaningful networks from such representations. In this paper, we present GINI, a machine learning system for inferring gene interaction networks from Drosophila embryonic ISH images. GINI builds on a computer-vision-inspired vector-space representation of the spatial pattern of gene expression in ISH images, enabled by our recently developed system; and a new multi-instance-kernel algorithm that learns a sparse Markov network model, in which, every gene (i.e., node) in the network is represented by a vector-valued spatial pattern rather than a scalar-valued gene intensity as in conventional approaches such as a Gaussian graphical model. By capturing the notion of spatial similarity of gene expression, and at the same time properly taking into account the presence of multiple images per gene via multi-instance kernels, GINI is well-positioned to infer statistically sound, and biologically meaningful gene interaction networks from image data. Using both synthetic data and a small manually curated data set, we demonstrate the effectiveness of our approach in network building. Furthermore, we report results on a large publicly available collection of Drosophila embryonic ISH images from the Berkeley Drosophila Genome Project, where GINI makes novel and interesting predictions of gene interactions. Software for GINI is available at http://sailing.cs.cmu.edu/Drosophila_ISH_images/

Computational and experimental validation of B and T-cell epitopes of the in vivo immune response to a novel malarial antigen.

Vaccine development efforts will be guided by algorithms that predict immunogenic epitopes. Such prediction methods rely on classification-based algorithms that are trained against curated data sets of known B and T cell epitopes. It is unclear whether this empirical approach can be applied prospectively to predict epitopes associated with protective immunity for novel antigens. We present a comprehensive comparison of in silico B and T cell epitope predictions with in vivo validation using an previously uncharacterized malaria antigen, CelTOS. CelTOS has no known conserved structural elements with any known proteins, and thus is not represented in any epitope databases used to train prediction algorithms. This analysis represents a blind assessment of this approach in the context of a novel, immunologically relevant antigen. The limited accuracy of the tested algorithms to predict the in vivo immune responses emphasizes the need to improve their predictive capabilities for use as tools in vaccine design.

From data point timelines to a well curated data set, data mining of experimental data and chemical structure data from scientific articles, problems and possible solutions

The scientific literature is important source of experimental and chemical structure data. Very often this data has been harvested into smaller or bigger data collections leaving the data quality and curation issues on shoulders of users. The current research presents a systematic and reproducible workflow for collecting series of data points from scientific literature and assembling a database that is suitable for the purposes of high quality modelling and decision support. The quality assurance aspect of the workflow is concerned with the curation of both chemical structures and associated toxicity values at (1) single data point level and (2) collection of data points level. The assembly of a database employs a novel "timeline" approach. The workflow is implemented as a software solution and its applicability is demonstrated on the example of the Tetrahymena pyriformis acute aquatic toxicity endpoint. A literature collection of 86 primary publications for T. pyriformis was found to contain 2,072 chemical compounds and 2,498 unique toxicity values, which divide into 2,440 numerical and 58 textual values. Every chemical compound was assigned to a preferred toxicity value. Examples for most common chemical and toxicological data curation scenarios are discussed.

Enhancing Training Collections for Image Annotation: An Instance-Weighted Mixture Modeling Approach

Tagged Web images provide an abundance of labeled training examples for visual concept learning. However, the performance of automatic training data selection is susceptible to highly inaccurate tags and atypical images. Consequently, manually curated training data sets are still a preferred choice for many image annotation systems. This paper introduces ARTEMIS - a scheme to enhance automatic selection of training images using an instance-weighted mixture modeling framework. An optimization algorithm is derived to learn instance-weights in addition to mixture parameter estimation, essentially adapting to the noise associated with each example. The mechanism of hypothetical local mapping is evoked so that data in diverse mathematical forms or modalities can be cohesively treated as the system maintains tractability in optimization. Finally, training examples are selected from top-ranked images of a likelihood-based image ranking. Experiments indicate that ARTEMIS exhibits higher resilience to noise than several baselines for large training data collection. The performance of ARTEMIS-trained image annotation system is comparable with usage of manually curated data sets.

