MyDas, an extensible Java DAS server.

PubMed

Salazar, Gustavo A; García, Leyla J; Jones, Philip; Jimenez, Rafael C; Quinn, Antony F; Jenkinson, Andrew M; Mulder, Nicola; Martin, Maria; Hunter, Sarah; Hermjakob, Henning

2012-01-01

A large number of diverse, complex, and distributed data resources are currently available in the Bioinformatics domain. The pace of discovery and the diversity of information means that centralised reference databases like UniProt and Ensembl cannot integrate all potentially relevant information sources. From a user perspective however, centralised access to all relevant information concerning a specific query is essential. The Distributed Annotation System (DAS) defines a communication protocol to exchange annotations on genomic and protein sequences; this standardisation enables clients to retrieve data from a myriad of sources, thus offering centralised access to end-users.We introduce MyDas, a web server that facilitates the publishing of biological annotations according to the DAS specification. It deals with the common functionality requirements of making data available, while also providing an extension mechanism in order to implement the specifics of data store interaction. MyDas allows the user to define where the required information is located along with its structure, and is then responsible for the communication protocol details.

DNApod: DNA polymorphism annotation database from next-generation sequence read archives.

PubMed

Mochizuki, Takako; Tanizawa, Yasuhiro; Fujisawa, Takatomo; Ohta, Tazro; Nikoh, Naruo; Shimizu, Tokurou; Toyoda, Atsushi; Fujiyama, Asao; Kurata, Nori; Nagasaki, Hideki; Kaminuma, Eli; Nakamura, Yasukazu

2017-01-01

With the rapid advances in next-generation sequencing (NGS), datasets for DNA polymorphisms among various species and strains have been produced, stored, and distributed. However, reliability varies among these datasets because the experimental and analytical conditions used differ among assays. Furthermore, such datasets have been frequently distributed from the websites of individual sequencing projects. It is desirable to integrate DNA polymorphism data into one database featuring uniform quality control that is distributed from a single platform at a single place. DNA polymorphism annotation database (DNApod; http://tga.nig.ac.jp/dnapod/) is an integrated database that stores genome-wide DNA polymorphism datasets acquired under uniform analytical conditions, and this includes uniformity in the quality of the raw data, the reference genome version, and evaluation algorithms. DNApod genotypic data are re-analyzed whole-genome shotgun datasets extracted from sequence read archives, and DNApod distributes genome-wide DNA polymorphism datasets and known-gene annotations for each DNA polymorphism. This new database was developed for storing genome-wide DNA polymorphism datasets of plants, with crops being the first priority. Here, we describe our analyzed data for 679, 404, and 66 strains of rice, maize, and sorghum, respectively. The analytical methods are available as a DNApod workflow in an NGS annotation system of the DNA Data Bank of Japan and a virtual machine image. Furthermore, DNApod provides tables of links of identifiers between DNApod genotypic data and public phenotypic data. To advance the sharing of organism knowledge, DNApod offers basic and ubiquitous functions for multiple alignment and phylogenetic tree construction by using orthologous gene information.

DNApod: DNA polymorphism annotation database from next-generation sequence read archives

PubMed Central

Mochizuki, Takako; Tanizawa, Yasuhiro; Fujisawa, Takatomo; Ohta, Tazro; Nikoh, Naruo; Shimizu, Tokurou; Toyoda, Atsushi; Fujiyama, Asao; Kurata, Nori; Nagasaki, Hideki; Kaminuma, Eli; Nakamura, Yasukazu

2017-01-01

With the rapid advances in next-generation sequencing (NGS), datasets for DNA polymorphisms among various species and strains have been produced, stored, and distributed. However, reliability varies among these datasets because the experimental and analytical conditions used differ among assays. Furthermore, such datasets have been frequently distributed from the websites of individual sequencing projects. It is desirable to integrate DNA polymorphism data into one database featuring uniform quality control that is distributed from a single platform at a single place. DNA polymorphism annotation database (DNApod; http://tga.nig.ac.jp/dnapod/) is an integrated database that stores genome-wide DNA polymorphism datasets acquired under uniform analytical conditions, and this includes uniformity in the quality of the raw data, the reference genome version, and evaluation algorithms. DNApod genotypic data are re-analyzed whole-genome shotgun datasets extracted from sequence read archives, and DNApod distributes genome-wide DNA polymorphism datasets and known-gene annotations for each DNA polymorphism. This new database was developed for storing genome-wide DNA polymorphism datasets of plants, with crops being the first priority. Here, we describe our analyzed data for 679, 404, and 66 strains of rice, maize, and sorghum, respectively. The analytical methods are available as a DNApod workflow in an NGS annotation system of the DNA Data Bank of Japan and a virtual machine image. Furthermore, DNApod provides tables of links of identifiers between DNApod genotypic data and public phenotypic data. To advance the sharing of organism knowledge, DNApod offers basic and ubiquitous functions for multiple alignment and phylogenetic tree construction by using orthologous gene information. PMID:28234924

Programming distributed memory architectures using Kali

NASA Technical Reports Server (NTRS)

Mehrotra, Piyush; Vanrosendale, John

1990-01-01

Programming nonshared memory systems is more difficult than programming shared memory systems, in part because of the relatively low level of current programming environments for such machines. A new programming environment is presented, Kali, which provides a global name space and allows direct access to remote data values. In order to retain efficiency, Kali provides a system on annotations, allowing the user to control those aspects of the program critical to performance, such as data distribution and load balancing. The primitives and constructs provided by the language is described, and some of the issues raised in translating a Kali program for execution on distributed memory systems are also discussed.

Computer systems for annotation of single molecule fragments

DOEpatents

Schwartz, David Charles; Severin, Jessica

2016-07-19

There are provided computer systems for visualizing and annotating single molecule images. Annotation systems in accordance with this disclosure allow a user to mark and annotate single molecules of interest and their restriction enzyme cut sites thereby determining the restriction fragments of single nucleic acid molecules. The markings and annotations may be automatically generated by the system in certain embodiments and they may be overlaid translucently onto the single molecule images. An image caching system may be implemented in the computer annotation systems to reduce image processing time. The annotation systems include one or more connectors connecting to one or more databases capable of storing single molecule data as well as other biomedical data. Such diverse array of data can be retrieved and used to validate the markings and annotations. The annotation systems may be implemented and deployed over a computer network. They may be ergonomically optimized to facilitate user interactions.

GANESH: software for customized annotation of genome regions.

PubMed

Huntley, Derek; Hummerich, Holger; Smedley, Damian; Kittivoravitkul, Sasivimol; McCarthy, Mark; Little, Peter; Sergot, Marek

2003-09-01

GANESH is a software package designed to support the genetic analysis of regions of human and other genomes. It provides a set of components that may be assembled to construct a self-updating database of DNA sequence, mapping data, and annotations of possible genome features. Once one or more remote sources of data for the target region have been identified, all sequences for that region are downloaded, assimilated, and subjected to a (configurable) set of standard database-searching and genome-analysis packages. The results are stored in compressed form in a relational database, and are updated automatically on a regular schedule so that they are always immediately available in their most up-to-date versions. A Java front-end, executed as a stand alone application or web applet, provides a graphical interface for navigating the database and for viewing the annotations. There are facilities for importing and exporting data in the format of the Distributed Annotation System (DAS), enabling a GANESH database to be used as a component of a DAS configuration. The system has been used to construct databases for about a dozen regions of human chromosomes and for three regions of mouse chromosomes.

Multimodal MSI in Conjunction with Broad Coverage Spatially Resolved MS 2 Increases Confidence in Both Molecular Identification and Localization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Veličković, Dušan; Chu, Rosalie K.; Carrell, Alyssa A.

One critical aspect of mass spectrometry imaging (MSI) is the need to confidently identify detected analytes. While orthogonal tandem MS (e.g., LC-MS 2) experiments from sample extracts can assist in annotating ions, the spatial information about these molecules is lost. Accordingly, this could cause mislead conclusions, especially in cases where isobaric species exhibit different distributions within a sample. In this Technical Note, we employed a multimodal imaging approach, using matrix assisted laser desorption/ionization (MALDI)-MSI and liquid extraction surface analysis (LESA)-MS 2I, to confidently annotate and One critical aspect of mass spectrometry imaging (MSI) is the need to confidently identify detectedmore » analytes. While orthogonal tandem MS (e.g., LC-MS2) experiments from sample extracts can assist in annotating ions, the spatial information about these molecules is lost. Accordingly, this could cause mislead conclusions, especially in cases where isobaric species exhibit different distributions within a sample. In this Technical Note, we employed a multimodal imaging approach, using matrix assisted laser desorption/ionization (MALDI)-MSI and liquid extraction surface analysis (LESA)-MS 2I, to confidently annotate and localize a broad range of metabolites involved in a tripartite symbiosis system of moss, cyanobacteria, and fungus. We found that the combination of these two imaging modalities generated very congruent ion images, providing the link between highly accurate structural information onfered by LESA and high spatial resolution attainable by MALDI. These results demonstrate how this combined methodology could be very useful in differentiating metabolite routes in complex systems.« less

PAPARA(ZZ)I: An open-source software interface for annotating photographs of the deep-sea

NASA Astrophysics Data System (ADS)

Marcon, Yann; Purser, Autun

PAPARA(ZZ)I is a lightweight and intuitive image annotation program developed for the study of benthic megafauna. It offers functionalities such as free, grid and random point annotation. Annotations may be made following existing classification schemes for marine biota and substrata or with the use of user defined, customised lists of keywords, which broadens the range of potential application of the software to other types of studies (e.g. marine litter distribution assessment). If Internet access is available, PAPARA(ZZ)I can also query and use standardised taxa names directly from the World Register of Marine Species (WoRMS). Program outputs include abundances, densities and size calculations per keyword (e.g. per taxon). These results are written into text files that can be imported into spreadsheet programs for further analyses. PAPARA(ZZ)I is open-source and is available at http://papara-zz-i.github.io. Compiled versions exist for most 64-bit operating systems: Windows, Mac OS X and Linux.

Networking and Information Technology Research and Development. Supplement to the President’s Budget for FY 2002

DTIC Science & Technology

2001-07-01

Web-based applications to improve health data systems and quality of care; innovative strategies for data collection in clinical settings; approaches...research to increase interoperability and integration of software in distributed systems ; protocols and tools for data annotation and management; and...Generation National Defense and National Security Systems .......................... 27 Improved Health Care Systems for All Citizens

Non-Formal Education and Agriculture: A Selected Annotated Bibliography. Annotated Bibliography #10.

ERIC Educational Resources Information Center

Sullivan, Karen Collamore; And Others

Intended for those actively engaged in nonformal education for development, this annotated bibliography contains approximately 300 references to documents that highlight issues concerning food production, distribution, and consumption. It also demonstrates education's role in enhancing developmental efforts to alleviate world hunger. Materials are…

Algorithms for Hidden Markov Models Restricted to Occurrences of Regular Expressions

PubMed Central

Tataru, Paula; Sand, Andreas; Hobolth, Asger; Mailund, Thomas; Pedersen, Christian N. S.

2013-01-01

Hidden Markov Models (HMMs) are widely used probabilistic models, particularly for annotating sequential data with an underlying hidden structure. Patterns in the annotation are often more relevant to study than the hidden structure itself. A typical HMM analysis consists of annotating the observed data using a decoding algorithm and analyzing the annotation to study patterns of interest. For example, given an HMM modeling genes in DNA sequences, the focus is on occurrences of genes in the annotation. In this paper, we define a pattern through a regular expression and present a restriction of three classical algorithms to take the number of occurrences of the pattern in the hidden sequence into account. We present a new algorithm to compute the distribution of the number of pattern occurrences, and we extend the two most widely used existing decoding algorithms to employ information from this distribution. We show experimentally that the expectation of the distribution of the number of pattern occurrences gives a highly accurate estimate, while the typical procedure can be biased in the sense that the identified number of pattern occurrences does not correspond to the true number. We furthermore show that using this distribution in the decoding algorithms improves the predictive power of the model. PMID:24833225

SEED Software Annotations.

ERIC Educational Resources Information Center

Bethke, Dee; And Others

This document provides a composite index of the first five sets of software annotations produced by Project SEED. The software has been indexed by title, subject area, and grade level, and it covers sets of annotations distributed in September 1986, April 1987, September 1987, November 1987, and February 1988. The date column in the index…

A High-Speed, Real-Time Visualization and State Estimation Platform for Monitoring and Control of Electric Distribution Systems: Implementation and Field Results

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lundstrom, Blake; Gotseff, Peter; Giraldez, Julieta

Continued deployment of renewable and distributed energy resources is fundamentally changing the way that electric distribution systems are controlled and operated; more sophisticated active system control and greater situational awareness are needed. Real-time measurements and distribution system state estimation (DSSE) techniques enable more sophisticated system control and, when combined with visualization applications, greater situational awareness. This paper presents a novel demonstration of a high-speed, real-time DSSE platform and related control and visualization functionalities, implemented using existing open-source software and distribution system monitoring hardware. Live scrolling strip charts of meter data and intuitive annotated map visualizations of the entire state (obtainedmore » via DSSE) of a real-world distribution circuit are shown. The DSSE implementation is validated to demonstrate provision of accurate voltage data. This platform allows for enhanced control and situational awareness using only a minimum quantity of distribution system measurement units and modest data and software infrastructure.« less

Integration of Geographical Information Systems and Geophysical Applications with Distributed Computing Technologies.

NASA Astrophysics Data System (ADS)

Pierce, M. E.; Aktas, M. S.; Aydin, G.; Fox, G. C.; Gadgil, H.; Sayar, A.

2005-12-01

We examine the application of Web Service Architectures and Grid-based distributed computing technologies to geophysics and geo-informatics. We are particularly interested in the integration of Geographical Information System (GIS) services with distributed data mining applications. GIS services provide the general purpose framework for building archival data services, real time streaming data services, and map-based visualization services that may be integrated with data mining and other applications through the use of distributed messaging systems and Web Service orchestration tools. Building upon on our previous work in these areas, we present our current research efforts. These include fundamental investigations into increasing XML-based Web service performance, supporting real time data streams, and integrating GIS mapping tools with audio/video collaboration systems for shared display and annotation.

Marky: a tool supporting annotation consistency in multi-user and iterative document annotation projects.

PubMed

Pérez-Pérez, Martín; Glez-Peña, Daniel; Fdez-Riverola, Florentino; Lourenço, Anália

2015-02-01

Document annotation is a key task in the development of Text Mining methods and applications. High quality annotated corpora are invaluable, but their preparation requires a considerable amount of resources and time. Although the existing annotation tools offer good user interaction interfaces to domain experts, project management and quality control abilities are still limited. Therefore, the current work introduces Marky, a new Web-based document annotation tool equipped to manage multi-user and iterative projects, and to evaluate annotation quality throughout the project life cycle. At the core, Marky is a Web application based on the open source CakePHP framework. User interface relies on HTML5 and CSS3 technologies. Rangy library assists in browser-independent implementation of common DOM range and selection tasks, and Ajax and JQuery technologies are used to enhance user-system interaction. Marky grants solid management of inter- and intra-annotator work. Most notably, its annotation tracking system supports systematic and on-demand agreement analysis and annotation amendment. Each annotator may work over documents as usual, but all the annotations made are saved by the tracking system and may be further compared. So, the project administrator is able to evaluate annotation consistency among annotators and across rounds of annotation, while annotators are able to reject or amend subsets of annotations made in previous rounds. As a side effect, the tracking system minimises resource and time consumption. Marky is a novel environment for managing multi-user and iterative document annotation projects. Compared to other tools, Marky offers a similar visually intuitive annotation experience while providing unique means to minimise annotation effort and enforce annotation quality, and therefore corpus consistency. Marky is freely available for non-commercial use at http://sing.ei.uvigo.es/marky. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Prokaryotic Contig Annotation Pipeline Server: Web Application for a Prokaryotic Genome Annotation Pipeline Based on the Shiny App Package.

PubMed

Park, Byeonghyeok; Baek, Min-Jeong; Min, Byoungnam; Choi, In-Geol

2017-09-01

Genome annotation is a primary step in genomic research. To establish a light and portable prokaryotic genome annotation pipeline for use in individual laboratories, we developed a Shiny app package designated as "P-CAPS" (Prokaryotic Contig Annotation Pipeline Server). The package is composed of R and Python scripts that integrate publicly available annotation programs into a server application. P-CAPS is not only a browser-based interactive application but also a distributable Shiny app package that can be installed on any personal computer. The final annotation is provided in various standard formats and is summarized in an R markdown document. Annotation can be visualized and examined with a public genome browser. A benchmark test showed that the annotation quality and completeness of P-CAPS were reliable and compatible with those of currently available public pipelines.

easyDAS: Automatic creation of DAS servers

PubMed Central

2011-01-01

Background The Distributed Annotation System (DAS) has proven to be a successful way to publish and share biological data. Although there are more than 750 active registered servers from around 50 organizations, setting up a DAS server comprises a fair amount of work, making it difficult for many research groups to share their biological annotations. Given the clear advantage that the generalized sharing of relevant biological data is for the research community it would be desirable to facilitate the sharing process. Results Here we present easyDAS, a web-based system enabling anyone to publish biological annotations with just some clicks. The system, available at http://www.ebi.ac.uk/panda-srv/easydas is capable of reading different standard data file formats, process the data and create a new publicly available DAS source in a completely automated way. The created sources are hosted on the EBI systems and can take advantage of its high storage capacity and network connection, freeing the data provider from any network management work. easyDAS is an open source project under the GNU LGPL license. Conclusions easyDAS is an automated DAS source creation system which can help many researchers in sharing their biological data, potentially increasing the amount of relevant biological data available to the scientific community. PMID:21244646

Semi-automatic semantic annotation of PubMed Queries: a study on quality, efficiency, satisfaction

PubMed Central

Névéol, Aurélie; Islamaj-Doğan, Rezarta; Lu, Zhiyong

2010-01-01

Information processing algorithms require significant amounts of annotated data for training and testing. The availability of such data is often hindered by the complexity and high cost of production. In this paper, we investigate the benefits of a state-of-the-art tool to help with the semantic annotation of a large set of biomedical information queries. Seven annotators were recruited to annotate a set of 10,000 PubMed® queries with 16 biomedical and bibliographic categories. About half of the queries were annotated from scratch, while the other half were automatically pre-annotated and manually corrected. The impact of the automatic pre-annotations was assessed on several aspects of the task: time, number of actions, annotator satisfaction, inter-annotator agreement, quality and number of the resulting annotations. The analysis of annotation results showed that the number of required hand annotations is 28.9% less when using pre-annotated results from automatic tools. As a result, the overall annotation time was substantially lower when pre-annotations were used, while inter-annotator agreement was significantly higher. In addition, there was no statistically significant difference in the semantic distribution or number of annotations produced when pre-annotations were used. The annotated query corpus is freely available to the research community. This study shows that automatic pre-annotations are found helpful by most annotators. Our experience suggests using an automatic tool to assist large-scale manual annotation projects. This helps speed-up the annotation time and improve annotation consistency while maintaining high quality of the final annotations. PMID:21094696

High throughput profile-profile based fold recognition for the entire human proteome.

PubMed

McGuffin, Liam J; Smith, Richard T; Bryson, Kevin; Sørensen, Søren-Aksel; Jones, David T

2006-06-07

In order to maintain the most comprehensive structural annotation databases we must carry out regular updates for each proteome using the latest profile-profile fold recognition methods. The ability to carry out these updates on demand is necessary to keep pace with the regular updates of sequence and structure databases. Providing the highest quality structural models requires the most intensive profile-profile fold recognition methods running with the very latest available sequence databases and fold libraries. However, running these methods on such a regular basis for every sequenced proteome requires large amounts of processing power. In this paper we describe and benchmark the JYDE (Job Yield Distribution Environment) system, which is a meta-scheduler designed to work above cluster schedulers, such as Sun Grid Engine (SGE) or Condor. We demonstrate the ability of JYDE to distribute the load of genomic-scale fold recognition across multiple independent Grid domains. We use the most recent profile-profile version of our mGenTHREADER software in order to annotate the latest version of the Human proteome against the latest sequence and structure databases in as short a time as possible. We show that our JYDE system is able to scale to large numbers of intensive fold recognition jobs running across several independent computer clusters. Using our JYDE system we have been able to annotate 99.9% of the protein sequences within the Human proteome in less than 24 hours, by harnessing over 500 CPUs from 3 independent Grid domains. This study clearly demonstrates the feasibility of carrying out on demand high quality structural annotations for the proteomes of major eukaryotic organisms. Specifically, we have shown that it is now possible to provide complete regular updates of profile-profile based fold recognition models for entire eukaryotic proteomes, through the use of Grid middleware such as JYDE.

Introducing meta-services for biomedical information extraction

PubMed Central

Leitner, Florian; Krallinger, Martin; Rodriguez-Penagos, Carlos; Hakenberg, Jörg; Plake, Conrad; Kuo, Cheng-Ju; Hsu, Chun-Nan; Tsai, Richard Tzong-Han; Hung, Hsi-Chuan; Lau, William W; Johnson, Calvin A; Sætre, Rune; Yoshida, Kazuhiro; Chen, Yan Hua; Kim, Sun; Shin, Soo-Yong; Zhang, Byoung-Tak; Baumgartner, William A; Hunter, Lawrence; Haddow, Barry; Matthews, Michael; Wang, Xinglong; Ruch, Patrick; Ehrler, Frédéric; Özgür, Arzucan; Erkan, Güneş; Radev, Dragomir R; Krauthammer, Michael; Luong, ThaiBinh; Hoffmann, Robert; Sander, Chris; Valencia, Alfonso

2008-01-01

We introduce the first meta-service for information extraction in molecular biology, the BioCreative MetaServer (BCMS; ). This prototype platform is a joint effort of 13 research groups and provides automatically generated annotations for PubMed/Medline abstracts. Annotation types cover gene names, gene IDs, species, and protein-protein interactions. The annotations are distributed by the meta-server in both human and machine readable formats (HTML/XML). This service is intended to be used by biomedical researchers and database annotators, and in biomedical language processing. The platform allows direct comparison, unified access, and result aggregation of the annotations. PMID:18834497

Large-scale machine learning and evaluation platform for real-time traffic surveillance

NASA Astrophysics Data System (ADS)

Eichel, Justin A.; Mishra, Akshaya; Miller, Nicholas; Jankovic, Nicholas; Thomas, Mohan A.; Abbott, Tyler; Swanson, Douglas; Keller, Joel

2016-09-01

In traffic engineering, vehicle detectors are trained on limited datasets, resulting in poor accuracy when deployed in real-world surveillance applications. Annotating large-scale high-quality datasets is challenging. Typically, these datasets have limited diversity; they do not reflect the real-world operating environment. There is a need for a large-scale, cloud-based positive and negative mining process and a large-scale learning and evaluation system for the application of automatic traffic measurements and classification. The proposed positive and negative mining process addresses the quality of crowd sourced ground truth data through machine learning review and human feedback mechanisms. The proposed learning and evaluation system uses a distributed cloud computing framework to handle data-scaling issues associated with large numbers of samples and a high-dimensional feature space. The system is trained using AdaBoost on 1,000,000 Haar-like features extracted from 70,000 annotated video frames. The trained real-time vehicle detector achieves an accuracy of at least 95% for 1/2 and about 78% for 19/20 of the time when tested on ˜7,500,000 video frames. At the end of 2016, the dataset is expected to have over 1 billion annotated video frames.

Evaluation of web-based annotation of ophthalmic images for multicentric clinical trials.

PubMed

Chalam, K V; Jain, P; Shah, V A; Shah, Gaurav Y

2006-06-01

An Internet browser-based annotation system can be used to identify and describe features in digitalized retinal images, in multicentric clinical trials, in real time. In this web-based annotation system, the user employs a mouse to draw and create annotations on a transparent layer, that encapsulates the observations and interpretations of a specific image. Multiple annotation layers may be overlaid on a single image. These layers may correspond to annotations by different users on the same image or annotations of a temporal sequence of images of a disease process, over a period of time. In addition, geometrical properties of annotated figures may be computed and measured. The annotations are stored in a central repository database on a server, which can be retrieved by multiple users in real time. This system facilitates objective evaluation of digital images and comparison of double-blind readings of digital photographs, with an identifiable audit trail. Annotation of ophthalmic images allowed clinically feasible and useful interpretation to track properties of an area of fundus pathology. This provided an objective method to monitor properties of pathologies over time, an essential component of multicentric clinical trials. The annotation system also allowed users to view stereoscopic images that are stereo pairs. This web-based annotation system is useful and valuable in monitoring patient care, in multicentric clinical trials, telemedicine, teaching and routine clinical settings.

A method for integrating and ranking the evidence for biochemical pathways by mining reactions from text

PubMed Central

Miwa, Makoto; Ohta, Tomoko; Rak, Rafal; Rowley, Andrew; Kell, Douglas B.; Pyysalo, Sampo; Ananiadou, Sophia

2013-01-01

Motivation: To create, verify and maintain pathway models, curators must discover and assess knowledge distributed over the vast body of biological literature. Methods supporting these tasks must understand both the pathway model representations and the natural language in the literature. These methods should identify and order documents by relevance to any given pathway reaction. No existing system has addressed all aspects of this challenge. Method: We present novel methods for associating pathway model reactions with relevant publications. Our approach extracts the reactions directly from the models and then turns them into queries for three text mining-based MEDLINE literature search systems. These queries are executed, and the resulting documents are combined and ranked according to their relevance to the reactions of interest. We manually annotate document-reaction pairs with the relevance of the document to the reaction and use this annotation to study several ranking methods, using various heuristic and machine-learning approaches. Results: Our evaluation shows that the annotated document-reaction pairs can be used to create a rule-based document ranking system, and that machine learning can be used to rank documents by their relevance to pathway reactions. We find that a Support Vector Machine-based system outperforms several baselines and matches the performance of the rule-based system. The success of the query extraction and ranking methods are used to update our existing pathway search system, PathText. Availability: An online demonstration of PathText 2 and the annotated corpus are available for research purposes at http://www.nactem.ac.uk/pathtext2/. Contact: makoto.miwa@manchester.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23813008

Non-Gaussian Distributions Affect Identification of Expression Patterns, Functional Annotation, and Prospective Classification in Human Cancer Genomes

PubMed Central

Marko, Nicholas F.; Weil, Robert J.

2012-01-01

Introduction Gene expression data is often assumed to be normally-distributed, but this assumption has not been tested rigorously. We investigate the distribution of expression data in human cancer genomes and study the implications of deviations from the normal distribution for translational molecular oncology research. Methods We conducted a central moments analysis of five cancer genomes and performed empiric distribution fitting to examine the true distribution of expression data both on the complete-experiment and on the individual-gene levels. We used a variety of parametric and nonparametric methods to test the effects of deviations from normality on gene calling, functional annotation, and prospective molecular classification using a sixth cancer genome. Results Central moments analyses reveal statistically-significant deviations from normality in all of the analyzed cancer genomes. We observe as much as 37% variability in gene calling, 39% variability in functional annotation, and 30% variability in prospective, molecular tumor subclassification associated with this effect. Conclusions Cancer gene expression profiles are not normally-distributed, either on the complete-experiment or on the individual-gene level. Instead, they exhibit complex, heavy-tailed distributions characterized by statistically-significant skewness and kurtosis. The non-Gaussian distribution of this data affects identification of differentially-expressed genes, functional annotation, and prospective molecular classification. These effects may be reduced in some circumstances, although not completely eliminated, by using nonparametric analytics. This analysis highlights two unreliable assumptions of translational cancer gene expression analysis: that “small” departures from normality in the expression data distributions are analytically-insignificant and that “robust” gene-calling algorithms can fully compensate for these effects. PMID:23118863

Discovery of numerous novel small genes in the intergenic regions of the Escherichia coli O157:H7 Sakai genome

PubMed Central

Hücker, Sarah M.; Ardern, Zachary; Goldberg, Tatyana; Schafferhans, Andrea; Bernhofer, Michael; Vestergaard, Gisle; Nelson, Chase W.; Schloter, Michael; Rost, Burkhard; Scherer, Siegfried

2017-01-01

In the past, short protein-coding genes were often disregarded by genome annotation pipelines. Transcriptome sequencing (RNAseq) signals outside of annotated genes have usually been interpreted to indicate either ncRNA or pervasive transcription. Therefore, in addition to the transcriptome, the translatome (RIBOseq) of the enteric pathogen Escherichia coli O157:H7 strain Sakai was determined at two optimal growth conditions and a severe stress condition combining low temperature and high osmotic pressure. All intergenic open reading frames potentially encoding a protein of ≥ 30 amino acids were investigated with regard to coverage by transcription and translation signals and their translatability expressed by the ribosomal coverage value. This led to discovery of 465 unique, putative novel genes not yet annotated in this E. coli strain, which are evenly distributed over both DNA strands of the genome. For 255 of the novel genes, annotated homologs in other bacteria were found, and a machine-learning algorithm, trained on small protein-coding E. coli genes, predicted that 89% of these translated open reading frames represent bona fide genes. The remaining 210 putative novel genes without annotated homologs were compared to the 255 novel genes with homologs and to 250 short annotated genes of this E. coli strain. All three groups turned out to be similar with respect to their translatability distribution, fractions of differentially regulated genes, secondary structure composition, and the distribution of evolutionary constraint, suggesting that both novel groups represent legitimate genes. However, the machine-learning algorithm only recognized a small fraction of the 210 genes without annotated homologs. It is possible that these genes represent a novel group of genes, which have unusual features dissimilar to the genes of the machine-learning algorithm training set. PMID:28902868

Ergatis: a web interface and scalable software system for bioinformatics workflows

PubMed Central

Orvis, Joshua; Crabtree, Jonathan; Galens, Kevin; Gussman, Aaron; Inman, Jason M.; Lee, Eduardo; Nampally, Sreenath; Riley, David; Sundaram, Jaideep P.; Felix, Victor; Whitty, Brett; Mahurkar, Anup; Wortman, Jennifer; White, Owen; Angiuoli, Samuel V.

2010-01-01

Motivation: The growth of sequence data has been accompanied by an increasing need to analyze data on distributed computer clusters. The use of these systems for routine analysis requires scalable and robust software for data management of large datasets. Software is also needed to simplify data management and make large-scale bioinformatics analysis accessible and reproducible to a wide class of target users. Results: We have developed a workflow management system named Ergatis that enables users to build, execute and monitor pipelines for computational analysis of genomics data. Ergatis contains preconfigured components and template pipelines for a number of common bioinformatics tasks such as prokaryotic genome annotation and genome comparisons. Outputs from many of these components can be loaded into a Chado relational database. Ergatis was designed to be accessible to a broad class of users and provides a user friendly, web-based interface. Ergatis supports high-throughput batch processing on distributed compute clusters and has been used for data management in a number of genome annotation and comparative genomics projects. Availability: Ergatis is an open-source project and is freely available at http://ergatis.sourceforge.net Contact: jorvis@users.sourceforge.net PMID:20413634

Filtered Push: Annotating Distributed Data for Quality Control and Fitness for Use Analysis

NASA Astrophysics Data System (ADS)

Morris, P. J.; Kelly, M. A.; Lowery, D. B.; Macklin, J. A.; Morris, R. A.; Tremonte, D.; Wang, Z.

2009-12-01

The single greatest problem with the federation of scientific data is the assessment of the quality and validity of the aggregated data in the context of particular research problems, that is, its fitness for use. There are three critical data quality issues in networks of distributed natural science collections data, as in all scientific data: identifying and correcting errors, maintaining currency, and assessing fitness for use. To this end, we have designed and implemented a prototype network in the domain of natural science collections. This prototype is built over the open source Map-Reduce platform Hadoop with a network client in the open source collections management system Specify 6. We call this network “Filtered Push” as, at its core, annotations are pushed from the network edges to relevant authoritative repositories, where humans and software filter the annotations before accepting them as changes to the authoritative data. The Filtered Push software is a domain-neutral framework for originating, distributing, and analyzing record-level annotations. Network participants can subscribe to notifications arising from ontology-based analyses of new annotations or of purpose-built queries against the network's global history of annotations. Quality and fitness for use of distributed natural science collections data can be addressed with Filtered Push software by implementing a network that allows data providers and consumers to define potential errors in data, develop metrics for those errors, specify workflows to analyze distributed data to detect potential errors, and to close the quality management cycle by providing a network architecture to pushing assertions about data quality such as corrections back to the curators of the participating data sets. Quality issues in distributed scientific data have several things in common: (1) Statements about data quality should be regarded as hypotheses about inconsistencies between perhaps several records, data sets, or practices of science. (2) Data quality problems often cannot be detected only from internal statistical correlations or logical analysis, but may need the application of defined workflows that signal illogical output. (3) Changes in scientific theory or practice over time can result in changes of what QC tests should be applied to legacy data. (4) The frequency of some classes of error in a data set may be identifiable without the ability to assert that a particular record is in error. To address these issues requires, as does science itself, framing QC hypotheses against data that may be anywhere and may arise at any time in the future. In short, QC for science data is a never ending process. It must provide for notice to an agent (human or software) that a given dataset supports a hypothesis of inconsistency with a current scientific resource or model, or with potential generalizations of the concepts in a metadata ontology. Like quality control in general, quality control of distributed data is a repeated cyclical process. In implementing a Filtered Push network for quality control, we have a model in which the cost of QC forever is not substantially greater than QC once.

Aspects, Wrappers and Events

NASA Technical Reports Server (NTRS)

Filman, Robert E.

2003-01-01

This viewgraph presentation provides information on Object Infrastructure Framework (OIF), an Aspect-Oriented Programming (AOP) system. The presentation begins with an introduction to the difficulties and requirements of distributed computing, including functional and non-functional requirements (ilities). The architecture of Distributed Object Technology includes stubs, proxies for implementation objects, and skeletons, proxies for client applications. The key OIF ideas (injecting behavior, annotated communications, thread contexts, and pragma) are discussed. OIF is an AOP mechanism; AOP is centered on: 1) Separate expression of crosscutting concerns; 2) Mechanisms to weave the separate expressions into a unified system. AOP is software engineering technology for separately expressing systematic properties while nevertheless producing running systems that embody these properties.

Prospective study of automated versus manual annotation of early time-lapse markers in the human preimplantation embryo.

PubMed

Kaser, Daniel J; Farland, Leslie V; Missmer, Stacey A; Racowsky, Catherine

2017-08-01

How does automated time-lapse annotation (Eeva™) compare to manual annotation of the same video images performed by embryologists certified in measuring durations of the 2-cell (P2; time to the 3-cell minus time to the 2-cell, or t3-t2) and 3-cell (P3; time to 4-cell minus time to the 3-cell, or t4-t3) stages? Manual annotation was superior to the automated annotation provided by Eeva™ version 2.2, because manual annotation assigned a rating to a higher proportion of embryos and yielded a greater sensitivity for blastocyst prediction than automated annotation. While use of the Eeva™ test has been shown to improve an embryologist's ability to predict blastocyst formation compared to Day 3 morphology alone, the accuracy of the automated image analysis employed by the Eeva™ system has never been compared to manual annotation of the same time-lapse markers by a trained embryologist. We conducted a prospective cohort study of embryos (n = 1477) cultured in the Eeva™ system (n = 8 microscopes) at our institution from August 2014 to February 2016. Embryos were assigned a blastocyst prediction rating of High (H), Medium (M), Low (L), or Not Rated (NR) by Eeva™ version 2.2 according to P2 and P3. An embryologist from a team of 10, then manually annotated each embryo and if the automated and manual ratings differed, a second embryologist independently annotated the embryo. If both embryologists disagreed with the automated Eeva™ rating, then the rating was classified as discordant. If the second embryologist agreed with the automated Eeva™ score, the rating was not considered discordant. Spearman's correlation (ρ), weighted kappa statistics and the intra-class correlation (ICC) coefficients with 95% confidence intervals (CI) between Eeva™ and manual annotation were calculated, as were the proportions of discordant embryos, and the sensitivity, specificity, positive predictive value (PPV) and NPV of each method for blastocyst prediction. The distribution of H, M and L ratings differed by annotation method (P < 0.0001). The correlation between Eeva™ and manual annotation was higher for P2 (ρ = 0.75; ICC = 0.82; 95% CI 0.82-0.83) than for P3 (ρ = 0.39; ICC = 0.20; 95% CI 0.16-0.26). Eeva™ was more likely than an embryologist to rate an embryo as NR (11.1% vs. 3.0%, P < 0.0001). Discordance occurred in 30.0% (443/1477) of all embryos and was not associated with factors such as Day 3 cell number, fragmentation, symmetry or presence of abnormal cleavage. Rather, discordance was associated with direct cleavage (P2 ≤ 5 h) and short P3 (≤0.25 h), and also factors intrinsic to the Eeva™ system, such as the automated rating (proportion of discordant embryos by rating: H: 9.3%; M: 18.1%; L: 41.3%; NR: 31.4%; P < 0.0001), microwell location (peripheral: 31.2%; central: 23.8%; P = 0.02) and Eeva™ microscope (n = 8; range 22.9-42.6%; P < 0.0001). Manual annotation upgraded 82.6% of all discordant embryos from a lower to a higher rating, and improved the sensitivity for predicting blastocyst formation. One team of embryologists performed the manual annotations; however, the study staff was trained and certified by the company sponsor. Only two time-lapse markers were evaluated, so the results are not generalizable to other parameters; likewise, the results are not generalizable to future versions of Eeva™ or other automated image analysis systems. Based on the proportion of discordance and the improved performance of manual annotation, clinics using the Eeva™ system should consider manual annotation of P2 and P3 to confirm the automated ratings generated by Eeva™. These data were acquired in a study funded by Progyny, Inc. There are no competing interests. N/A. © The Author 2017. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Inter-Annotator Agreement and the Upper Limit on Machine Performance: Evidence from Biomedical Natural Language Processing.

PubMed

Boguslav, Mayla; Cohen, Kevin Bretonnel

2017-01-01

Human-annotated data is a fundamental part of natural language processing system development and evaluation. The quality of that data is typically assessed by calculating the agreement between the annotators. It is widely assumed that this agreement between annotators is the upper limit on system performance in natural language processing: if humans can't agree with each other about the classification more than some percentage of the time, we don't expect a computer to do any better. We trace the logical positivist roots of the motivation for measuring inter-annotator agreement, demonstrate the prevalence of the widely-held assumption about the relationship between inter-annotator agreement and system performance, and present data that suggest that inter-annotator agreement is not, in fact, an upper bound on language processing system performance.

Next Generation Models for Storage and Representation of Microbial Biological Annotation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Quest, Daniel J; Land, Miriam L; Brettin, Thomas S

2010-01-01

Background Traditional genome annotation systems were developed in a very different computing era, one where the World Wide Web was just emerging. Consequently, these systems are built as centralized black boxes focused on generating high quality annotation submissions to GenBank/EMBL supported by expert manual curation. The exponential growth of sequence data drives a growing need for increasingly higher quality and automatically generated annotation. Typical annotation pipelines utilize traditional database technologies, clustered computing resources, Perl, C, and UNIX file systems to process raw sequence data, identify genes, and predict and categorize gene function. These technologies tightly couple the annotation software systemmore » to hardware and third party software (e.g. relational database systems and schemas). This makes annotation systems hard to reproduce, inflexible to modification over time, difficult to assess, difficult to partition across multiple geographic sites, and difficult to understand for those who are not domain experts. These systems are not readily open to scrutiny and therefore not scientifically tractable. The advent of Semantic Web standards such as Resource Description Framework (RDF) and OWL Web Ontology Language (OWL) enables us to construct systems that address these challenges in a new comprehensive way. Results Here, we develop a framework for linking traditional data to OWL-based ontologies in genome annotation. We show how data standards can decouple hardware and third party software tools from annotation pipelines, thereby making annotation pipelines easier to reproduce and assess. An illustrative example shows how TURTLE (Terse RDF Triple Language) can be used as a human readable, but also semantically-aware, equivalent to GenBank/EMBL files. Conclusions The power of this approach lies in its ability to assemble annotation data from multiple databases across multiple locations into a representation that is understandable to researchers. In this way, all researchers, experimental and computational, will more easily understand the informatics processes constructing genome annotation and ultimately be able to help improve the systems that produce them.« less

Spot: A Programming Language for Verified Flight Software

NASA Technical Reports Server (NTRS)

Bocchino, Robert L., Jr.; Gamble, Edward; Gostelow, Kim P.; Some, Raphael R.

2014-01-01

The C programming language is widely used for programming space flight software and other safety-critical real time systems. C, however, is far from ideal for this purpose: as is well known, it is both low-level and unsafe. This paper describes Spot, a language derived from C for programming space flight systems. Spot aims to maintain compatibility with existing C code while improving the language and supporting verification with the SPIN model checker. The major features of Spot include actor-based concurrency, distributed state with message passing and transactional updates, and annotations for testing and verification. Spot also supports domain-specific annotations for managing spacecraft state, e.g., communicating telemetry information to the ground. We describe the motivation and design rationale for Spot, give an overview of the design, provide examples of Spot's capabilities, and discuss the current status of the implementation.