CSAR Data Set Release 2012: Ligands, Affinities, Complexes, and Docking Decoys

A major goal in drug design is the improvement of computational methods for docking and scoring. The Community Structure Activity Resource (CSAR) has collected several data sets from industry and added in-house data sets that may be used for this purpose (www.csardock.org). CSAR has currently obtained data from Abbott, GlaxoSmithKline, and Vertex and is working on obtaining data from several others. Combined with our in-house projects, we are providing a data set consisting of 6 protein targets, 647 compounds with biological affinities, and 82 crystal structures. Multiple congeneric series are available for several targets with a few representative crystal structures of each of the series. These series generally contain a few inactive compounds, usually not available in the literature, to provide an upper bound to the affinity range. The affinity ranges are typically 3–4 orders of magnitude per series. For our in-house projects, we have had compounds synthesized for biological testing. Affinities were measured by Thermofluor, Octet RED, and isothermal titration calorimetry for the most soluble. This allows the direct comparison of the biological affinities for those compounds, providing a measure of the variance in the experimental affinity. It appears that there can be considerable variance in the absolute value of the affinity, making the prediction of the absolute value ill-defined. However, the relative rankings within the methods are much better, and this fits with the observation that predicting relative ranking is a more tractable problem computationally. For those in-house compounds, we also have measured the following physical properties: logD, logP, thermodynamic solubility, and pKa. This data set also provides a substantial decoy set for each target consisting of diverse conformations covering the entire active site for all of the 58 CSAR-quality crystal structures. The CSAR data sets (CSAR-NRC HiQ and the 2012 release) provide substantial, publically available, curated data sets for use in parametrizing and validating docking and scoring methods.

De novo prediction of the genomic components and capabilities for microbial plant biomass degradation from (meta-)genomes

BackgroundUnderstanding the biological mechanisms used by microorganisms for plant biomass degradation is of considerable biotechnological interest. Despite of the growing number of sequenced (meta)genomes of plant biomass-degrading microbes, there is currently no technique for the systematic determination of the genomic components of this process from these data.ResultsWe describe a computational method for the discovery of the protein domains and CAZy families involved in microbial plant biomass degradation. Our method furthermore accurately predicts the capability to degrade plant biomass for microbial species from their genome sequences. Application to a large, manually curated data set of microbial degraders and non-degraders identified gene families of enzymes known by physiological and biochemical tests to be implicated in cellulose degradation, such as GH5 and GH6. Additionally, genes of enzymes that degrade other plant polysaccharides, such as hemicellulose, pectins and oligosaccharides, were found, as well as gene families which have not previously been related to the process. For draft genomes reconstructed from a cow rumen metagenome our method predicted Bacteroidetes-affiliated species and a relative to a known plant biomass degrader to be plant biomass degraders. This was supported by the presence of genes encoding enzymatically active glycoside hydrolases in these genomes.ConclusionsOur results show the potential of the method for generating novel insights into microbial plant biomass degradation from (meta-)genome data, where there is an increasing production of genome assemblages for uncultured microbes.

NSort/DB: An Intranuclear Compartment Protein Database

Distinct substructures within the nucleus are associated with a wide variety of important nuclear processes. Structures such as chromatin and nuclear pores have specific roles, while others such as Cajal bodies are more functionally varied. Understanding the roles of these membraneless intra-nuclear compartments requires extensive data sets covering nuclear and compartment-associated proteins. NSort/DB is a database providing access to intra- or sub-nuclear compartment associations for the mouse nuclear proteome. Based on resources ranging from large-scale curated data sets to detailed experiments, this data set provides a high-quality set of annotations of non-exclusive association of nuclear proteins with structures such as promyelocytic leukaemia bodies and chromatin. The database is searchable by protein identifier or compartment, and has a documented web service API. The search interface, web service and data download are all freely available online at http://www.nsort.org/db/. Availability of this data set will enable systematic analyses of the protein complements of nuclear compartments, improving our understanding of the diverse functional repertoire of these structures.

Predictive Toxicology Modeling: Protocols for Exploring hERG Classification and Tetrahymena pyriformis End Point Predictions

The inclusion and accessibility of different methodologies to explore chemical data sets has been beneficial to the field of predictive modeling, specifically in the chemical sciences in the field of Quantitative Structure-Activity Relationship (QSAR) modeling. This study discusses using contemporary protocols and QSAR modeling methods to properly model two biomolecular systems that have historically not performed well using traditional and three-dimensional QSAR methodologies. Herein, we explore, analyze, and discuss the creation of a classification human Ether-a-go-go Related Gene (hERG) potassium channel model and a continuous Tetrahymena pyriformis (T. pyriformis) model using Support Vector Machine (SVM) and Support Vector Regression (SVR), respectively. The models are constructed with three types of molecular descriptors that capture the gross physicochemical features of the compounds: (i) 2D, 2 1/2D, and 3D physical features, (ii) VolSurf-like molecular interaction fields, and (iii) 4D-Fingerprints. The best hERG SVM model achieved 89% accuracy and the three-best SVM models were able to screen a Pubchem data set with an accuracy of 97%. The best T. pyriformis model had an R(2) value of 0.924 for the training set and was able to predict the continuous end points for two test sets with R(2) values of 0.832 and 0.620, respectively. The studies presented within demonstrate the predictive ability (classification and continuous end points) of QSAR models constructed from curated data sets, biologically relevant molecular descriptors, and Support Vector Machines and Support Vector Regression. The ability of these protocols and methodologies to accommodate large data sets (several thousands compounds) that are chemically diverse - and in the case of classification modeling unbalanced (one experimental outcome dominates the data set) - allows scientists to further explore a remarkable amount of biological and chemical information.