HAMAP in 2013, new developments in the protein family classification and annotation system

PubMed Central

Pedruzzi, Ivo; Rivoire, Catherine; Auchincloss, Andrea H.; Coudert, Elisabeth; Keller, Guillaume; de Castro, Edouard; Baratin, Delphine; Cuche, Béatrice A.; Bougueleret, Lydie; Poux, Sylvain; Redaschi, Nicole; Xenarios, Ioannis; Bridge, Alan

2013-01-01

HAMAP (High-quality Automated and Manual Annotation of Proteins—available at http://hamap.expasy.org/) is a system for the classification and annotation of protein sequences. It consists of a collection of manually curated family profiles for protein classification, and associated annotation rules that specify annotations that apply to family members. HAMAP was originally developed to support the manual curation of UniProtKB/Swiss-Prot records describing microbial proteins. Here we describe new developments in HAMAP, including the extension of HAMAP to eukaryotic proteins, the use of HAMAP in the automated annotation of UniProtKB/TrEMBL, providing high-quality annotation for millions of protein sequences, and the future integration of HAMAP into a unified system for UniProtKB annotation, UniRule. HAMAP is continuously updated by expert curators with new family profiles and annotation rules as new protein families are characterized. The collection of HAMAP family classification profiles and annotation rules can be browsed and viewed on the HAMAP website, which also provides an interface to scan user sequences against HAMAP profiles. PMID:23193261

Annotating images by mining image search results.

PubMed

Wang, Xin-Jing; Zhang, Lei; Li, Xirong; Ma, Wei-Ying

2008-11-01

Although it has been studied for years by the computer vision and machine learning communities, image annotation is still far from practical. In this paper, we propose a novel attempt at model-free image annotation, which is a data-driven approach that annotates images by mining their search results. Some 2.4 million images with their surrounding text are collected from a few photo forums to support this approach. The entire process is formulated in a divide-and-conquer framework where a query keyword is provided along with the uncaptioned image to improve both the effectiveness and efficiency. This is helpful when the collected data set is not dense everywhere. In this sense, our approach contains three steps: 1) the search process to discover visually and semantically similar search results, 2) the mining process to identify salient terms from textual descriptions of the search results, and 3) the annotation rejection process to filter out noisy terms yielded by Step 2. To ensure real-time annotation, two key techniques are leveraged-one is to map the high-dimensional image visual features into hash codes, the other is to implement it as a distributed system, of which the search and mining processes are provided as Web services. As a typical result, the entire process finishes in less than 1 second. Since no training data set is required, our approach enables annotating with unlimited vocabulary and is highly scalable and robust to outliers. Experimental results on both real Web images and a benchmark image data set show the effectiveness and efficiency of the proposed algorithm. It is also worth noting that, although the entire approach is illustrated within the divide-and conquer framework, a query keyword is not crucial to our current implementation. We provide experimental results to prove this.

A proposal for the annotation of recurrent colorectal cancer: the 'Sheffield classification'.

PubMed

Majeed, A W; Shorthouse, A J; Blakeborough, A; Bird, N C

2011-11-01

Current classification systems of large bowel cancer only refer to metastatic disease as M0, M1 or Mx. Recurrent colorectal cancer primarily occurs in the liver, lungs, nodes or peritoneum. The management of each of these sites of recurrence has made significant advances and each is a subspecialty in its own right. The aim of this paper was to devise a classification system which accurately describes the site and extent of metastatic spread. An amendment of the current system is proposed in which liver, lung and peritoneal metastases are annotated by 'Liv 0,1', 'Pul 0,1' and 'Per 0,1' in describing the primary presentation. These are then subclassified, taking into account the chronology, size, number and geographical distribution of metastatic disease or logoregional recurrence and its K-Ras status. This discussion document proposes a classification system which is logical and simple to use. We plan to validate it prospectively. © 2011 The Authors. Colorectal Disease © 2011 The Association of Coloproctology of Great Britain and Ireland.

An approach to describing and analysing bulk biological annotation quality: a case study using UniProtKB.

PubMed

Bell, Michael J; Gillespie, Colin S; Swan, Daniel; Lord, Phillip

2012-09-15

Annotations are a key feature of many biological databases, used to convey our knowledge of a sequence to the reader. Ideally, annotations are curated manually, however manual curation is costly, time consuming and requires expert knowledge and training. Given these issues and the exponential increase of data, many databases implement automated annotation pipelines in an attempt to avoid un-annotated entries. Both manual and automated annotations vary in quality between databases and annotators, making assessment of annotation reliability problematic for users. The community lacks a generic measure for determining annotation quality and correctness, which we look at addressing within this article. Specifically we investigate word reuse within bulk textual annotations and relate this to Zipf's Principle of Least Effort. We use the UniProt Knowledgebase (UniProtKB) as a case study to demonstrate this approach since it allows us to compare annotation change, both over time and between automated and manually curated annotations. By applying power-law distributions to word reuse in annotation, we show clear trends in UniProtKB over time, which are consistent with existing studies of quality on free text English. Further, we show a clear distinction between manual and automated analysis and investigate cohorts of protein records as they mature. These results suggest that this approach holds distinct promise as a mechanism for judging annotation quality. Source code is available at the authors website: http://homepages.cs.ncl.ac.uk/m.j.bell1/annotation. phillip.lord@newcastle.ac.uk.

ProbOnto: ontology and knowledge base of probability distributions.

PubMed

Swat, Maciej J; Grenon, Pierre; Wimalaratne, Sarala

2016-09-01

Probability distributions play a central role in mathematical and statistical modelling. The encoding, annotation and exchange of such models could be greatly simplified by a resource providing a common reference for the definition of probability distributions. Although some resources exist, no suitably detailed and complex ontology exists nor any database allowing programmatic access. ProbOnto, is an ontology-based knowledge base of probability distributions, featuring more than 80 uni- and multivariate distributions with their defining functions, characteristics, relationships and re-parameterization formulas. It can be used for model annotation and facilitates the encoding of distribution-based models, related functions and quantities. http://probonto.org mjswat@ebi.ac.uk Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ames, L.L.; Rai, D.; Serne, R.J.

The annotated bibliography is divided into sections on chemistry and geochemistry, migration and accumulation, cultural distributions, natural distributions, and bibliographies and annual reviews. (LK)

Design and Applications of a GeoSemantic Framework for Integration of Data and Model Resources in Hydrologic Systems

NASA Astrophysics Data System (ADS)

Elag, M.; Kumar, P.

2016-12-01

Hydrologists today have to integrate resources such as data and models, which originate and reside in multiple autonomous and heterogeneous repositories over the Web. Several resource management systems have emerged within geoscience communities for sharing long-tail data, which are collected by individual or small research groups, and long-tail models, which are developed by scientists or small modeling communities. While these systems have increased the availability of resources within geoscience domains, deficiencies remain due to the heterogeneity in the methods, which are used to describe, encode, and publish information about resources over the Web. This heterogeneity limits our ability to access the right information in the right context so that it can be efficiently retrieved and understood without the Hydrologist's mediation. A primary challenge of the Web today is the lack of the semantic interoperability among the massive number of resources, which already exist and are continually being generated at rapid rates. To address this challenge, we have developed a decentralized GeoSemantic (GS) framework, which provides three sets of micro-web services to support (i) semantic annotation of resources, (ii) semantic alignment between the metadata of two resources, and (iii) semantic mediation among Standard Names. Here we present the design of the framework and demonstrate its application for semantic integration between data and models used in the IML-CZO. First we show how the IML-CZO data are annotated using the Semantic Annotation Services. Then we illustrate how the Resource Alignment Services and Knowledge Integration Services are used to create a semantic workflow among TopoFlow model, which is a spatially-distributed hydrologic model and the annotated data. Results of this work are (i) a demonstration of how the GS framework advances the integration of heterogeneous data and models of water-related disciplines by seamless handling of their semantic heterogeneity, (ii) an introduction of new paradigm for reusing existing and new standards as well as tools and models without the need of their implementation in the Cyberinfrastructures of water-related disciplines, and (iii) an investigation of a methodology by which distributed models can be coupled in a workflow using the GS services.

Performance and Architecture Lab Modeling Tool

DOE Office of Scientific and Technical Information (OSTI.GOV)

2014-06-19

Analytical application performance models are critical for diagnosing performance-limiting resources, optimizing systems, and designing machines. Creating models, however, is difficult. Furthermore, models are frequently expressed in forms that are hard to distribute and validate. The Performance and Architecture Lab Modeling tool, or Palm, is a modeling tool designed to make application modeling easier. Palm provides a source code modeling annotation language. Not only does the modeling language divide the modeling task into sub problems, it formally links an application's source code with its model. This link is important because a model's purpose is to capture application behavior. Furthermore, this linkmore » makes it possible to define rules for generating models according to source code organization. Palm generates hierarchical models according to well-defined rules. Given an application, a set of annotations, and a representative execution environment, Palm will generate the same model. A generated model is a an executable program whose constituent parts directly correspond to the modeled application. Palm generates models by combining top-down (human-provided) semantic insight with bottom-up static and dynamic analysis. A model's hierarchy is defined by static and dynamic source code structure. Because Palm coordinates models and source code, Palm's models are 'first-class' and reproducible. Palm automates common modeling tasks. For instance, Palm incorporates measurements to focus attention, represent constant behavior, and validate models. Palm's workflow is as follows. The workflow's input is source code annotated with Palm modeling annotations. The most important annotation models an instance of a block of code. Given annotated source code, the Palm Compiler produces executables and the Palm Monitor collects a representative performance profile. The Palm Generator synthesizes a model based on the static and dynamic mapping of annotations to program behavior. The model -- an executable program -- is a hierarchical composition of annotation functions, synthesized functions, statistics for runtime values, and performance measurements.« less

Automated clinical annotation of tissue bank specimens.

PubMed

Gilbertson, John R; Gupta, Rajnish; Nie, Yimin; Patel, Ashokkumar A; Becich, Michael J

2004-01-01

Modern, molecular bio-medicine is driving a growing demand for extensively annotated tissue bank specimens. With careful clinical, pathologic and outcomes annotation, samples can be better matched to the research question at hand and experimental results better understood and verified. However, the difficulty and expense of detailed specimen annotation is well beyond the capability of most banks and has made access to well documented tissue a major limitation in medical re-search. In this context, we have implemented automated annotation of banked tissue by integrating data from three clinical systems--the cancer registry, the pathology LIS and the tissue bank inventory system--through a classical data warehouse environment. The project required modification of clinical systems, development of methods to identify patients between and map data elements across systems and the creation of de-identified data in data marts for use by researchers. The result has been much more extensive and accurate initial tissue annotation with less effort in the tissue bank, as well as dynamic ongoing annotation as the cancer registry follows patients over time.

Evaluating Hierarchical Structure in Music Annotations

PubMed Central

McFee, Brian; Nieto, Oriol; Farbood, Morwaread M.; Bello, Juan Pablo

2017-01-01

Music exhibits structure at multiple scales, ranging from motifs to large-scale functional components. When inferring the structure of a piece, different listeners may attend to different temporal scales, which can result in disagreements when they describe the same piece. In the field of music informatics research (MIR), it is common to use corpora annotated with structural boundaries at different levels. By quantifying disagreements between multiple annotators, previous research has yielded several insights relevant to the study of music cognition. First, annotators tend to agree when structural boundaries are ambiguous. Second, this ambiguity seems to depend on musical features, time scale, and genre. Furthermore, it is possible to tune current annotation evaluation metrics to better align with these perceptual differences. However, previous work has not directly analyzed the effects of hierarchical structure because the existing methods for comparing structural annotations are designed for “flat” descriptions, and do not readily generalize to hierarchical annotations. In this paper, we extend and generalize previous work on the evaluation of hierarchical descriptions of musical structure. We derive an evaluation metric which can compare hierarchical annotations holistically across multiple levels. sing this metric, we investigate inter-annotator agreement on the multilevel annotations of two different music corpora, investigate the influence of acoustic properties on hierarchical annotations, and evaluate existing hierarchical segmentation algorithms against the distribution of inter-annotator agreement. PMID:28824514

Improved methods and resources for paramecium genomics: transcription units, gene annotation and gene expression.

PubMed

Arnaiz, Olivier; Van Dijk, Erwin; Bétermier, Mireille; Lhuillier-Akakpo, Maoussi; de Vanssay, Augustin; Duharcourt, Sandra; Sallet, Erika; Gouzy, Jérôme; Sperling, Linda

2017-06-26

The 15 sibling species of the Paramecium aurelia cryptic species complex emerged after a whole genome duplication that occurred tens of millions of years ago. Given extensive knowledge of the genetics and epigenetics of Paramecium acquired over the last century, this species complex offers a uniquely powerful system to investigate the consequences of whole genome duplication in a unicellular eukaryote as well as the genetic and epigenetic mechanisms that drive speciation. High quality Paramecium gene models are important for research using this system. The major aim of the work reported here was to build an improved gene annotation pipeline for the Paramecium lineage. We generated oriented RNA-Seq transcriptome data across the sexual process of autogamy for the model species Paramecium tetraurelia. We determined, for the first time in a ciliate, candidate P. tetraurelia transcription start sites using an adapted Cap-Seq protocol. We developed TrUC, multi-threaded Perl software that in conjunction with TopHat mapping of RNA-Seq data to a reference genome, predicts transcription units for the annotation pipeline. We used EuGene software to combine annotation evidence. The high quality gene structural annotations obtained for P. tetraurelia were used as evidence to improve published annotations for 3 other Paramecium species. The RNA-Seq data were also used for differential gene expression analysis, providing a gene expression atlas that is more sensitive than the previously established microarray resource. We have developed a gene annotation pipeline tailored for the compact genomes and tiny introns of Paramecium species. A novel component of this pipeline, TrUC, predicts transcription units using Cap-Seq and oriented RNA-Seq data. TrUC could prove useful beyond Paramecium, especially in the case of high gene density. Accurate predictions of 3' and 5' UTR will be particularly valuable for studies of gene expression (e.g. nucleosome positioning, identification of cis regulatory motifs). The P. tetraurelia improved transcriptome resource, gene annotations for P. tetraurelia, P. biaurelia, P. sexaurelia and P. caudatum, and Paramecium-trained EuGene configuration are available through ParameciumDB ( http://paramecium.i2bc.paris-saclay.fr ). TrUC software is freely distributed under a GNU GPL v3 licence ( https://github.com/oarnaiz/TrUC ).

Is the Juice Worth the Squeeze? Costs and Benefits of Multiple Human Annotators for Clinical Text De-identification.

PubMed

Carrell, David S; Cronkite, David J; Malin, Bradley A; Aberdeen, John S; Hirschman, Lynette

2016-08-05

Clinical text contains valuable information but must be de-identified before it can be used for secondary purposes. Accurate annotation of personally identifiable information (PII) is essential to the development of automated de-identification systems and to manual redaction of PII. Yet the accuracy of annotations may vary considerably across individual annotators and annotation is costly. As such, the marginal benefit of incorporating additional annotators has not been well characterized. This study models the costs and benefits of incorporating increasing numbers of independent human annotators to identify the instances of PII in a corpus. We used a corpus with gold standard annotations to evaluate the performance of teams of annotators of increasing size. Four annotators independently identified PII in a 100-document corpus consisting of randomly selected clinical notes from Family Practice clinics in a large integrated health care system. These annotations were pooled and validated to generate a gold standard corpus for evaluation. Recall rates for all PII types ranged from 0.90 to 0.98 for individual annotators to 0.998 to 1.0 for teams of three, when meas-ured against the gold standard. Median cost per PII instance discovered during corpus annotation ranged from $ 0.71 for an individual annotator to $ 377 for annotations discovered only by a fourth annotator. Incorporating a second annotator into a PII annotation process reduces unredacted PII and improves the quality of annotations to 0.99 recall, yielding clear benefit at reasonable cost; the cost advantages of annotation teams larger than two diminish rapidly.

Generation of silver standard concept annotations from biomedical texts with special relevance to phenotypes.

PubMed

Oellrich, Anika; Collier, Nigel; Smedley, Damian; Groza, Tudor

2015-01-01

Electronic health records and scientific articles possess differing linguistic characteristics that may impact the performance of natural language processing tools developed for one or the other. In this paper, we investigate the performance of four extant concept recognition tools: the clinical Text Analysis and Knowledge Extraction System (cTAKES), the National Center for Biomedical Ontology (NCBO) Annotator, the Biomedical Concept Annotation System (BeCAS) and MetaMap. Each of the four concept recognition systems is applied to four different corpora: the i2b2 corpus of clinical documents, a PubMed corpus of Medline abstracts, a clinical trails corpus and the ShARe/CLEF corpus. In addition, we assess the individual system performances with respect to one gold standard annotation set, available for the ShARe/CLEF corpus. Furthermore, we built a silver standard annotation set from the individual systems' output and assess the quality as well as the contribution of individual systems to the quality of the silver standard. Our results demonstrate that mainly the NCBO annotator and cTAKES contribute to the silver standard corpora (F1-measures in the range of 21% to 74%) and their quality (best F1-measure of 33%), independent from the type of text investigated. While BeCAS and MetaMap can contribute to the precision of silver standard annotations (precision of up to 42%), the F1-measure drops when combined with NCBO Annotator and cTAKES due to a low recall. In conclusion, the performances of individual systems need to be improved independently from the text types, and the leveraging strategies to best take advantage of individual systems' annotations need to be revised. The textual content of the PubMed corpus, accession numbers for the clinical trials corpus, and assigned annotations of the four concept recognition systems as well as the generated silver standard annotation sets are available from http://purl.org/phenotype/resources. The textual content of the ShARe/CLEF (https://sites.google.com/site/shareclefehealth/data) and i2b2 (https://i2b2.org/NLP/DataSets/) corpora needs to be requested with the individual corpus providers.

Wide coverage biomedical event extraction using multiple partially overlapping corpora

PubMed Central

2013-01-01

Background Biomedical events are key to understanding physiological processes and disease, and wide coverage extraction is required for comprehensive automatic analysis of statements describing biomedical systems in the literature. In turn, the training and evaluation of extraction methods requires manually annotated corpora. However, as manual annotation is time-consuming and expensive, any single event-annotated corpus can only cover a limited number of semantic types. Although combined use of several such corpora could potentially allow an extraction system to achieve broad semantic coverage, there has been little research into learning from multiple corpora with partially overlapping semantic annotation scopes. Results We propose a method for learning from multiple corpora with partial semantic annotation overlap, and implement this method to improve our existing event extraction system, EventMine. An evaluation using seven event annotated corpora, including 65 event types in total, shows that learning from overlapping corpora can produce a single, corpus-independent, wide coverage extraction system that outperforms systems trained on single corpora and exceeds previously reported results on two established event extraction tasks from the BioNLP Shared Task 2011. Conclusions The proposed method allows the training of a wide-coverage, state-of-the-art event extraction system from multiple corpora with partial semantic annotation overlap. The resulting single model makes broad-coverage extraction straightforward in practice by removing the need to either select a subset of compatible corpora or semantic types, or to merge results from several models trained on different individual corpora. Multi-corpus learning also allows annotation efforts to focus on covering additional semantic types, rather than aiming for exhaustive coverage in any single annotation effort, or extending the coverage of semantic types annotated in existing corpora. PMID:23731785

RELATIONSHIP BETWEEN PHYLOGENETIC DISTRIBUTION AND GENOMIC FEATURES IN NEUROSPORA CRASSA

USDA-ARS?s Scientific Manuscript database

In the post-genome era, insufficient functional annotation of predicted genes greatly restricts the potential of mining genome data. We demonstrate that an evolutionary approach, which is independent of functional annotation, has great potential as a tool for genome analysis. We chose the genome o...

An annotation system for 3D fluid flow visualization

NASA Technical Reports Server (NTRS)

Loughlin, Maria M.; Hughes, John F.

1995-01-01

Annotation is a key activity of data analysis. However, current systems for data analysis focus almost exclusively on visualization. We propose a system which integrates annotations into a visualization system. Annotations are embedded in 3D data space, using the Post-it metaphor. This embedding allows contextual-based information storage and retrieval, and facilitates information sharing in collaborative environments. We provide a traditional database filter and a Magic Lens filter to create specialized views of the data. The system has been customized for fluid flow applications, with features which allow users to store parameters of visualization tools and sketch 3D volumes.

Chado controller: advanced annotation management with a community annotation system.

PubMed

Guignon, Valentin; Droc, Gaëtan; Alaux, Michael; Baurens, Franc-Christophe; Garsmeur, Olivier; Poiron, Claire; Carver, Tim; Rouard, Mathieu; Bocs, Stéphanie

2012-04-01

We developed a controller that is compliant with the Chado database schema, GBrowse and genome annotation-editing tools such as Artemis and Apollo. It enables the management of public and private data, monitors manual annotation (with controlled vocabularies, structural and functional annotation controls) and stores versions of annotation for all modified features. The Chado controller uses PostgreSQL and Perl. The Chado Controller package is available for download at http://www.gnpannot.org/content/chado-controller and runs on any Unix-like operating system, and documentation is available at http://www.gnpannot.org/content/chado-controller-doc The system can be tested using the GNPAnnot Sandbox at http://www.gnpannot.org/content/gnpannot-sandbox-form valentin.guignon@cirad.fr; stephanie.sidibe-bocs@cirad.fr Supplementary data are available at Bioinformatics online.

Annotation of UAV surveillance video

NASA Astrophysics Data System (ADS)

Howlett, Todd; Robertson, Mark A.; Manthey, Dan; Krol, John

2004-08-01

Significant progress toward the development of a video annotation capability is presented in this paper. Research and development of an object tracking algorithm applicable for UAV video is described. Object tracking is necessary for attaching the annotations to the objects of interest. A methodology and format is defined for encoding video annotations using the SMPTE Key-Length-Value encoding standard. This provides the following benefits: a non-destructive annotation, compliance with existing standards, video playback in systems that are not annotation enabled and support for a real-time implementation. A model real-time video annotation system is also presented, at a high level, using the MPEG-2 Transport Stream as the transmission medium. This work was accomplished to meet the Department of Defense"s (DoD"s) need for a video annotation capability. Current practices for creating annotated products are to capture a still image frame, annotate it using an Electric Light Table application, and then pass the annotated image on as a product. That is not adequate for reporting or downstream cueing. It is too slow and there is a severe loss of information. This paper describes a capability for annotating directly on the video.

Comprehensive phylogenetic analysis of bacterial reverse transcriptases.

PubMed

Toro, Nicolás; Nisa-Martínez, Rafael

2014-01-01

Much less is known about reverse transcriptases (RTs) in prokaryotes than in eukaryotes, with most prokaryotic enzymes still uncharacterized. Two surveys involving BLAST searches for RT genes in prokaryotic genomes revealed the presence of large numbers of diverse, uncharacterized RTs and RT-like sequences. Here, using consistent annotation across all sequenced bacterial species from GenBank and other sources via RAST, available from the PATRIC (Pathogenic Resource Integration Center) platform, we have compiled the data for currently annotated reverse transcriptases from completely sequenced bacterial genomes. RT sequences are broadly distributed across bacterial phyla, but green sulfur bacteria and cyanobacteria have the highest levels of RT sequence diversity (≤85% identity) per genome. By contrast, phylum Actinobacteria, for which a large number of genomes have been sequenced, was found to have a low RT sequence diversity. Phylogenetic analyses revealed that bacterial RTs could be classified into 17 main groups: group II introns, retrons/retron-like RTs, diversity-generating retroelements (DGRs), Abi-like RTs, CRISPR-Cas-associated RTs, group II-like RTs (G2L), and 11 other groups of RTs of unknown function. Proteobacteria had the highest potential functional diversity, as they possessed most of the RT groups. Group II introns and DGRs were the most widely distributed RTs in bacterial phyla. Our results provide insights into bacterial RT phylogeny and the basis for an update of annotation systems based on sequence/domain homology.

Comprehensive Phylogenetic Analysis of Bacterial Reverse Transcriptases

PubMed Central

Toro, Nicolás; Nisa-Martínez, Rafael

2014-01-01

Much less is known about reverse transcriptases (RTs) in prokaryotes than in eukaryotes, with most prokaryotic enzymes still uncharacterized. Two surveys involving BLAST searches for RT genes in prokaryotic genomes revealed the presence of large numbers of diverse, uncharacterized RTs and RT-like sequences. Here, using consistent annotation across all sequenced bacterial species from GenBank and other sources via RAST, available from the PATRIC (Pathogenic Resource Integration Center) platform, we have compiled the data for currently annotated reverse transcriptases from completely sequenced bacterial genomes. RT sequences are broadly distributed across bacterial phyla, but green sulfur bacteria and cyanobacteria have the highest levels of RT sequence diversity (≤85% identity) per genome. By contrast, phylum Actinobacteria, for which a large number of genomes have been sequenced, was found to have a low RT sequence diversity. Phylogenetic analyses revealed that bacterial RTs could be classified into 17 main groups: group II introns, retrons/retron-like RTs, diversity-generating retroelements (DGRs), Abi-like RTs, CRISPR-Cas-associated RTs, group II-like RTs (G2L), and 11 other groups of RTs of unknown function. Proteobacteria had the highest potential functional diversity, as they possessed most of the RT groups. Group II introns and DGRs were the most widely distributed RTs in bacterial phyla. Our results provide insights into bacterial RT phylogeny and the basis for an update of annotation systems based on sequence/domain homology. PMID:25423096

Game-powered machine learning

PubMed Central

Barrington, Luke; Turnbull, Douglas; Lanckriet, Gert

2012-01-01

Searching for relevant content in a massive amount of multimedia information is facilitated by accurately annotating each image, video, or song with a large number of relevant semantic keywords, or tags. We introduce game-powered machine learning, an integrated approach to annotating multimedia content that combines the effectiveness of human computation, through online games, with the scalability of machine learning. We investigate this framework for labeling music. First, a socially-oriented music annotation game called Herd It collects reliable music annotations based on the “wisdom of the crowds.” Second, these annotated examples are used to train a supervised machine learning system. Third, the machine learning system actively directs the annotation games to collect new data that will most benefit future model iterations. Once trained, the system can automatically annotate a corpus of music much larger than what could be labeled using human computation alone. Automatically annotated songs can be retrieved based on their semantic relevance to text-based queries (e.g., “funky jazz with saxophone,” “spooky electronica,” etc.). Based on the results presented in this paper, we find that actively coupling annotation games with machine learning provides a reliable and scalable approach to making searchable massive amounts of multimedia data. PMID:22460786

Game-powered machine learning.

PubMed

Barrington, Luke; Turnbull, Douglas; Lanckriet, Gert

2012-04-24

Searching for relevant content in a massive amount of multimedia information is facilitated by accurately annotating each image, video, or song with a large number of relevant semantic keywords, or tags. We introduce game-powered machine learning, an integrated approach to annotating multimedia content that combines the effectiveness of human computation, through online games, with the scalability of machine learning. We investigate this framework for labeling music. First, a socially-oriented music annotation game called Herd It collects reliable music annotations based on the "wisdom of the crowds." Second, these annotated examples are used to train a supervised machine learning system. Third, the machine learning system actively directs the annotation games to collect new data that will most benefit future model iterations. Once trained, the system can automatically annotate a corpus of music much larger than what could be labeled using human computation alone. Automatically annotated songs can be retrieved based on their semantic relevance to text-based queries (e.g., "funky jazz with saxophone," "spooky electronica," etc.). Based on the results presented in this paper, we find that actively coupling annotation games with machine learning provides a reliable and scalable approach to making searchable massive amounts of multimedia data.

Systems Theory and Communication. Annotated Bibliography.

ERIC Educational Resources Information Center

Covington, William G., Jr.

This annotated bibliography presents annotations of 31 books and journal articles dealing with systems theory and its relation to organizational communication, marketing, information theory, and cybernetics. Materials were published between 1963 and 1992 and are listed alphabetically by author. (RS)

The Protein Information Resource: an integrated public resource of functional annotation of proteins

PubMed Central

Wu, Cathy H.; Huang, Hongzhan; Arminski, Leslie; Castro-Alvear, Jorge; Chen, Yongxing; Hu, Zhang-Zhi; Ledley, Robert S.; Lewis, Kali C.; Mewes, Hans-Werner; Orcutt, Bruce C.; Suzek, Baris E.; Tsugita, Akira; Vinayaka, C. R.; Yeh, Lai-Su L.; Zhang, Jian; Barker, Winona C.

2002-01-01

The Protein Information Resource (PIR) serves as an integrated public resource of functional annotation of protein data to support genomic/proteomic research and scientific discovery. The PIR, in collaboration with the Munich Information Center for Protein Sequences (MIPS) and the Japan International Protein Information Database (JIPID), produces the PIR-International Protein Sequence Database (PSD), the major annotated protein sequence database in the public domain, containing about 250 000 proteins. To improve protein annotation and the coverage of experimentally validated data, a bibliography submission system is developed for scientists to submit, categorize and retrieve literature information. Comprehensive protein information is available from iProClass, which includes family classification at the superfamily, domain and motif levels, structural and functional features of proteins, as well as cross-references to over 40 biological databases. To provide timely and comprehensive protein data with source attribution, we have introduced a non-redundant reference protein database, PIR-NREF. The database consists of about 800 000 proteins collected from PIR-PSD, SWISS-PROT, TrEMBL, GenPept, RefSeq and PDB, with composite protein names and literature data. To promote database interoperability, we provide XML data distribution and open database schema, and adopt common ontologies. The PIR web site (http://pir.georgetown.edu/) features data mining and sequence analysis tools for information retrieval and functional identification of proteins based on both sequence and annotation information. The PIR databases and other files are also available by FTP (ftp://nbrfa.georgetown.edu/pir_databases). PMID:11752247

Annotation Graphs: A Graph-Based Visualization for Meta-Analysis of Data Based on User-Authored Annotations.

PubMed

Zhao, Jian; Glueck, Michael; Breslav, Simon; Chevalier, Fanny; Khan, Azam

2017-01-01

User-authored annotations of data can support analysts in the activity of hypothesis generation and sensemaking, where it is not only critical to document key observations, but also to communicate insights between analysts. We present annotation graphs, a dynamic graph visualization that enables meta-analysis of data based on user-authored annotations. The annotation graph topology encodes annotation semantics, which describe the content of and relations between data selections, comments, and tags. We present a mixed-initiative approach to graph layout that integrates an analyst's manual manipulations with an automatic method based on similarity inferred from the annotation semantics. Various visual graph layout styles reveal different perspectives on the annotation semantics. Annotation graphs are implemented within C8, a system that supports authoring annotations during exploratory analysis of a dataset. We apply principles of Exploratory Sequential Data Analysis (ESDA) in designing C8, and further link these to an existing task typology in the visualization literature. We develop and evaluate the system through an iterative user-centered design process with three experts, situated in the domain of analyzing HCI experiment data. The results suggest that annotation graphs are effective as a method of visually extending user-authored annotations to data meta-analysis for discovery and organization of ideas.

BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models

PubMed Central

2010-01-01

Background Quantitative models of biochemical and cellular systems are used to answer a variety of questions in the biological sciences. The number of published quantitative models is growing steadily thanks to increasing interest in the use of models as well as the development of improved software systems and the availability of better, cheaper computer hardware. To maximise the benefits of this growing body of models, the field needs centralised model repositories that will encourage, facilitate and promote model dissemination and reuse. Ideally, the models stored in these repositories should be extensively tested and encoded in community-supported and standardised formats. In addition, the models and their components should be cross-referenced with other resources in order to allow their unambiguous identification. Description BioModels Database http://www.ebi.ac.uk/biomodels/ is aimed at addressing exactly these needs. It is a freely-accessible online resource for storing, viewing, retrieving, and analysing published, peer-reviewed quantitative models of biochemical and cellular systems. The structure and behaviour of each simulation model distributed by BioModels Database are thoroughly checked; in addition, model elements are annotated with terms from controlled vocabularies as well as linked to relevant data resources. Models can be examined online or downloaded in various formats. Reaction network diagrams generated from the models are also available in several formats. BioModels Database also provides features such as online simulation and the extraction of components from large scale models into smaller submodels. Finally, the system provides a range of web services that external software systems can use to access up-to-date data from the database. Conclusions BioModels Database has become a recognised reference resource for systems biology. It is being used by the community in a variety of ways; for example, it is used to benchmark different simulation systems, and to study the clustering of models based upon their annotations. Model deposition to the database today is advised by several publishers of scientific journals. The models in BioModels Database are freely distributed and reusable; the underlying software infrastructure is also available from SourceForge https://sourceforge.net/projects/biomodels/ under the GNU General Public License. PMID:20587024

Collaborative Annotation System Environment (CASE) for Online Learning

ERIC Educational Resources Information Center

Glover, Ian; Hardaker, Glenn; Xu, Zhijie

2004-01-01

This paper outlines the design and development process of an online annotation system and how it is applied to the sphere of collaborative online learning. The architecture and design of the annotation system, illustrated in this paper, have been developed to enrich collaborative learning content through adding a layer of information in online…

Supporting community annotation and user collaboration in the integrated microbial genomes (IMG) system.

PubMed

Chen, I-Min A; Markowitz, Victor M; Palaniappan, Krishna; Szeto, Ernest; Chu, Ken; Huang, Jinghua; Ratner, Anna; Pillay, Manoj; Hadjithomas, Michalis; Huntemann, Marcel; Mikhailova, Natalia; Ovchinnikova, Galina; Ivanova, Natalia N; Kyrpides, Nikos C

2016-04-26

The exponential growth of genomic data from next generation technologies renders traditional manual expert curation effort unsustainable. Many genomic systems have included community annotation tools to address the problem. Most of these systems adopted a "Wiki-based" approach to take advantage of existing wiki technologies, but encountered obstacles in issues such as usability, authorship recognition, information reliability and incentive for community participation. Here, we present a different approach, relying on tightly integrated method rather than "Wiki-based" method, to support community annotation and user collaboration in the Integrated Microbial Genomes (IMG) system. The IMG approach allows users to use existing IMG data warehouse and analysis tools to add gene, pathway and biosynthetic cluster annotations, to analyze/reorganize contigs, genes and functions using workspace datasets, and to share private user annotations and workspace datasets with collaborators. We show that the annotation effort using IMG can be part of the research process to overcome the user incentive and authorship recognition problems thus fostering collaboration among domain experts. The usability and reliability issues are addressed by the integration of curated information and analysis tools in IMG, together with DOE Joint Genome Institute (JGI) expert review. By incorporating annotation operations into IMG, we provide an integrated environment for users to perform deeper and extended data analysis and annotation in a single system that can lead to publications and community knowledge sharing as shown in the case studies.

Chado Controller: advanced annotation management with a community annotation system

PubMed Central

Guignon, Valentin; Droc, Gaëtan; Alaux, Michael; Baurens, Franc-Christophe; Garsmeur, Olivier; Poiron, Claire; Carver, Tim; Rouard, Mathieu; Bocs, Stéphanie

2012-01-01

Summary: We developed a controller that is compliant with the Chado database schema, GBrowse and genome annotation-editing tools such as Artemis and Apollo. It enables the management of public and private data, monitors manual annotation (with controlled vocabularies, structural and functional annotation controls) and stores versions of annotation for all modified features. The Chado controller uses PostgreSQL and Perl. Availability: The Chado Controller package is available for download at http://www.gnpannot.org/content/chado-controller and runs on any Unix-like operating system, and documentation is available at http://www.gnpannot.org/content/chado-controller-doc The system can be tested using the GNPAnnot Sandbox at http://www.gnpannot.org/content/gnpannot-sandbox-form Contact: valentin.guignon@cirad.fr; stephanie.sidibe-bocs@cirad.fr Supplementary information: Supplementary data are available at Bioinformatics online. PMID:22285827

IMG ER: a system for microbial genome annotation expert review and curation.

PubMed

Markowitz, Victor M; Mavromatis, Konstantinos; Ivanova, Natalia N; Chen, I-Min A; Chu, Ken; Kyrpides, Nikos C

2009-09-01

A rapidly increasing number of microbial genomes are sequenced by organizations worldwide and are eventually included into various public genome data resources. The quality of the annotations depends largely on the original dataset providers, with erroneous or incomplete annotations often carried over into the public resources and difficult to correct. We have developed an Expert Review (ER) version of the Integrated Microbial Genomes (IMG) system, with the goal of supporting systematic and efficient revision of microbial genome annotations. IMG ER provides tools for the review and curation of annotations of both new and publicly available microbial genomes within IMG's rich integrated genome framework. New genome datasets are included into IMG ER prior to their public release either with their native annotations or with annotations generated by IMG ER's annotation pipeline. IMG ER tools allow addressing annotation problems detected with IMG's comparative analysis tools, such as genes missed by gene prediction pipelines or genes without an associated function. Over the past year, IMG ER was used for improving the annotations of about 150 microbial genomes.

AnnoSys—implementation of a generic annotation system for schema-based data using the example of biodiversity collection data

PubMed Central

Kusber, W.-H.; Tschöpe, O.; Güntsch, A.; Berendsohn, W. G.

2017-01-01

Abstract Biological research collections holding billions of specimens world-wide provide the most important baseline information for systematic biodiversity research. Increasingly, specimen data records become available in virtual herbaria and data portals. The traditional (physical) annotation procedure fails here, so that an important pathway of research documentation and data quality control is broken. In order to create an online annotation system, we analysed, modeled and adapted traditional specimen annotation workflows. The AnnoSys system accesses collection data from either conventional web resources or the Biological Collection Access Service (BioCASe) and accepts XML-based data standards like ABCD or DarwinCore. It comprises a searchable annotation data repository, a user interface, and a subscription based message system. We describe the main components of AnnoSys and its current and planned interoperability with biodiversity data portals and networks. Details are given on the underlying architectural model, which implements the W3C OpenAnnotation model and allows the adaptation of AnnoSys to different problem domains. Advantages and disadvantages of different digital annotation and feedback approaches are discussed. For the biodiversity domain, AnnoSys proposes best practice procedures for digital annotations of complex records. Database URL: https://annosys.bgbm.fu-berlin.de/AnnoSys/AnnoSys PMID:28365735

Directions in parallel programming: HPF, shared virtual memory and object parallelism in pC++

NASA Technical Reports Server (NTRS)

Bodin, Francois; Priol, Thierry; Mehrotra, Piyush; Gannon, Dennis

1994-01-01

Fortran and C++ are the dominant programming languages used in scientific computation. Consequently, extensions to these languages are the most popular for programming massively parallel computers. We discuss two such approaches to parallel Fortran and one approach to C++. The High Performance Fortran Forum has designed HPF with the intent of supporting data parallelism on Fortran 90 applications. HPF works by asking the user to help the compiler distribute and align the data structures with the distributed memory modules in the system. Fortran-S takes a different approach in which the data distribution is managed by the operating system and the user provides annotations to indicate parallel control regions. In the case of C++, we look at pC++ which is based on a concurrent aggregate parallel model.

Building a comprehensive syntactic and semantic corpus of Chinese clinical texts.

PubMed

He, Bin; Dong, Bin; Guan, Yi; Yang, Jinfeng; Jiang, Zhipeng; Yu, Qiubin; Cheng, Jianyi; Qu, Chunyan

2017-05-01

To build a comprehensive corpus covering syntactic and semantic annotations of Chinese clinical texts with corresponding annotation guidelines and methods as well as to develop tools trained on the annotated corpus, which supplies baselines for research on Chinese texts in the clinical domain. An iterative annotation method was proposed to train annotators and to develop annotation guidelines. Then, by using annotation quality assurance measures, a comprehensive corpus was built, containing annotations of part-of-speech (POS) tags, syntactic tags, entities, assertions, and relations. Inter-annotator agreement (IAA) was calculated to evaluate the annotation quality and a Chinese clinical text processing and information extraction system (CCTPIES) was developed based on our annotated corpus. The syntactic corpus consists of 138 Chinese clinical documents with 47,426 tokens and 2612 full parsing trees, while the semantic corpus includes 992 documents that annotated 39,511 entities with their assertions and 7693 relations. IAA evaluation shows that this comprehensive corpus is of good quality, and the system modules are effective. The annotated corpus makes a considerable contribution to natural language processing (NLP) research into Chinese texts in the clinical domain. However, this corpus has a number of limitations. Some additional types of clinical text should be introduced to improve corpus coverage and active learning methods should be utilized to promote annotation efficiency. In this study, several annotation guidelines and an annotation method for Chinese clinical texts were proposed, and a comprehensive corpus with its NLP modules were constructed, providing a foundation for further study of applying NLP techniques to Chinese texts in the clinical domain. Copyright © 2017. Published by Elsevier Inc.

Scripps Genome ADVISER: Annotation and Distributed Variant Interpretation SERver

PubMed Central

Pham, Phillip H.; Shipman, William J.; Erikson, Galina A.; Schork, Nicholas J.; Torkamani, Ali

2015-01-01

Interpretation of human genomes is a major challenge. We present the Scripps Genome ADVISER (SG-ADVISER) suite, which aims to fill the gap between data generation and genome interpretation by performing holistic, in-depth, annotations and functional predictions on all variant types and effects. The SG-ADVISER suite includes a de-identification tool, a variant annotation web-server, and a user interface for inheritance and annotation-based filtration. SG-ADVISER allows users with no bioinformatics expertise to manipulate large volumes of variant data with ease – without the need to download large reference databases, install software, or use a command line interface. SG-ADVISER is freely available at genomics.scripps.edu/ADVISER. PMID:25706643

GeneFarm, structural and functional annotation of Arabidopsis gene and protein families by a network of experts

PubMed Central

Aubourg, Sébastien; Brunaud, Véronique; Bruyère, Clémence; Cock, Mark; Cooke, Richard; Cottet, Annick; Couloux, Arnaud; Déhais, Patrice; Deléage, Gilbert; Duclert, Aymeric; Echeverria, Manuel; Eschbach, Aimée; Falconet, Denis; Filippi, Ghislain; Gaspin, Christine; Geourjon, Christophe; Grienenberger, Jean-Michel; Houlné, Guy; Jamet, Elisabeth; Lechauve, Frédéric; Leleu, Olivier; Leroy, Philippe; Mache, Régis; Meyer, Christian; Nedjari, Hafed; Negrutiu, Ioan; Orsini, Valérie; Peyretaillade, Eric; Pommier, Cyril; Raes, Jeroen; Risler, Jean-Loup; Rivière, Stéphane; Rombauts, Stéphane; Rouzé, Pierre; Schneider, Michel; Schwob, Philippe; Small, Ian; Soumayet-Kampetenga, Ghislain; Stankovski, Darko; Toffano, Claire; Tognolli, Michael; Caboche, Michel; Lecharny, Alain

2005-01-01

Genomic projects heavily depend on genome annotations and are limited by the current deficiencies in the published predictions of gene structure and function. It follows that, improved annotation will allow better data mining of genomes, and more secure planning and design of experiments. The purpose of the GeneFarm project is to obtain homogeneous, reliable, documented and traceable annotations for Arabidopsis nuclear genes and gene products, and to enter them into an added-value database. This re-annotation project is being performed exhaustively on every member of each gene family. Performing a family-wide annotation makes the task easier and more efficient than a gene-by-gene approach since many features obtained for one gene can be extrapolated to some or all the other genes of a family. A complete annotation procedure based on the most efficient prediction tools available is being used by 16 partner laboratories, each contributing annotated families from its field of expertise. A database, named GeneFarm, and an associated user-friendly interface to query the annotations have been developed. More than 3000 genes distributed over 300 families have been annotated and are available at http://genoplante-info.infobiogen.fr/Genefarm/. Furthermore, collaboration with the Swiss Institute of Bioinformatics is underway to integrate the GeneFarm data into the protein knowledgebase Swiss-Prot. PMID:15608279

Improving Microbial Genome Annotations in an Integrated Database Context

PubMed Central

Chen, I-Min A.; Markowitz, Victor M.; Chu, Ken; Anderson, Iain; Mavromatis, Konstantinos; Kyrpides, Nikos C.; Ivanova, Natalia N.

2013-01-01

Effective comparative analysis of microbial genomes requires a consistent and complete view of biological data. Consistency regards the biological coherence of annotations, while completeness regards the extent and coverage of functional characterization for genomes. We have developed tools that allow scientists to assess and improve the consistency and completeness of microbial genome annotations in the context of the Integrated Microbial Genomes (IMG) family of systems. All publicly available microbial genomes are characterized in IMG using different functional annotation and pathway resources, thus providing a comprehensive framework for identifying and resolving annotation discrepancies. A rule based system for predicting phenotypes in IMG provides a powerful mechanism for validating functional annotations, whereby the phenotypic traits of an organism are inferred based on the presence of certain metabolic reactions and pathways and compared to experimentally observed phenotypes. The IMG family of systems are available at http://img.jgi.doe.gov/. PMID:23424620

Supporting community annotation and user collaboration in the integrated microbial genomes (IMG) system

DOE PAGES

Chen, I-Min A.; Markowitz, Victor M.; Palaniappan, Krishna; ...

2016-04-26

Background: The exponential growth of genomic data from next generation technologies renders traditional manual expert curation effort unsustainable. Many genomic systems have included community annotation tools to address the problem. Most of these systems adopted a "Wiki-based" approach to take advantage of existing wiki technologies, but encountered obstacles in issues such as usability, authorship recognition, information reliability and incentive for community participation. Results: Here, we present a different approach, relying on tightly integrated method rather than "Wiki-based" method, to support community annotation and user collaboration in the Integrated Microbial Genomes (IMG) system. The IMG approach allows users to use existingmore » IMG data warehouse and analysis tools to add gene, pathway and biosynthetic cluster annotations, to analyze/reorganize contigs, genes and functions using workspace datasets, and to share private user annotations and workspace datasets with collaborators. We show that the annotation effort using IMG can be part of the research process to overcome the user incentive and authorship recognition problems thus fostering collaboration among domain experts. The usability and reliability issues are addressed by the integration of curated information and analysis tools in IMG, together with DOE Joint Genome Institute (JGI) expert review. Conclusion: By incorporating annotation operations into IMG, we provide an integrated environment for users to perform deeper and extended data analysis and annotation in a single system that can lead to publications and community knowledge sharing as shown in the case studies.« less

Supporting community annotation and user collaboration in the integrated microbial genomes (IMG) system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, I-Min A.; Markowitz, Victor M.; Palaniappan, Krishna

Background: The exponential growth of genomic data from next generation technologies renders traditional manual expert curation effort unsustainable. Many genomic systems have included community annotation tools to address the problem. Most of these systems adopted a "Wiki-based" approach to take advantage of existing wiki technologies, but encountered obstacles in issues such as usability, authorship recognition, information reliability and incentive for community participation. Results: Here, we present a different approach, relying on tightly integrated method rather than "Wiki-based" method, to support community annotation and user collaboration in the Integrated Microbial Genomes (IMG) system. The IMG approach allows users to use existingmore » IMG data warehouse and analysis tools to add gene, pathway and biosynthetic cluster annotations, to analyze/reorganize contigs, genes and functions using workspace datasets, and to share private user annotations and workspace datasets with collaborators. We show that the annotation effort using IMG can be part of the research process to overcome the user incentive and authorship recognition problems thus fostering collaboration among domain experts. The usability and reliability issues are addressed by the integration of curated information and analysis tools in IMG, together with DOE Joint Genome Institute (JGI) expert review. Conclusion: By incorporating annotation operations into IMG, we provide an integrated environment for users to perform deeper and extended data analysis and annotation in a single system that can lead to publications and community knowledge sharing as shown in the case studies.« less

A web-based video annotation system for crowdsourcing surveillance videos

NASA Astrophysics Data System (ADS)

Gadgil, Neeraj J.; Tahboub, Khalid; Kirsh, David; Delp, Edward J.

2014-03-01

Video surveillance systems are of a great value to prevent threats and identify/investigate criminal activities. Manual analysis of a huge amount of video data from several cameras over a long period of time often becomes impracticable. The use of automatic detection methods can be challenging when the video contains many objects with complex motion and occlusions. Crowdsourcing has been proposed as an effective method for utilizing human intelligence to perform several tasks. Our system provides a platform for the annotation of surveillance video in an organized and controlled way. One can monitor a surveillance system using a set of tools such as training modules, roles and labels, task management. This system can be used in a real-time streaming mode to detect any potential threats or as an investigative tool to analyze past events. Annotators can annotate video contents assigned to them for suspicious activity or criminal acts. First responders are then able to view the collective annotations and receive email alerts about a newly reported incident. They can also keep track of the annotators' training performance, manage their activities and reward their success. By providing this system, the process of video analysis is made more efficient.

A User-Driven Annotation Framework for Scientific Data

ERIC Educational Resources Information Center

Li, Qinglan

2013-01-01

Annotations play an increasingly crucial role in scientific exploration and discovery, as the amount of data and the level of collaboration among scientists increases. There are many systems today focusing on annotation management, querying, and propagation. Although all such systems are implemented to take user input (i.e., the annotations…

The Proteome Folding Project: Proteome-scale prediction of structure and function

PubMed Central

Drew, Kevin; Winters, Patrick; Butterfoss, Glenn L.; Berstis, Viktors; Uplinger, Keith; Armstrong, Jonathan; Riffle, Michael; Schweighofer, Erik; Bovermann, Bill; Goodlett, David R.; Davis, Trisha N.; Shasha, Dennis; Malmström, Lars; Bonneau, Richard

2011-01-01

The incompleteness of proteome structure and function annotation is a critical problem for biologists and, in particular, severely limits interpretation of high-throughput and next-generation experiments. We have developed a proteome annotation pipeline based on structure prediction, where function and structure annotations are generated using an integration of sequence comparison, fold recognition, and grid-computing-enabled de novo structure prediction. We predict protein domain boundaries and three-dimensional (3D) structures for protein domains from 94 genomes (including human, Arabidopsis, rice, mouse, fly, yeast, Escherichia coli, and worm). De novo structure predictions were distributed on a grid of more than 1.5 million CPUs worldwide (World Community Grid). We generated significant numbers of new confident fold annotations (9% of domains that are otherwise unannotated in these genomes). We demonstrate that predicted structures can be combined with annotations from the Gene Ontology database to predict new and more specific molecular functions. PMID:21824995

Multimodal MSI in Conjunction with Broad Coverage Spatially Resolved MS2 Increases Confidence in Both Molecular Identification and Localization.

PubMed

Veličković, Dušan; Chu, Rosalie K; Carrell, Alyssa A; Thomas, Mathew; Paša-Tolić, Ljiljana; Weston, David J; Anderton, Christopher R

2018-01-02

One critical aspect of mass spectrometry imaging (MSI) is the need to confidently identify detected analytes. While orthogonal tandem MS (e.g., LC-MS 2 ) experiments from sample extracts can assist in annotating ions, the spatial information about these molecules is lost. Accordingly, this could cause mislead conclusions, especially in cases where isobaric species exhibit different distributions within a sample. In this Technical Note, we employed a multimodal imaging approach, using matrix assisted laser desorption/ionization (MALDI)-MSI and liquid extraction surface analysis (LESA)-MS 2 I, to confidently annotate and localize a broad range of metabolites involved in a tripartite symbiosis system of moss, cyanobacteria, and fungus. We found that the combination of these two imaging modalities generated very congruent ion images, providing the link between highly accurate structural information onfered by LESA and high spatial resolution attainable by MALDI. These results demonstrate how this combined methodology could be very useful in differentiating metabolite routes in complex systems.

CycADS: an annotation database system to ease the development and update of BioCyc databases

PubMed Central

Vellozo, Augusto F.; Véron, Amélie S.; Baa-Puyoulet, Patrice; Huerta-Cepas, Jaime; Cottret, Ludovic; Febvay, Gérard; Calevro, Federica; Rahbé, Yvan; Douglas, Angela E.; Gabaldón, Toni; Sagot, Marie-France; Charles, Hubert; Colella, Stefano

2011-01-01

In recent years, genomes from an increasing number of organisms have been sequenced, but their annotation remains a time-consuming process. The BioCyc databases offer a framework for the integrated analysis of metabolic networks. The Pathway tool software suite allows the automated construction of a database starting from an annotated genome, but it requires prior integration of all annotations into a specific summary file or into a GenBank file. To allow the easy creation and update of a BioCyc database starting from the multiple genome annotation resources available over time, we have developed an ad hoc data management system that we called Cyc Annotation Database System (CycADS). CycADS is centred on a specific database model and on a set of Java programs to import, filter and export relevant information. Data from GenBank and other annotation sources (including for example: KAAS, PRIAM, Blast2GO and PhylomeDB) are collected into a database to be subsequently filtered and extracted to generate a complete annotation file. This file is then used to build an enriched BioCyc database using the PathoLogic program of Pathway Tools. The CycADS pipeline for annotation management was used to build the AcypiCyc database for the pea aphid (Acyrthosiphon pisum) whose genome was recently sequenced. The AcypiCyc database webpage includes also, for comparative analyses, two other metabolic reconstruction BioCyc databases generated using CycADS: TricaCyc for Tribolium castaneum and DromeCyc for Drosophila melanogaster. Linked to its flexible design, CycADS offers a powerful software tool for the generation and regular updating of enriched BioCyc databases. The CycADS system is particularly suited for metabolic gene annotation and network reconstruction in newly sequenced genomes. Because of the uniform annotation used for metabolic network reconstruction, CycADS is particularly useful for comparative analysis of the metabolism of different organisms. Database URL: http://www.cycadsys.org PMID:21474551

Perceived Usefulness of a Strategy-Based Peer Annotation System for Improving Academic Reading Comprehension

ERIC Educational Resources Information Center

Chen, I-Jung; Chen, Wen-Chun

2016-01-01

This study examines the enhancing effect of peer annotation on the academic English reading of nonnative-Englishspeaking graduate students. To facilitate peer collaboration, the present study included the development of a strategybased online reading system. Through peer annotation, the students not only achieved enhanced reading comprehension but…

Automatic medical image annotation and keyword-based image retrieval using relevance feedback.

PubMed

Ko, Byoung Chul; Lee, JiHyeon; Nam, Jae-Yeal

2012-08-01

This paper presents novel multiple keywords annotation for medical images, keyword-based medical image retrieval, and relevance feedback method for image retrieval for enhancing image retrieval performance. For semantic keyword annotation, this study proposes a novel medical image classification method combining local wavelet-based center symmetric-local binary patterns with random forests. For keyword-based image retrieval, our retrieval system use the confidence score that is assigned to each annotated keyword by combining probabilities of random forests with predefined body relation graph. To overcome the limitation of keyword-based image retrieval, we combine our image retrieval system with relevance feedback mechanism based on visual feature and pattern classifier. Compared with other annotation and relevance feedback algorithms, the proposed method shows both improved annotation performance and accurate retrieval results.

BG7: A New Approach for Bacterial Genome Annotation Designed for Next Generation Sequencing Data

PubMed Central

Pareja-Tobes, Pablo; Manrique, Marina; Pareja-Tobes, Eduardo; Pareja, Eduardo; Tobes, Raquel

2012-01-01

BG7 is a new system for de novo bacterial, archaeal and viral genome annotation based on a new approach specifically designed for annotating genomes sequenced with next generation sequencing technologies. The system is versatile and able to annotate genes even in the step of preliminary assembly of the genome. It is especially efficient detecting unexpected genes horizontally acquired from bacterial or archaeal distant genomes, phages, plasmids, and mobile elements. From the initial phases of the gene annotation process, BG7 exploits the massive availability of annotated protein sequences in databases. BG7 predicts ORFs and infers their function based on protein similarity with a wide set of reference proteins, integrating ORF prediction and functional annotation phases in just one step. BG7 is especially tolerant to sequencing errors in start and stop codons, to frameshifts, and to assembly or scaffolding errors. The system is also tolerant to the high level of gene fragmentation which is frequently found in not fully assembled genomes. BG7 current version – which is developed in Java, takes advantage of Amazon Web Services (AWS) cloud computing features, but it can also be run locally in any operating system. BG7 is a fast, automated and scalable system that can cope with the challenge of analyzing the huge amount of genomes that are being sequenced with NGS technologies. Its capabilities and efficiency were demonstrated in the 2011 EHEC Germany outbreak in which BG7 was used to get the first annotations right the next day after the first entero-hemorrhagic E. coli genome sequences were made publicly available. The suitability of BG7 for genome annotation has been proved for Illumina, 454, Ion Torrent, and PacBio sequencing technologies. Besides, thanks to its plasticity, our system could be very easily adapted to work with new technologies in the future. PMID:23185310

Chemical annotation of small and peptide-like molecules at the Protein Data Bank

PubMed Central

Young, Jasmine Y.; Feng, Zukang; Dimitropoulos, Dimitris; Sala, Raul; Westbrook, John; Zhuravleva, Marina; Shao, Chenghua; Quesada, Martha; Peisach, Ezra; Berman, Helen M.

2013-01-01

Over the past decade, the number of polymers and their complexes with small molecules in the Protein Data Bank archive (PDB) has continued to increase significantly. To support scientific advancements and ensure the best quality and completeness of the data files over the next 10 years and beyond, the Worldwide PDB partnership that manages the PDB archive is developing a new deposition and annotation system. This system focuses on efficient data capture across all supported experimental methods. The new deposition and annotation system is composed of four major modules that together support all of the processing requirements for a PDB entry. In this article, we describe one such module called the Chemical Component Annotation Tool. This tool uses information from both the Chemical Component Dictionary and Biologically Interesting molecule Reference Dictionary to aid in annotation. Benchmark studies have shown that the Chemical Component Annotation Tool provides significant improvements in processing efficiency and data quality. Database URL: http://wwpdb.org PMID:24291661

Chemical annotation of small and peptide-like molecules at the Protein Data Bank.

PubMed

Young, Jasmine Y; Feng, Zukang; Dimitropoulos, Dimitris; Sala, Raul; Westbrook, John; Zhuravleva, Marina; Shao, Chenghua; Quesada, Martha; Peisach, Ezra; Berman, Helen M

2013-01-01

Over the past decade, the number of polymers and their complexes with small molecules in the Protein Data Bank archive (PDB) has continued to increase significantly. To support scientific advancements and ensure the best quality and completeness of the data files over the next 10 years and beyond, the Worldwide PDB partnership that manages the PDB archive is developing a new deposition and annotation system. This system focuses on efficient data capture across all supported experimental methods. The new deposition and annotation system is composed of four major modules that together support all of the processing requirements for a PDB entry. In this article, we describe one such module called the Chemical Component Annotation Tool. This tool uses information from both the Chemical Component Dictionary and Biologically Interesting molecule Reference Dictionary to aid in annotation. Benchmark studies have shown that the Chemical Component Annotation Tool provides significant improvements in processing efficiency and data quality. Database URL: http://wwpdb.org.

DREAM: Classification scheme for dialog acts in clinical research query mediation.

PubMed

Hoxha, Julia; Chandar, Praveen; He, Zhe; Cimino, James; Hanauer, David; Weng, Chunhua

2016-02-01

Clinical data access involves complex but opaque communication between medical researchers and query analysts. Understanding such communication is indispensable for designing intelligent human-machine dialog systems that automate query formulation. This study investigates email communication and proposes a novel scheme for classifying dialog acts in clinical research query mediation. We analyzed 315 email messages exchanged in the communication for 20 data requests obtained from three institutions. The messages were segmented into 1333 utterance units. Through a rigorous process, we developed a classification scheme and applied it for dialog act annotation of the extracted utterances. Evaluation results with high inter-annotator agreement demonstrate the reliability of this scheme. This dataset is used to contribute preliminary understanding of dialog acts distribution and conversation flow in this dialog space. Copyright © 2015 Elsevier Inc. All rights reserved.

A UIMA wrapper for the NCBO annotator.

PubMed

Roeder, Christophe; Jonquet, Clement; Shah, Nigam H; Baumgartner, William A; Verspoor, Karin; Hunter, Lawrence

2010-07-15

The Unstructured Information Management Architecture (UIMA) framework and web services are emerging as useful tools for integrating biomedical text mining tools. This note describes our work, which wraps the National Center for Biomedical Ontology (NCBO) Annotator-an ontology-based annotation service-to make it available as a component in UIMA workflows. This wrapper is freely available on the web at http://bionlp-uima.sourceforge.net/ as part of the UIMA tools distribution from the Center for Computational Pharmacology (CCP) at the University of Colorado School of Medicine. It has been implemented in Java for support on Mac OS X, Linux and MS Windows.

Rapid storage and retrieval of genomic intervals from a relational database system using nested containment lists

PubMed Central

Wiley, Laura K.; Sivley, R. Michael; Bush, William S.

2013-01-01

Efficient storage and retrieval of genomic annotations based on range intervals is necessary, given the amount of data produced by next-generation sequencing studies. The indexing strategies of relational database systems (such as MySQL) greatly inhibit their use in genomic annotation tasks. This has led to the development of stand-alone applications that are dependent on flat-file libraries. In this work, we introduce MyNCList, an implementation of the NCList data structure within a MySQL database. MyNCList enables the storage, update and rapid retrieval of genomic annotations from the convenience of a relational database system. Range-based annotations of 1 million variants are retrieved in under a minute, making this approach feasible for whole-genome annotation tasks. Database URL: https://github.com/bushlab/mynclist PMID:23894185

Rapid storage and retrieval of genomic intervals from a relational database system using nested containment lists.

PubMed

Wiley, Laura K; Sivley, R Michael; Bush, William S

2013-01-01

Efficient storage and retrieval of genomic annotations based on range intervals is necessary, given the amount of data produced by next-generation sequencing studies. The indexing strategies of relational database systems (such as MySQL) greatly inhibit their use in genomic annotation tasks. This has led to the development of stand-alone applications that are dependent on flat-file libraries. In this work, we introduce MyNCList, an implementation of the NCList data structure within a MySQL database. MyNCList enables the storage, update and rapid retrieval of genomic annotations from the convenience of a relational database system. Range-based annotations of 1 million variants are retrieved in under a minute, making this approach feasible for whole-genome annotation tasks. Database URL: https://github.com/bushlab/mynclist.

Annotated Bibliography. Conference Materials: Strategy Conference on Education and the Economy (Washington, D.C., September 22-24, 1981).

ERIC Educational Resources Information Center

Center for Law and Education, Boston, MA.

The 22 items included in a packet distributed to participants at a 1981 conference on education and the economy are listed in this annotated bibliography, with sources for the items identified. The materials include articles, chapters from books, and monographs, as well as a resource guide and bibliography concerning rural economic and community…

GONUTS: the Gene Ontology Normal Usage Tracking System

PubMed Central

Renfro, Daniel P.; McIntosh, Brenley K.; Venkatraman, Anand; Siegele, Deborah A.; Hu, James C.

2012-01-01

The Gene Ontology Normal Usage Tracking System (GONUTS) is a community-based browser and usage guide for Gene Ontology (GO) terms and a community system for general GO annotation of proteins. GONUTS uses wiki technology to allow registered users to share and edit notes on the use of each term in GO, and to contribute annotations for specific genes of interest. By providing a site for generation of third-party documentation at the granularity of individual terms, GONUTS complements the official documentation of the Gene Ontology Consortium. To provide examples for community users, GONUTS displays the complete GO annotations from seven model organisms: Saccharomyces cerevisiae, Dictyostelium discoideum, Caenorhabditis elegans, Drosophila melanogaster, Danio rerio, Mus musculus and Arabidopsis thaliana. To support community annotation, GONUTS allows automated creation of gene pages for gene products in UniProt. GONUTS will improve the consistency of annotation efforts across genome projects, and should be useful in training new annotators and consumers in the production of GO annotations and the use of GO terms. GONUTS can be accessed at http://gowiki.tamu.edu. The source code for generating the content of GONUTS is available upon request. PMID:22110029

MetaStorm: A Public Resource for Customizable Metagenomics Annotation

PubMed Central

Arango-Argoty, Gustavo; Singh, Gargi; Heath, Lenwood S.; Pruden, Amy; Xiao, Weidong; Zhang, Liqing

2016-01-01

Metagenomics is a trending research area, calling for the need to analyze large quantities of data generated from next generation DNA sequencing technologies. The need to store, retrieve, analyze, share, and visualize such data challenges current online computational systems. Interpretation and annotation of specific information is especially a challenge for metagenomic data sets derived from environmental samples, because current annotation systems only offer broad classification of microbial diversity and function. Moreover, existing resources are not configured to readily address common questions relevant to environmental systems. Here we developed a new online user-friendly metagenomic analysis server called MetaStorm (http://bench.cs.vt.edu/MetaStorm/), which facilitates customization of computational analysis for metagenomic data sets. Users can upload their own reference databases to tailor the metagenomics annotation to focus on various taxonomic and functional gene markers of interest. MetaStorm offers two major analysis pipelines: an assembly-based annotation pipeline and the standard read annotation pipeline used by existing web servers. These pipelines can be selected individually or together. Overall, MetaStorm provides enhanced interactive visualization to allow researchers to explore and manipulate taxonomy and functional annotation at various levels of resolution. PMID:27632579

MetaStorm: A Public Resource for Customizable Metagenomics Annotation.

PubMed

Arango-Argoty, Gustavo; Singh, Gargi; Heath, Lenwood S; Pruden, Amy; Xiao, Weidong; Zhang, Liqing

2016-01-01

Metagenomics is a trending research area, calling for the need to analyze large quantities of data generated from next generation DNA sequencing technologies. The need to store, retrieve, analyze, share, and visualize such data challenges current online computational systems. Interpretation and annotation of specific information is especially a challenge for metagenomic data sets derived from environmental samples, because current annotation systems only offer broad classification of microbial diversity and function. Moreover, existing resources are not configured to readily address common questions relevant to environmental systems. Here we developed a new online user-friendly metagenomic analysis server called MetaStorm (http://bench.cs.vt.edu/MetaStorm/), which facilitates customization of computational analysis for metagenomic data sets. Users can upload their own reference databases to tailor the metagenomics annotation to focus on various taxonomic and functional gene markers of interest. MetaStorm offers two major analysis pipelines: an assembly-based annotation pipeline and the standard read annotation pipeline used by existing web servers. These pipelines can be selected individually or together. Overall, MetaStorm provides enhanced interactive visualization to allow researchers to explore and manipulate taxonomy and functional annotation at various levels of resolution.

Anomalous Ground State of the Electrons in Nano-confined Water

DTIC Science & Technology

2016-06-13

confined water system, Nafion, is so different from that of bulk water that the weakly electrostatically interacting molecule model of water is clearly...assume that water is made up molecules weakly interacting(on the scale of the zero point bond energy~.2eV) electrostatically with its neighbors2-3. In an...not possible for a collection of molecules interacting weakly electrostatically . These changes in the spatial distribution of valence electrons in

EXTRACT: interactive extraction of environment metadata and term suggestion for metagenomic sample annotation.

PubMed

Pafilis, Evangelos; Buttigieg, Pier Luigi; Ferrell, Barbra; Pereira, Emiliano; Schnetzer, Julia; Arvanitidis, Christos; Jensen, Lars Juhl

2016-01-01

The microbial and molecular ecology research communities have made substantial progress on developing standards for annotating samples with environment metadata. However, sample manual annotation is a highly labor intensive process and requires familiarity with the terminologies used. We have therefore developed an interactive annotation tool, EXTRACT, which helps curators identify and extract standard-compliant terms for annotation of metagenomic records and other samples. Behind its web-based user interface, the system combines published methods for named entity recognition of environment, organism, tissue and disease terms. The evaluators in the BioCreative V Interactive Annotation Task found the system to be intuitive, useful, well documented and sufficiently accurate to be helpful in spotting relevant text passages and extracting organism and environment terms. Comparison of fully manual and text-mining-assisted curation revealed that EXTRACT speeds up annotation by 15-25% and helps curators to detect terms that would otherwise have been missed. Database URL: https://extract.hcmr.gr/. © The Author(s) 2016. Published by Oxford University Press.

EXTRACT: Interactive extraction of environment metadata and term suggestion for metagenomic sample annotation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pafilis, Evangelos; Buttigieg, Pier Luigi; Ferrell, Barbra

The microbial and molecular ecology research communities have made substantial progress on developing standards for annotating samples with environment metadata. However, sample manual annotation is a highly labor intensive process and requires familiarity with the terminologies used. We have therefore developed an interactive annotation tool, EXTRACT, which helps curators identify and extract standard-compliant terms for annotation of metagenomic records and other samples. Behind its web-based user interface, the system combines published methods for named entity recognition of environment, organism, tissue and disease terms. The evaluators in the BioCreative V Interactive Annotation Task found the system to be intuitive, useful, wellmore » documented and sufficiently accurate to be helpful in spotting relevant text passages and extracting organism and environment terms. Here the comparison of fully manual and text-mining-assisted curation revealed that EXTRACT speeds up annotation by 15–25% and helps curators to detect terms that would otherwise have been missed.« less

EXTRACT: Interactive extraction of environment metadata and term suggestion for metagenomic sample annotation

DOE PAGES

Pafilis, Evangelos; Buttigieg, Pier Luigi; Ferrell, Barbra; ...

2016-01-01

The microbial and molecular ecology research communities have made substantial progress on developing standards for annotating samples with environment metadata. However, sample manual annotation is a highly labor intensive process and requires familiarity with the terminologies used. We have therefore developed an interactive annotation tool, EXTRACT, which helps curators identify and extract standard-compliant terms for annotation of metagenomic records and other samples. Behind its web-based user interface, the system combines published methods for named entity recognition of environment, organism, tissue and disease terms. The evaluators in the BioCreative V Interactive Annotation Task found the system to be intuitive, useful, wellmore » documented and sufficiently accurate to be helpful in spotting relevant text passages and extracting organism and environment terms. Here the comparison of fully manual and text-mining-assisted curation revealed that EXTRACT speeds up annotation by 15–25% and helps curators to detect terms that would otherwise have been missed.« less

SIRSALE: integrated video database management tools

NASA Astrophysics Data System (ADS)

Brunie, Lionel; Favory, Loic; Gelas, J. P.; Lefevre, Laurent; Mostefaoui, Ahmed; Nait-Abdesselam, F.

2002-07-01

Video databases became an active field of research during the last decade. The main objective in such systems is to provide users with capabilities to friendly search, access and playback distributed stored video data in the same way as they do for traditional distributed databases. Hence, such systems need to deal with hard issues : (a) video documents generate huge volumes of data and are time sensitive (streams must be delivered at a specific bitrate), (b) contents of video data are very hard to be automatically extracted and need to be humanly annotated. To cope with these issues, many approaches have been proposed in the literature including data models, query languages, video indexing etc. In this paper, we present SIRSALE : a set of video databases management tools that allow users to manipulate video documents and streams stored in large distributed repositories. All the proposed tools are based on generic models that can be customized for specific applications using ad-hoc adaptation modules. More precisely, SIRSALE allows users to : (a) browse video documents by structures (sequences, scenes, shots) and (b) query the video database content by using a graphical tool, adapted to the nature of the target video documents. This paper also presents an annotating interface which allows archivists to describe the content of video documents. All these tools are coupled to a video player integrating remote VCR functionalities and are based on active network technology. So, we present how dedicated active services allow an optimized video transport for video streams (with Tamanoir active nodes). We then describe experiments of using SIRSALE on an archive of news video and soccer matches. The system has been demonstrated to professionals with a positive feedback. Finally, we discuss open issues and present some perspectives.

An Annotated and Cross-Referenced Bibliography on Computer Security and Access Control in Computer Systems.

ERIC Educational Resources Information Center

Bergart, Jeffrey G.; And Others

This paper represents a careful study of published works on computer security and access control in computer systems. The study includes a selective annotated bibliography of some eighty-five important published results in the field and, based on these papers, analyzes the state of the art. In annotating these works, the authors try to be…

Effects of Annotations and Homework on Learning Achievement: An Empirical Study of Scratch Programming Pedagogy

ERIC Educational Resources Information Center

Su, Addison Y. S.; Huang, Chester S. J.; Yang, Stephen J. H.; Ding, T. J.; Hsieh, Y. Z.

2015-01-01

In Taiwan elementary schools, Scratch programming has been taught for more than four years. Previous studies have shown that personal annotations is a useful learning method that improve learning performance. An annotation-based Scratch programming (ASP) system provides for the creation, share, and review of annotations and homework solutions in…

Extending SEQenv: a taxa-centric approach to environmental annotations of 16S rDNA sequences

PubMed Central

Jeffries, Thomas C.; Ijaz, Umer Z.; Hamonts, Kelly

2017-01-01

Understanding how the environment selects a given taxon and the diversity patterns that emerge as a result of environmental filtering can dramatically improve our ability to analyse any environment in depth as well as advancing our knowledge on how the response of different taxa can impact each other and ecosystem functions. Most of the work investigating microbial biogeography has been site-specific, and logical environmental factors, rather than geographical location, may be more influential on microbial diversity. SEQenv, a novel pipeline aiming to provide environmental annotations of sequences emerged to provide a consistent description of the environmental niches using the ENVO ontology. While the pipeline provides a list of environmental terms on the basis of sample datasets and, therefore, the annotations obtained are at the dataset level, it lacks a taxa centric approach to environmental annotation. The work here describes an extension developed to enhance the SEQenv pipeline, which provided the means to directly generate environmental annotations for taxa under different contexts. 16S rDNA amplicon datasets belonging to distinct biomes were selected to illustrate the applicability of the extended SEQenv pipeline. A literature survey of the results demonstrates the immense importance of sequence level environmental annotations by illustrating the distribution of both taxa across environments as well as the various environmental sources of a specific taxon. Significantly enhancing the SEQenv pipeline in the process, this information would be valuable to any biologist seeking to understand the various taxa present in the habitat and the environment they originated from, enabling a more thorough analysis of which lineages are abundant in certain habitats and the recovery of patterns in taxon distribution across different habitats and environmental gradients. PMID:29038749

ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data

PubMed Central

2010-01-01

Background Chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing (ChIP-seq) or ChIP followed by genome tiling array analysis (ChIP-chip) have become standard technologies for genome-wide identification of DNA-binding protein target sites. A number of algorithms have been developed in parallel that allow identification of binding sites from ChIP-seq or ChIP-chip datasets and subsequent visualization in the University of California Santa Cruz (UCSC) Genome Browser as custom annotation tracks. However, summarizing these tracks can be a daunting task, particularly if there are a large number of binding sites or the binding sites are distributed widely across the genome. Results We have developed ChIPpeakAnno as a Bioconductor package within the statistical programming environment R to facilitate batch annotation of enriched peaks identified from ChIP-seq, ChIP-chip, cap analysis of gene expression (CAGE) or any experiments resulting in a large number of enriched genomic regions. The binding sites annotated with ChIPpeakAnno can be viewed easily as a table, a pie chart or plotted in histogram form, i.e., the distribution of distances to the nearest genes for each set of peaks. In addition, we have implemented functionalities for determining the significance of overlap between replicates or binding sites among transcription factors within a complex, and for drawing Venn diagrams to visualize the extent of the overlap between replicates. Furthermore, the package includes functionalities to retrieve sequences flanking putative binding sites for PCR amplification, cloning, or motif discovery, and to identify Gene Ontology (GO) terms associated with adjacent genes. Conclusions ChIPpeakAnno enables batch annotation of the binding sites identified from ChIP-seq, ChIP-chip, CAGE or any technology that results in a large number of enriched genomic regions within the statistical programming environment R. Allowing users to pass their own annotation data such as a different Chromatin immunoprecipitation (ChIP) preparation and a dataset from literature, or existing annotation packages, such as GenomicFeatures and BSgenome, provides flexibility. Tight integration to the biomaRt package enables up-to-date annotation retrieval from the BioMart database. PMID:20459804

Considerations to improve functional annotations in biological databases.

PubMed

Benítez-Páez, Alfonso

2009-12-01

Despite the great effort to design efficient systems allowing the electronic indexation of information concerning genes, proteins, structures, and interactions published daily in scientific journals, some problems are still observed in specific tasks such as functional annotation. The annotation of function is a critical issue for bioinformatic routines, such as for instance, in functional genomics and the further prediction of unknown protein function, which are highly dependent of the quality of existing annotations. Some information management systems evolve to efficiently incorporate information from large-scale projects, but often, annotation of single records from the literature is difficult and slow. In this short report, functional characterizations of a representative sample of the entire set of uncharacterized proteins from Escherichia coli K12 was compiled from Swiss-Prot, PubMed, and EcoCyc and demonstrate a functional annotation deficit in biological databases. Some issues are postulated as causes of the lack of annotation, and different solutions are evaluated and proposed to avoid them. The hope is that as a consequence of these observations, there will be new impetus to improve the speed and quality of functional annotation and ultimately provide updated, reliable information to the scientific community.

Integrating sequence and structural biology with DAS

PubMed Central

Prlić, Andreas; Down, Thomas A; Kulesha, Eugene; Finn, Robert D; Kähäri, Andreas; Hubbard, Tim JP

2007-01-01

Background The Distributed Annotation System (DAS) is a network protocol for exchanging biological data. It is frequently used to share annotations of genomes and protein sequence. Results Here we present several extensions to the current DAS 1.5 protocol. These provide new commands to share alignments, three dimensional molecular structure data, add the possibility for registration and discovery of DAS servers, and provide a convention how to provide different types of data plots. We present examples of web sites and applications that use the new extensions. We operate a public registry of DAS sources, which now includes entries for more than 250 distinct sources. Conclusion Our DAS extensions are essential for the management of the growing number of services and exchange of diverse biological data sets. In addition the extensions allow new types of applications to be developed and scientific questions to be addressed. The registry of DAS sources is available at PMID:17850653

Improved Genome Assembly and Annotation for the Rock Pigeon (Columba livia)

PubMed Central

Holt, Carson; Campbell, Michael; Keays, David A.; Edelman, Nathaniel; Kapusta, Aurélie; Maclary, Emily; T. Domyan, Eric; Suh, Alexander; Warren, Wesley C.; Yandell, Mark; Gilbert, M. Thomas P.; Shapiro, Michael D.

2018-01-01

The domestic rock pigeon (Columba livia) is among the most widely distributed and phenotypically diverse avian species. C. livia is broadly studied in ecology, genetics, physiology, behavior, and evolutionary biology, and has recently emerged as a model for understanding the molecular basis of anatomical diversity, the magnetic sense, and other key aspects of avian biology. Here we report an update to the C. livia genome reference assembly and gene annotation dataset. Greatly increased scaffold lengths in the updated reference assembly, along with an updated annotation set, provide improved tools for evolutionary and functional genetic studies of the pigeon, and for comparative avian genomics in general. PMID:29519939

Semantic annotation of consumer health questions.

PubMed

Kilicoglu, Halil; Ben Abacha, Asma; Mrabet, Yassine; Shooshan, Sonya E; Rodriguez, Laritza; Masterton, Kate; Demner-Fushman, Dina

2018-02-06

Consumers increasingly use online resources for their health information needs. While current search engines can address these needs to some extent, they generally do not take into account that most health information needs are complex and can only fully be expressed in natural language. Consumer health question answering (QA) systems aim to fill this gap. A major challenge in developing consumer health QA systems is extracting relevant semantic content from the natural language questions (question understanding). To develop effective question understanding tools, question corpora semantically annotated for relevant question elements are needed. In this paper, we present a two-part consumer health question corpus annotated with several semantic categories: named entities, question triggers/types, question frames, and question topic. The first part (CHQA-email) consists of relatively long email requests received by the U.S. National Library of Medicine (NLM) customer service, while the second part (CHQA-web) consists of shorter questions posed to MedlinePlus search engine as queries. Each question has been annotated by two annotators. The annotation methodology is largely the same between the two parts of the corpus; however, we also explain and justify the differences between them. Additionally, we provide information about corpus characteristics, inter-annotator agreement, and our attempts to measure annotation confidence in the absence of adjudication of annotations. The resulting corpus consists of 2614 questions (CHQA-email: 1740, CHQA-web: 874). Problems are the most frequent named entities, while treatment and general information questions are the most common question types. Inter-annotator agreement was generally modest: question types and topics yielded highest agreement, while the agreement for more complex frame annotations was lower. Agreement in CHQA-web was consistently higher than that in CHQA-email. Pairwise inter-annotator agreement proved most useful in estimating annotation confidence. To our knowledge, our corpus is the first focusing on annotation of uncurated consumer health questions. It is currently used to develop machine learning-based methods for question understanding. We make the corpus publicly available to stimulate further research on consumer health QA.

Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization.

PubMed

Misirli, Goksel; Cavaliere, Matteo; Waites, William; Pocock, Matthew; Madsen, Curtis; Gilfellon, Owen; Honorato-Zimmer, Ricardo; Zuliani, Paolo; Danos, Vincent; Wipat, Anil

2016-03-15

Biological systems are complex and challenging to model and therefore model reuse is highly desirable. To promote model reuse, models should include both information about the specifics of simulations and the underlying biology in the form of metadata. The availability of computationally tractable metadata is especially important for the effective automated interpretation and processing of models. Metadata are typically represented as machine-readable annotations which enhance programmatic access to information about models. Rule-based languages have emerged as a modelling framework to represent the complexity of biological systems. Annotation approaches have been widely used for reaction-based formalisms such as SBML. However, rule-based languages still lack a rich annotation framework to add semantic information, such as machine-readable descriptions, to the components of a model. We present an annotation framework and guidelines for annotating rule-based models, encoded in the commonly used Kappa and BioNetGen languages. We adapt widely adopted annotation approaches to rule-based models. We initially propose a syntax to store machine-readable annotations and describe a mapping between rule-based modelling entities, such as agents and rules, and their annotations. We then describe an ontology to both annotate these models and capture the information contained therein, and demonstrate annotating these models using examples. Finally, we present a proof of concept tool for extracting annotations from a model that can be queried and analyzed in a uniform way. The uniform representation of the annotations can be used to facilitate the creation, analysis, reuse and visualization of rule-based models. Although examples are given, using specific implementations the proposed techniques can be applied to rule-based models in general. The annotation ontology for rule-based models can be found at http://purl.org/rbm/rbmo The krdf tool and associated executable examples are available at http://purl.org/rbm/rbmo/krdf anil.wipat@newcastle.ac.uk or vdanos@inf.ed.ac.uk. © The Author 2015. Published by Oxford University Press.

Community annotation experiment for ground truth generation for the i2b2 medication challenge

PubMed Central

Solti, Imre; Xia, Fei; Cadag, Eithon

2010-01-01

Objective Within the context of the Third i2b2 Workshop on Natural Language Processing Challenges for Clinical Records, the authors (also referred to as ‘the i2b2 medication challenge team’ or ‘the i2b2 team’ for short) organized a community annotation experiment. Design For this experiment, the authors released annotation guidelines and a small set of annotated discharge summaries. They asked the participants of the Third i2b2 Workshop to annotate 10 discharge summaries per person; each discharge summary was annotated by two annotators from two different teams, and a third annotator from a third team resolved disagreements. Measurements In order to evaluate the reliability of the annotations thus produced, the authors measured community inter-annotator agreement and compared it with the inter-annotator agreement of expert annotators when both the community and the expert annotators generated ground truth based on pooled system outputs. For this purpose, the pool consisted of the three most densely populated automatic annotations of each record. The authors also compared the community inter-annotator agreement with expert inter-annotator agreement when the experts annotated raw records without using the pool. Finally, they measured the quality of the community ground truth by comparing it with the expert ground truth. Results and conclusions The authors found that the community annotators achieved comparable inter-annotator agreement to expert annotators, regardless of whether the experts annotated from the pool. Furthermore, the ground truth generated by the community obtained F-measures above 0.90 against the ground truth of the experts, indicating the value of the community as a source of high-quality ground truth even on intricate and domain-specific annotation tasks. PMID:20819855

Bootstrapping a de-identification system for narrative patient records: cost-performance tradeoffs.

PubMed

Hanauer, David; Aberdeen, John; Bayer, Samuel; Wellner, Benjamin; Clark, Cheryl; Zheng, Kai; Hirschman, Lynette

2013-09-01

We describe an experiment to build a de-identification system for clinical records using the open source MITRE Identification Scrubber Toolkit (MIST). We quantify the human annotation effort needed to produce a system that de-identifies at high accuracy. Using two types of clinical records (history and physical notes, and social work notes), we iteratively built statistical de-identification models by annotating 10 notes, training a model, applying the model to another 10 notes, correcting the model's output, and training from the resulting larger set of annotated notes. This was repeated for 20 rounds of 10 notes each, and then an additional 6 rounds of 20 notes each, and a final round of 40 notes. At each stage, we measured precision, recall, and F-score, and compared these to the amount of annotation time needed to complete the round. After the initial 10-note round (33min of annotation time) we achieved an F-score of 0.89. After just over 8h of annotation time (round 21) we achieved an F-score of 0.95. Number of annotation actions needed, as well as time needed, decreased in later rounds as model performance improved. Accuracy on history and physical notes exceeded that of social work notes, suggesting that the wider variety and contexts for protected health information (PHI) in social work notes is more difficult to model. It is possible, with modest effort, to build a functioning de-identification system de novo using the MIST framework. The resulting system achieved performance comparable to other high-performing de-identification systems. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Communication spaces

PubMed Central

Coiera, Enrico

2014-01-01

Background and objective Annotations to physical workspaces such as signs and notes are ubiquitous. When densely annotated, work areas become communication spaces. This study aims to characterize the types and purpose of such annotations. Methods A qualitative observational study was undertaken in two wards and the radiology department of a 440-bed metropolitan teaching hospital. Images were purposefully sampled; 39 were analyzed after excluding inferior images. Results Annotation functions included signaling identity, location, capability, status, availability, and operation. They encoded data, rules or procedural descriptions. Most aggregated into groups that either created a workflow by referencing each other, supported a common workflow without reference to each other, or were heterogeneous, referring to many workflows. Higher-level assemblies of such groupings were also observed. Discussion Annotations make visible the gap between work done and the capability of a space to support work. Annotations are repairs of an environment, improving fitness for purpose, fixing inadequacy in design, or meeting emergent needs. Annotations thus record the missing information needed to undertake tasks, typically added post-implemented. Measuring annotation levels post-implementation could help assess the fit of technology to task. Physical and digital spaces could meet broader user needs by formally supporting user customization, ‘programming through annotation’. Augmented reality systems could also directly support annotation, addressing existing information gaps, and enhancing work with context sensitive annotation. Conclusions Communication spaces offer a model of how work unfolds. Annotations make visible local adaptation that makes technology fit for purpose post-implementation and suggest an important role for annotatable information systems and digital augmentation of the physical environment. PMID:24005797

A Semantic Web-based System for Mining Genetic Mutations in Cancer Clinical Trials.

PubMed

Priya, Sambhawa; Jiang, Guoqian; Dasari, Surendra; Zimmermann, Michael T; Wang, Chen; Heflin, Jeff; Chute, Christopher G

2015-01-01

Textual eligibility criteria in clinical trial protocols contain important information about potential clinically relevant pharmacogenomic events. Manual curation for harvesting this evidence is intractable as it is error prone and time consuming. In this paper, we develop and evaluate a Semantic Web-based system that captures and manages mutation evidences and related contextual information from cancer clinical trials. The system has 2 main components: an NLP-based annotator and a Semantic Web ontology-based annotation manager. We evaluated the performance of the annotator in terms of precision and recall. We demonstrated the usefulness of the system by conducting case studies in retrieving relevant clinical trials using a collection of mutations identified from TCGA Leukemia patients and Atlas of Genetics and Cytogenetics in Oncology and Haematology. In conclusion, our system using Semantic Web technologies provides an effective framework for extraction, annotation, standardization and management of genetic mutations in cancer clinical trials.

MEGANTE: A Web-Based System for Integrated Plant Genome Annotation

PubMed Central

Numa, Hisataka; Itoh, Takeshi

2014-01-01

The recent advancement of high-throughput genome sequencing technologies has resulted in a considerable increase in demands for large-scale genome annotation. While annotation is a crucial step for downstream data analyses and experimental studies, this process requires substantial expertise and knowledge of bioinformatics. Here we present MEGANTE, a web-based annotation system that makes plant genome annotation easy for researchers unfamiliar with bioinformatics. Without any complicated configuration, users can perform genomic sequence annotations simply by uploading a sequence and selecting the species to query. MEGANTE automatically runs several analysis programs and integrates the results to select the appropriate consensus exon–intron structures and to predict open reading frames (ORFs) at each locus. Functional annotation, including a similarity search against known proteins and a functional domain search, are also performed for the predicted ORFs. The resultant annotation information is visualized with a widely used genome browser, GBrowse. For ease of analysis, the results can be downloaded in Microsoft Excel format. All of the query sequences and annotation results are stored on the server side so that users can access their own data from virtually anywhere on the web. The current release of MEGANTE targets 24 plant species from the Brassicaceae, Fabaceae, Musaceae, Poaceae, Salicaceae, Solanaceae, Rosaceae and Vitaceae families, and it allows users to submit a sequence up to 10 Mb in length and to save up to 100 sequences with the annotation information on the server. The MEGANTE web service is available at https://megante.dna.affrc.go.jp/. PMID:24253915

Superior ab initio identification, annotation and characterisation of TEs and segmental duplications from genome assemblies.

PubMed

Zeng, Lu; Kortschak, R Daniel; Raison, Joy M; Bertozzi, Terry; Adelson, David L

2018-01-01

Transposable Elements (TEs) are mobile DNA sequences that make up significant fractions of amniote genomes. However, they are difficult to detect and annotate ab initio because of their variable features, lengths and clade-specific variants. We have addressed this problem by refining and developing a Comprehensive ab initio Repeat Pipeline (CARP) to identify and cluster TEs and other repetitive sequences in genome assemblies. The pipeline begins with a pairwise alignment using krishna, a custom aligner. Single linkage clustering is then carried out to produce families of repetitive elements. Consensus sequences are then filtered for protein coding genes and then annotated using Repbase and a custom library of retrovirus and reverse transcriptase sequences. This process yields three types of family: fully annotated, partially annotated and unannotated. Fully annotated families reflect recently diverged/young known TEs present in Repbase. The remaining two types of families contain a mixture of novel TEs and segmental duplications. These can be resolved by aligning these consensus sequences back to the genome to assess copy number vs. length distribution. Our pipeline has three significant advantages compared to other methods for ab initio repeat identification: 1) we generate not only consensus sequences, but keep the genomic intervals for the original aligned sequences, allowing straightforward analysis of evolutionary dynamics, 2) consensus sequences represent low-divergence, recently/currently active TE families, 3) segmental duplications are annotated as a useful by-product. We have compared our ab initio repeat annotations for 7 genome assemblies to other methods and demonstrate that CARP compares favourably with RepeatModeler, the most widely used repeat annotation package.

Superior ab initio identification, annotation and characterisation of TEs and segmental duplications from genome assemblies

PubMed Central

Zeng, Lu; Kortschak, R. Daniel; Raison, Joy M.

2018-01-01

Transposable Elements (TEs) are mobile DNA sequences that make up significant fractions of amniote genomes. However, they are difficult to detect and annotate ab initio because of their variable features, lengths and clade-specific variants. We have addressed this problem by refining and developing a Comprehensive ab initio Repeat Pipeline (CARP) to identify and cluster TEs and other repetitive sequences in genome assemblies. The pipeline begins with a pairwise alignment using krishna, a custom aligner. Single linkage clustering is then carried out to produce families of repetitive elements. Consensus sequences are then filtered for protein coding genes and then annotated using Repbase and a custom library of retrovirus and reverse transcriptase sequences. This process yields three types of family: fully annotated, partially annotated and unannotated. Fully annotated families reflect recently diverged/young known TEs present in Repbase. The remaining two types of families contain a mixture of novel TEs and segmental duplications. These can be resolved by aligning these consensus sequences back to the genome to assess copy number vs. length distribution. Our pipeline has three significant advantages compared to other methods for ab initio repeat identification: 1) we generate not only consensus sequences, but keep the genomic intervals for the original aligned sequences, allowing straightforward analysis of evolutionary dynamics, 2) consensus sequences represent low-divergence, recently/currently active TE families, 3) segmental duplications are annotated as a useful by-product. We have compared our ab initio repeat annotations for 7 genome assemblies to other methods and demonstrate that CARP compares favourably with RepeatModeler, the most widely used repeat annotation package. PMID:29538441

Vocational Instructional Materials for Distributive Education Available from Federal Agencies.

ERIC Educational Resources Information Center

Northwest Regional Educational Lab., Portland, OR.

This annotated bibliography lists curriculum materials for distributive education which were produced by Federal agencies and are appropriate for these subject matter areas: (1) advertising, (2) apparel and accessories, (3) automotive, (4) finance and credit, (5) food distribution and services, (6) general merchandise, (7) floristry, (8) hardware,…

Sensor Control And Film Annotation For Long Range, Standoff Reconnaissance

NASA Astrophysics Data System (ADS)

Schmidt, Thomas G.; Peters, Owen L.; Post, Lawrence H.

1984-12-01

This paper describes a Reconnaissance Data Annotation System that incorporates off-the-shelf technology and system designs providing a high degree of adaptability and interoperability to satisfy future reconnaissance data requirements. The history of data annotation for reconnaissance is reviewed in order to provide the base from which future developments can be assessed and technical risks minimized. The system described will accommodate new developments in recording head assemblies and the incorporation of advanced cameras of both the film and electro-optical type. Use of microprocessor control and digital bus inter-face form the central design philosophy. For long range, high altitude, standoff missions, the Data Annotation System computes the projected latitude and longitude of central target position from aircraft position and attitude. This complements the use of longer ranges and high altitudes for reconnaissance missions.

A multilingual gold-standard corpus for biomedical concept recognition: the Mantra GSC

PubMed Central

Clematide, Simon; Akhondi, Saber A; van Mulligen, Erik M; Rebholz-Schuhmann, Dietrich

2015-01-01

Objective To create a multilingual gold-standard corpus for biomedical concept recognition. Materials and methods We selected text units from different parallel corpora (Medline abstract titles, drug labels, biomedical patent claims) in English, French, German, Spanish, and Dutch. Three annotators per language independently annotated the biomedical concepts, based on a subset of the Unified Medical Language System and covering a wide range of semantic groups. To reduce the annotation workload, automatically generated preannotations were provided. Individual annotations were automatically harmonized and then adjudicated, and cross-language consistency checks were carried out to arrive at the final annotations. Results The number of final annotations was 5530. Inter-annotator agreement scores indicate good agreement (median F-score 0.79), and are similar to those between individual annotators and the gold standard. The automatically generated harmonized annotation set for each language performed equally well as the best annotator for that language. Discussion The use of automatic preannotations, harmonized annotations, and parallel corpora helped to keep the manual annotation efforts manageable. The inter-annotator agreement scores provide a reference standard for gauging the performance of automatic annotation techniques. Conclusion To our knowledge, this is the first gold-standard corpus for biomedical concept recognition in languages other than English. Other distinguishing features are the wide variety of semantic groups that are being covered, and the diversity of text genres that were annotated. PMID:25948699

U-Science (Invited)

NASA Astrophysics Data System (ADS)

Borne, K. D.

2009-12-01

The emergence of e-Science over the past decade as a paradigm for Internet-based science was an inevitable evolution of science that built upon the web protocols and access patterns that were prevalent at that time, including Web Services, XML-based information exchange, machine-to-machine communication, service registries, the Grid, and distributed data. We now see a major shift in web behavior patterns to social networks, user-provided content (e.g., tags and annotations), ubiquitous devices, user-centric experiences, and user-led activities. The inevitable accrual of these social networking patterns and protocols by scientists and science projects leads to U-Science as a new paradigm for online scientific research (i.e., ubiquitous, user-led, untethered, You-centered science). U-Science applications include components from semantic e-science (ontologies, taxonomies, folksonomies, tagging, annotations, and classification systems), which is much more than Web 2.0-based science (Wikis, blogs, and online environments like Second Life). Among the best examples of U-Science are Citizen Science projects, including Galaxy Zoo, Stardust@Home, Project Budburst, Volksdata, CoCoRaHS (the Community Collaborative Rain, Hail and Snow network), and projects utilizing Volunteer Geographic Information (VGI). There are also scientist-led projects for scientists that engage a wider community in building knowledge through user-provided content. Among the semantic-based U-Science projects for scientists are those that specifically enable user-based annotation of scientific results in databases. These include the Heliophysics Knowledgebase, BioDAS, WikiProteins, The Entity Describer, and eventually AstroDAS. Such collaborative tagging of scientific data addresses several petascale data challenges for scientists: how to find the most relevant data, how to reuse those data, how to integrate data from multiple sources, how to mine and discover new knowledge in large databases, how to represent and encode the new knowledge, and how to curate the discovered knowledge. This talk will address the emergence of U-Science as a type of Semantic e-Science, and will explore challenges, implementations, and results. Semantic e-Science and U-Science applications and concepts will be discussed within the context of one particular implementation (AstroDAS: Astronomy Distributed Annotation System) and its applicability to petascale science projects such as the LSST (Large Synoptic Survey Telescope), coming online within the next few years.

Multimodal MSI in Conjunction with Broad Coverage Spatially Resolved MS 2 Increases Confidence in Both Molecular Identification and Localization

DOE PAGES

Veličković, Dušan; Chu, Rosalie K.; Carrell, Alyssa A.; ...

2017-12-06

One critical aspect of mass spectrometry imaging (MSI) is the need to confidently identify detected analytes. While orthogonal tandem MS (e.g., LC–MS 2) experiments from sample extracts can assist in annotating ions, the spatial information about these molecules is lost. Accordingly, this could cause mislead conclusions, especially in cases where isobaric species exhibit different distributions within a sample. In this Technical Note, we employed a multimodal imaging approach, using matrix assisted laser desorption/ionization (MALDI)-MSI and liquid extraction surface analysis (LESA)-MS 2I, to confidently annotate and localize a broad range of metabolites involved in a tripartite symbiosis system of moss, cyanobacteria,more » and fungus. In conclusion, we found that the combination of these two imaging modalities generated very congruent ion images, providing the link between highly accurate structural information onfered by LESA and high spatial resolution attainable by MALDI. These results demonstrate how this combined methodology could be very useful in differentiating metabolite routes in complex systems.« less

Multimodal MSI in Conjunction with Broad Coverage Spatially Resolved MS 2 Increases Confidence in Both Molecular Identification and Localization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Veličković, Dušan; Chu, Rosalie K.; Carrell, Alyssa A.

One critical aspect of mass spectrometry imaging (MSI) is the need to confidently identify detected analytes. While orthogonal tandem MS (e.g., LC–MS 2) experiments from sample extracts can assist in annotating ions, the spatial information about these molecules is lost. Accordingly, this could cause mislead conclusions, especially in cases where isobaric species exhibit different distributions within a sample. In this Technical Note, we employed a multimodal imaging approach, using matrix assisted laser desorption/ionization (MALDI)-MSI and liquid extraction surface analysis (LESA)-MS 2I, to confidently annotate and localize a broad range of metabolites involved in a tripartite symbiosis system of moss, cyanobacteria,more » and fungus. In conclusion, we found that the combination of these two imaging modalities generated very congruent ion images, providing the link between highly accurate structural information onfered by LESA and high spatial resolution attainable by MALDI. These results demonstrate how this combined methodology could be very useful in differentiating metabolite routes in complex systems.« less

Knowledge Extraction and Semantic Annotation of Text from the Encyclopedia of Life

PubMed Central

Thessen, Anne E.; Parr, Cynthia Sims

2014-01-01

Numerous digitization and ontological initiatives have focused on translating biological knowledge from narrative text to machine-readable formats. In this paper, we describe two workflows for knowledge extraction and semantic annotation of text data objects featured in an online biodiversity aggregator, the Encyclopedia of Life. One workflow tags text with DBpedia URIs based on keywords. Another workflow finds taxon names in text using GNRD for the purpose of building a species association network. Both workflows work well: the annotation workflow has an F1 Score of 0.941 and the association algorithm has an F1 Score of 0.885. Existing text annotators such as Terminizer and DBpedia Spotlight performed well, but require some optimization to be useful in the ecology and evolution domain. Important future work includes scaling up and improving accuracy through the use of distributional semantics. PMID:24594988

Impact of ontology evolution on functional analyses.

PubMed

Groß, Anika; Hartung, Michael; Prüfer, Kay; Kelso, Janet; Rahm, Erhard

2012-10-15

Ontologies are used in the annotation and analysis of biological data. As knowledge accumulates, ontologies and annotation undergo constant modifications to reflect this new knowledge. These modifications may influence the results of statistical applications such as functional enrichment analyses that describe experimental data in terms of ontological groupings. Here, we investigate to what degree modifications of the Gene Ontology (GO) impact these statistical analyses for both experimental and simulated data. The analysis is based on new measures for the stability of result sets and considers different ontology and annotation changes. Our results show that past changes in the GO are non-uniformly distributed over different branches of the ontology. Considering the semantic relatedness of significant categories in analysis results allows a more realistic stability assessment for functional enrichment studies. We observe that the results of term-enrichment analyses tend to be surprisingly stable despite changes in ontology and annotation.

Temporal Annotation in the Clinical Domain

PubMed Central

Styler, William F.; Bethard, Steven; Finan, Sean; Palmer, Martha; Pradhan, Sameer; de Groen, Piet C; Erickson, Brad; Miller, Timothy; Lin, Chen; Savova, Guergana; Pustejovsky, James

2014-01-01

This article discusses the requirements of a formal specification for the annotation of temporal information in clinical narratives. We discuss the implementation and extension of ISO-TimeML for annotating a corpus of clinical notes, known as the THYME corpus. To reflect the information task and the heavily inference-based reasoning demands in the domain, a new annotation guideline has been developed, “the THYME Guidelines to ISO-TimeML (THYME-TimeML)”. To clarify what relations merit annotation, we distinguish between linguistically-derived and inferentially-derived temporal orderings in the text. We also apply a top performing TempEval 2013 system against this new resource to measure the difficulty of adapting systems to the clinical domain. The corpus is available to the community and has been proposed for use in a SemEval 2015 task. PMID:29082229

Combining rules, background knowledge and change patterns to maintain semantic annotations.

PubMed

Cardoso, Silvio Domingos; Chantal, Reynaud-Delaître; Da Silveira, Marcos; Pruski, Cédric

2017-01-01

Knowledge Organization Systems (KOS) play a key role in enriching biomedical information in order to make it machine-understandable and shareable. This is done by annotating medical documents, or more specifically, associating concept labels from KOS with pieces of digital information, e.g., images or texts. However, the dynamic nature of KOS may impact the annotations, thus creating a mismatch between the evolved concept and the associated information. To solve this problem, methods to maintain the quality of the annotations are required. In this paper, we define a framework based on rules, background knowledge and change patterns to drive the annotation adaption process. We evaluate experimentally the proposed approach in realistic cases-studies and demonstrate the overall performance of our approach in different KOS considering the precision, recall, F1-score and AUC value of the system.

Combining rules, background knowledge and change patterns to maintain semantic annotations

PubMed Central

Cardoso, Silvio Domingos; Chantal, Reynaud-Delaître; Da Silveira, Marcos; Pruski, Cédric

2017-01-01

Knowledge Organization Systems (KOS) play a key role in enriching biomedical information in order to make it machine-understandable and shareable. This is done by annotating medical documents, or more specifically, associating concept labels from KOS with pieces of digital information, e.g., images or texts. However, the dynamic nature of KOS may impact the annotations, thus creating a mismatch between the evolved concept and the associated information. To solve this problem, methods to maintain the quality of the annotations are required. In this paper, we define a framework based on rules, background knowledge and change patterns to drive the annotation adaption process. We evaluate experimentally the proposed approach in realistic cases-studies and demonstrate the overall performance of our approach in different KOS considering the precision, recall, F1-score and AUC value of the system. PMID:29854115

Type-Separated Bytecode - Its Construction and Evaluation

NASA Astrophysics Data System (ADS)

Adler, Philipp; Amme, Wolfram

A lot of constrained systems still use interpreters to run mobile applications written in Java. These interpreters demand for only a few resources. On the other hand, it is difficult to apply optimizations during the runtime of the application. Annotations could be used to achieve a simpler and faster code analysis, which would allow optimizations even for interpreters on constrained devices. Unfortunately, there is no viable way of transporting annotations to and verifying them at the code consumer. In this paper we present type-separated bytecode as an intermediate representation which allows to safely transport annotations as type-extensions. We have implemented several versions of this system and show that it is possible to obtain a performance comparable to Java Bytecode, even though we use a type-separated system with annotations.

An annotated bibliography of completed and in-progress behavioral research for the Office of Buildings and Community Systems. [About 1000 items, usually with abstracts

DOE Office of Scientific and Technical Information (OSTI.GOV)

Weijo, R.O.; Roberson, B.F.; Eckert, R.

This report provides an annotated bibliography of completed and in-progress consumer decision research useful for technology transfer and commercialization planning by the US Department of Energy's (DOE) Office of Buildings and Community Systems (OBCS). This report attempts to integrate the consumer research studies conducted across several public and private organizations over the last four to five years. Some of the sources of studies included in this annotated bibliography are DOE National Laboratories, public and private utilities, trade associations, states, and nonprofit organizations. This study divides the articles identified in this annotated bibliography into sections that are consistent with or similarmore » to the system of organization used by OBCS.« less

Integrating UIMA annotators in a web-based text processing framework.

PubMed

Chen, Xiang; Arnold, Corey W

2013-01-01

The Unstructured Information Management Architecture (UIMA) [1] framework is a growing platform for natural language processing (NLP) applications. However, such applications may be difficult for non-technical users deploy. This project presents a web-based framework that wraps UIMA-based annotator systems into a graphical user interface for researchers and clinicians, and a web service for developers. An annotator that extracts data elements from lung cancer radiology reports is presented to illustrate the use of the system. Annotation results from the web system can be exported to multiple formats for users to utilize in other aspects of their research and workflow. This project demonstrates the benefits of a lay-user interface for complex NLP applications. Efforts such as this can lead to increased interest and support for NLP work in the clinical domain.

THE DIMENSIONS OF COMPOSITION ANNOTATION.

ERIC Educational Resources Information Center

MCCOLLY, WILLIAM

ENGLISH TEACHER ANNOTATIONS WERE STUDIED TO DETERMINE THE DIMENSIONS AND PROPERTIES OF THE ENTIRE SYSTEM FOR WRITING CORRECTIONS AND CRITICISMS ON COMPOSITIONS. FOUR SETS OF COMPOSITIONS WERE WRITTEN BY STUDENTS IN GRADES 9 THROUGH 13. TYPESCRIPTS OF THE COMPOSITIONS WERE ANNOTATED BY CLASSROOM ENGLISH TEACHERS. THEN, 32 ENGLISH TEACHERS JUDGED…

K-Nearest Neighbors Relevance Annotation Model for Distance Education

ERIC Educational Resources Information Center

Ke, Xiao; Li, Shaozi; Cao, Donglin

2011-01-01

With the rapid development of Internet technologies, distance education has become a popular educational mode. In this paper, the authors propose an online image automatic annotation distance education system, which could effectively help children learn interrelations between image content and corresponding keywords. Image automatic annotation is…

Data annotation, recording and mapping system for the US open skies aircraft

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, B.W.; Goede, W.F.; Farmer, R.G.

1996-11-01

This paper discusses the system developed by Northrop Grumman for the Defense Nuclear Agency (DNA), US Air Force, and the On-Site Inspection Agency (OSIA) to comply with the data annotation and reporting provisions of the Open Skies Treaty. This system, called the Data Annotation, Recording and Mapping System (DARMS), has been installed on the US OC-135 and meets or exceeds all annotation requirements for the Open Skies Treaty. The Open Skies Treaty, which will enter into force in the near future, allows any of the 26 signatory countries to fly fixed wing aircraft with imaging sensors over any of themore » other treaty participants, upon very short notice, and with no restricted flight areas. Sensor types presently allowed by the treaty are: optical framing and panoramic film cameras; video cameras ranging from analog PAL color television cameras to the more sophisticated digital monochrome and color line scanning or framing cameras; infrared line scanners; and synthetic aperture radars. Each sensor type has specific performance parameters which are limited by the treaty, as well as specific annotation requirements which must be achieved upon full entry into force. DARMS supports U.S. compliance with the Opens Skies Treaty by means of three subsystems: the Data Annotation Subsytem (DAS), which annotates sensor media with data obtained from sensors and the aircraft`s avionics system; the Data Recording System (DRS), which records all sensor and flight events on magnetic media for later use in generating Treaty mandated mission reports; and the Dynamic Sensor Mapping Subsystem (DSMS), which provides observers and sensor operators with a real-time moving map displays of the progress of the mission, complete with instantaneous and cumulative sensor coverages. This paper will describe DARMS and its subsystems in greater detail, along with the supporting avionics sub-systems. 7 figs.« less

Experimental annotation of post-translational features and translated coding regions in the pathogen Salmonella Typhimurium

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ansong, Charles; Tolic, Nikola; Purvine, Samuel O.

Complete and accurate genome annotation is crucial for comprehensive and systematic studies of biological systems. For example systems biology-oriented genome scale modeling efforts greatly benefit from accurate annotation of protein-coding genes to develop proper functioning models. However, determining protein-coding genes for most new genomes is almost completely performed by inference, using computational predictions with significant documented error rates (> 15%). Furthermore, gene prediction programs provide no information on biologically important post-translational processing events critical for protein function. With the ability to directly measure peptides arising from expressed proteins, mass spectrometry-based proteomics approaches can be used to augment and verify codingmore » regions of a genomic sequence and importantly detect post-translational processing events. In this study we utilized “shotgun” proteomics to guide accurate primary genome annotation of the bacterial pathogen Salmonella Typhimurium 14028 to facilitate a systems-level understanding of Salmonella biology. The data provides protein-level experimental confirmation for 44% of predicted protein-coding genes, suggests revisions to 48 genes assigned incorrect translational start sites, and uncovers 13 non-annotated genes missed by gene prediction programs. We also present a comprehensive analysis of post-translational processing events in Salmonella, revealing a wide range of complex chemical modifications (70 distinct modifications) and confirming more than 130 signal peptide and N-terminal methionine cleavage events in Salmonella. This study highlights several ways in which proteomics data applied during the primary stages of annotation can improve the quality of genome annotations, especially with regards to the annotation of mature protein products.« less

BEACON: automated tool for Bacterial GEnome Annotation ComparisON.

PubMed

Kalkatawi, Manal; Alam, Intikhab; Bajic, Vladimir B

2015-08-18

Genome annotation is one way of summarizing the existing knowledge about genomic characteristics of an organism. There has been an increased interest during the last several decades in computer-based structural and functional genome annotation. Many methods for this purpose have been developed for eukaryotes and prokaryotes. Our study focuses on comparison of functional annotations of prokaryotic genomes. To the best of our knowledge there is no fully automated system for detailed comparison of functional genome annotations generated by different annotation methods (AMs). The presence of many AMs and development of new ones introduce needs to: a/ compare different annotations for a single genome, and b/ generate annotation by combining individual ones. To address these issues we developed an Automated Tool for Bacterial GEnome Annotation ComparisON (BEACON) that benefits both AM developers and annotation analysers. BEACON provides detailed comparison of gene function annotations of prokaryotic genomes obtained by different AMs and generates extended annotations through combination of individual ones. For the illustration of BEACON's utility, we provide a comparison analysis of multiple different annotations generated for four genomes and show on these examples that the extended annotation can increase the number of genes annotated by putative functions up to 27%, while the number of genes without any function assignment is reduced. We developed BEACON, a fast tool for an automated and a systematic comparison of different annotations of single genomes. The extended annotation assigns putative functions to many genes with unknown functions. BEACON is available under GNU General Public License version 3.0 and is accessible at: http://www.cbrc.kaust.edu.sa/BEACON/ .

Improved Genome Assembly and Annotation for the Rock Pigeon (Columba livia).

PubMed

Holt, Carson; Campbell, Michael; Keays, David A; Edelman, Nathaniel; Kapusta, Aurélie; Maclary, Emily; T Domyan, Eric; Suh, Alexander; Warren, Wesley C; Yandell, Mark; Gilbert, M Thomas P; Shapiro, Michael D

2018-05-04

The domestic rock pigeon ( Columba livia ) is among the most widely distributed and phenotypically diverse avian species. C. livia is broadly studied in ecology, genetics, physiology, behavior, and evolutionary biology, and has recently emerged as a model for understanding the molecular basis of anatomical diversity, the magnetic sense, and other key aspects of avian biology. Here we report an update to the C. livia genome reference assembly and gene annotation dataset. Greatly increased scaffold lengths in the updated reference assembly, along with an updated annotation set, provide improved tools for evolutionary and functional genetic studies of the pigeon, and for comparative avian genomics in general. Copyright © 2018 Holt et al.

An annotated catalogue of the mayfly fauna of Turkey (Insecta, Ephemeroptera)

PubMed Central

Salur, Ali; Darilmaz, Mustafa Cemal; Bauernfeind, Ernst

2016-01-01

Abstract The mayfly fauna of Turkey was reviewed including all hitherto known distribution records together with references and a few new records. Additionally, comments on taxonomy, identification and nomenclature are provided. Two species are new for the Turkish fauna: Ephemera romantzovi Kluge, 1988 and Thraulus thraker Jacob, 1988. A list of taxa including their recorded distribution in Turkey (according to provinces) is provided in the annotated catalogue. The type locality is also given for each species originally described from Turkey. According to the literature and the new records, 157 mayfly taxa representing 33 genera and 14 families were described from Turkey. Among them, 24 species are considered endemic to Anatolia. PMID:27853408

Towards the VWO Annotation Service: a Success Story of the IMAGE RPI Expert Rating System

NASA Astrophysics Data System (ADS)

Reinisch, B. W.; Galkin, I. A.; Fung, S. F.; Benson, R. F.; Kozlov, A. V.; Khmyrov, G. M.; Garcia, L. N.

2010-12-01

Interpretation of Heliophysics wave data requires specialized knowledge of wave phenomena. Users of the virtual wave observatory (VWO) will greatly benefit from a data annotation service that will allow querying of data by phenomenon type, thus helping accomplish the VWO goal to make Heliophysics wave data searchable, understandable, and usable by the scientific community. Individual annotations can be sorted by phenomenon type and reduced into event lists (catalogs). However, in contrast to the event lists, annotation records allow a greater flexibility of collaborative management by more easily admitting operations of addition, revision, or deletion. They can therefore become the building blocks for an interactive Annotation Service with a suitable graphic user interface to the VWO middleware. The VWO Annotation Service vision is an interactive, collaborative sharing of domain expert knowledge with fellow scientists and students alike. An effective prototype of the VWO Annotation Service has been in operation at the University of Massachusetts Lowell since 2001. An expert rating system (ERS) was developed for annotating the IMAGE radio plasma imager (RPI) active sounding data containing 1.2 million plasmagrams. The RPI data analysts can use ERS to submit expert ratings of plasmagram features, such as presence of echo traces resulted from reflected RPI signals from distant plasma structures. Since its inception in 2001, the RPI ERS has accumulated 7351 expert plasmagram ratings in 16 phenomenon categories, together with free-text descriptions and other metadata. In addition to human expert ratings, the system holds 225,125 ratings submitted by the CORPRAL data prospecting software that employs a model of the human pre-attentive vision to select images potentially containing interesting features. The annotation records proved to be instrumental in a number of investigations where manual data exploration would have been prohibitively tedious and expensive. Especially useful are queries of the annotation database for successive plasmagrams containing echo traces. Several success stories of the RPI ERS using this capability will be discussed, particularly in terms of how they may be extended to develop the VWO Annotation Service.

A multilingual gold-standard corpus for biomedical concept recognition: the Mantra GSC.

PubMed

Kors, Jan A; Clematide, Simon; Akhondi, Saber A; van Mulligen, Erik M; Rebholz-Schuhmann, Dietrich

2015-09-01

To create a multilingual gold-standard corpus for biomedical concept recognition. We selected text units from different parallel corpora (Medline abstract titles, drug labels, biomedical patent claims) in English, French, German, Spanish, and Dutch. Three annotators per language independently annotated the biomedical concepts, based on a subset of the Unified Medical Language System and covering a wide range of semantic groups. To reduce the annotation workload, automatically generated preannotations were provided. Individual annotations were automatically harmonized and then adjudicated, and cross-language consistency checks were carried out to arrive at the final annotations. The number of final annotations was 5530. Inter-annotator agreement scores indicate good agreement (median F-score 0.79), and are similar to those between individual annotators and the gold standard. The automatically generated harmonized annotation set for each language performed equally well as the best annotator for that language. The use of automatic preannotations, harmonized annotations, and parallel corpora helped to keep the manual annotation efforts manageable. The inter-annotator agreement scores provide a reference standard for gauging the performance of automatic annotation techniques. To our knowledge, this is the first gold-standard corpus for biomedical concept recognition in languages other than English. Other distinguishing features are the wide variety of semantic groups that are being covered, and the diversity of text genres that were annotated. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

The standard operating procedure of the DOE-JGI Microbial Genome Annotation Pipeline (MGAP v.4).

PubMed

Huntemann, Marcel; Ivanova, Natalia N; Mavromatis, Konstantinos; Tripp, H James; Paez-Espino, David; Palaniappan, Krishnaveni; Szeto, Ernest; Pillay, Manoj; Chen, I-Min A; Pati, Amrita; Nielsen, Torben; Markowitz, Victor M; Kyrpides, Nikos C

2015-01-01

The DOE-JGI Microbial Genome Annotation Pipeline performs structural and functional annotation of microbial genomes that are further included into the Integrated Microbial Genome comparative analysis system. MGAP is applied to assembled nucleotide sequence datasets that are provided via the IMG submission site. Dataset submission for annotation first requires project and associated metadata description in GOLD. The MGAP sequence data processing consists of feature prediction including identification of protein-coding genes, non-coding RNAs and regulatory RNA features, as well as CRISPR elements. Structural annotation is followed by assignment of protein product names and functions.

Systemic Planning: An Annotated Bibliography and Literature Guide. Exchange Bibliography No. 91.

ERIC Educational Resources Information Center

Catanese, Anthony James

Systemic planning is an operational approach to using scientific rigor and qualitative judgment in a complementary manner. It integrates rigorous techniques and methods from systems analysis, cybernetics, decision theory, and work programing. The annotated reference sources in this bibliography include those works that have been most influential…

CommWalker: correctly evaluating modules in molecular networks in light of annotation bias.

PubMed

Luecken, M D; Page, M J T; Crosby, A J; Mason, S; Reinert, G; Deane, C M

2018-03-15

Detecting novel functional modules in molecular networks is an important step in biological research. In the absence of gold standard functional modules, functional annotations are often used to verify whether detected modules/communities have biological meaning. However, as we show, the uneven distribution of functional annotations means that such evaluation methods favor communities of well-studied proteins. We propose a novel framework for the evaluation of communities as functional modules. Our proposed framework, CommWalker, takes communities as inputs and evaluates them in their local network environment by performing short random walks. We test CommWalker's ability to overcome annotation bias using input communities from four community detection methods on two protein interaction networks. We find that modules accepted by CommWalker are similarly co-expressed as those accepted by current methods. Crucially, CommWalker performs well not only in well-annotated regions, but also in regions otherwise obscured by poor annotation. CommWalker community prioritization both faithfully captures well-validated communities and identifies functional modules that may correspond to more novel biology. The CommWalker algorithm is freely available at opig.stats.ox.ac.uk/resources or as a docker image on the Docker Hub at hub.docker.com/r/lueckenmd/commwalker/. deane@stats.ox.ac.uk. Supplementary data are available at Bioinformatics online.

Comparison of Natural Language Processing Rules-based and Machine-learning Systems to Identify Lumbar Spine Imaging Findings Related to Low Back Pain.

PubMed

Tan, W Katherine; Hassanpour, Saeed; Heagerty, Patrick J; Rundell, Sean D; Suri, Pradeep; Huhdanpaa, Hannu T; James, Kathryn; Carrell, David S; Langlotz, Curtis P; Organ, Nancy L; Meier, Eric N; Sherman, Karen J; Kallmes, David F; Luetmer, Patrick H; Griffith, Brent; Nerenz, David R; Jarvik, Jeffrey G

2018-03-28

To evaluate a natural language processing (NLP) system built with open-source tools for identification of lumbar spine imaging findings related to low back pain on magnetic resonance and x-ray radiology reports from four health systems. We used a limited data set (de-identified except for dates) sampled from lumbar spine imaging reports of a prospectively assembled cohort of adults. From N = 178,333 reports, we randomly selected N = 871 to form a reference-standard dataset, consisting of N = 413 x-ray reports and N = 458 MR reports. Using standardized criteria, four spine experts annotated the presence of 26 findings, where 71 reports were annotated by all four experts and 800 were each annotated by two experts. We calculated inter-rater agreement and finding prevalence from annotated data. We randomly split the annotated data into development (80%) and testing (20%) sets. We developed an NLP system from both rule-based and machine-learned models. We validated the system using accuracy metrics such as sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The multirater annotated dataset achieved inter-rater agreement of Cohen's kappa > 0.60 (substantial agreement) for 25 of 26 findings, with finding prevalence ranging from 3% to 89%. In the testing sample, rule-based and machine-learned predictions both had comparable average specificity (0.97 and 0.95, respectively). The machine-learned approach had a higher average sensitivity (0.94, compared to 0.83 for rules-based), and a higher overall AUC (0.98, compared to 0.90 for rules-based). Our NLP system performed well in identifying the 26 lumbar spine findings, as benchmarked by reference-standard annotation by medical experts. Machine-learned models provided substantial gains in model sensitivity with slight loss of specificity, and overall higher AUC. Copyright © 2018 The Association of University Radiologists. All rights reserved.

Year 2 Report: Protein Function Prediction Platform

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhou, C E

2012-04-27

Upon completion of our second year of development in a 3-year development cycle, we have completed a prototype protein structure-function annotation and function prediction system: Protein Function Prediction (PFP) platform (v.0.5). We have met our milestones for Years 1 and 2 and are positioned to continue development in completion of our original statement of work, or a reasonable modification thereof, in service to DTRA Programs involved in diagnostics and medical countermeasures research and development. The PFP platform is a multi-scale computational modeling system for protein structure-function annotation and function prediction. As of this writing, PFP is the only existing fullymore » automated, high-throughput, multi-scale modeling, whole-proteome annotation platform, and represents a significant advance in the field of genome annotation (Fig. 1). PFP modules perform protein functional annotations at the sequence, systems biology, protein structure, and atomistic levels of biological complexity (Fig. 2). Because these approaches provide orthogonal means of characterizing proteins and suggesting protein function, PFP processing maximizes the protein functional information that can currently be gained by computational means. Comprehensive annotation of pathogen genomes is essential for bio-defense applications in pathogen characterization, threat assessment, and medical countermeasure design and development in that it can short-cut the time and effort required to select and characterize protein biomarkers.« less

The role of fine-grained annotations in supervised recognition of risk factors for heart disease from EHRs.

PubMed

Roberts, Kirk; Shooshan, Sonya E; Rodriguez, Laritza; Abhyankar, Swapna; Kilicoglu, Halil; Demner-Fushman, Dina

2015-12-01

This paper describes a supervised machine learning approach for identifying heart disease risk factors in clinical text, and assessing the impact of annotation granularity and quality on the system's ability to recognize these risk factors. We utilize a series of support vector machine models in conjunction with manually built lexicons to classify triggers specific to each risk factor. The features used for classification were quite simple, utilizing only lexical information and ignoring higher-level linguistic information such as syntax and semantics. Instead, we incorporated high-quality data to train the models by annotating additional information on top of a standard corpus. Despite the relative simplicity of the system, it achieves the highest scores (micro- and macro-F1, and micro- and macro-recall) out of the 20 participants in the 2014 i2b2/UTHealth Shared Task. This system obtains a micro- (macro-) precision of 0.8951 (0.8965), recall of 0.9625 (0.9611), and F1-measure of 0.9276 (0.9277). Additionally, we perform a series of experiments to assess the value of the annotated data we created. These experiments show how manually-labeled negative annotations can improve information extraction performance, demonstrating the importance of high-quality, fine-grained natural language annotations. Copyright © 2015 Elsevier Inc. All rights reserved.

Improved Annotation of 3′ Untranslated Regions and Complex Loci by Combination of Strand-Specific Direct RNA Sequencing, RNA-Seq and ESTs

PubMed Central

Song, Junfang; Duc, Céline; Storey, Kate G.; McLean, W. H. Irwin; Brown, Sara J.; Simpson, Gordon G.; Barton, Geoffrey J.

2014-01-01

The reference annotations made for a genome sequence provide the framework for all subsequent analyses of the genome. Correct and complete annotation in addition to the underlying genomic sequence is particularly important when interpreting the results of RNA-seq experiments where short sequence reads are mapped against the genome and assigned to genes according to the annotation. Inconsistencies in annotations between the reference and the experimental system can lead to incorrect interpretation of the effect on RNA expression of an experimental treatment or mutation in the system under study. Until recently, the genome-wide annotation of 3′ untranslated regions received less attention than coding regions and the delineation of intron/exon boundaries. In this paper, data produced for samples in Human, Chicken and A. thaliana by the novel single-molecule, strand-specific, Direct RNA Sequencing technology from Helicos Biosciences which locates 3′ polyadenylation sites to within +/− 2 nt, were combined with archival EST and RNA-Seq data. Nine examples are illustrated where this combination of data allowed: (1) gene and 3′ UTR re-annotation (including extension of one 3′ UTR by 5.9 kb); (2) disentangling of gene expression in complex regions; (3) clearer interpretation of small RNA expression and (4) identification of novel genes. While the specific examples displayed here may become obsolete as genome sequences and their annotations are refined, the principles laid out in this paper will be of general use both to those annotating genomes and those seeking to interpret existing publically available annotations in the context of their own experimental data. PMID:24722185

An Annotated Bibliography of MANPRINT-Related Assessments and Evaluations Conducted by the U.S. Army, 2nd Edition: 1953 to 2009. Volume 2 - MANPRINT Assessment and Evaluations

DTIC Science & Technology

2010-02-01

Research Laboratory Aberdeen Proving Ground, MD 21005-5425 ARL-SR-189 February 2010 An Annotated Bibliography of MANPRINT-Related...Middlebrooks, Ph.D. Human Research and Engineering Directorate, ARL Approved for public release; distribution...7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) U.S. Army Research Laboratory ATTN: RDRL-HRM-AV Fort Hood, TX 76544-5073 8. PERFORMING

Protein Annotators' Assistant: A Novel Application of Information Retrieval Techniques.

ERIC Educational Resources Information Center

Wise, Michael J.

2000-01-01

Protein Annotators' Assistant (PAA) is a software system which assists protein annotators in assigning functions to newly sequenced proteins. PAA employs a number of information retrieval techniques in a novel setting and is thus related to text categorization, where multiple categories may be suggested, except that in this case none of the…

Collective dynamics of social annotation

PubMed Central

Cattuto, Ciro; Barrat, Alain; Baldassarri, Andrea; Schehr, Gregory; Loreto, Vittorio

2009-01-01

The enormous increase of popularity and use of the worldwide web has led in the recent years to important changes in the ways people communicate. An interesting example of this fact is provided by the now very popular social annotation systems, through which users annotate resources (such as web pages or digital photographs) with keywords known as “tags.” Understanding the rich emergent structures resulting from the uncoordinated actions of users calls for an interdisciplinary effort. In particular concepts borrowed from statistical physics, such as random walks (RWs), and complex networks theory, can effectively contribute to the mathematical modeling of social annotation systems. Here, we show that the process of social annotation can be seen as a collective but uncoordinated exploration of an underlying semantic space, pictured as a graph, through a series of RWs. This modeling framework reproduces several aspects, thus far unexplained, of social annotation, among which are the peculiar growth of the size of the vocabulary used by the community and its complex network structure that represents an externalization of semantic structures grounded in cognition and that are typically hard to access. PMID:19506244

BIOSSES: a semantic sentence similarity estimation system for the biomedical domain.

PubMed

Sogancioglu, Gizem; Öztürk, Hakime; Özgür, Arzucan

2017-07-15

The amount of information available in textual format is rapidly increasing in the biomedical domain. Therefore, natural language processing (NLP) applications are becoming increasingly important to facilitate the retrieval and analysis of these data. Computing the semantic similarity between sentences is an important component in many NLP tasks including text retrieval and summarization. A number of approaches have been proposed for semantic sentence similarity estimation for generic English. However, our experiments showed that such approaches do not effectively cover biomedical knowledge and produce poor results for biomedical text. We propose several approaches for sentence-level semantic similarity computation in the biomedical domain, including string similarity measures and measures based on the distributed vector representations of sentences learned in an unsupervised manner from a large biomedical corpus. In addition, ontology-based approaches are presented that utilize general and domain-specific ontologies. Finally, a supervised regression based model is developed that effectively combines the different similarity computation metrics. A benchmark data set consisting of 100 sentence pairs from the biomedical literature is manually annotated by five human experts and used for evaluating the proposed methods. The experiments showed that the supervised semantic sentence similarity computation approach obtained the best performance (0.836 correlation with gold standard human annotations) and improved over the state-of-the-art domain-independent systems up to 42.6% in terms of the Pearson correlation metric. A web-based system for biomedical semantic sentence similarity computation, the source code, and the annotated benchmark data set are available at: http://tabilab.cmpe.boun.edu.tr/BIOSSES/ . gizemsogancioglu@gmail.com or arzucan.ozgur@boun.edu.tr. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations

PubMed Central

Miyao, Yusuke; Collier, Nigel

2017-01-01

Background Work on pharmacovigilance systems using texts from PubMed and Twitter typically target at different elements and use different annotation guidelines resulting in a scenario where there is no comparable set of documents from both Twitter and PubMed annotated in the same manner. Objective This study aimed to provide a comparable corpus of texts from PubMed and Twitter that can be used to study drug reports from these two sources of information, allowing researchers in the area of pharmacovigilance using natural language processing (NLP) to perform experiments to better understand the similarities and differences between drug reports in Twitter and PubMed. Methods We produced a corpus comprising 1000 tweets and 1000 PubMed sentences selected using the same strategy and annotated at entity level by the same experts (pharmacists) using the same set of guidelines. Results The resulting corpus, annotated by two pharmacists, comprises semantically correct annotations for a set of drugs, diseases, and symptoms. This corpus contains the annotations for 3144 entities, 2749 relations, and 5003 attributes. Conclusions We present a corpus that is unique in its characteristics as this is the first corpus for pharmacovigilance curated from Twitter messages and PubMed sentences using the same data selection and annotation strategies. We believe this corpus will be of particular interest for researchers willing to compare results from pharmacovigilance systems (eg, classifiers and named entity recognition systems) when using data from Twitter and from PubMed. We hope that given the comprehensive set of drug names and the annotated entities and relations, this corpus becomes a standard resource to compare results from different pharmacovigilance studies in the area of NLP. PMID:28468748

TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations.

PubMed

Alvaro, Nestor; Miyao, Yusuke; Collier, Nigel

2017-05-03

Work on pharmacovigilance systems using texts from PubMed and Twitter typically target at different elements and use different annotation guidelines resulting in a scenario where there is no comparable set of documents from both Twitter and PubMed annotated in the same manner. This study aimed to provide a comparable corpus of texts from PubMed and Twitter that can be used to study drug reports from these two sources of information, allowing researchers in the area of pharmacovigilance using natural language processing (NLP) to perform experiments to better understand the similarities and differences between drug reports in Twitter and PubMed. We produced a corpus comprising 1000 tweets and 1000 PubMed sentences selected using the same strategy and annotated at entity level by the same experts (pharmacists) using the same set of guidelines. The resulting corpus, annotated by two pharmacists, comprises semantically correct annotations for a set of drugs, diseases, and symptoms. This corpus contains the annotations for 3144 entities, 2749 relations, and 5003 attributes. We present a corpus that is unique in its characteristics as this is the first corpus for pharmacovigilance curated from Twitter messages and PubMed sentences using the same data selection and annotation strategies. We believe this corpus will be of particular interest for researchers willing to compare results from pharmacovigilance systems (eg, classifiers and named entity recognition systems) when using data from Twitter and from PubMed. We hope that given the comprehensive set of drug names and the annotated entities and relations, this corpus becomes a standard resource to compare results from different pharmacovigilance studies in the area of NLP. ©Nestor Alvaro, Yusuke Miyao, Nigel Collier. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 03.05.2017.

Coreference annotation and resolution in the Colorado Richly Annotated Full Text (CRAFT) corpus of biomedical journal articles.

PubMed

Cohen, K Bretonnel; Lanfranchi, Arrick; Choi, Miji Joo-Young; Bada, Michael; Baumgartner, William A; Panteleyeva, Natalya; Verspoor, Karin; Palmer, Martha; Hunter, Lawrence E

2017-08-17

Coreference resolution is the task of finding strings in text that have the same referent as other strings. Failures of coreference resolution are a common cause of false negatives in information extraction from the scientific literature. In order to better understand the nature of the phenomenon of coreference in biomedical publications and to increase performance on the task, we annotated the Colorado Richly Annotated Full Text (CRAFT) corpus with coreference relations. The corpus was manually annotated with coreference relations, including identity and appositives for all coreferring base noun phrases. The OntoNotes annotation guidelines, with minor adaptations, were used. Interannotator agreement ranges from 0.480 (entity-based CEAF) to 0.858 (Class-B3), depending on the metric that is used to assess it. The resulting corpus adds nearly 30,000 annotations to the previous release of the CRAFT corpus. Differences from related projects include a much broader definition of markables, connection to extensive annotation of several domain-relevant semantic classes, and connection to complete syntactic annotation. Tool performance was benchmarked on the data. A publicly available out-of-the-box, general-domain coreference resolution system achieved an F-measure of 0.14 (B3), while a simple domain-adapted rule-based system achieved an F-measure of 0.42. An ensemble of the two reached F of 0.46. Following the IDENTITY chains in the data would add 106,263 additional named entities in the full 97-paper corpus, for an increase of 76% percent in the semantic classes of the eight ontologies that have been annotated in earlier versions of the CRAFT corpus. The project produced a large data set for further investigation of coreference and coreference resolution in the scientific literature. The work raised issues in the phenomenon of reference in this domain and genre, and the paper proposes that many mentions that would be considered generic in the general domain are not generic in the biomedical domain due to their referents to specific classes in domain-specific ontologies. The comparison of the performance of a publicly available and well-understood coreference resolution system with a domain-adapted system produced results that are consistent with the notion that the requirements for successful coreference resolution in this genre are quite different from those of the general domain, and also suggest that the baseline performance difference is quite large.

Laboratory E-Notebooks: A Learning Object-Based Repository

ERIC Educational Resources Information Center

Abari, Ilior; Pierre, Samuel; Saliah-Hassane, Hamadou

2006-01-01

During distributed virtual laboratory experiment sessions, a major problem is to be able to collect, store, manage and share heterogeneous data (intermediate results, analysis, annotations, etc) manipulated simultaneously by geographically distributed teammates composing a virtual team. The electronic notebook is a possible response to this…

Resource Guide to Competency-Based Vocational Education: Distributive Education.

ERIC Educational Resources Information Center

Foster, Phillip R., Comp.

This resource guide for classroom teachers contains annotations of resources representing recent instructional development in competency-based education for distributive education. It is also intended to assist curriculum specialists, administrators, and supervisors in development of performance-based instructional programs. The guide is divided…

INDIGO – INtegrated Data Warehouse of MIcrobial GenOmes with Examples from the Red Sea Extremophiles

PubMed Central

Alam, Intikhab; Antunes, André; Kamau, Allan Anthony; Ba alawi, Wail; Kalkatawi, Manal; Stingl, Ulrich; Bajic, Vladimir B.

2013-01-01

Background The next generation sequencing technologies substantially increased the throughput of microbial genome sequencing. To functionally annotate newly sequenced microbial genomes, a variety of experimental and computational methods are used. Integration of information from different sources is a powerful approach to enhance such annotation. Functional analysis of microbial genomes, necessary for downstream experiments, crucially depends on this annotation but it is hampered by the current lack of suitable information integration and exploration systems for microbial genomes. Results We developed a data warehouse system (INDIGO) that enables the integration of annotations for exploration and analysis of newly sequenced microbial genomes. INDIGO offers an opportunity to construct complex queries and combine annotations from multiple sources starting from genomic sequence to protein domain, gene ontology and pathway levels. This data warehouse is aimed at being populated with information from genomes of pure cultures and uncultured single cells of Red Sea bacteria and Archaea. Currently, INDIGO contains information from Salinisphaera shabanensis, Haloplasma contractile, and Halorhabdus tiamatea - extremophiles isolated from deep-sea anoxic brine lakes of the Red Sea. We provide examples of utilizing the system to gain new insights into specific aspects on the unique lifestyle and adaptations of these organisms to extreme environments. Conclusions We developed a data warehouse system, INDIGO, which enables comprehensive integration of information from various resources to be used for annotation, exploration and analysis of microbial genomes. It will be regularly updated and extended with new genomes. It is aimed to serve as a resource dedicated to the Red Sea microbes. In addition, through INDIGO, we provide our Automatic Annotation of Microbial Genomes (AAMG) pipeline. The INDIGO web server is freely available at http://www.cbrc.kaust.edu.sa/indigo. PMID:24324765

INDIGO - INtegrated data warehouse of microbial genomes with examples from the red sea extremophiles.

PubMed

Alam, Intikhab; Antunes, André; Kamau, Allan Anthony; Ba Alawi, Wail; Kalkatawi, Manal; Stingl, Ulrich; Bajic, Vladimir B

2013-01-01

The next generation sequencing technologies substantially increased the throughput of microbial genome sequencing. To functionally annotate newly sequenced microbial genomes, a variety of experimental and computational methods are used. Integration of information from different sources is a powerful approach to enhance such annotation. Functional analysis of microbial genomes, necessary for downstream experiments, crucially depends on this annotation but it is hampered by the current lack of suitable information integration and exploration systems for microbial genomes. We developed a data warehouse system (INDIGO) that enables the integration of annotations for exploration and analysis of newly sequenced microbial genomes. INDIGO offers an opportunity to construct complex queries and combine annotations from multiple sources starting from genomic sequence to protein domain, gene ontology and pathway levels. This data warehouse is aimed at being populated with information from genomes of pure cultures and uncultured single cells of Red Sea bacteria and Archaea. Currently, INDIGO contains information from Salinisphaera shabanensis, Haloplasma contractile, and Halorhabdus tiamatea - extremophiles isolated from deep-sea anoxic brine lakes of the Red Sea. We provide examples of utilizing the system to gain new insights into specific aspects on the unique lifestyle and adaptations of these organisms to extreme environments. We developed a data warehouse system, INDIGO, which enables comprehensive integration of information from various resources to be used for annotation, exploration and analysis of microbial genomes. It will be regularly updated and extended with new genomes. It is aimed to serve as a resource dedicated to the Red Sea microbes. In addition, through INDIGO, we provide our Automatic Annotation of Microbial Genomes (AAMG) pipeline. The INDIGO web server is freely available at http://www.cbrc.kaust.edu.sa/indigo.

A National State of the Art Study of Curriculum Instructional Materials for Distributive Education. Volume 1.

ERIC Educational Resources Information Center

Hirshfeld, Marvin; Leventhal, Jerome I.

Volume 1 of the two-volume annotated bibliography provides a partial listing of available materials for curriculum and instructional enrichment in distributive education. The grouping of all materials was made according to the U. S. Office of Education Classification of Instructional Programs for Distributive Education. Alphabetized by title under…

A National State of the Art Study of Curriculum Instructional Materials for Distributive Education. Volume 2.

ERIC Educational Resources Information Center

Hirshfeld, Marvin; Leventhal, Jerome I.

Volume 2 of the two-volume annotated bibliography provides a partial listing of available materials for curriculum and instructional enrichment in distributive education. The grouping of all materials was made according to the U. S. Office of Education Classification of Instructional Programs for Distributive Education. Alphabetized by title under…

A Novel Approach to Semantic and Coreference Annotation at LLNL

DOE Office of Scientific and Technical Information (OSTI.GOV)

Firpo, M

A case is made for the importance of high quality semantic and coreference annotation. The challenges of providing such annotation are described. Asperger's Syndrome is introduced, and the connections are drawn between the needs of text annotation and the abilities of persons with Asperger's Syndrome to meet those needs. Finally, a pilot program is recommended wherein semantic annotation is performed by people with Asperger's Syndrome. The primary points embodied in this paper are as follows: (1) Document annotation is essential to the Natural Language Processing (NLP) projects at Lawrence Livermore National Laboratory (LLNL); (2) LLNL does not currently have amore » system in place to meet its need for text annotation; (3) Text annotation is challenging for a variety of reasons, many related to its very rote nature; (4) Persons with Asperger's Syndrome are particularly skilled at rote verbal tasks, and behavioral experts agree that they would excel at text annotation; and (6) A pilot study is recommend in which two to three people with Asperger's Syndrome annotate documents and then the quality and throughput of their work is evaluated relative to that of their neuro-typical peers.« less

AnnotCompute: annotation-based exploration and meta-analysis of genomics experiments

PubMed Central

Zheng, Jie; Stoyanovich, Julia; Manduchi, Elisabetta; Liu, Junmin; Stoeckert, Christian J.

2011-01-01

The ever-increasing scale of biological data sets, particularly those arising in the context of high-throughput technologies, requires the development of rich data exploration tools. In this article, we present AnnotCompute, an information discovery platform for repositories of functional genomics experiments such as ArrayExpress. Our system leverages semantic annotations of functional genomics experiments with controlled vocabulary and ontology terms, such as those from the MGED Ontology, to compute conceptual dissimilarities between pairs of experiments. These dissimilarities are then used to support two types of exploratory analysis—clustering and query-by-example. We show that our proposed dissimilarity measures correspond to a user's intuition about conceptual dissimilarity, and can be used to support effective query-by-example. We also evaluate the quality of clustering based on these measures. While AnnotCompute can support a richer data exploration experience, its effectiveness is limited in some cases, due to the quality of available annotations. Nonetheless, tools such as AnnotCompute may provide an incentive for richer annotations of experiments. Code is available for download at http://www.cbil.upenn.edu/downloads/AnnotCompute. Database URL: http://www.cbil.upenn.edu/annotCompute/ PMID:22190598

Automated eukaryotic gene structure annotation using EVidenceModeler and the Program to Assemble Spliced Alignments

PubMed Central

Haas, Brian J; Salzberg, Steven L; Zhu, Wei; Pertea, Mihaela; Allen, Jonathan E; Orvis, Joshua; White, Owen; Buell, C Robin; Wortman, Jennifer R

2008-01-01

EVidenceModeler (EVM) is presented as an automated eukaryotic gene structure annotation tool that reports eukaryotic gene structures as a weighted consensus of all available evidence. EVM, when combined with the Program to Assemble Spliced Alignments (PASA), yields a comprehensive, configurable annotation system that predicts protein-coding genes and alternatively spliced isoforms. Our experiments on both rice and human genome sequences demonstrate that EVM produces automated gene structure annotation approaching the quality of manual curation. PMID:18190707

ANALYTiC: An Active Learning System for Trajectory Classification.

PubMed

Soares Junior, Amilcar; Renso, Chiara; Matwin, Stan

2017-01-01

The increasing availability and use of positioning devices has resulted in large volumes of trajectory data. However, semantic annotations for such data are typically added by domain experts, which is a time-consuming task. Machine-learning algorithms can help infer semantic annotations from trajectory data by learning from sets of labeled data. Specifically, active learning approaches can minimize the set of trajectories to be annotated while preserving good performance measures. The ANALYTiC web-based interactive tool visually guides users through this annotation process.

The standard operating procedure of the DOE-JGI Microbial Genome Annotation Pipeline (MGAP v.4)

DOE PAGES

Huntemann, Marcel; Ivanova, Natalia N.; Mavromatis, Konstantinos; ...

2015-10-26

The DOE-JGI Microbial Genome Annotation Pipeline performs structural and functional annotation of microbial genomes that are further included into the Integrated Microbial Genome comparative analysis system. MGAP is applied to assembled nucleotide sequence datasets that are provided via the IMG submission site. Dataset submission for annotation first requires project and associated metadata description in GOLD. The MGAP sequence data processing consists of feature prediction including identification of protein-coding genes, non-coding RNAs and regulatory RNA features, as well as CRISPR elements. In conclusion, structural annotation is followed by assignment of protein product names and functions.

The standard operating procedure of the DOE-JGI Microbial Genome Annotation Pipeline (MGAP v.4)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huntemann, Marcel; Ivanova, Natalia N.; Mavromatis, Konstantinos

The DOE-JGI Microbial Genome Annotation Pipeline performs structural and functional annotation of microbial genomes that are further included into the Integrated Microbial Genome comparative analysis system. MGAP is applied to assembled nucleotide sequence datasets that are provided via the IMG submission site. Dataset submission for annotation first requires project and associated metadata description in GOLD. The MGAP sequence data processing consists of feature prediction including identification of protein-coding genes, non-coding RNAs and regulatory RNA features, as well as CRISPR elements. In conclusion, structural annotation is followed by assignment of protein product names and functions.

A Linked Data-Based Collaborative Annotation System for Increasing Learning Achievements

ERIC Educational Resources Information Center

Zarzour, Hafed; Sellami, Mokhtar

2017-01-01

With the emergence of the Web 2.0, collaborative annotation practices have become more mature in the field of learning. In this context, several recent studies have shown the powerful effects of the integration of annotation mechanism in learning process. However, most of these studies provide poor support for semantically structured resources,…

Forecasting Reading Anxiety for Promoting English-Language Reading Performance Based on Reading Annotation Behavior

ERIC Educational Resources Information Center

Chen, Chih-Ming; Wang, Jung-Ying; Chen, Yong-Ting; Wu, Jhih-Hao

2016-01-01

To reduce effectively the reading anxiety of learners while reading English articles, a C4.5 decision tree, a widely used data mining technique, was used to develop a personalized reading anxiety prediction model (PRAPM) based on individual learners' reading annotation behavior in a collaborative digital reading annotation system (CDRAS). In…

A Semantic-Oriented Approach for Organizing and Developing Annotation for E-Learning

ERIC Educational Resources Information Center

Brut, Mihaela M.; Sedes, Florence; Dumitrescu, Stefan D.

2011-01-01

This paper presents a solution to extend the IEEE LOM standard with ontology-based semantic annotations for efficient use of learning objects outside Learning Management Systems. The data model corresponding to this approach is first presented. The proposed indexing technique for this model development in order to acquire a better annotation of…

A System for the Semantic Multimodal Analysis of News Audio-Visual Content

NASA Astrophysics Data System (ADS)

Mezaris, Vasileios; Gidaros, Spyros; Papadopoulos, GeorgiosTh; Kasper, Walter; Steffen, Jörg; Ordelman, Roeland; Huijbregts, Marijn; de Jong, Franciska; Kompatsiaris, Ioannis; Strintzis, MichaelG

2010-12-01

News-related content is nowadays among the most popular types of content for users in everyday applications. Although the generation and distribution of news content has become commonplace, due to the availability of inexpensive media capturing devices and the development of media sharing services targeting both professional and user-generated news content, the automatic analysis and annotation that is required for supporting intelligent search and delivery of this content remains an open issue. In this paper, a complete architecture for knowledge-assisted multimodal analysis of news-related multimedia content is presented, along with its constituent components. The proposed analysis architecture employs state-of-the-art methods for the analysis of each individual modality (visual, audio, text) separately and proposes a novel fusion technique based on the particular characteristics of news-related content for the combination of the individual modality analysis results. Experimental results on news broadcast video illustrate the usefulness of the proposed techniques in the automatic generation of semantic annotations.

GeneTools--application for functional annotation and statistical hypothesis testing.

PubMed

Beisvag, Vidar; Jünge, Frode K R; Bergum, Hallgeir; Jølsum, Lars; Lydersen, Stian; Günther, Clara-Cecilie; Ramampiaro, Heri; Langaas, Mette; Sandvik, Arne K; Laegreid, Astrid

2006-10-24

Modern biology has shifted from "one gene" approaches to methods for genomic-scale analysis like microarray technology, which allow simultaneous measurement of thousands of genes. This has created a need for tools facilitating interpretation of biological data in "batch" mode. However, such tools often leave the investigator with large volumes of apparently unorganized information. To meet this interpretation challenge, gene-set, or cluster testing has become a popular analytical tool. Many gene-set testing methods and software packages are now available, most of which use a variety of statistical tests to assess the genes in a set for biological information. However, the field is still evolving, and there is a great need for "integrated" solutions. GeneTools is a web-service providing access to a database that brings together information from a broad range of resources. The annotation data are updated weekly, guaranteeing that users get data most recently available. Data submitted by the user are stored in the database, where it can easily be updated, shared between users and exported in various formats. GeneTools provides three different tools: i) NMC Annotation Tool, which offers annotations from several databases like UniGene, Entrez Gene, SwissProt and GeneOntology, in both single- and batch search mode. ii) GO Annotator Tool, where users can add new gene ontology (GO) annotations to genes of interest. These user defined GO annotations can be used in further analysis or exported for public distribution. iii) eGOn, a tool for visualization and statistical hypothesis testing of GO category representation. As the first GO tool, eGOn supports hypothesis testing for three different situations (master-target situation, mutually exclusive target-target situation and intersecting target-target situation). An important additional function is an evidence-code filter that allows users, to select the GO annotations for the analysis. GeneTools is the first "all in one" annotation tool, providing users with a rapid extraction of highly relevant gene annotation data for e.g. thousands of genes or clones at once. It allows a user to define and archive new GO annotations and it supports hypothesis testing related to GO category representations. GeneTools is freely available through www.genetools.no

Biases in the Experimental Annotations of Protein Function and Their Effect on Our Understanding of Protein Function Space

PubMed Central

Schnoes, Alexandra M.; Ream, David C.; Thorman, Alexander W.; Babbitt, Patricia C.; Friedberg, Iddo

2013-01-01

The ongoing functional annotation of proteins relies upon the work of curators to capture experimental findings from scientific literature and apply them to protein sequence and structure data. However, with the increasing use of high-throughput experimental assays, a small number of experimental studies dominate the functional protein annotations collected in databases. Here, we investigate just how prevalent is the “few articles - many proteins” phenomenon. We examine the experimentally validated annotation of proteins provided by several groups in the GO Consortium, and show that the distribution of proteins per published study is exponential, with 0.14% of articles providing the source of annotations for 25% of the proteins in the UniProt-GOA compilation. Since each of the dominant articles describes the use of an assay that can find only one function or a small group of functions, this leads to substantial biases in what we know about the function of many proteins. Mass-spectrometry, microscopy and RNAi experiments dominate high throughput experiments. Consequently, the functional information derived from these experiments is mostly of the subcellular location of proteins, and of the participation of proteins in embryonic developmental pathways. For some organisms, the information provided by different studies overlap by a large amount. We also show that the information provided by high throughput experiments is less specific than those provided by low throughput experiments. Given the experimental techniques available, certain biases in protein function annotation due to high-throughput experiments are unavoidable. Knowing that these biases exist and understanding their characteristics and extent is important for database curators, developers of function annotation programs, and anyone who uses protein function annotation data to plan experiments. PMID:23737737

FIGENIX: Intelligent automation of genomic annotation: expertise integration in a new software platform

PubMed Central

Gouret, Philippe; Vitiello, Vérane; Balandraud, Nathalie; Gilles, André; Pontarotti, Pierre; Danchin, Etienne GJ

2005-01-01

Background Two of the main objectives of the genomic and post-genomic era are to structurally and functionally annotate genomes which consists of detecting genes' position and structure, and inferring their function (as well as of other features of genomes). Structural and functional annotation both require the complex chaining of numerous different software, algorithms and methods under the supervision of a biologist. The automation of these pipelines is necessary to manage huge amounts of data released by sequencing projects. Several pipelines already automate some of these complex chaining but still necessitate an important contribution of biologists for supervising and controlling the results at various steps. Results Here we propose an innovative automated platform, FIGENIX, which includes an expert system capable to substitute to human expertise at several key steps. FIGENIX currently automates complex pipelines of structural and functional annotation under the supervision of the expert system (which allows for example to make key decisions, check intermediate results or refine the dataset). The quality of the results produced by FIGENIX is comparable to those obtained by expert biologists with a drastic gain in terms of time costs and avoidance of errors due to the human manipulation of data. Conclusion The core engine and expert system of the FIGENIX platform currently handle complex annotation processes of broad interest for the genomic community. They could be easily adapted to new, or more specialized pipelines, such as for example the annotation of miRNAs, the classification of complex multigenic families, annotation of regulatory elements and other genomic features of interest. PMID:16083500

Ecology of the Nevada Test Site. I. Geographic and ecologic distributions of the vascular flora (annotated checklist)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Beatley, J C

1965-04-01

A checklist of vascular plants of the Nevada Test Site is presented for use in studies of plant ecology. Data on the occurrence and distribution of plant species are included. Collections were made from both undisturbed and disturbed sites.

Vocational Instructional Materials for Marketing and Distributive Education Available from Federal Agencies.

ERIC Educational Resources Information Center

Hill, Carol L., Comp.

This listing of federally produced curriculum and instructional materials for marketing and distributive education is one of eight annotated bibliographies that provide information for vocational educators at the secondary, postsecondary, and adult levels. Introductory information given includes a description of how to use the listing and sources…

C-ME: A 3D Community-Based, Real-Time Collaboration Tool for Scientific Research and Training

PubMed Central

Kolatkar, Anand; Kennedy, Kevin; Halabuk, Dan; Kunken, Josh; Marrinucci, Dena; Bethel, Kelly; Guzman, Rodney; Huckaby, Tim; Kuhn, Peter

2008-01-01

The need for effective collaboration tools is growing as multidisciplinary proteome-wide projects and distributed research teams become more common. The resulting data is often quite disparate, stored in separate locations, and not contextually related. Collaborative Molecular Modeling Environment (C-ME) is an interactive community-based collaboration system that allows researchers to organize information, visualize data on a two-dimensional (2-D) or three-dimensional (3-D) basis, and share and manage that information with collaborators in real time. C-ME stores the information in industry-standard databases that are immediately accessible by appropriate permission within the computer network directory service or anonymously across the internet through the C-ME application or through a web browser. The system addresses two important aspects of collaboration: context and information management. C-ME allows a researcher to use a 3-D atomic structure model or a 2-D image as a contextual basis on which to attach and share annotations to specific atoms or molecules or to specific regions of a 2-D image. These annotations provide additional information about the atomic structure or image data that can then be evaluated, amended or added to by other project members. PMID:18286178

Protein Sequence Annotation Tool (PSAT): A centralized web-based meta-server for high-throughput sequence annotations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leung, Elo; Huang, Amy; Cadag, Eithon

In this study, we introduce the Protein Sequence Annotation Tool (PSAT), a web-based, sequence annotation meta-server for performing integrated, high-throughput, genome-wide sequence analyses. Our goals in building PSAT were to (1) create an extensible platform for integration of multiple sequence-based bioinformatics tools, (2) enable functional annotations and enzyme predictions over large input protein fasta data sets, and (3) provide a web interface for convenient execution of the tools. In this paper, we demonstrate the utility of PSAT by annotating the predicted peptide gene products of Herbaspirillum sp. strain RV1423, importing the results of PSAT into EC2KEGG, and using the resultingmore » functional comparisons to identify a putative catabolic pathway, thereby distinguishing RV1423 from a well annotated Herbaspirillum species. This analysis demonstrates that high-throughput enzyme predictions, provided by PSAT processing, can be used to identify metabolic potential in an otherwise poorly annotated genome. Lastly, PSAT is a meta server that combines the results from several sequence-based annotation and function prediction codes, and is available at http://psat.llnl.gov/psat/. PSAT stands apart from other sequencebased genome annotation systems in providing a high-throughput platform for rapid de novo enzyme predictions and sequence annotations over large input protein sequence data sets in FASTA. PSAT is most appropriately applied in annotation of large protein FASTA sets that may or may not be associated with a single genome.« less

Protein Sequence Annotation Tool (PSAT): A centralized web-based meta-server for high-throughput sequence annotations

DOE PAGES

Leung, Elo; Huang, Amy; Cadag, Eithon; ...

2016-01-20

In this study, we introduce the Protein Sequence Annotation Tool (PSAT), a web-based, sequence annotation meta-server for performing integrated, high-throughput, genome-wide sequence analyses. Our goals in building PSAT were to (1) create an extensible platform for integration of multiple sequence-based bioinformatics tools, (2) enable functional annotations and enzyme predictions over large input protein fasta data sets, and (3) provide a web interface for convenient execution of the tools. In this paper, we demonstrate the utility of PSAT by annotating the predicted peptide gene products of Herbaspirillum sp. strain RV1423, importing the results of PSAT into EC2KEGG, and using the resultingmore » functional comparisons to identify a putative catabolic pathway, thereby distinguishing RV1423 from a well annotated Herbaspirillum species. This analysis demonstrates that high-throughput enzyme predictions, provided by PSAT processing, can be used to identify metabolic potential in an otherwise poorly annotated genome. Lastly, PSAT is a meta server that combines the results from several sequence-based annotation and function prediction codes, and is available at http://psat.llnl.gov/psat/. PSAT stands apart from other sequencebased genome annotation systems in providing a high-throughput platform for rapid de novo enzyme predictions and sequence annotations over large input protein sequence data sets in FASTA. PSAT is most appropriately applied in annotation of large protein FASTA sets that may or may not be associated with a single genome.« less

CPS-Rater: Automated Sequential Annotation for Conversations in Collaborative Problem-Solving Activities. Research Report. ETS RR-17-58

ERIC Educational Resources Information Center

Hao, Jiangang; Chen, Lei; Flor, Michael; Liu, Lei; von Davier, Alina A.

2017-01-01

Conversations in collaborative problem-solving activities can be used to probe the collaboration skills of the team members. Annotating the conversations into different collaboration skills by human raters is laborious and time consuming. In this report, we report our work on developing an automated annotation system, CPS-rater, for conversational…

FALDO: a semantic standard for describing the location of nucleotide and protein feature annotation.

PubMed

Bolleman, Jerven T; Mungall, Christopher J; Strozzi, Francesco; Baran, Joachim; Dumontier, Michel; Bonnal, Raoul J P; Buels, Robert; Hoehndorf, Robert; Fujisawa, Takatomo; Katayama, Toshiaki; Cock, Peter J A

2016-06-13

Nucleotide and protein sequence feature annotations are essential to understand biology on the genomic, transcriptomic, and proteomic level. Using Semantic Web technologies to query biological annotations, there was no standard that described this potentially complex location information as subject-predicate-object triples. We have developed an ontology, the Feature Annotation Location Description Ontology (FALDO), to describe the positions of annotated features on linear and circular sequences. FALDO can be used to describe nucleotide features in sequence records, protein annotations, and glycan binding sites, among other features in coordinate systems of the aforementioned "omics" areas. Using the same data format to represent sequence positions that are independent of file formats allows us to integrate sequence data from multiple sources and data types. The genome browser JBrowse is used to demonstrate accessing multiple SPARQL endpoints to display genomic feature annotations, as well as protein annotations from UniProt mapped to genomic locations. Our ontology allows users to uniformly describe - and potentially merge - sequence annotations from multiple sources. Data sources using FALDO can prospectively be retrieved using federalised SPARQL queries against public SPARQL endpoints and/or local private triple stores.

FALDO: a semantic standard for describing the location of nucleotide and protein feature annotation

DOE PAGES

Bolleman, Jerven T.; Mungall, Christopher J.; Strozzi, Francesco; ...

2016-06-13

Nucleotide and protein sequence feature annotations are essential to understand biology on the genomic, transcriptomic, and proteomic level. Using Semantic Web technologies to query biological annotations, there was no standard that described this potentially complex location information as subject-predicate-object triples. In this paper, we have developed an ontology, the Feature Annotation Location Description Ontology (FALDO), to describe the positions of annotated features on linear and circular sequences. FALDO can be used to describe nucleotide features in sequence records, protein annotations, and glycan binding sites, among other features in coordinate systems of the aforementioned “omics” areas. Using the same data formatmore » to represent sequence positions that are independent of file formats allows us to integrate sequence data from multiple sources and data types. The genome browser JBrowse is used to demonstrate accessing multiple SPARQL endpoints to display genomic feature annotations, as well as protein annotations from UniProt mapped to genomic locations. Our ontology allows users to uniformly describe – and potentially merge – sequence annotations from multiple sources. Finally, data sources using FALDO can prospectively be retrieved using federalised SPARQL queries against public SPARQL endpoints and/or local private triple stores.« less

Using comparative genome analysis to identify problems in annotated microbial genomes.

PubMed

Poptsova, Maria S; Gogarten, J Peter

2010-07-01

Genome annotation is a tedious task that is mostly done by automated methods; however, the accuracy of these approaches has been questioned since the beginning of the sequencing era. Genome annotation is a multilevel process, and errors can emerge at different stages: during sequencing, as a result of gene-calling procedures, and in the process of assigning gene functions. Missed or wrongly annotated genes differentially impact different types of analyses. Here we discuss and demonstrate how the methods of comparative genome analysis can refine annotations by locating missing orthologues. We also discuss possible reasons for errors and show that the second-generation annotation systems, which combine multiple gene-calling programs with similarity-based methods, perform much better than the first annotation tools. Since old errors may propagate to the newly sequenced genomes, we emphasize that the problem of continuously updating popular public databases is an urgent and unresolved one. Due to the progress in genome-sequencing technologies, automated annotation techniques will remain the main approach in the future. Researchers need to be aware of the existing errors in the annotation of even well-studied genomes, such as Escherichia coli, and consider additional quality control for their results.

FALDO: a semantic standard for describing the location of nucleotide and protein feature annotation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bolleman, Jerven T.; Mungall, Christopher J.; Strozzi, Francesco

Nucleotide and protein sequence feature annotations are essential to understand biology on the genomic, transcriptomic, and proteomic level. Using Semantic Web technologies to query biological annotations, there was no standard that described this potentially complex location information as subject-predicate-object triples. In this paper, we have developed an ontology, the Feature Annotation Location Description Ontology (FALDO), to describe the positions of annotated features on linear and circular sequences. FALDO can be used to describe nucleotide features in sequence records, protein annotations, and glycan binding sites, among other features in coordinate systems of the aforementioned “omics” areas. Using the same data formatmore » to represent sequence positions that are independent of file formats allows us to integrate sequence data from multiple sources and data types. The genome browser JBrowse is used to demonstrate accessing multiple SPARQL endpoints to display genomic feature annotations, as well as protein annotations from UniProt mapped to genomic locations. Our ontology allows users to uniformly describe – and potentially merge – sequence annotations from multiple sources. Finally, data sources using FALDO can prospectively be retrieved using federalised SPARQL queries against public SPARQL endpoints and/or local private triple stores.« less

Active Deep Learning-Based Annotation of Electroencephalography Reports for Cohort Identification

PubMed Central

Maldonado, Ramon; Goodwin, Travis R; Harabagiu, Sanda M

2017-01-01

The annotation of a large corpus of Electroencephalography (EEG) reports is a crucial step in the development of an EEG-specific patient cohort retrieval system. The annotation of multiple types of EEG-specific medical concepts, along with their polarity and modality, is challenging, especially when automatically performed on Big Data. To address this challenge, we present a novel framework which combines the advantages of active and deep learning while producing annotations that capture a variety of attributes of medical concepts. Results obtained through our novel framework show great promise. PMID:28815135

Feeling Expression Using Avatars and Its Consistency for Subjective Annotation

NASA Astrophysics Data System (ADS)

Ito, Fuyuko; Sasaki, Yasunari; Hiroyasu, Tomoyuki; Miki, Mitsunori

Consumer Generated Media(CGM) is growing rapidly and the amount of content is increasing. However, it is often difficult for users to extract important contents and the existence of contents recording their experiences can easily be forgotten. As there are no methods or systems to indicate the subjective value of the contents or ways to reuse them, subjective annotation appending subjectivity, such as feelings and intentions, to contents is needed. Representation of subjectivity depends on not only verbal expression, but also nonverbal expression. Linguistically expressed annotation, typified by collaborative tagging in social bookmarking systems, has come into widespread use, but there is no system of nonverbally expressed annotation on the web. We propose the utilization of controllable avatars as a means of nonverbal expression of subjectivity, and confirmed the consistency of feelings elicited by avatars over time for an individual and in a group. In addition, we compared the expressiveness and ease of subjective annotation between collaborative tagging and controllable avatars. The result indicates that the feelings evoked by avatars are consistent in both cases, and using controllable avatars is easier than collaborative tagging for representing feelings elicited by contents that do not express meaning, such as photos.

PSI:Biology-Materials Repository: A Biologist’s Resource for Protein Expression Plasmids

PubMed Central

Cormier, Catherine Y.; Park, Jin G.; Fiacco, Michael; Steel, Jason; Hunter, Preston; Kramer, Jason; Singla, Rajeev; LaBaer, Joshua

2011-01-01

The Protein Structure Initiative:Biology-Materials Repository (PSI:Biology-MR; MR; http://psimr.asu.edu) sequence-verifies, annotates, stores, and distributes the protein expression plasmids and vectors created by the Protein Structure Initiative (PSI). The MR has developed an informatics and sample processing pipeline that manages this process for thousands of samples per month from nearly a dozen PSI centers. DNASU (http://dnasu.asu.edu), a freely searchable database, stores the plasmid annotations, which include the full-length sequence, vector information, and associated publications for over 130,000 plasmids created by our laboratory, by the PSI and other consortia, and by individual laboratories for distribution to researchers worldwide. Each plasmid links to external resources, including the PSI Structural Biology Knowledgebase (http://sbkb.org), which facilitates cross-referencing of a particular plasmid to additional protein annotations and experimental data. To expedite and simplify plasmid requests, the MR uses an expedited material transfer agreement (EP-MTA) network, where researchers from network institutions can order and receive PSI plasmids without institutional delays. Currently over 39,000 protein expression plasmids and 78 empty vectors from the PSI are available upon request from DNASU. Overall, the MR’s repository of expression-ready plasmids, its automated pipeline, and the rapid process for receiving and distributing these plasmids more effectively allows the research community to dissect the biological function of proteins whose structures have been studied by the PSI. PMID:21360289

Finding Important Terms for Patients in Their Electronic Health Records: A Learning-to-Rank Approach Using Expert Annotations

PubMed Central

Zheng, Jiaping; Yu, Hong

2016-01-01

Background Many health organizations allow patients to access their own electronic health record (EHR) notes through online patient portals as a way to enhance patient-centered care. However, EHR notes are typically long and contain abundant medical jargon that can be difficult for patients to understand. In addition, many medical terms in patients’ notes are not directly related to their health care needs. One way to help patients better comprehend their own notes is to reduce information overload and help them focus on medical terms that matter most to them. Interventions can then be developed by giving them targeted education to improve their EHR comprehension and the quality of care. Objective We aimed to develop a supervised natural language processing (NLP) system called Finding impOrtant medical Concepts most Useful to patientS (FOCUS) that automatically identifies and ranks medical terms in EHR notes based on their importance to the patients. Methods First, we built an expert-annotated corpus. For each EHR note, 2 physicians independently identified medical terms important to the patient. Using the physicians’ agreement as the gold standard, we developed and evaluated FOCUS. FOCUS first identifies candidate terms from each EHR note using MetaMap and then ranks the terms using a support vector machine-based learn-to-rank algorithm. We explored rich learning features, including distributed word representation, Unified Medical Language System semantic type, topic features, and features derived from consumer health vocabulary. We compared FOCUS with 2 strong baseline NLP systems. Results Physicians annotated 90 EHR notes and identified a mean of 9 (SD 5) important terms per note. The Cohen’s kappa annotation agreement was .51. The 10-fold cross-validation results show that FOCUS achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.940 for ranking candidate terms from EHR notes to identify important terms. When including term identification, the performance of FOCUS for identifying important terms from EHR notes was 0.866 AUC-ROC. Both performance scores significantly exceeded the corresponding baseline system scores (P<.001). Rich learning features contributed to FOCUS’s performance substantially. Conclusions FOCUS can automatically rank terms from EHR notes based on their importance to patients. It may help develop future interventions that improve quality of care. PMID:27903489

Finding Important Terms for Patients in Their Electronic Health Records: A Learning-to-Rank Approach Using Expert Annotations.

PubMed

Chen, Jinying; Zheng, Jiaping; Yu, Hong

2016-11-30

Many health organizations allow patients to access their own electronic health record (EHR) notes through online patient portals as a way to enhance patient-centered care. However, EHR notes are typically long and contain abundant medical jargon that can be difficult for patients to understand. In addition, many medical terms in patients' notes are not directly related to their health care needs. One way to help patients better comprehend their own notes is to reduce information overload and help them focus on medical terms that matter most to them. Interventions can then be developed by giving them targeted education to improve their EHR comprehension and the quality of care. We aimed to develop a supervised natural language processing (NLP) system called Finding impOrtant medical Concepts most Useful to patientS (FOCUS) that automatically identifies and ranks medical terms in EHR notes based on their importance to the patients. First, we built an expert-annotated corpus. For each EHR note, 2 physicians independently identified medical terms important to the patient. Using the physicians' agreement as the gold standard, we developed and evaluated FOCUS. FOCUS first identifies candidate terms from each EHR note using MetaMap and then ranks the terms using a support vector machine-based learn-to-rank algorithm. We explored rich learning features, including distributed word representation, Unified Medical Language System semantic type, topic features, and features derived from consumer health vocabulary. We compared FOCUS with 2 strong baseline NLP systems. Physicians annotated 90 EHR notes and identified a mean of 9 (SD 5) important terms per note. The Cohen's kappa annotation agreement was .51. The 10-fold cross-validation results show that FOCUS achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.940 for ranking candidate terms from EHR notes to identify important terms. When including term identification, the performance of FOCUS for identifying important terms from EHR notes was 0.866 AUC-ROC. Both performance scores significantly exceeded the corresponding baseline system scores (P<.001). Rich learning features contributed to FOCUS's performance substantially. FOCUS can automatically rank terms from EHR notes based on their importance to patients. It may help develop future interventions that improve quality of care. ©Jinying Chen, Jiaping Zheng, Hong Yu. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 30.11.2016.

Viral genome analysis and knowledge management.

PubMed

Kuiken, Carla; Yoon, Hyejin; Abfalterer, Werner; Gaschen, Brian; Lo, Chienchi; Korber, Bette

2013-01-01

One of the challenges of genetic data analysis is to combine information from sources that are distributed around the world and accessible through a wide array of different methods and interfaces. The HIV database and its footsteps, the hepatitis C virus (HCV) and hemorrhagic fever virus (HFV) databases, have made it their mission to make different data types easily available to their users. This involves a large amount of behind-the-scenes processing, including quality control and analysis of the sequences and their annotation. Gene and protein sequences are distilled from the sequences that are stored in GenBank; to this end, both submitter annotation and script-generated sequences are used. Alignments of both nucleotide and amino acid sequences are generated, manually curated, distilled into an alignment model, and regenerated in an iterative cycle that results in ever better new alignments. Annotation of epidemiological and clinical information is parsed, checked, and added to the database. User interfaces are updated, and new interfaces are added based upon user requests. Vital for its success, the database staff are heavy users of the system, which enables them to fix bugs and find opportunities for improvement. In this chapter we describe some of the infrastructure that keeps these heavily used analysis platforms alive and vital after nearly 25 years of use. The database/analysis platforms described in this chapter can be accessed at http://hiv.lanl.gov http://hcv.lanl.gov http://hfv.lanl.gov.

Calling on a million minds for community annotation in WikiProteins

PubMed Central

Mons, Barend; Ashburner, Michael; Chichester, Christine; van Mulligen, Erik; Weeber, Marc; den Dunnen, Johan; van Ommen, Gert-Jan; Musen, Mark; Cockerill, Matthew; Hermjakob, Henning; Mons, Albert; Packer, Abel; Pacheco, Roberto; Lewis, Suzanna; Berkeley, Alfred; Melton, William; Barris, Nickolas; Wales, Jimmy; Meijssen, Gerard; Moeller, Erik; Roes, Peter Jan; Borner, Katy; Bairoch, Amos

2008-01-01

WikiProteins enables community annotation in a Wiki-based system. Extracts of major data sources have been fused into an editable environment that links out to the original sources. Data from community edits create automatic copies of the original data. Semantic technology captures concepts co-occurring in one sentence and thus potential factual statements. In addition, indirect associations between concepts have been calculated. We call on a 'million minds' to annotate a 'million concepts' and to collect facts from the literature with the reward of collaborative knowledge discovery. The system is available for beta testing at . PMID:18507872

Irrigation in the Rio Grande Valley, New Mexico: A study and annotated bibliography of the development of irrigation systems

Treesearch

Frank E. Wozniak

1998-01-01

This publication reviews both published and unpublished sources on Puebloan, Hispanic, and AngloAmerican irrigation systems in the Rio Grande Valley. Settlement patterns and Spanish and Mexican land grants in the valley are also discussed. The volume includes an annotated bibliography.

A Selected Annotated Bibliography on the Analysis of Water Resources System, Volume 2.

ERIC Educational Resources Information Center

Kriss, Carol; And Others

Presented is an annotated bibliography of some recent selected publications pertaining to the application of systems analysis techniques for defining and evaluating alternative solutions to water resource problems. Both subject and author indices are provided. Keywords are listed at the end of each abstract. The abstracted material emphasizes the…

A Selected Annotated Bibliography on the Analysis of Water Resource Systems.

ERIC Educational Resources Information Center

Gysi, Marshall; And Others

Presented is an annotated bibliography of some selected publications pertaining to the application of systems analysis techniques to water resource problems. The majority of the references included in this bibliography have been published within the last five years. About half of the entries have informative abstracts and keywords following the…

Sma3s: a three-step modular annotator for large sequence datasets.

PubMed

Muñoz-Mérida, Antonio; Viguera, Enrique; Claros, M Gonzalo; Trelles, Oswaldo; Pérez-Pulido, Antonio J

2014-08-01

Automatic sequence annotation is an essential component of modern 'omics' studies, which aim to extract information from large collections of sequence data. Most existing tools use sequence homology to establish evolutionary relationships and assign putative functions to sequences. However, it can be difficult to define a similarity threshold that achieves sufficient coverage without sacrificing annotation quality. Defining the correct configuration is critical and can be challenging for non-specialist users. Thus, the development of robust automatic annotation techniques that generate high-quality annotations without needing expert knowledge would be very valuable for the research community. We present Sma3s, a tool for automatically annotating very large collections of biological sequences from any kind of gene library or genome. Sma3s is composed of three modules that progressively annotate query sequences using either: (i) very similar homologues, (ii) orthologous sequences or (iii) terms enriched in groups of homologous sequences. We trained the system using several random sets of known sequences, demonstrating average sensitivity and specificity values of ~85%. In conclusion, Sma3s is a versatile tool for high-throughput annotation of a wide variety of sequence datasets that outperforms the accuracy of other well-established annotation algorithms, and it can enrich existing database annotations and uncover previously hidden features. Importantly, Sma3s has already been used in the functional annotation of two published transcriptomes. © The Author 2014. Published by Oxford University Press on behalf of Kazusa DNA Research Institute.

Federated ontology-based queries over cancer data

PubMed Central

2012-01-01

Background Personalised medicine provides patients with treatments that are specific to their genetic profiles. It requires efficient data sharing of disparate data types across a variety of scientific disciplines, such as molecular biology, pathology, radiology and clinical practice. Personalised medicine aims to offer the safest and most effective therapeutic strategy based on the gene variations of each subject. In particular, this is valid in oncology, where knowledge about genetic mutations has already led to new therapies. Current molecular biology techniques (microarrays, proteomics, epigenetic technology and improved DNA sequencing technology) enable better characterisation of cancer tumours. The vast amounts of data, however, coupled with the use of different terms - or semantic heterogeneity - in each discipline makes the retrieval and integration of information difficult. Results Existing software infrastructures for data-sharing in the cancer domain, such as caGrid, support access to distributed information. caGrid follows a service-oriented model-driven architecture. Each data source in caGrid is associated with metadata at increasing levels of abstraction, including syntactic, structural, reference and domain metadata. The domain metadata consists of ontology-based annotations associated with the structural information of each data source. However, caGrid's current querying functionality is given at the structural metadata level, without capitalising on the ontology-based annotations. This paper presents the design of and theoretical foundations for distributed ontology-based queries over cancer research data. Concept-based queries are reformulated to the target query language, where join conditions between multiple data sources are found by exploiting the semantic annotations. The system has been implemented, as a proof of concept, over the caGrid infrastructure. The approach is applicable to other model-driven architectures. A graphical user interface has been developed, supporting ontology-based queries over caGrid data sources. An extensive evaluation of the query reformulation technique is included. Conclusions To support personalised medicine in oncology, it is crucial to retrieve and integrate molecular, pathology, radiology and clinical data in an efficient manner. The semantic heterogeneity of the data makes this a challenging task. Ontologies provide a formal framework to support querying and integration. This paper provides an ontology-based solution for querying distributed databases over service-oriented, model-driven infrastructures. PMID:22373043

ezTag: tagging biomedical concepts via interactive learning.

PubMed

Kwon, Dongseop; Kim, Sun; Wei, Chih-Hsuan; Leaman, Robert; Lu, Zhiyong

2018-05-18

Recently, advanced text-mining techniques have been shown to speed up manual data curation by providing human annotators with automated pre-annotations generated by rules or machine learning models. Due to the limited training data available, however, current annotation systems primarily focus only on common concept types such as genes or diseases. To support annotating a wide variety of biological concepts with or without pre-existing training data, we developed ezTag, a web-based annotation tool that allows curators to perform annotation and provide training data with humans in the loop. ezTag supports both abstracts in PubMed and full-text articles in PubMed Central. It also provides lexicon-based concept tagging as well as the state-of-the-art pre-trained taggers such as TaggerOne, GNormPlus and tmVar. ezTag is freely available at http://eztag.bioqrator.org.

DEVA: An extensible ontology-based annotation model for visual document collections

NASA Astrophysics Data System (ADS)

Jelmini, Carlo; Marchand-Maillet, Stephane

2003-01-01

The description of visual documents is a fundamental aspect of any efficient information management system, but the process of manually annotating large collections of documents is tedious and far from being perfect. The need for a generic and extensible annotation model therefore arises. In this paper, we present DEVA, an open, generic and expressive multimedia annotation framework. DEVA is an extension of the Dublin Core specification. The model can represent the semantic content of any visual document. It is described in the ontology language DAML+OIL and can easily be extended with external specialized ontologies, adapting the vocabulary to the given application domain. In parallel, we present the Magritte annotation tool, which is an early prototype that validates the DEVA features. Magritte allows to manually annotating image collections. It is designed with a modular and extensible architecture, which enables the user to dynamically adapt the user interface to specialized ontologies merged into DEVA.

Annotated Bibliography on Apartheid.

ERIC Educational Resources Information Center

Totten, Sam, ed.

1985-01-01

This annotated listing on apartheid in South Africa cites general resources, classroom materials, fiction, poetry, audio visuals, and organizations and associations. Also included are a glossary and a brief chronology of South Africa's apartheid system. (RM)

Coding gestural behavior with the NEUROGES--ELAN system.

PubMed

Lausberg, Hedda; Sloetjes, Han

2009-08-01

We present a coding system combined with an annotation tool for the analysis of gestural behavior. The NEUROGES coding system consists of three modules that progress from gesture kinetics to gesture function. Grounded on empirical neuropsychological and psychological studies, the theoretical assumption behind NEUROGES is that its main kinetic and functional movement categories are differentially associated with specific cognitive, emotional, and interactive functions. ELAN is a free, multimodal annotation tool for digital audio and video media. It supports multileveled transcription and complies with such standards as XML and Unicode. ELAN allows gesture categories to be stored with associated vocabularies that are reusable by means of template files. The combination of the NEUROGES coding system and the annotation tool ELAN creates an effective tool for empirical research on gestural behavior.

Sequence- and Structure-Based Functional Annotation and Assessment of Metabolic Transporters in Aspergillus oryzae: A Representative Case Study

PubMed Central

Raethong, Nachon; Wong-ekkabut, Jirasak; Laoteng, Kobkul; Vongsangnak, Wanwipa

2016-01-01

Aspergillus oryzae is widely used for the industrial production of enzymes. In A. oryzae metabolism, transporters appear to play crucial roles in controlling the flux of molecules for energy generation, nutrients delivery, and waste elimination in the cell. While the A. oryzae genome sequence is available, transporter annotation remains limited and thus the connectivity of metabolic networks is incomplete. In this study, we developed a metabolic annotation strategy to understand the relationship between the sequence, structure, and function for annotation of A. oryzae metabolic transporters. Sequence-based analysis with manual curation showed that 58 genes of 12,096 total genes in the A. oryzae genome encoded metabolic transporters. Under consensus integrative databases, 55 unambiguous metabolic transporter genes were distributed into channels and pores (7 genes), electrochemical potential-driven transporters (33 genes), and primary active transporters (15 genes). To reveal the transporter functional role, a combination of homology modeling and molecular dynamics simulation was implemented to assess the relationship between sequence to structure and structure to function. As in the energy metabolism of A. oryzae, the H+-ATPase encoded by the AO090005000842 gene was selected as a representative case study of multilevel linkage annotation. Our developed strategy can be used for enhancing metabolic network reconstruction. PMID:27274991

Sequence- and Structure-Based Functional Annotation and Assessment of Metabolic Transporters in Aspergillus oryzae: A Representative Case Study.

PubMed

Raethong, Nachon; Wong-Ekkabut, Jirasak; Laoteng, Kobkul; Vongsangnak, Wanwipa

2016-01-01

Aspergillus oryzae is widely used for the industrial production of enzymes. In A. oryzae metabolism, transporters appear to play crucial roles in controlling the flux of molecules for energy generation, nutrients delivery, and waste elimination in the cell. While the A. oryzae genome sequence is available, transporter annotation remains limited and thus the connectivity of metabolic networks is incomplete. In this study, we developed a metabolic annotation strategy to understand the relationship between the sequence, structure, and function for annotation of A. oryzae metabolic transporters. Sequence-based analysis with manual curation showed that 58 genes of 12,096 total genes in the A. oryzae genome encoded metabolic transporters. Under consensus integrative databases, 55 unambiguous metabolic transporter genes were distributed into channels and pores (7 genes), electrochemical potential-driven transporters (33 genes), and primary active transporters (15 genes). To reveal the transporter functional role, a combination of homology modeling and molecular dynamics simulation was implemented to assess the relationship between sequence to structure and structure to function. As in the energy metabolism of A. oryzae, the H(+)-ATPase encoded by the AO090005000842 gene was selected as a representative case study of multilevel linkage annotation. Our developed strategy can be used for enhancing metabolic network reconstruction.

Reducing Labeling Effort for Structured Prediction Tasks

DTIC Science & Technology

2005-01-01

correctly annotated for the instance to be of use to the learner. Traditional active learning addresses this problem by optimizing the order in which the...than for others. We propose a new active learning paradigm which reduces not only how many instances the annotator must label, but also how difficult...We validate this active learning framework in an interactive information extraction system, reducing the total number of annotation actions by 22%.

Multivendor Spectral-Domain Optical Coherence Tomography Dataset, Observer Annotation Performance Evaluation, and Standardized Evaluation Framework for Intraretinal Cystoid Fluid Segmentation.

PubMed

Wu, Jing; Philip, Ana-Maria; Podkowinski, Dominika; Gerendas, Bianca S; Langs, Georg; Simader, Christian; Waldstein, Sebastian M; Schmidt-Erfurth, Ursula M

2016-01-01

Development of image analysis and machine learning methods for segmentation of clinically significant pathology in retinal spectral-domain optical coherence tomography (SD-OCT), used in disease detection and prediction, is limited due to the availability of expertly annotated reference data. Retinal segmentation methods use datasets that either are not publicly available, come from only one device, or use different evaluation methodologies making them difficult to compare. Thus we present and evaluate a multiple expert annotated reference dataset for the problem of intraretinal cystoid fluid (IRF) segmentation, a key indicator in exudative macular disease. In addition, a standardized framework for segmentation accuracy evaluation, applicable to other pathological structures, is presented. Integral to this work is the dataset used which must be fit for purpose for IRF segmentation algorithm training and testing. We describe here a multivendor dataset comprised of 30 scans. Each OCT scan for system training has been annotated by multiple graders using a proprietary system. Evaluation of the intergrader annotations shows a good correlation, thus making the reproducibly annotated scans suitable for the training and validation of image processing and machine learning based segmentation methods. The dataset will be made publicly available in the form of a segmentation Grand Challenge.

Multivendor Spectral-Domain Optical Coherence Tomography Dataset, Observer Annotation Performance Evaluation, and Standardized Evaluation Framework for Intraretinal Cystoid Fluid Segmentation

PubMed Central

Wu, Jing; Philip, Ana-Maria; Podkowinski, Dominika; Gerendas, Bianca S.; Langs, Georg; Simader, Christian

2016-01-01

Development of image analysis and machine learning methods for segmentation of clinically significant pathology in retinal spectral-domain optical coherence tomography (SD-OCT), used in disease detection and prediction, is limited due to the availability of expertly annotated reference data. Retinal segmentation methods use datasets that either are not publicly available, come from only one device, or use different evaluation methodologies making them difficult to compare. Thus we present and evaluate a multiple expert annotated reference dataset for the problem of intraretinal cystoid fluid (IRF) segmentation, a key indicator in exudative macular disease. In addition, a standardized framework for segmentation accuracy evaluation, applicable to other pathological structures, is presented. Integral to this work is the dataset used which must be fit for purpose for IRF segmentation algorithm training and testing. We describe here a multivendor dataset comprised of 30 scans. Each OCT scan for system training has been annotated by multiple graders using a proprietary system. Evaluation of the intergrader annotations shows a good correlation, thus making the reproducibly annotated scans suitable for the training and validation of image processing and machine learning based segmentation methods. The dataset will be made publicly available in the form of a segmentation Grand Challenge. PMID:27579177

LAILAPS: the plant science search engine.

PubMed

Esch, Maria; Chen, Jinbo; Colmsee, Christian; Klapperstück, Matthias; Grafahrend-Belau, Eva; Scholz, Uwe; Lange, Matthias

2015-01-01

With the number of sequenced plant genomes growing, the number of predicted genes and functional annotations is also increasing. The association between genes and phenotypic traits is currently of great interest. Unfortunately, the information available today is widely scattered over a number of different databases. Information retrieval (IR) has become an all-encompassing bioinformatics methodology for extracting knowledge from complex, heterogeneous and distributed databases, and therefore can be a useful tool for obtaining a comprehensive view of plant genomics, from genes to traits. Here we describe LAILAPS (http://lailaps.ipk-gatersleben.de), an IR system designed to link plant genomic data in the context of phenotypic attributes for a detailed forward genetic research. LAILAPS comprises around 65 million indexed documents, encompassing >13 major life science databases with around 80 million links to plant genomic resources. The LAILAPS search engine allows fuzzy querying for candidate genes linked to specific traits over a loosely integrated system of indexed and interlinked genome databases. Query assistance and an evidence-based annotation system enable time-efficient and comprehensive information retrieval. An artificial neural network incorporating user feedback and behavior tracking allows relevance sorting of results. We fully describe LAILAPS's functionality and capabilities by comparing this system's performance with other widely used systems and by reporting both a validation in maize and a knowledge discovery use-case focusing on candidate genes in barley. © The Author 2014. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Talmage, S.S.

This bibliography, containing 537 references from the 1977 literature, is the seventh in a series of annotated bibliographies on the effects of heat on aquatic organisms. The effects of thermal discharges at power plant sites are emphasized. Laboratory and field studies on temperature tolerance and the effects of temperature changes on reproduction, development, growth, distribution, physiology, and sensitivity to other stresses are included. References in the bibliography are divided into three subject categories: marine systems, freshwater systems, and estuaries. The references are arranged alphabetically by first author. Indexes are provided for author, keywords, subject category, geographic location of the study,more » taxon, and title (alphabetical listing of keywords-in-context of nontrivial words in the title).« less

Expert system for computer-assisted annotation of MS/MS spectra.

PubMed

Neuhauser, Nadin; Michalski, Annette; Cox, Jürgen; Mann, Matthias

2012-11-01

An important step in mass spectrometry (MS)-based proteomics is the identification of peptides by their fragment spectra. Regardless of the identification score achieved, almost all tandem-MS (MS/MS) spectra contain remaining peaks that are not assigned by the search engine. These peaks may be explainable by human experts but the scale of modern proteomics experiments makes this impractical. In computer science, Expert Systems are a mature technology to implement a list of rules generated by interviews with practitioners. We here develop such an Expert System, making use of literature knowledge as well as a large body of high mass accuracy and pure fragmentation spectra. Interestingly, we find that even with high mass accuracy data, rule sets can quickly become too complex, leading to over-annotation. Therefore we establish a rigorous false discovery rate, calculated by random insertion of peaks from a large collection of other MS/MS spectra, and use it to develop an optimized knowledge base. This rule set correctly annotates almost all peaks of medium or high abundance. For high resolution HCD data, median intensity coverage of fragment peaks in MS/MS spectra increases from 58% by search engine annotation alone to 86%. The resulting annotation performance surpasses a human expert, especially on complex spectra such as those of larger phosphorylated peptides. Our system is also applicable to high resolution collision-induced dissociation data. It is available both as a part of MaxQuant and via a webserver that only requires an MS/MS spectrum and the corresponding peptides sequence, and which outputs publication quality, annotated MS/MS spectra (www.biochem.mpg.de/mann/tools/). It provides expert knowledge to beginners in the field of MS-based proteomics and helps advanced users to focus on unusual and possibly novel types of fragment ions.

Expert System for Computer-assisted Annotation of MS/MS Spectra*

PubMed Central

Neuhauser, Nadin; Michalski, Annette; Cox, Jürgen; Mann, Matthias

2012-01-01

An important step in mass spectrometry (MS)-based proteomics is the identification of peptides by their fragment spectra. Regardless of the identification score achieved, almost all tandem-MS (MS/MS) spectra contain remaining peaks that are not assigned by the search engine. These peaks may be explainable by human experts but the scale of modern proteomics experiments makes this impractical. In computer science, Expert Systems are a mature technology to implement a list of rules generated by interviews with practitioners. We here develop such an Expert System, making use of literature knowledge as well as a large body of high mass accuracy and pure fragmentation spectra. Interestingly, we find that even with high mass accuracy data, rule sets can quickly become too complex, leading to over-annotation. Therefore we establish a rigorous false discovery rate, calculated by random insertion of peaks from a large collection of other MS/MS spectra, and use it to develop an optimized knowledge base. This rule set correctly annotates almost all peaks of medium or high abundance. For high resolution HCD data, median intensity coverage of fragment peaks in MS/MS spectra increases from 58% by search engine annotation alone to 86%. The resulting annotation performance surpasses a human expert, especially on complex spectra such as those of larger phosphorylated peptides. Our system is also applicable to high resolution collision-induced dissociation data. It is available both as a part of MaxQuant and via a webserver that only requires an MS/MS spectrum and the corresponding peptides sequence, and which outputs publication quality, annotated MS/MS spectra (www.biochem.mpg.de/mann/tools/). It provides expert knowledge to beginners in the field of MS-based proteomics and helps advanced users to focus on unusual and possibly novel types of fragment ions. PMID:22888147

Social networks to biological networks: systems biology of Mycobacterium tuberculosis.

PubMed

Vashisht, Rohit; Bhardwaj, Anshu; Osdd Consortium; Brahmachari, Samir K

2013-07-01

Contextualizing relevant information to construct a network that represents a given biological process presents a fundamental challenge in the network science of biology. The quality of network for the organism of interest is critically dependent on the extent of functional annotation of its genome. Mostly the automated annotation pipelines do not account for unstructured information present in volumes of literature and hence large fraction of genome remains poorly annotated. However, if used, this information could substantially enhance the functional annotation of a genome, aiding the development of a more comprehensive network. Mining unstructured information buried in volumes of literature often requires manual intervention to a great extent and thus becomes a bottleneck for most of the automated pipelines. In this review, we discuss the potential of scientific social networking as a solution for systematic manual mining of data. Focusing on Mycobacterium tuberculosis, as a case study, we discuss our open innovative approach for the functional annotation of its genome. Furthermore, we highlight the strength of such collated structured data in the context of drug target prediction based on systems level analysis of pathogen.

Scholarometer: a social framework for analyzing impact across disciplines.

PubMed

Kaur, Jasleen; Hoang, Diep Thi; Sun, Xiaoling; Possamai, Lino; Jafariasbagh, Mohsen; Patil, Snehal; Menczer, Filippo

2012-01-01

The use of quantitative metrics to gauge the impact of scholarly publications, authors, and disciplines is predicated on the availability of reliable usage and annotation data. Citation and download counts are widely available from digital libraries. However, current annotation systems rely on proprietary labels, refer to journals but not articles or authors, and are manually curated. To address these limitations, we propose a social framework based on crowdsourced annotations of scholars, designed to keep up with the rapidly evolving disciplinary and interdisciplinary landscape. We describe a system called Scholarometer, which provides a service to scholars by computing citation-based impact measures. This creates an incentive for users to provide disciplinary annotations of authors, which in turn can be used to compute disciplinary metrics. We first present the system architecture and several heuristics to deal with noisy bibliographic and annotation data. We report on data sharing and interactive visualization services enabled by Scholarometer. Usage statistics, illustrating the data collected and shared through the framework, suggest that the proposed crowdsourcing approach can be successful. Secondly, we illustrate how the disciplinary bibliometric indicators elicited by Scholarometer allow us to implement for the first time a universal impact measure proposed in the literature. Our evaluation suggests that this metric provides an effective means for comparing scholarly impact across disciplinary boundaries.

Scholarometer: A Social Framework for Analyzing Impact across Disciplines

PubMed Central

Sun, Xiaoling; Possamai, Lino; JafariAsbagh, Mohsen; Patil, Snehal; Menczer, Filippo

2012-01-01

The use of quantitative metrics to gauge the impact of scholarly publications, authors, and disciplines is predicated on the availability of reliable usage and annotation data. Citation and download counts are widely available from digital libraries. However, current annotation systems rely on proprietary labels, refer to journals but not articles or authors, and are manually curated. To address these limitations, we propose a social framework based on crowdsourced annotations of scholars, designed to keep up with the rapidly evolving disciplinary and interdisciplinary landscape. We describe a system called Scholarometer, which provides a service to scholars by computing citation-based impact measures. This creates an incentive for users to provide disciplinary annotations of authors, which in turn can be used to compute disciplinary metrics. We first present the system architecture and several heuristics to deal with noisy bibliographic and annotation data. We report on data sharing and interactive visualization services enabled by Scholarometer. Usage statistics, illustrating the data collected and shared through the framework, suggest that the proposed crowdsourcing approach can be successful. Secondly, we illustrate how the disciplinary bibliometric indicators elicited by Scholarometer allow us to implement for the first time a universal impact measure proposed in the literature. Our evaluation suggests that this metric provides an effective means for comparing scholarly impact across disciplinary boundaries. PMID:22984414

The KIT Motion-Language Dataset.

PubMed

Plappert, Matthias; Mandery, Christian; Asfour, Tamim

2016-12-01

Linking human motion and natural language is of great interest for the generation of semantic representations of human activities as well as for the generation of robot activities based on natural language input. However, although there have been years of research in this area, no standardized and openly available data set exists to support the development and evaluation of such systems. We, therefore, propose the Karlsruhe Institute of Technology (KIT) Motion-Language Dataset, which is large, open, and extensible. We aggregate data from multiple motion capture databases and include them in our data set using a unified representation that is independent of the capture system or marker set, making it easy to work with the data regardless of its origin. To obtain motion annotations in natural language, we apply a crowd-sourcing approach and a web-based tool that was specifically build for this purpose, the Motion Annotation Tool. We thoroughly document the annotation process itself and discuss gamification methods that we used to keep annotators motivated. We further propose a novel method, perplexity-based selection, which systematically selects motions for further annotation that are either under-represented in our data set or that have erroneous annotations. We show that our method mitigates the two aforementioned problems and ensures a systematic annotation process. We provide an in-depth analysis of the structure and contents of our resulting data set, which, as of October 10, 2016, contains 3911 motions with a total duration of 11.23 hours and 6278 annotations in natural language that contain 52,903 words. We believe this makes our data set an excellent choice that enables more transparent and comparable research in this important area.

P2RP: a Web-based framework for the identification and analysis of regulatory proteins in prokaryotic genomes.

PubMed

Barakat, Mohamed; Ortet, Philippe; Whitworth, David E

2013-04-20

Regulatory proteins (RPs) such as transcription factors (TFs) and two-component system (TCS) proteins control how prokaryotic cells respond to changes in their external and/or internal state. Identification and annotation of TFs and TCSs is non-trivial, and between-genome comparisons are often confounded by different standards in annotation. There is a need for user-friendly, fast and convenient tools to allow researchers to overcome the inherent variability in annotation between genome sequences. We have developed the web-server P2RP (Predicted Prokaryotic Regulatory Proteins), which enables users to identify and annotate TFs and TCS proteins within their sequences of interest. Users can input amino acid or genomic DNA sequences, and predicted proteins therein are scanned for the possession of DNA-binding domains and/or TCS domains. RPs identified in this manner are categorised into families, unambiguously annotated, and a detailed description of their features generated, using an integrated software pipeline. P2RP results can then be outputted in user-specified formats. Biologists have an increasing need for fast and intuitively usable tools, which is why P2RP has been developed as an interactive system. As well as assisting experimental biologists to interrogate novel sequence data, it is hoped that P2RP will be built into genome annotation pipelines and re-annotation processes, to increase the consistency of RP annotation in public genomic sequences. P2RP is the first publicly available tool for predicting and analysing RP proteins in users' sequences. The server is freely available and can be accessed along with documentation at http://www.p2rp.org.

The standard operating procedure of the DOE-JGI Metagenome Annotation Pipeline (MAP v.4)

DOE PAGES

Huntemann, Marcel; Ivanova, Natalia N.; Mavromatis, Konstantinos; ...

2016-02-24

The DOE-JGI Metagenome Annotation Pipeline (MAP v.4) performs structural and functional annotation for metagenomic sequences that are submitted to the Integrated Microbial Genomes with Microbiomes (IMG/M) system for comparative analysis. The pipeline runs on nucleotide sequences provide d via the IMG submission site. Users must first define their analysis projects in GOLD and then submit the associated sequence datasets consisting of scaffolds/contigs with optional coverage information and/or unassembled reads in fasta and fastq file formats. The MAP processing consists of feature prediction including identification of protein-coding genes, non-coding RNAs and regulatory RNAs, as well as CRISPR elements. Structural annotation ismore » followed by functional annotation including assignment of protein product names and connection to various protein family databases.« less

The standard operating procedure of the DOE-JGI Metagenome Annotation Pipeline (MAP v.4)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huntemann, Marcel; Ivanova, Natalia N.; Mavromatis, Konstantinos

The DOE-JGI Metagenome Annotation Pipeline (MAP v.4) performs structural and functional annotation for metagenomic sequences that are submitted to the Integrated Microbial Genomes with Microbiomes (IMG/M) system for comparative analysis. The pipeline runs on nucleotide sequences provide d via the IMG submission site. Users must first define their analysis projects in GOLD and then submit the associated sequence datasets consisting of scaffolds/contigs with optional coverage information and/or unassembled reads in fasta and fastq file formats. The MAP processing consists of feature prediction including identification of protein-coding genes, non-coding RNAs and regulatory RNAs, as well as CRISPR elements. Structural annotation ismore » followed by functional annotation including assignment of protein product names and connection to various protein family databases.« less

Lynx web services for annotations and systems analysis of multi-gene disorders.

PubMed

Sulakhe, Dinanath; Taylor, Andrew; Balasubramanian, Sandhya; Feng, Bo; Xie, Bingqing; Börnigen, Daniela; Dave, Utpal J; Foster, Ian T; Gilliam, T Conrad; Maltsev, Natalia

2014-07-01

Lynx is a web-based integrated systems biology platform that supports annotation and analysis of experimental data and generation of weighted hypotheses on molecular mechanisms contributing to human phenotypes and disorders of interest. Lynx has integrated multiple classes of biomedical data (genomic, proteomic, pathways, phenotypic, toxicogenomic, contextual and others) from various public databases as well as manually curated data from our group and collaborators (LynxKB). Lynx provides tools for gene list enrichment analysis using multiple functional annotations and network-based gene prioritization. Lynx provides access to the integrated database and the analytical tools via REST based Web Services (http://lynx.ci.uchicago.edu/webservices.html). This comprises data retrieval services for specific functional annotations, services to search across the complete LynxKB (powered by Lucene), and services to access the analytical tools built within the Lynx platform. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Rule Mining Techniques to Predict Prokaryotic Metabolic Pathways.

PubMed

Saidi, Rabie; Boudellioua, Imane; Martin, Maria J; Solovyev, Victor

2017-01-01

It is becoming more evident that computational methods are needed for the identification and the mapping of pathways in new genomes. We introduce an automatic annotation system (ARBA4Path Association Rule-Based Annotator for Pathways) that utilizes rule mining techniques to predict metabolic pathways across wide range of prokaryotes. It was demonstrated that specific combinations of protein domains (recorded in our rules) strongly determine pathways in which proteins are involved and thus provide information that let us very accurately assign pathway membership (with precision of 0.999 and recall of 0.966) to proteins of a given prokaryotic taxon. Our system can be used to enhance the quality of automatically generated annotations as well as annotating proteins with unknown function. The prediction models are represented in the form of human-readable rules, and they can be used effectively to add absent pathway information to many proteins in UniProtKB/TrEMBL database.

Entropy growth in emotional online dialogues

NASA Astrophysics Data System (ADS)

Sienkiewicz, J.; Skowron, M.; Paltoglou, G.; Hołyst, Janusz A.

2013-02-01

We analyze emotionally annotated massive data from IRC (Internet Relay Chat) and model the dialogues between its participants by assuming that the driving force for the discussion is the entropy growth of emotional probability distribution.

The economics of biobanking and pharmacogenetics databasing: the case of an adaptive platform on breast cancer.

PubMed

Huttin, Christine C; Liebman, Michael N

2013-01-01

This paper aims to discuss the economics of biobanking. Among the critical issues in evaluating potential ROI for creation of a bio-bank are: scale (e.g. local, national, international), centralized versus virtual/distributed, degree of sample annotation/QC procedures, targeted end-users and uses, types of samples, potential characterization, both of samples and annotations. The paper presents a review on cost models for an economic analysis of biobanking for different steps: data collection (e.g. biospecimens in different types of sites, storage, transport and distribution, information management for the different types of information (e.g. biological information such as cell, gene, and protein)). It also provides additional concepts to process biospecimens from laboratory to clinical practice and will help to identify how changing paradigms in translational medicine affect the economic modeling.

STINGRAY: system for integrated genomic resources and analysis.

PubMed

Wagner, Glauber; Jardim, Rodrigo; Tschoeke, Diogo A; Loureiro, Daniel R; Ocaña, Kary A C S; Ribeiro, Antonio C B; Emmel, Vanessa E; Probst, Christian M; Pitaluga, André N; Grisard, Edmundo C; Cavalcanti, Maria C; Campos, Maria L M; Mattoso, Marta; Dávila, Alberto M R

2014-03-07

The STINGRAY system has been conceived to ease the tasks of integrating, analyzing, annotating and presenting genomic and expression data from Sanger and Next Generation Sequencing (NGS) platforms. STINGRAY includes: (a) a complete and integrated workflow (more than 20 bioinformatics tools) ranging from functional annotation to phylogeny; (b) a MySQL database schema, suitable for data integration and user access control; and (c) a user-friendly graphical web-based interface that makes the system intuitive, facilitating the tasks of data analysis and annotation. STINGRAY showed to be an easy to use and complete system for analyzing sequencing data. While both Sanger and NGS platforms are supported, the system could be faster using Sanger data, since the large NGS datasets could potentially slow down the MySQL database usage. STINGRAY is available at http://stingray.biowebdb.org and the open source code at http://sourceforge.net/projects/stingray-biowebdb/.

STINGRAY: system for integrated genomic resources and analysis

PubMed Central

2014-01-01

Background The STINGRAY system has been conceived to ease the tasks of integrating, analyzing, annotating and presenting genomic and expression data from Sanger and Next Generation Sequencing (NGS) platforms. Findings STINGRAY includes: (a) a complete and integrated workflow (more than 20 bioinformatics tools) ranging from functional annotation to phylogeny; (b) a MySQL database schema, suitable for data integration and user access control; and (c) a user-friendly graphical web-based interface that makes the system intuitive, facilitating the tasks of data analysis and annotation. Conclusion STINGRAY showed to be an easy to use and complete system for analyzing sequencing data. While both Sanger and NGS platforms are supported, the system could be faster using Sanger data, since the large NGS datasets could potentially slow down the MySQL database usage. STINGRAY is available at http://stingray.biowebdb.org and the open source code at http://sourceforge.net/projects/stingray-biowebdb/. PMID:24606808

An annotated catalogue of the gamasid mites associated with small mammals in Asiatic Russia. The family Haemogamasidae (Acari: Mesostigmata: Gamasina).

PubMed

Vinarski, Maxim V; Korallo-Vinarskaya, Natalia P

2017-06-01

We provide a list of the species of the family Haemogamasidae Oudemans, 1926 living in Asiatic Russia, with data on their synonymy, distribution, and relationships with mammal hosts. In total, 23 species of mites distributed between two genera (Eulaelaps Berlese, 1903, and Haemogamasus Berlese, 1889) are covered.

BioCatalogue: a universal catalogue of web services for the life sciences

PubMed Central

Bhagat, Jiten; Tanoh, Franck; Nzuobontane, Eric; Laurent, Thomas; Orlowski, Jerzy; Roos, Marco; Wolstencroft, Katy; Aleksejevs, Sergejs; Stevens, Robert; Pettifer, Steve; Lopez, Rodrigo; Goble, Carole A.

2010-01-01

The use of Web Services to enable programmatic access to on-line bioinformatics is becoming increasingly important in the Life Sciences. However, their number, distribution and the variable quality of their documentation can make their discovery and subsequent use difficult. A Web Services registry with information on available services will help to bring together service providers and their users. The BioCatalogue (http://www.biocatalogue.org/) provides a common interface for registering, browsing and annotating Web Services to the Life Science community. Services in the BioCatalogue can be described and searched in multiple ways based upon their technical types, bioinformatics categories, user tags, service providers or data inputs and outputs. They are also subject to constant monitoring, allowing the identification of service problems and changes and the filtering-out of unavailable or unreliable resources. The system is accessible via a human-readable ‘Web 2.0’-style interface and a programmatic Web Service interface. The BioCatalogue follows a community approach in which all services can be registered, browsed and incrementally documented with annotations by any member of the scientific community. PMID:20484378

BioCatalogue: a universal catalogue of web services for the life sciences.

PubMed

Bhagat, Jiten; Tanoh, Franck; Nzuobontane, Eric; Laurent, Thomas; Orlowski, Jerzy; Roos, Marco; Wolstencroft, Katy; Aleksejevs, Sergejs; Stevens, Robert; Pettifer, Steve; Lopez, Rodrigo; Goble, Carole A

2010-07-01

The use of Web Services to enable programmatic access to on-line bioinformatics is becoming increasingly important in the Life Sciences. However, their number, distribution and the variable quality of their documentation can make their discovery and subsequent use difficult. A Web Services registry with information on available services will help to bring together service providers and their users. The BioCatalogue (http://www.biocatalogue.org/) provides a common interface for registering, browsing and annotating Web Services to the Life Science community. Services in the BioCatalogue can be described and searched in multiple ways based upon their technical types, bioinformatics categories, user tags, service providers or data inputs and outputs. They are also subject to constant monitoring, allowing the identification of service problems and changes and the filtering-out of unavailable or unreliable resources. The system is accessible via a human-readable 'Web 2.0'-style interface and a programmatic Web Service interface. The BioCatalogue follows a community approach in which all services can be registered, browsed and incrementally documented with annotations by any member of the scientific community.

Integrated genome browser: visual analytics platform for genomics.

PubMed

Freese, Nowlan H; Norris, David C; Loraine, Ann E

2016-07-15

Genome browsers that support fast navigation through vast datasets and provide interactive visual analytics functions can help scientists achieve deeper insight into biological systems. Toward this end, we developed Integrated Genome Browser (IGB), a highly configurable, interactive and fast open source desktop genome browser. Here we describe multiple updates to IGB, including all-new capabilities to display and interact with data from high-throughput sequencing experiments. To demonstrate, we describe example visualizations and analyses of datasets from RNA-Seq, ChIP-Seq and bisulfite sequencing experiments. Understanding results from genome-scale experiments requires viewing the data in the context of reference genome annotations and other related datasets. To facilitate this, we enhanced IGB's ability to consume data from diverse sources, including Galaxy, Distributed Annotation and IGB-specific Quickload servers. To support future visualization needs as new genome-scale assays enter wide use, we transformed the IGB codebase into a modular, extensible platform for developers to create and deploy all-new visualizations of genomic data. IGB is open source and is freely available from http://bioviz.org/igb aloraine@uncc.edu. © The Author 2016. Published by Oxford University Press.

Does Evidence-Based PTS Treatment Reduce PTS Symptoms and Suicide in Iraq and Afghanistan Veterans Seeking VA Care

DTIC Science & Technology

We succeeded in developing a Natural Language Processing ( NLP ) System with excellent performance characteristics for determining the type of...people (quadruple-annotated) and7,226 of which were double annotated. We also developed an NLP system to extract PT Checklist (PCL) scores from clinical notes with excellent accuracy (98 positive predictive value).

Toward autonomous driving: The CMU Navlab. II - Architecture and systems

NASA Technical Reports Server (NTRS)

Thorpe, Charles; Hebert, Martial; Kanade, Takeo; Shafer, Steven

1991-01-01

A description is given of EDDIE, the architecture for the Navlab mobile robot which provides a toolkit for building specific systems quickly and easily. Included in the discussion are the annotated maps used by EDDIE and the Navlab's road-following system, called the Autonomous Mail Vehicle, which was built using EDDIE and its annotated maps as a basis. The contributions of the Navlab project and the lessons learned from it are examined.

MPEG-7 based video annotation and browsing

NASA Astrophysics Data System (ADS)

Hoeynck, Michael; Auweiler, Thorsten; Wellhausen, Jens

2003-11-01

The huge amount of multimedia data produced worldwide requires annotation in order to enable universal content access and to provide content-based search-and-retrieval functionalities. Since manual video annotation can be time consuming, automatic annotation systems are required. We review recent approaches to content-based indexing and annotation of videos for different kind of sports and describe our approach to automatic annotation of equestrian sports videos. We especially concentrate on MPEG-7 based feature extraction and content description, where we apply different visual descriptors for cut detection. Further, we extract the temporal positions of single obstacles on the course by analyzing MPEG-7 edge information. Having determined single shot positions as well as the visual highlights, the information is jointly stored with meta-textual information in an MPEG-7 description scheme. Based on this information, we generate content summaries which can be utilized in a user-interface in order to provide content-based access to the video stream, but further for media browsing on a streaming server.

Accelerating Cancer Systems Biology Research through Semantic Web Technology

PubMed Central

Wang, Zhihui; Sagotsky, Jonathan; Taylor, Thomas; Shironoshita, Patrick; Deisboeck, Thomas S.

2012-01-01

Cancer systems biology is an interdisciplinary, rapidly expanding research field in which collaborations are a critical means to advance the field. Yet the prevalent database technologies often isolate data rather than making it easily accessible. The Semantic Web has the potential to help facilitate web-based collaborative cancer research by presenting data in a manner that is self-descriptive, human and machine readable, and easily sharable. We have created a semantically linked online Digital Model Repository (DMR) for storing, managing, executing, annotating, and sharing computational cancer models. Within the DMR, distributed, multidisciplinary, and inter-organizational teams can collaborate on projects, without forfeiting intellectual property. This is achieved by the introduction of a new stakeholder to the collaboration workflow, the institutional licensing officer, part of the Technology Transfer Office. Furthermore, the DMR has achieved silver level compatibility with the National Cancer Institute’s caBIG®, so users can not only interact with the DMR through a web browser but also through a semantically annotated and secure web service. We also discuss the technology behind the DMR leveraging the Semantic Web, ontologies, and grid computing to provide secure inter-institutional collaboration on cancer modeling projects, online grid-based execution of shared models, and the collaboration workflow protecting researchers’ intellectual property. PMID:23188758

Accelerating cancer systems biology research through Semantic Web technology.

PubMed

Wang, Zhihui; Sagotsky, Jonathan; Taylor, Thomas; Shironoshita, Patrick; Deisboeck, Thomas S

2013-01-01

Cancer systems biology is an interdisciplinary, rapidly expanding research field in which collaborations are a critical means to advance the field. Yet the prevalent database technologies often isolate data rather than making it easily accessible. The Semantic Web has the potential to help facilitate web-based collaborative cancer research by presenting data in a manner that is self-descriptive, human and machine readable, and easily sharable. We have created a semantically linked online Digital Model Repository (DMR) for storing, managing, executing, annotating, and sharing computational cancer models. Within the DMR, distributed, multidisciplinary, and inter-organizational teams can collaborate on projects, without forfeiting intellectual property. This is achieved by the introduction of a new stakeholder to the collaboration workflow, the institutional licensing officer, part of the Technology Transfer Office. Furthermore, the DMR has achieved silver level compatibility with the National Cancer Institute's caBIG, so users can interact with the DMR not only through a web browser but also through a semantically annotated and secure web service. We also discuss the technology behind the DMR leveraging the Semantic Web, ontologies, and grid computing to provide secure inter-institutional collaboration on cancer modeling projects, online grid-based execution of shared models, and the collaboration workflow protecting researchers' intellectual property. Copyright © 2012 Wiley Periodicals, Inc.

A Digital Approach to Learning Petrology

NASA Astrophysics Data System (ADS)

Reid, M. R.

2011-12-01

In the undergraduate igneous and metamorphic petrology course at Northern Arizona University, we are employing petrographic microscopes equipped with relatively inexpensive ( $200) digital cameras that are linked to pen-tablet computers. The camera-tablet systems can assist student learning in a variety of ways. Images provided by the tablet computers can be used for helping students filter the visually complex specimens they examine. Instructors and students can simultaneously view the same petrographic features captured by the cameras and exchange information about them by pointing to salient features using the tablet pen. These images can become part of a virtual mineral/rock/texture portfolio tailored to individual student's needs. Captured digital illustrations can be annotated with digital ink or computer graphics tools; this activity emulates essential features of more traditional line drawings (visualizing an appropriate feature and selecting a representative image of it, internalizing the feature through studying and annotating it) while minimizing the frustration that many students feel about drawing. In these ways, we aim to help a student progress more efficiently from novice to expert. A number of our petrology laboratory exercises involve use of the camera-tablet systems for collaborative learning. Observational responsibilities are distributed among individual members of teams in order to increase interdependence and accountability, and to encourage efficiency. Annotated digital images are used to share students' findings and arrive at an understanding of an entire rock suite. This interdependence increases the individual's sense of responsibility for their work, and reporting out encourages students to practice use of technical vocabulary and to defend their observations. Pre- and post-course student interest in the camera-tablet systems has been assessed. In a post-course survey, the majority of students reported that, if available, they would use camera-tablet systems to capture microscope images (77%) and to make notes on images (71%). An informal focus group recommended introducing the cameras as soon as possible and having them available for making personal mineralogy/petrology portfolios. Because the stakes are perceived as high, use of the camera-tablet systems for peer-peer learning has been progressively modified to bolster student confidence in their collaborative efforts.

Ontology design patterns to disambiguate relations between genes and gene products in GENIA

PubMed Central

2011-01-01

Motivation Annotated reference corpora play an important role in biomedical information extraction. A semantic annotation of the natural language texts in these reference corpora using formal ontologies is challenging due to the inherent ambiguity of natural language. The provision of formal definitions and axioms for semantic annotations offers the means for ensuring consistency as well as enables the development of verifiable annotation guidelines. Consistent semantic annotations facilitate the automatic discovery of new information through deductive inferences. Results We provide a formal characterization of the relations used in the recent GENIA corpus annotations. For this purpose, we both select existing axiom systems based on the desired properties of the relations within the domain and develop new axioms for several relations. To apply this ontology of relations to the semantic annotation of text corpora, we implement two ontology design patterns. In addition, we provide a software application to convert annotated GENIA abstracts into OWL ontologies by combining both the ontology of relations and the design patterns. As a result, the GENIA abstracts become available as OWL ontologies and are amenable for automated verification, deductive inferences and other knowledge-based applications. Availability Documentation, implementation and examples are available from http://www-tsujii.is.s.u-tokyo.ac.jp/GENIA/. PMID:22166341

The caBIG annotation and image Markup project.

PubMed

Channin, David S; Mongkolwat, Pattanasak; Kleper, Vladimir; Sepukar, Kastubh; Rubin, Daniel L

2010-04-01

Image annotation and markup are at the core of medical interpretation in both the clinical and the research setting. Digital medical images are managed with the DICOM standard format. While DICOM contains a large amount of meta-data about whom, where, and how the image was acquired, DICOM says little about the content or meaning of the pixel data. An image annotation is the explanatory or descriptive information about the pixel data of an image that is generated by a human or machine observer. An image markup is the graphical symbols placed over the image to depict an annotation. While DICOM is the standard for medical image acquisition, manipulation, transmission, storage, and display, there are no standards for image annotation and markup. Many systems expect annotation to be reported verbally, while markups are stored in graphical overlays or proprietary formats. This makes it difficult to extract and compute with both of them. The goal of the Annotation and Image Markup (AIM) project is to develop a mechanism, for modeling, capturing, and serializing image annotation and markup data that can be adopted as a standard by the medical imaging community. The AIM project produces both human- and machine-readable artifacts. This paper describes the AIM information model, schemas, software libraries, and tools so as to prepare researchers and developers for their use of AIM.

Phylotastic! Making tree-of-life knowledge accessible, reusable and convenient.

PubMed

Stoltzfus, Arlin; Lapp, Hilmar; Matasci, Naim; Deus, Helena; Sidlauskas, Brian; Zmasek, Christian M; Vaidya, Gaurav; Pontelli, Enrico; Cranston, Karen; Vos, Rutger; Webb, Campbell O; Harmon, Luke J; Pirrung, Megan; O'Meara, Brian; Pennell, Matthew W; Mirarab, Siavash; Rosenberg, Michael S; Balhoff, James P; Bik, Holly M; Heath, Tracy A; Midford, Peter E; Brown, Joseph W; McTavish, Emily Jane; Sukumaran, Jeet; Westneat, Mark; Alfaro, Michael E; Steele, Aaron; Jordan, Greg

2013-05-13

Scientists rarely reuse expert knowledge of phylogeny, in spite of years of effort to assemble a great "Tree of Life" (ToL). A notable exception involves the use of Phylomatic, which provides tools to generate custom phylogenies from a large, pre-computed, expert phylogeny of plant taxa. This suggests great potential for a more generalized system that, starting with a query consisting of a list of any known species, would rectify non-standard names, identify expert phylogenies containing the implicated taxa, prune away unneeded parts, and supply branch lengths and annotations, resulting in a custom phylogeny suited to the user's needs. Such a system could become a sustainable community resource if implemented as a distributed system of loosely coupled parts that interact through clearly defined interfaces. With the aim of building such a "phylotastic" system, the NESCent Hackathons, Interoperability, Phylogenies (HIP) working group recruited 2 dozen scientist-programmers to a weeklong programming hackathon in June 2012. During the hackathon (and a three-month follow-up period), 5 teams produced designs, implementations, documentation, presentations, and tests including: (1) a generalized scheme for integrating components; (2) proof-of-concept pruners and controllers; (3) a meta-API for taxonomic name resolution services; (4) a system for storing, finding, and retrieving phylogenies using semantic web technologies for data exchange, storage, and querying; (5) an innovative new service, DateLife.org, which synthesizes pre-computed, time-calibrated phylogenies to assign ages to nodes; and (6) demonstration projects. These outcomes are accessible via a public code repository (GitHub.com), a website (http://www.phylotastic.org), and a server image. Approximately 9 person-months of effort (centered on a software development hackathon) resulted in the design and implementation of proof-of-concept software for 4 core phylotastic components, 3 controllers, and 3 end-user demonstration tools. While these products have substantial limitations, they suggest considerable potential for a distributed system that makes phylogenetic knowledge readily accessible in computable form. Widespread use of phylotastic systems will create an electronic marketplace for sharing phylogenetic knowledge that will spur innovation in other areas of the ToL enterprise, such as annotation of sources and methods and third-party methods of quality assessment.

Genome Annotation Generator: a simple tool for generating and correcting WGS annotation tables for NCBI submission.

PubMed

Geib, Scott M; Hall, Brian; Derego, Theodore; Bremer, Forest T; Cannoles, Kyle; Sim, Sheina B

2018-04-01

One of the most overlooked, yet critical, components of a whole genome sequencing (WGS) project is the submission and curation of the data to a genomic repository, most commonly the National Center for Biotechnology Information (NCBI). While large genome centers or genome groups have developed software tools for post-annotation assembly filtering, annotation, and conversion into the NCBI's annotation table format, these tools typically require back-end setup and connection to an Structured Query Language (SQL) database and/or some knowledge of programming (Perl, Python) to implement. With WGS becoming commonplace, genome sequencing projects are moving away from the genome centers and into the ecology or biology lab, where fewer resources are present to support the process of genome assembly curation. To fill this gap, we developed software to assess, filter, and transfer annotation and convert a draft genome assembly and annotation set into the NCBI annotation table (.tbl) format, facilitating submission to the NCBI Genome Assembly database. This software has no dependencies, is compatible across platforms, and utilizes a simple command to perform a variety of simple and complex post-analysis, pre-NCBI submission WGS project tasks. The Genome Annotation Generator is a consistent and user-friendly bioinformatics tool that can be used to generate a .tbl file that is consistent with the NCBI submission pipeline. The Genome Annotation Generator achieves the goal of providing a publicly available tool that will facilitate the submission of annotated genome assemblies to the NCBI. It is useful for any individual researcher or research group that wishes to submit a genome assembly of their study system to the NCBI.

Genome Annotation Generator: a simple tool for generating and correcting WGS annotation tables for NCBI submission

PubMed Central

Hall, Brian; Derego, Theodore; Bremer, Forest T; Cannoles, Kyle

2018-01-01

Abstract Background One of the most overlooked, yet critical, components of a whole genome sequencing (WGS) project is the submission and curation of the data to a genomic repository, most commonly the National Center for Biotechnology Information (NCBI). While large genome centers or genome groups have developed software tools for post-annotation assembly filtering, annotation, and conversion into the NCBI’s annotation table format, these tools typically require back-end setup and connection to an Structured Query Language (SQL) database and/or some knowledge of programming (Perl, Python) to implement. With WGS becoming commonplace, genome sequencing projects are moving away from the genome centers and into the ecology or biology lab, where fewer resources are present to support the process of genome assembly curation. To fill this gap, we developed software to assess, filter, and transfer annotation and convert a draft genome assembly and annotation set into the NCBI annotation table (.tbl) format, facilitating submission to the NCBI Genome Assembly database. This software has no dependencies, is compatible across platforms, and utilizes a simple command to perform a variety of simple and complex post-analysis, pre-NCBI submission WGS project tasks. Findings The Genome Annotation Generator is a consistent and user-friendly bioinformatics tool that can be used to generate a .tbl file that is consistent with the NCBI submission pipeline Conclusions The Genome Annotation Generator achieves the goal of providing a publicly available tool that will facilitate the submission of annotated genome assemblies to the NCBI. It is useful for any individual researcher or research group that wishes to submit a genome assembly of their study system to the NCBI. PMID:29635297

Metadata Standard and Data Exchange Specifications to Describe, Model, and Integrate Complex and Diverse High-Throughput Screening Data from the Library of Integrated Network-based Cellular Signatures (LINCS).

PubMed

Vempati, Uma D; Chung, Caty; Mader, Chris; Koleti, Amar; Datar, Nakul; Vidović, Dušica; Wrobel, David; Erickson, Sean; Muhlich, Jeremy L; Berriz, Gabriel; Benes, Cyril H; Subramanian, Aravind; Pillai, Ajay; Shamu, Caroline E; Schürer, Stephan C

2014-06-01

The National Institutes of Health Library of Integrated Network-based Cellular Signatures (LINCS) program is generating extensive multidimensional data sets, including biochemical, genome-wide transcriptional, and phenotypic cellular response signatures to a variety of small-molecule and genetic perturbations with the goal of creating a sustainable, widely applicable, and readily accessible systems biology knowledge resource. Integration and analysis of diverse LINCS data sets depend on the availability of sufficient metadata to describe the assays and screening results and on their syntactic, structural, and semantic consistency. Here we report metadata specifications for the most important molecular and cellular components and recommend them for adoption beyond the LINCS project. We focus on the minimum required information to model LINCS assays and results based on a number of use cases, and we recommend controlled terminologies and ontologies to annotate assays with syntactic consistency and semantic integrity. We also report specifications for a simple annotation format (SAF) to describe assays and screening results based on our metadata specifications with explicit controlled vocabularies. SAF specifically serves to programmatically access and exchange LINCS data as a prerequisite for a distributed information management infrastructure. We applied the metadata specifications to annotate large numbers of LINCS cell lines, proteins, and small molecules. The resources generated and presented here are freely available. © 2014 Society for Laboratory Automation and Screening.

Community-based Ontology Development, Annotation and Discussion with MediaWiki extension Ontokiwi and Ontokiwi-based Ontobedia

PubMed Central

Ong, Edison; He, Yongqun

2016-01-01

Hundreds of biological and biomedical ontologies have been developed to support data standardization, integration and analysis. Although ontologies are typically developed for community usage, community efforts in ontology development are limited. To support ontology visualization, distribution, and community-based annotation and development, we have developed Ontokiwi, an ontology extension to the MediaWiki software. Ontokiwi displays hierarchical classes and ontological axioms. Ontology classes and axioms can be edited and added using Ontokiwi form or MediaWiki source editor. Ontokiwi also inherits MediaWiki features such as Wikitext editing and version control. Based on the Ontokiwi/MediaWiki software package, we have developed Ontobedia, which targets to support community-based development and annotations of biological and biomedical ontologies. As demonstrations, we have loaded the Ontology of Adverse Events (OAE) and the Cell Line Ontology (CLO) into Ontobedia. Our studies showed that Ontobedia was able to achieve expected Ontokiwi features. PMID:27570653

An annotated checklist of the Greek Stonefly Fauna (Insecta: Plecoptera).

PubMed

Karaouzas, Ioannis; Andriopoulou, Argyro; Kouvarda, Theodora; Murányi, Dávid

2016-05-17

An overview of the Greek stonefly (Plecoptera) fauna is presented as an annotated index of all available published records. These records have resulted in an updated species list reflecting current taxonomy and species distributions of the Greek peninsula and islands. Currently, a total of 71 species and seven subspecies belonging to seven families and 19 genera are reported from Greece. There is high species endemicity of the Leuctridae and Nemouridae, particularly on the Greek islands. The endemics known from Greece comprise thirty species representing 42% of the Greek stonefly fauna. The remaining taxa are typical Balkan and Mediterranean species.

GAMOLA2, a Comprehensive Software Package for the Annotation and Curation of Draft and Complete Microbial Genomes

PubMed Central

Altermann, Eric; Lu, Jingli; McCulloch, Alan

2017-01-01

Expert curated annotation remains one of the critical steps in achieving a reliable biological relevant annotation. Here we announce the release of GAMOLA2, a user friendly and comprehensive software package to process, annotate and curate draft and complete bacterial, archaeal, and viral genomes. GAMOLA2 represents a wrapping tool to combine gene model determination, functional Blast, COG, Pfam, and TIGRfam analyses with structural predictions including detection of tRNAs, rRNA genes, non-coding RNAs, signal protein cleavage sites, transmembrane helices, CRISPR repeats and vector sequence contaminations. GAMOLA2 has already been validated in a wide range of bacterial and archaeal genomes, and its modular concept allows easy addition of further functionality in future releases. A modified and adapted version of the Artemis Genome Viewer (Sanger Institute) has been developed to leverage the additional features and underlying information provided by the GAMOLA2 analysis, and is part of the software distribution. In addition to genome annotations, GAMOLA2 features, among others, supplemental modules that assist in the creation of custom Blast databases, annotation transfers between genome versions, and the preparation of Genbank files for submission via the NCBI Sequin tool. GAMOLA2 is intended to be run under a Linux environment, whereas the subsequent visualization and manual curation in Artemis is mobile and platform independent. The development of GAMOLA2 is ongoing and community driven. New functionality can easily be added upon user requests, ensuring that GAMOLA2 provides information relevant to microbiologists. The software is available free of charge for academic use. PMID:28386247

GAMOLA2, a Comprehensive Software Package for the Annotation and Curation of Draft and Complete Microbial Genomes.

PubMed

Altermann, Eric; Lu, Jingli; McCulloch, Alan

2017-01-01

Expert curated annotation remains one of the critical steps in achieving a reliable biological relevant annotation. Here we announce the release of GAMOLA2, a user friendly and comprehensive software package to process, annotate and curate draft and complete bacterial, archaeal, and viral genomes. GAMOLA2 represents a wrapping tool to combine gene model determination, functional Blast, COG, Pfam, and TIGRfam analyses with structural predictions including detection of tRNAs, rRNA genes, non-coding RNAs, signal protein cleavage sites, transmembrane helices, CRISPR repeats and vector sequence contaminations. GAMOLA2 has already been validated in a wide range of bacterial and archaeal genomes, and its modular concept allows easy addition of further functionality in future releases. A modified and adapted version of the Artemis Genome Viewer (Sanger Institute) has been developed to leverage the additional features and underlying information provided by the GAMOLA2 analysis, and is part of the software distribution. In addition to genome annotations, GAMOLA2 features, among others, supplemental modules that assist in the creation of custom Blast databases, annotation transfers between genome versions, and the preparation of Genbank files for submission via the NCBI Sequin tool. GAMOLA2 is intended to be run under a Linux environment, whereas the subsequent visualization and manual curation in Artemis is mobile and platform independent. The development of GAMOLA2 is ongoing and community driven. New functionality can easily be added upon user requests, ensuring that GAMOLA2 provides information relevant to microbiologists. The software is available free of charge for academic use.

Eliciting the Functional Taxonomy from protein annotations and taxa

PubMed Central

Falda, Marco; Lavezzo, Enrico; Fontana, Paolo; Bianco, Luca; Berselli, Michele; Formentin, Elide; Toppo, Stefano

2016-01-01

The advances of omics technologies have triggered the production of an enormous volume of data coming from thousands of species. Meanwhile, joint international efforts like the Gene Ontology (GO) consortium have worked to provide functional information for a vast amount of proteins. With these data available, we have developed FunTaxIS, a tool that is the first attempt to infer functional taxonomy (i.e. how functions are distributed over taxa) combining functional and taxonomic information. FunTaxIS is able to define a taxon specific functional space by exploiting annotation frequencies in order to establish if a function can or cannot be used to annotate a certain species. The tool generates constraints between GO terms and taxa and then propagates these relations over the taxonomic tree and the GO graph. Since these constraints nearly cover the whole taxonomy, it is possible to obtain the mapping of a function over the taxonomy. FunTaxIS can be used to make functional comparative analyses among taxa, to detect improper associations between taxa and functions, and to discover how functional knowledge is either distributed or missing. A benchmark test set based on six different model species has been devised to get useful insights on the generated taxonomic rules. PMID:27534507

Desiderata for ontologies to be used in semantic annotation of biomedical documents.

PubMed

Bada, Michael; Hunter, Lawrence

2011-02-01

A wealth of knowledge valuable to the translational research scientist is contained within the vast biomedical literature, but this knowledge is typically in the form of natural language. Sophisticated natural-language-processing systems are needed to translate text into unambiguous formal representations grounded in high-quality consensus ontologies, and these systems in turn rely on gold-standard corpora of annotated documents for training and testing. To this end, we are constructing the Colorado Richly Annotated Full-Text (CRAFT) Corpus, a collection of 97 full-text biomedical journal articles that are being manually annotated with the entire sets of terms from select vocabularies, predominantly from the Open Biomedical Ontologies (OBO) library. Our efforts in building this corpus has illuminated infelicities of these ontologies with respect to the semantic annotation of biomedical documents, and we propose desiderata whose implementation could substantially improve their utility in this task; these include the integration of overlapping terms across OBOs, the resolution of OBO-specific ambiguities, the integration of the BFO with the OBOs and the use of mid-level ontologies, the inclusion of noncanonical instances, and the expansion of relations and realizable entities. Copyright © 2010 Elsevier Inc. All rights reserved.

High-throughput comparison, functional annotation, and metabolic modeling of plant genomes using the PlantSEED resource

PubMed Central

Seaver, Samuel M. D.; Gerdes, Svetlana; Frelin, Océane; Lerma-Ortiz, Claudia; Bradbury, Louis M. T.; Zallot, Rémi; Hasnain, Ghulam; Niehaus, Thomas D.; El Yacoubi, Basma; Pasternak, Shiran; Olson, Robert; Pusch, Gordon; Overbeek, Ross; Stevens, Rick; de Crécy-Lagard, Valérie; Ware, Doreen; Hanson, Andrew D.; Henry, Christopher S.

2014-01-01

The increasing number of sequenced plant genomes is placing new demands on the methods applied to analyze, annotate, and model these genomes. Today’s annotation pipelines result in inconsistent gene assignments that complicate comparative analyses and prevent efficient construction of metabolic models. To overcome these problems, we have developed the PlantSEED, an integrated, metabolism-centric database to support subsystems-based annotation and metabolic model reconstruction for plant genomes. PlantSEED combines SEED subsystems technology, first developed for microbial genomes, with refined protein families and biochemical data to assign fully consistent functional annotations to orthologous genes, particularly those encoding primary metabolic pathways. Seamless integration with its parent, the prokaryotic SEED database, makes PlantSEED a unique environment for cross-kingdom comparative analysis of plant and bacterial genomes. The consistent annotations imposed by PlantSEED permit rapid reconstruction and modeling of primary metabolism for all plant genomes in the database. This feature opens the unique possibility of model-based assessment of the completeness and accuracy of gene annotation and thus allows computational identification of genes and pathways that are restricted to certain genomes or need better curation. We demonstrate the PlantSEED system by producing consistent annotations for 10 reference genomes. We also produce a functioning metabolic model for each genome, gapfilling to identify missing annotations and proposing gene candidates for missing annotations. Models are built around an extended biomass composition representing the most comprehensive published to date. To our knowledge, our models are the first to be published for seven of the genomes analyzed. PMID:24927599

High-throughput comparison, functional annotation, and metabolic modeling of plant genomes using the PlantSEED resource.

PubMed

Seaver, Samuel M D; Gerdes, Svetlana; Frelin, Océane; Lerma-Ortiz, Claudia; Bradbury, Louis M T; Zallot, Rémi; Hasnain, Ghulam; Niehaus, Thomas D; El Yacoubi, Basma; Pasternak, Shiran; Olson, Robert; Pusch, Gordon; Overbeek, Ross; Stevens, Rick; de Crécy-Lagard, Valérie; Ware, Doreen; Hanson, Andrew D; Henry, Christopher S

2014-07-01

The increasing number of sequenced plant genomes is placing new demands on the methods applied to analyze, annotate, and model these genomes. Today's annotation pipelines result in inconsistent gene assignments that complicate comparative analyses and prevent efficient construction of metabolic models. To overcome these problems, we have developed the PlantSEED, an integrated, metabolism-centric database to support subsystems-based annotation and metabolic model reconstruction for plant genomes. PlantSEED combines SEED subsystems technology, first developed for microbial genomes, with refined protein families and biochemical data to assign fully consistent functional annotations to orthologous genes, particularly those encoding primary metabolic pathways. Seamless integration with its parent, the prokaryotic SEED database, makes PlantSEED a unique environment for cross-kingdom comparative analysis of plant and bacterial genomes. The consistent annotations imposed by PlantSEED permit rapid reconstruction and modeling of primary metabolism for all plant genomes in the database. This feature opens the unique possibility of model-based assessment of the completeness and accuracy of gene annotation and thus allows computational identification of genes and pathways that are restricted to certain genomes or need better curation. We demonstrate the PlantSEED system by producing consistent annotations for 10 reference genomes. We also produce a functioning metabolic model for each genome, gapfilling to identify missing annotations and proposing gene candidates for missing annotations. Models are built around an extended biomass composition representing the most comprehensive published to date. To our knowledge, our models are the first to be published for seven of the genomes analyzed.

SNPit: a federated data integration system for the purpose of functional SNP annotation.

PubMed

Shen, Terry H; Carlson, Christopher S; Tarczy-Hornoch, Peter

2009-08-01

Genome wide association studies can potentially identify the genetic causes behind the majority of human diseases. With the advent of more advanced genotyping techniques, there is now an explosion of data gathered on single nucleotide polymorphisms (SNPs). The need exists for an integrated system that can provide up-to-date functional annotation information on SNPs. We have developed the SNP Integration Tool (SNPit) system to address this need. Built upon a federated data integration system, SNPit provides current information on a comprehensive list of SNP data sources. Additional logical inference analysis was included through an inference engine plug in. The SNPit web servlet is available online for use. SNPit allows users to go to one source for up-to-date information on the functional annotation of SNPs. A tool that can help to integrate and analyze the potential functional significance of SNPs is important for understanding the results from genome wide association studies.

Paraconsistent Annotated Logic in Viability Analysis: an Approach to Product Launching

NASA Astrophysics Data System (ADS)

Romeu de Carvalho, Fábio; Brunstein, Israel; Abe, Jair Minoro

2004-08-01

In this paper we present an application of the Para-analyzer, a logical analyzer based on the Paraconsistent Annotated Logic Pτ, introduced by Da Silva Filho and Abe in the decision-making systems. An example is analyzed in detail showing how uncertainty, inconsistency and paracompleteness can be elegantly handled with this logical system. As application for the Para-analyzer in decision-making, we developed the BAM — Baricenter Analysis Method. In order to make the presentation easier, we present the BAM applied in the viability analysis of product launching. Some of the techniques of Paraconsistent Annotated Logic have been applied in Artificial Intelligence, Robotics, Information Technolgy (Computer Sciences), etc..

Windows .NET Network Distributed Basic Local Alignment Search Toolkit (W.ND-BLAST)

PubMed Central

Dowd, Scot E; Zaragoza, Joaquin; Rodriguez, Javier R; Oliver, Melvin J; Payton, Paxton R

2005-01-01

Background BLAST is one of the most common and useful tools for Genetic Research. This paper describes a software application we have termed Windows .NET Distributed Basic Local Alignment Search Toolkit (W.ND-BLAST), which enhances the BLAST utility by improving usability, fault recovery, and scalability in a Windows desktop environment. Our goal was to develop an easy to use, fault tolerant, high-throughput BLAST solution that incorporates a comprehensive BLAST result viewer with curation and annotation functionality. Results W.ND-BLAST is a comprehensive Windows-based software toolkit that targets researchers, including those with minimal computer skills, and provides the ability increase the performance of BLAST by distributing BLAST queries to any number of Windows based machines across local area networks (LAN). W.ND-BLAST provides intuitive Graphic User Interfaces (GUI) for BLAST database creation, BLAST execution, BLAST output evaluation and BLAST result exportation. This software also provides several layers of fault tolerance and fault recovery to prevent loss of data if nodes or master machines fail. This paper lays out the functionality of W.ND-BLAST. W.ND-BLAST displays close to 100% performance efficiency when distributing tasks to 12 remote computers of the same performance class. A high throughput BLAST job which took 662.68 minutes (11 hours) on one average machine was completed in 44.97 minutes when distributed to 17 nodes, which included lower performance class machines. Finally, there is a comprehensive high-throughput BLAST Output Viewer (BOV) and Annotation Engine components, which provides comprehensive exportation of BLAST hits to text files, annotated fasta files, tables, or association files. Conclusion W.ND-BLAST provides an interactive tool that allows scientists to easily utilizing their available computing resources for high throughput and comprehensive sequence analyses. The install package for W.ND-BLAST is freely downloadable from . With registration the software is free, installation, networking, and usage instructions are provided as well as a support forum. PMID:15819992

Gene Ontology annotation of the rice blast fungus, Magnaporthe oryzae

PubMed Central

Meng, Shaowu; Brown, Douglas E; Ebbole, Daniel J; Torto-Alalibo, Trudy; Oh, Yeon Yee; Deng, Jixin; Mitchell, Thomas K; Dean, Ralph A

2009-01-01

Background Magnaporthe oryzae, the causal agent of blast disease of rice, is the most destructive disease of rice worldwide. The genome of this fungal pathogen has been sequenced and an automated annotation has recently been updated to Version 6 . However, a comprehensive manual curation remains to be performed. Gene Ontology (GO) annotation is a valuable means of assigning functional information using standardized vocabulary. We report an overview of the GO annotation for Version 5 of M. oryzae genome assembly. Methods A similarity-based (i.e., computational) GO annotation with manual review was conducted, which was then integrated with a literature-based GO annotation with computational assistance. For similarity-based GO annotation a stringent reciprocal best hits method was used to identify similarity between predicted proteins of M. oryzae and GO proteins from multiple organisms with published associations to GO terms. Significant alignment pairs were manually reviewed. Functional assignments were further cross-validated with manually reviewed data, conserved domains, or data determined by wet lab experiments. Additionally, biological appropriateness of the functional assignments was manually checked. Results In total, 6,286 proteins received GO term assignment via the homology-based annotation, including 2,870 hypothetical proteins. Literature-based experimental evidence, such as microarray, MPSS, T-DNA insertion mutation, or gene knockout mutation, resulted in 2,810 proteins being annotated with GO terms. Of these, 1,673 proteins were annotated with new terms developed for Plant-Associated Microbe Gene Ontology (PAMGO). In addition, 67 experiment-determined secreted proteins were annotated with PAMGO terms. Integration of the two data sets resulted in 7,412 proteins (57%) being annotated with 1,957 distinct and specific GO terms. Unannotated proteins were assigned to the 3 root terms. The Version 5 GO annotation is publically queryable via the GO site . Additionally, the genome of M. oryzae is constantly being refined and updated as new information is incorporated. For the latest GO annotation of Version 6 genome, please visit our website . The preliminary GO annotation of Version 6 genome is placed at a local MySql database that is publically queryable via a user-friendly interface Adhoc Query System. Conclusion Our analysis provides comprehensive and robust GO annotations of the M. oryzae genome assemblies that will be solid foundations for further functional interrogation of M. oryzae. PMID:19278556

Distributed Structure Searchable Toxicity

EPA Pesticide Factsheets

The Distributed Structure Searchable Toxicity (DSSTox) online resource provides high quality chemical structures and annotations in association with toxicity data. It helps to build a data foundation for improved structure-activity relationships and predictive toxicology. DSSTox publishes summarized chemical activity representations for structure-activity modeling and provides a structure browser. This tool also houses the chemical inventories for the ToxCast and Tox21 projects.

Current and future trends in marine image annotation software

NASA Astrophysics Data System (ADS)

Gomes-Pereira, Jose Nuno; Auger, Vincent; Beisiegel, Kolja; Benjamin, Robert; Bergmann, Melanie; Bowden, David; Buhl-Mortensen, Pal; De Leo, Fabio C.; Dionísio, Gisela; Durden, Jennifer M.; Edwards, Luke; Friedman, Ariell; Greinert, Jens; Jacobsen-Stout, Nancy; Lerner, Steve; Leslie, Murray; Nattkemper, Tim W.; Sameoto, Jessica A.; Schoening, Timm; Schouten, Ronald; Seager, James; Singh, Hanumant; Soubigou, Olivier; Tojeira, Inês; van den Beld, Inge; Dias, Frederico; Tempera, Fernando; Santos, Ricardo S.

2016-12-01

Given the need to describe, analyze and index large quantities of marine imagery data for exploration and monitoring activities, a range of specialized image annotation tools have been developed worldwide. Image annotation - the process of transposing objects or events represented in a video or still image to the semantic level, may involve human interactions and computer-assisted solutions. Marine image annotation software (MIAS) have enabled over 500 publications to date. We review the functioning, application trends and developments, by comparing general and advanced features of 23 different tools utilized in underwater image analysis. MIAS requiring human input are basically a graphical user interface, with a video player or image browser that recognizes a specific time code or image code, allowing to log events in a time-stamped (and/or geo-referenced) manner. MIAS differ from similar software by the capability of integrating data associated to video collection, the most simple being the position coordinates of the video recording platform. MIAS have three main characteristics: annotating events in real time, posteriorly to annotation and interact with a database. These range from simple annotation interfaces, to full onboard data management systems, with a variety of toolboxes. Advanced packages allow to input and display data from multiple sensors or multiple annotators via intranet or internet. Posterior human-mediated annotation often include tools for data display and image analysis, e.g. length, area, image segmentation, point count; and in a few cases the possibility of browsing and editing previous dive logs or to analyze the annotations. The interaction with a database allows the automatic integration of annotations from different surveys, repeated annotation and collaborative annotation of shared datasets, browsing and querying of data. Progress in the field of automated annotation is mostly in post processing, for stable platforms or still images. Integration into available MIAS is currently limited to semi-automated processes of pixel recognition through computer-vision modules that compile expert-based knowledge. Important topics aiding the choice of a specific software are outlined, the ideal software is discussed and future trends are presented.

Utilization of Automatic Tagging Using Web Information to Datamining

NASA Astrophysics Data System (ADS)

Sugimura, Hiroshi; Matsumoto, Kazunori

This paper proposes a data annotation system using the automatic tagging approach. Although annotations of data are useful for deep analysis and mining of it, the cost of providing them becomes huge in most of the cases. In order to solve this problem, we develop a semi-automatic method that consists of two stages. In the first stage, it searches the Web space for relating information, and discovers candidates of effective annotations. The second stage uses knowledge of a human user. The candidates are investigated and refined by the user, and then they become annotations. We in this paper focus on time-series data, and show effectiveness of a GUI tool that supports the above process.

Mitochondrial Disease Sequence Data Resource (MSeqDR): a global grass-roots consortium to facilitate deposition, curation, annotation, and integrated analysis of genomic data for the mitochondrial disease clinical and research communities.

PubMed

Falk, Marni J; Shen, Lishuang; Gonzalez, Michael; Leipzig, Jeremy; Lott, Marie T; Stassen, Alphons P M; Diroma, Maria Angela; Navarro-Gomez, Daniel; Yeske, Philip; Bai, Renkui; Boles, Richard G; Brilhante, Virginia; Ralph, David; DaRe, Jeana T; Shelton, Robert; Terry, Sharon F; Zhang, Zhe; Copeland, William C; van Oven, Mannis; Prokisch, Holger; Wallace, Douglas C; Attimonelli, Marcella; Krotoski, Danuta; Zuchner, Stephan; Gai, Xiaowu

2015-03-01

Success rates for genomic analyses of highly heterogeneous disorders can be greatly improved if a large cohort of patient data is assembled to enhance collective capabilities for accurate sequence variant annotation, analysis, and interpretation. Indeed, molecular diagnostics requires the establishment of robust data resources to enable data sharing that informs accurate understanding of genes, variants, and phenotypes. The "Mitochondrial Disease Sequence Data Resource (MSeqDR) Consortium" is a grass-roots effort facilitated by the United Mitochondrial Disease Foundation to identify and prioritize specific genomic data analysis needs of the global mitochondrial disease clinical and research community. A central Web portal (https://mseqdr.org) facilitates the coherent compilation, organization, annotation, and analysis of sequence data from both nuclear and mitochondrial genomes of individuals and families with suspected mitochondrial disease. This Web portal provides users with a flexible and expandable suite of resources to enable variant-, gene-, and exome-level sequence analysis in a secure, Web-based, and user-friendly fashion. Users can also elect to share data with other MSeqDR Consortium members, or even the general public, either by custom annotation tracks or through the use of a convenient distributed annotation system (DAS) mechanism. A range of data visualization and analysis tools are provided to facilitate user interrogation and understanding of genomic, and ultimately phenotypic, data of relevance to mitochondrial biology and disease. Currently available tools for nuclear and mitochondrial gene analyses include an MSeqDR GBrowse instance that hosts optimized mitochondrial disease and mitochondrial DNA (mtDNA) specific annotation tracks, as well as an MSeqDR locus-specific database (LSDB) that curates variant data on more than 1300 genes that have been implicated in mitochondrial disease and/or encode mitochondria-localized proteins. MSeqDR is integrated with a diverse array of mtDNA data analysis tools that are both freestanding and incorporated into an online exome-level dataset curation and analysis resource (GEM.app) that is being optimized to support needs of the MSeqDR community. In addition, MSeqDR supports mitochondrial disease phenotyping and ontology tools, and provides variant pathogenicity assessment features that enable community review, feedback, and integration with the public ClinVar variant annotation resource. A centralized Web-based informed consent process is being developed, with implementation of a Global Unique Identifier (GUID) system to integrate data deposited on a given individual from different sources. Community-based data deposition into MSeqDR has already begun. Future efforts will enhance capabilities to incorporate phenotypic data that enhance genomic data analyses. MSeqDR will fill the existing void in bioinformatics tools and centralized knowledge that are necessary to enable efficient nuclear and mtDNA genomic data interpretation by a range of shareholders across both clinical diagnostic and research settings. Ultimately, MSeqDR is focused on empowering the global mitochondrial disease community to better define and explore mitochondrial diseases. Copyright © 2014 Elsevier Inc. All rights reserved.

Mitochondrial Disease Sequence Data Resource (MSeqDR): A global grass-roots consortium to facilitate deposition, curation, annotation, and integrated analysis of genomic data for the mitochondrial disease clinical and research communities

PubMed Central

Falk, Marni J.; Shen, Lishuang; Gonzalez, Michael; Leipzig, Jeremy; Lott, Marie T.; Stassen, Alphons P.M.; Diroma, Maria Angela; Navarro-Gomez, Daniel; Yeske, Philip; Bai, Renkui; Boles, Richard G.; Brilhante, Virginia; Ralph, David; DaRe, Jeana T.; Shelton, Robert; Terry, Sharon; Zhang, Zhe; Copeland, William C.; van Oven, Mannis; Prokisch, Holger; Wallace, Douglas C.; Attimonelli, Marcella; Krotoski, Danuta; Zuchner, Stephan; Gai, Xiaowu

2014-01-01

Success rates for genomic analyses of highly heterogeneous disorders can be greatly improved if a large cohort of patient data is assembled to enhance collective capabilities for accurate sequence variant annotation, analysis, and interpretation. Indeed, molecular diagnostics requires the establishment of robust data resources to enable data sharing that informs accurate understanding of genes, variants, and phenotypes. The “Mitochondrial Disease Sequence Data Resource (MSeqDR) Consortium” is a grass-roots effort facilitated by the United Mitochondrial Disease Foundation to identify and prioritize specific genomic data analysis needs of the global mitochondrial disease clinical and research community. A central Web portal (https://mseqdr.org) facilitates the coherent compilation, organization, annotation, and analysis of sequence data from both nuclear and mitochondrial genomes of individuals and families with suspected mitochondrial disease. This Web portal provides users with a flexible and expandable suite of resources to enable variant-, gene-, and exome-level sequence analysis in a secure, Web-based, and user-friendly fashion. Users can also elect to share data with other MSeqDR Consortium members, or even the general public, either by custom annotation tracks or through use of a convenient distributed annotation system (DAS) mechanism. A range of data visualization and analysis tools are provided to facilitate user interrogation and understanding of genomic, and ultimately phenotypic, data of relevance to mitochondrial biology and disease. Currently available tools for nuclear and mitochondrial gene analyses include an MSeqDR GBrowse instance that hosts optimized mitochondrial disease and mitochondrial DNA (mtDNA) specific annotation tracks, as well as an MSeqDR locus-specific database (LSDB) that curates variant data on more than 1,300 genes that have been implicated in mitochondrial disease and/or encode mitochondria-localized proteins. MSeqDR is integrated with a diverse array of mtDNA data analysis tools that are both freestanding and incorporated into an online exome-level dataset curation and analysis resource (GEM.app) that is being optimized to support needs of the MSeqDR community. In addition, MSeqDR supports mitochondrial disease phenotyping and ontology tools, and provides variant pathogenicity assessment features that enable community review, feedback, and integration with the public ClinVar variant annotation resource. A centralized Web-based informed consent process is being developed, with implementation of a Global Unique Identifier (GUID) system to integrate data deposited on a given individual from different sources. Community-based data deposition into MSeqDR has already begun. Future efforts will enhance capabilities to incorporate phenotypic data that enhance genomic data analyses. MSeqDR will fill the existing void in bioinformatics tools and centralized knowledge that are necessary to enable efficient nuclear and mtDNA genomic data interpretation by a range of shareholders across both clinical diagnostic and research settings. Ultimately, MSeqDR is focused on empowering the global mitochondrial disease community to better define and explore mitochondrial disease. PMID:25542617

It’s about This and That: A Description of Anaphoric Expressions in Clinical Text

PubMed Central

Wang, Yan; Melton, Genevieve B.; Pakhomov, Serguei

2011-01-01

Although anaphoric expressions are very common in biomedical and clinical documents, little work has been done to systematically characterize their use in clinical text. Samples of ‘it’, ‘this’, and ‘that’ expressions occurring in inpatient clinical notes from four metropolitan hospitals were analyzed using a combination of semi-automated and manual annotation techniques. We developed a rule-based approach to filter potential non-referential expressions. A physician then manually annotated 1000 potential referential instances to determine referent status and the antecedent of each referent expression. A distributional analysis of the three referring expressions in the entire corpus of notes demonstrates a high prevalence of anaphora and large variance in distributions of referential expressions with different notes. Our results confirm that anaphoric expressions are common in clinical texts. Effective co-reference resolution with anaphoric expressions remains an important challenge in medical natural language processing research. PMID:22195211

BioModels.net Web Services, a free and integrated toolkit for computational modelling software.

PubMed

Li, Chen; Courtot, Mélanie; Le Novère, Nicolas; Laibe, Camille

2010-05-01

Exchanging and sharing scientific results are essential for researchers in the field of computational modelling. BioModels.net defines agreed-upon standards for model curation. A fundamental one, MIRIAM (Minimum Information Requested in the Annotation of Models), standardises the annotation and curation process of quantitative models in biology. To support this standard, MIRIAM Resources maintains a set of standard data types for annotating models, and provides services for manipulating these annotations. Furthermore, BioModels.net creates controlled vocabularies, such as SBO (Systems Biology Ontology) which strictly indexes, defines and links terms used in Systems Biology. Finally, BioModels Database provides a free, centralised, publicly accessible database for storing, searching and retrieving curated and annotated computational models. Each resource provides a web interface to submit, search, retrieve and display its data. In addition, the BioModels.net team provides a set of Web Services which allows the community to programmatically access the resources. A user is then able to perform remote queries, such as retrieving a model and resolving all its MIRIAM Annotations, as well as getting the details about the associated SBO terms. These web services use established standards. Communications rely on SOAP (Simple Object Access Protocol) messages and the available queries are described in a WSDL (Web Services Description Language) file. Several libraries are provided in order to simplify the development of client software. BioModels.net Web Services make one step further for the researchers to simulate and understand the entirety of a biological system, by allowing them to retrieve biological models in their own tool, combine queries in workflows and efficiently analyse models.

Developing a disease outbreak event corpus.

PubMed

Conway, Mike; Kawazoe, Ai; Chanlekha, Hutchatai; Collier, Nigel

2010-09-28

In recent years, there has been a growth in work on the use of information extraction technologies for tracking disease outbreaks from online news texts, yet publicly available evaluation standards (and associated resources) for this new area of research have been noticeably lacking. This study seeks to create a "gold standard" data set against which to test how accurately disease outbreak information extraction systems can identify the semantics of disease outbreak events. Additionally, we hope that the provision of an annotation scheme (and associated corpus) to the community will encourage open evaluation in this new and growing application area. We developed an annotation scheme for identifying infectious disease outbreak events in news texts. An event--in the context of our annotation scheme--consists minimally of geographical (eg, country and province) and disease name information. However, the scheme also allows for the rich encoding of other domain salient concepts (eg, international travel, species, and food contamination). The work resulted in a 200-document corpus of event-annotated disease outbreak reports that can be used to evaluate the accuracy of event detection algorithms (in this case, for the BioCaster biosurveillance online news information extraction system). In the 200 documents, 394 distinct events were identified (mean 1.97 events per document, range 0-25 events per document). We also provide a download script and graphical user interface (GUI)-based event browsing software to facilitate corpus exploration. In summary, we present an annotation scheme and corpus that can be used in the evaluation of disease outbreak event extraction algorithms. The annotation scheme and corpus were designed both with the particular evaluation requirements of the BioCaster system in mind as well as the wider need for further evaluation resources in this growing research area.

Hymenoptera Genome Database: integrating genome annotations in HymenopteraMine

PubMed Central

Elsik, Christine G.; Tayal, Aditi; Diesh, Colin M.; Unni, Deepak R.; Emery, Marianne L.; Nguyen, Hung N.; Hagen, Darren E.

2016-01-01

We report an update of the Hymenoptera Genome Database (HGD) (http://HymenopteraGenome.org), a model organism database for insect species of the order Hymenoptera (ants, bees and wasps). HGD maintains genomic data for 9 bee species, 10 ant species and 1 wasp, including the versions of genome and annotation data sets published by the genome sequencing consortiums and those provided by NCBI. A new data-mining warehouse, HymenopteraMine, based on the InterMine data warehousing system, integrates the genome data with data from external sources and facilitates cross-species analyses based on orthology. New genome browsers and annotation tools based on JBrowse/WebApollo provide easy genome navigation, and viewing of high throughput sequence data sets and can be used for collaborative genome annotation. All of the genomes and annotation data sets are combined into a single BLAST server that allows users to select and combine sequence data sets to search. PMID:26578564

An overview of the BioCreative 2012 Workshop Track III: interactive text mining task.

PubMed

Arighi, Cecilia N; Carterette, Ben; Cohen, K Bretonnel; Krallinger, Martin; Wilbur, W John; Fey, Petra; Dodson, Robert; Cooper, Laurel; Van Slyke, Ceri E; Dahdul, Wasila; Mabee, Paula; Li, Donghui; Harris, Bethany; Gillespie, Marc; Jimenez, Silvia; Roberts, Phoebe; Matthews, Lisa; Becker, Kevin; Drabkin, Harold; Bello, Susan; Licata, Luana; Chatr-aryamontri, Andrew; Schaeffer, Mary L; Park, Julie; Haendel, Melissa; Van Auken, Kimberly; Li, Yuling; Chan, Juancarlos; Muller, Hans-Michael; Cui, Hong; Balhoff, James P; Chi-Yang Wu, Johnny; Lu, Zhiyong; Wei, Chih-Hsuan; Tudor, Catalina O; Raja, Kalpana; Subramani, Suresh; Natarajan, Jeyakumar; Cejuela, Juan Miguel; Dubey, Pratibha; Wu, Cathy

2013-01-01

In many databases, biocuration primarily involves literature curation, which usually involves retrieving relevant articles, extracting information that will translate into annotations and identifying new incoming literature. As the volume of biological literature increases, the use of text mining to assist in biocuration becomes increasingly relevant. A number of groups have developed tools for text mining from a computer science/linguistics perspective, and there are many initiatives to curate some aspect of biology from the literature. Some biocuration efforts already make use of a text mining tool, but there have not been many broad-based systematic efforts to study which aspects of a text mining tool contribute to its usefulness for a curation task. Here, we report on an effort to bring together text mining tool developers and database biocurators to test the utility and usability of tools. Six text mining systems presenting diverse biocuration tasks participated in a formal evaluation, and appropriate biocurators were recruited for testing. The performance results from this evaluation indicate that some of the systems were able to improve efficiency of curation by speeding up the curation task significantly (∼1.7- to 2.5-fold) over manual curation. In addition, some of the systems were able to improve annotation accuracy when compared with the performance on the manually curated set. In terms of inter-annotator agreement, the factors that contributed to significant differences for some of the systems included the expertise of the biocurator on the given curation task, the inherent difficulty of the curation and attention to annotation guidelines. After the task, annotators were asked to complete a survey to help identify strengths and weaknesses of the various systems. The analysis of this survey highlights how important task completion is to the biocurators' overall experience of a system, regardless of the system's high score on design, learnability and usability. In addition, strategies to refine the annotation guidelines and systems documentation, to adapt the tools to the needs and query types the end user might have and to evaluate performance in terms of efficiency, user interface, result export and traditional evaluation metrics have been analyzed during this task. This analysis will help to plan for a more intense study in BioCreative IV.

The Génolevures database.

PubMed

Martin, Tiphaine; Sherman, David J; Durrens, Pascal

2011-01-01

The Génolevures online database (URL: http://www.genolevures.org) stores and provides the data and results obtained by the Génolevures Consortium through several campaigns of genome annotation of the yeasts in the Saccharomycotina subphylum (hemiascomycetes). This database is dedicated to large-scale comparison of these genomes, storing not only the different chromosomal elements detected in the sequences, but also the logical relations between them. The database is divided into a public part, accessible to anyone through Internet, and a private part where the Consortium members make genome annotations with our Magus annotation system; this system is used to annotate several related genomes in parallel. The public database is widely consulted and offers structured data, organized using a REST web site architecture that allows for automated requests. The implementation of the database, as well as its associated tools and methods, is evolving to cope with the influx of genome sequences produced by Next Generation Sequencing (NGS). Copyright © 2011 Académie des sciences. Published by Elsevier SAS. All rights reserved.

Computational annotation of genes differentially expressed along olive fruit development

PubMed Central

Galla, Giulio; Barcaccia, Gianni; Ramina, Angelo; Collani, Silvio; Alagna, Fiammetta; Baldoni, Luciana; Cultrera, Nicolò GM; Martinelli, Federico; Sebastiani, Luca; Tonutti, Pietro

2009-01-01

Background Olea europaea L. is a traditional tree crop of the Mediterranean basin with a worldwide economical high impact. Differently from other fruit tree species, little is known about the physiological and molecular basis of the olive fruit development and a few sequences of genes and gene products are available for olive in public databases. This study deals with the identification of large sets of differentially expressed genes in developing olive fruits and the subsequent computational annotation by means of different software. Results mRNA from fruits of the cv. Leccino sampled at three different stages [i.e., initial fruit set (stage 1), completed pit hardening (stage 2) and veraison (stage 3)] was used for the identification of differentially expressed genes putatively involved in main processes along fruit development. Four subtractive hybridization libraries were constructed: forward and reverse between stage 1 and 2 (libraries A and B), and 2 and 3 (libraries C and D). All sequenced clones (1,132 in total) were analyzed through BlastX against non-redundant NCBI databases and about 60% of them showed similarity to known proteins. A total of 89 out of 642 differentially expressed unique sequences was further investigated by Real-Time PCR, showing a validation of the SSH results as high as 69%. Library-specific cDNA repertories were annotated according to the three main vocabularies of the gene ontology (GO): cellular component, biological process and molecular function. BlastX analysis, GO terms mapping and annotation analysis were performed using the Blast2GO software, a research tool designed with the main purpose of enabling GO based data mining on sequence sets for which no GO annotation is yet available. Bioinformatic analysis pointed out a significantly different distribution of the annotated sequences for each GO category, when comparing the three fruit developmental stages. The olive fruit-specific transcriptome dataset was used to query all known KEGG (Kyoto Encyclopaedia of Genes and Genomes) metabolic pathways for characterizing and positioning retrieved EST records. The integration of the olive sequence datasets within the MapMan platform for microarray analysis allowed the identification of specific biosynthetic pathways useful for the definition of key functional categories in time course analyses for gene groups. Conclusion The bioinformatic annotation of all gene sequences was useful to shed light on metabolic pathways and transcriptional aspects related to carbohydrates, fatty acids, secondary metabolites, transcription factors and hormones as well as response to biotic and abiotic stresses throughout olive drupe development. These results represent a first step toward both functional genomics and systems biology research for understanding the gene functions and regulatory networks in olive fruit growth and ripening. PMID:19852839

A review and evaluation of the Langley Research Center's Scientific and Technical Information Program. Results of phase 5. Design and evaluation of STI systems: A selected, annotated bibliography

NASA Technical Reports Server (NTRS)

Pinelli, T. E.; Hinnebusch, P. A.; Jaffe, J. M.

1981-01-01

A selected, annotated bibliography of literature citations related to the design and evaluation of STI systems is presented. The use of manual and machine-readable literature searches; the review of numerous books, periodicals reports, and papers; and the selection and annotation of literature citations were required. The bibliography was produced because the information was needed to develop the methodology for the review and evaluation project, and a survey of the literature did not reveal the existence of a single published source of information pertinent to the subject. Approximately 200 citations are classified in four subject areas. The areas include information - general; information systems - design and evaluation, including information products and services; information - use and need; and information - economics.

A semi-supervised learning framework for biomedical event extraction based on hidden topics.

PubMed

Zhou, Deyu; Zhong, Dayou

2015-05-01

Scientists have devoted decades of efforts to understanding the interaction between proteins or RNA production. The information might empower the current knowledge on drug reactions or the development of certain diseases. Nevertheless, due to the lack of explicit structure, literature in life science, one of the most important sources of this information, prevents computer-based systems from accessing. Therefore, biomedical event extraction, automatically acquiring knowledge of molecular events in research articles, has attracted community-wide efforts recently. Most approaches are based on statistical models, requiring large-scale annotated corpora to precisely estimate models' parameters. However, it is usually difficult to obtain in practice. Therefore, employing un-annotated data based on semi-supervised learning for biomedical event extraction is a feasible solution and attracts more interests. In this paper, a semi-supervised learning framework based on hidden topics for biomedical event extraction is presented. In this framework, sentences in the un-annotated corpus are elaborately and automatically assigned with event annotations based on their distances to these sentences in the annotated corpus. More specifically, not only the structures of the sentences, but also the hidden topics embedded in the sentences are used for describing the distance. The sentences and newly assigned event annotations, together with the annotated corpus, are employed for training. Experiments were conducted on the multi-level event extraction corpus, a golden standard corpus. Experimental results show that more than 2.2% improvement on F-score on biomedical event extraction is achieved by the proposed framework when compared to the state-of-the-art approach. The results suggest that by incorporating un-annotated data, the proposed framework indeed improves the performance of the state-of-the-art event extraction system and the similarity between sentences might be precisely described by hidden topics and structures of the sentences. Copyright © 2015 Elsevier B.V. All rights reserved.

A fully automatic end-to-end method for content-based image retrieval of CT scans with similar liver lesion annotations.

PubMed

Spanier, A B; Caplan, N; Sosna, J; Acar, B; Joskowicz, L

2018-01-01

The goal of medical content-based image retrieval (M-CBIR) is to assist radiologists in the decision-making process by retrieving medical cases similar to a given image. One of the key interests of radiologists is lesions and their annotations, since the patient treatment depends on the lesion diagnosis. Therefore, a key feature of M-CBIR systems is the retrieval of scans with the most similar lesion annotations. To be of value, M-CBIR systems should be fully automatic to handle large case databases. We present a fully automatic end-to-end method for the retrieval of CT scans with similar liver lesion annotations. The input is a database of abdominal CT scans labeled with liver lesions, a query CT scan, and optionally one radiologist-specified lesion annotation of interest. The output is an ordered list of the database CT scans with the most similar liver lesion annotations. The method starts by automatically segmenting the liver in the scan. It then extracts a histogram-based features vector from the segmented region, learns the features' relative importance, and ranks the database scans according to the relative importance measure. The main advantages of our method are that it fully automates the end-to-end querying process, that it uses simple and efficient techniques that are scalable to large datasets, and that it produces quality retrieval results using an unannotated CT scan. Our experimental results on 9 CT queries on a dataset of 41 volumetric CT scans from the 2014 Image CLEF Liver Annotation Task yield an average retrieval accuracy (Normalized Discounted Cumulative Gain index) of 0.77 and 0.84 without/with annotation, respectively. Fully automatic end-to-end retrieval of similar cases based on image information alone, rather that on disease diagnosis, may help radiologists to better diagnose liver lesions.

Self-evaluation and peer-feedback of medical students' communication skills using a web-based video annotation system. Exploring content and specificity.

PubMed

Hulsman, Robert L; van der Vloodt, Jane

2015-03-01

Self-evaluation and peer-feedback are important strategies within the reflective practice paradigm for the development and maintenance of professional competencies like medical communication. Characteristics of the self-evaluation and peer-feedback annotations of medical students' video recorded communication skills were analyzed. Twenty-five year 4 medical students recorded history-taking consultations with a simulated patient, uploaded the video to a web-based platform, marked and annotated positive and negative events. Peers reviewed the video and self-evaluations and provided feedback. Analyzed were the number of marked positive and negative annotations and the amount of text entered. Topics and specificity of the annotations were coded and analyzed qualitatively. Students annotated on average more negative than positive events. Additional peer-feedback was more often positive. Topics most often related to structuring the consultation. Students were most critical about their biomedical topics. Negative annotations were more specific than positive annotations. Self-evaluations were more specific than peer-feedback and both show a significant correlation. Four response patterns were detected that negatively bias specificity assessment ratings. Teaching students to be more specific in their self-evaluations may be effective for receiving more specific peer-feedback. Videofragmentrating is a convenient tool to implement reflective practice activities like self-evaluation and peer-feedback to the classroom in the teaching of clinical skills. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Hardware, Languages, and Architectures for Defense Against Hostile Operating Systems

DTIC Science & Technology

2015-05-14

Executive Service Directorate (0704-0188). Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to...in privileged system services . ExpressOS requires only a modest annotation burden (annotations were about 3% of code of the kernel), modest performance...INT benchmarks . In addition to enabling the development of an architecture- neutral instrumentation framework, our approach can take advantage of the

DSSTox and Chemical Information Technologies in Support of PredictiveToxicology

EPA Science Inventory

The EPA NCCT Distributed Structure-Searchable Toxicity (DSSTox) Database project initially focused on the curation and publication of high-quality, standardized, chemical structure-annotated toxicity databases for use in structure-activity relationship (SAR) modeling. In recent y...

MimoSA: a system for minimotif annotation

PubMed Central

2010-01-01

Background Minimotifs are short peptide sequences within one protein, which are recognized by other proteins or molecules. While there are now several minimotif databases, they are incomplete. There are reports of many minimotifs in the primary literature, which have yet to be annotated, while entirely novel minimotifs continue to be published on a weekly basis. Our recently proposed function and sequence syntax for minimotifs enables us to build a general tool that will facilitate structured annotation and management of minimotif data from the biomedical literature. Results We have built the MimoSA application for minimotif annotation. The application supports management of the Minimotif Miner database, literature tracking, and annotation of new minimotifs. MimoSA enables the visualization, organization, selection and editing functions of minimotifs and their attributes in the MnM database. For the literature components, Mimosa provides paper status tracking and scoring of papers for annotation through a freely available machine learning approach, which is based on word correlation. The paper scoring algorithm is also available as a separate program, TextMine. Form-driven annotation of minimotif attributes enables entry of new minimotifs into the MnM database. Several supporting features increase the efficiency of annotation. The layered architecture of MimoSA allows for extensibility by separating the functions of paper scoring, minimotif visualization, and database management. MimoSA is readily adaptable to other annotation efforts that manually curate literature into a MySQL database. Conclusions MimoSA is an extensible application that facilitates minimotif annotation and integrates with the Minimotif Miner database. We have built MimoSA as an application that integrates dynamic abstract scoring with a high performance relational model of minimotif syntax. MimoSA's TextMine, an efficient paper-scoring algorithm, can be used to dynamically rank papers with respect to context. PMID:20565705

dbWFA: a web-based database for functional annotation of Triticum aestivum transcripts

PubMed Central

Vincent, Jonathan; Dai, Zhanwu; Ravel, Catherine; Choulet, Frédéric; Mouzeyar, Said; Bouzidi, M. Fouad; Agier, Marie; Martre, Pierre

2013-01-01

The functional annotation of genes based on sequence homology with genes from model species genomes is time-consuming because it is necessary to mine several unrelated databases. The aim of the present work was to develop a functional annotation database for common wheat Triticum aestivum (L.). The database, named dbWFA, is based on the reference NCBI UniGene set, an expressed gene catalogue built by expressed sequence tag clustering, and on full-length coding sequences retrieved from the TriFLDB database. Information from good-quality heterogeneous sources, including annotations for model plant species Arabidopsis thaliana (L.) Heynh. and Oryza sativa L., was gathered and linked to T. aestivum sequences through BLAST-based homology searches. Even though the complexity of the transcriptome cannot yet be fully appreciated, we developed a tool to easily and promptly obtain information from multiple functional annotation systems (Gene Ontology, MapMan bin codes, MIPS Functional Categories, PlantCyc pathway reactions and TAIR gene families). The use of dbWFA is illustrated here with several query examples. We were able to assign a putative function to 45% of the UniGenes and 81% of the full-length coding sequences from TriFLDB. Moreover, comparison of the annotation of the whole T. aestivum UniGene set along with curated annotations of the two model species assessed the accuracy of the annotation provided by dbWFA. To further illustrate the use of dbWFA, genes specifically expressed during the early cell division or late storage polymer accumulation phases of T. aestivum grain development were identified using a clustering analysis and then annotated using dbWFA. The annotation of these two sets of genes was consistent with previous analyses of T. aestivum grain transcriptomes and proteomes. Database URL: urgi.versailles.inra.fr/dbWFA/ PMID:23660284

Biowep: a workflow enactment portal for bioinformatics applications.

PubMed

Romano, Paolo; Bartocci, Ezio; Bertolini, Guglielmo; De Paoli, Flavio; Marra, Domenico; Mauri, Giancarlo; Merelli, Emanuela; Milanesi, Luciano

2007-03-08

The huge amount of biological information, its distribution over the Internet and the heterogeneity of available software tools makes the adoption of new data integration and analysis network tools a necessity in bioinformatics. ICT standards and tools, like Web Services and Workflow Management Systems (WMS), can support the creation and deployment of such systems. Many Web Services are already available and some WMS have been proposed. They assume that researchers know which bioinformatics resources can be reached through a programmatic interface and that they are skilled in programming and building workflows. Therefore, they are not viable to the majority of unskilled researchers. A portal enabling these to take profit from new technologies is still missing. We designed biowep, a web based client application that allows for the selection and execution of a set of predefined workflows. The system is available on-line. Biowep architecture includes a Workflow Manager, a User Interface and a Workflow Executor. The task of the Workflow Manager is the creation and annotation of workflows. These can be created by using either the Taverna Workbench or BioWMS. Enactment of workflows is carried out by FreeFluo for Taverna workflows and by BioAgent/Hermes, a mobile agent-based middleware, for BioWMS ones. Main workflows' processing steps are annotated on the basis of their input and output, elaboration type and application domain by using a classification of bioinformatics data and tasks. The interface supports users authentication and profiling. Workflows can be selected on the basis of users' profiles and can be searched through their annotations. Results can be saved. We developed a web system that support the selection and execution of predefined workflows, thus simplifying access for all researchers. The implementation of Web Services allowing specialized software to interact with an exhaustive set of biomedical databases and analysis software and the creation of effective workflows can significantly improve automation of in-silico analysis. Biowep is available for interested researchers as a reference portal. They are invited to submit their workflows to the workflow repository. Biowep is further being developed in the sphere of the Laboratory of Interdisciplinary Technologies in Bioinformatics - LITBIO.

Biowep: a workflow enactment portal for bioinformatics applications

PubMed Central

Romano, Paolo; Bartocci, Ezio; Bertolini, Guglielmo; De Paoli, Flavio; Marra, Domenico; Mauri, Giancarlo; Merelli, Emanuela; Milanesi, Luciano

2007-01-01

Background The huge amount of biological information, its distribution over the Internet and the heterogeneity of available software tools makes the adoption of new data integration and analysis network tools a necessity in bioinformatics. ICT standards and tools, like Web Services and Workflow Management Systems (WMS), can support the creation and deployment of such systems. Many Web Services are already available and some WMS have been proposed. They assume that researchers know which bioinformatics resources can be reached through a programmatic interface and that they are skilled in programming and building workflows. Therefore, they are not viable to the majority of unskilled researchers. A portal enabling these to take profit from new technologies is still missing. Results We designed biowep, a web based client application that allows for the selection and execution of a set of predefined workflows. The system is available on-line. Biowep architecture includes a Workflow Manager, a User Interface and a Workflow Executor. The task of the Workflow Manager is the creation and annotation of workflows. These can be created by using either the Taverna Workbench or BioWMS. Enactment of workflows is carried out by FreeFluo for Taverna workflows and by BioAgent/Hermes, a mobile agent-based middleware, for BioWMS ones. Main workflows' processing steps are annotated on the basis of their input and output, elaboration type and application domain by using a classification of bioinformatics data and tasks. The interface supports users authentication and profiling. Workflows can be selected on the basis of users' profiles and can be searched through their annotations. Results can be saved. Conclusion We developed a web system that support the selection and execution of predefined workflows, thus simplifying access for all researchers. The implementation of Web Services allowing specialized software to interact with an exhaustive set of biomedical databases and analysis software and the creation of effective workflows can significantly improve automation of in-silico analysis. Biowep is available for interested researchers as a reference portal. They are invited to submit their workflows to the workflow repository. Biowep is further being developed in the sphere of the Laboratory of Interdisciplinary Technologies in Bioinformatics – LITBIO. PMID:17430563

Annotation an effective device for student feedback: a critical review of the literature.

PubMed

Ball, Elaine C

2010-05-01

The paper examines hand-written annotation, its many features, difficulties and strengths as a feedback tool. It extends and clarifies what modest evidence is in the public domain and offers an evaluation of how to use annotation effectively in the support of student feedback [Marshall, C.M., 1998a. The Future of Annotation in a Digital (paper) World. Presented at the 35th Annual GLSLIS Clinic: Successes and Failures of Digital Libraries, June 20-24, University of Illinois at Urbana-Champaign, March 24, pp. 1-20; Marshall, C.M., 1998b. Toward an ecology of hypertext annotation. Hypertext. In: Proceedings of the Ninth ACM Conference on Hypertext and Hypermedia, June 20-24, Pittsburgh Pennsylvania, US, pp. 40-49; Wolfe, J.L., Nuewirth, C.M., 2001. From the margins to the centre: the future of annotation. Journal of Business and Technical Communication, 15(3), 333-371; Diyanni, R., 2002. One Hundred Great Essays. Addison-Wesley, New York; Wolfe, J.L., 2002. Marginal pedagogy: how annotated texts affect writing-from-source texts. Written Communication, 19(2), 297-333; Liu, K., 2006. Annotation as an index to critical writing. Urban Education, 41, 192-207; Feito, A., Donahue, P., 2008. Minding the gap annotation as preparation for discussion. Arts and Humanities in Higher Education, 7(3), 295-307; Ball, E., 2009. A participatory action research study on handwritten annotation feedback and its impact on staff and students. Systemic Practice and Action Research, 22(2), 111-124; Ball, E., Franks, H., McGrath, M., Leigh, J., 2009. Annotation is a valuable tool to enhance learning and assessment in student essays. Nurse Education Today, 29(3), 284-291]. Although a significant number of studies examine annotation, this is largely related to on-line tools and computer mediated communication and not hand-written annotation as comment, phrase or sign written on the student essay to provide critique. Little systematic research has been conducted to consider how this latter form of annotation influences student learning and assessment or, indeed, helps tutors to employ better annotative practices [Juwah, C., Macfarlane-Dick, D., Matthew, B., Nicol, D., Ross, D., Smith, B., 2004. Enhancing student learning through effective formative feedback. The Higher Education Academy, 1-40; Jewitt, C., Kress, G., 2005. English in classrooms: only write down what you need to know: annotation for what? English in Education, 39(1), 5-18]. There is little evidence on ways to heighten students' self-awareness when their essays are returned with annotated feedback [Storch, N., Tapper, J., 1997. Student annotations: what NNS and NS university students say about their own writing. Journal of Second Language Writing, 6(3), 245-265]. The literature review clarifies forms of annotation as feedback practice and offers a summary of the challenges and usefulness of annotation. Copyright 2009. Published by Elsevier Ltd.

Annotated Bibliography: Research on Enlisted Attrition in the U.S. Army

DTIC Science & Technology

2007-07-01

Assignment Research Unit Michael G. Rumsey, Chief July 2007 United States Army Research Institute for the Behavioral and Social ...Sciences Approved for public release; distribution is unlimited. U.S. Army Research Institute for the Behavioral and Social Sciences A Directorate...correspondence concerning distribution of reports to: U.S. Army Research Institute for the Behavioral and Social Sciences, Attn: DAPE-ARI-MS, 2511

An overview of the BioCreative 2012 Workshop Track III: interactive text mining task

PubMed Central

Arighi, Cecilia N.; Carterette, Ben; Cohen, K. Bretonnel; Krallinger, Martin; Wilbur, W. John; Fey, Petra; Dodson, Robert; Cooper, Laurel; Van Slyke, Ceri E.; Dahdul, Wasila; Mabee, Paula; Li, Donghui; Harris, Bethany; Gillespie, Marc; Jimenez, Silvia; Roberts, Phoebe; Matthews, Lisa; Becker, Kevin; Drabkin, Harold; Bello, Susan; Licata, Luana; Chatr-aryamontri, Andrew; Schaeffer, Mary L.; Park, Julie; Haendel, Melissa; Van Auken, Kimberly; Li, Yuling; Chan, Juancarlos; Muller, Hans-Michael; Cui, Hong; Balhoff, James P.; Chi-Yang Wu, Johnny; Lu, Zhiyong; Wei, Chih-Hsuan; Tudor, Catalina O.; Raja, Kalpana; Subramani, Suresh; Natarajan, Jeyakumar; Cejuela, Juan Miguel; Dubey, Pratibha; Wu, Cathy

2013-01-01

In many databases, biocuration primarily involves literature curation, which usually involves retrieving relevant articles, extracting information that will translate into annotations and identifying new incoming literature. As the volume of biological literature increases, the use of text mining to assist in biocuration becomes increasingly relevant. A number of groups have developed tools for text mining from a computer science/linguistics perspective, and there are many initiatives to curate some aspect of biology from the literature. Some biocuration efforts already make use of a text mining tool, but there have not been many broad-based systematic efforts to study which aspects of a text mining tool contribute to its usefulness for a curation task. Here, we report on an effort to bring together text mining tool developers and database biocurators to test the utility and usability of tools. Six text mining systems presenting diverse biocuration tasks participated in a formal evaluation, and appropriate biocurators were recruited for testing. The performance results from this evaluation indicate that some of the systems were able to improve efficiency of curation by speeding up the curation task significantly (∼1.7- to 2.5-fold) over manual curation. In addition, some of the systems were able to improve annotation accuracy when compared with the performance on the manually curated set. In terms of inter-annotator agreement, the factors that contributed to significant differences for some of the systems included the expertise of the biocurator on the given curation task, the inherent difficulty of the curation and attention to annotation guidelines. After the task, annotators were asked to complete a survey to help identify strengths and weaknesses of the various systems. The analysis of this survey highlights how important task completion is to the biocurators’ overall experience of a system, regardless of the system’s high score on design, learnability and usability. In addition, strategies to refine the annotation guidelines and systems documentation, to adapt the tools to the needs and query types the end user might have and to evaluate performance in terms of efficiency, user interface, result export and traditional evaluation metrics have been analyzed during this task. This analysis will help to plan for a more intense study in BioCreative IV. PMID:23327936

Genome-wide transcription start site profiling in biofilm-grown Burkholderia cenocepacia J2315.

PubMed

Sass, Andrea M; Van Acker, Heleen; Förstner, Konrad U; Van Nieuwerburgh, Filip; Deforce, Dieter; Vogel, Jörg; Coenye, Tom

2015-10-13

Burkholderia cenocepacia is a soil-dwelling Gram-negative Betaproteobacterium with an important role as opportunistic pathogen in humans. Infections with B. cenocepacia are very difficult to treat due to their high intrinsic resistance to most antibiotics. Biofilm formation further adds to their antibiotic resistance. B. cenocepacia harbours a large, multi-replicon genome with a high GC-content, the reference genome of strain J2315 includes 7374 annotated genes. This study aims to annotate transcription start sites and identify novel transcripts on a whole genome scale. RNA extracted from B. cenocepacia J2315 biofilms was analysed by differential RNA-sequencing and the resulting dataset compared to data derived from conventional, global RNA-sequencing. Transcription start sites were annotated and further analysed according to their position relative to annotated genes. Four thousand ten transcription start sites were mapped over the whole B. cenocepacia genome and the primary transcription start site of 2089 genes expressed in B. cenocepacia biofilms were defined. For 64 genes a start codon alternative to the annotated one was proposed. Substantial antisense transcription for 105 genes and two novel protein coding sequences were identified. The distribution of internal transcription start sites can be used to identify genomic islands in B. cenocepacia. A potassium pump strongly induced only under biofilm conditions was found and 15 non-coding small RNAs highly expressed in biofilms were discovered. Mapping transcription start sites across the B. cenocepacia genome added relevant information to the J2315 annotation. Genes and novel regulatory RNAs putatively involved in B. cenocepacia biofilm formation were identified. These findings will help in understanding regulation of B. cenocepacia biofilm formation.

Generating Customized Verifiers for Automatically Generated Code

NASA Technical Reports Server (NTRS)

Denney, Ewen; Fischer, Bernd

2008-01-01

Program verification using Hoare-style techniques requires many logical annotations. We have previously developed a generic annotation inference algorithm that weaves in all annotations required to certify safety properties for automatically generated code. It uses patterns to capture generator- and property-specific code idioms and property-specific meta-program fragments to construct the annotations. The algorithm is customized by specifying the code patterns and integrating them with the meta-program fragments for annotation construction. However, this is difficult since it involves tedious and error-prone low-level term manipulations. Here, we describe an annotation schema compiler that largely automates this customization task using generative techniques. It takes a collection of high-level declarative annotation schemas tailored towards a specific code generator and safety property, and generates all customized analysis functions and glue code required for interfacing with the generic algorithm core, thus effectively creating a customized annotation inference algorithm. The compiler raises the level of abstraction and simplifies schema development and maintenance. It also takes care of some more routine aspects of formulating patterns and schemas, in particular handling of irrelevant program fragments and irrelevant variance in the program structure, which reduces the size, complexity, and number of different patterns and annotation schemas that are required. The improvements described here make it easier and faster to customize the system to a new safety property or a new generator, and we demonstrate this by customizing it to certify frame safety of space flight navigation code that was automatically generated from Simulink models by MathWorks' Real-Time Workshop.

Generation of an annotated reference standard for vaccine adverse event reports.

PubMed

Foster, Matthew; Pandey, Abhishek; Kreimeyer, Kory; Botsis, Taxiarchis

2018-07-05

As part of a collaborative project between the US Food and Drug Administration (FDA) and the Centers for Disease Control and Prevention for the development of a web-based natural language processing (NLP) workbench, we created a corpus of 1000 Vaccine Adverse Event Reporting System (VAERS) reports annotated for 36,726 clinical features, 13,365 temporal features, and 22,395 clinical-temporal links. This paper describes the final corpus, as well as the methodology used to create it, so that clinical NLP researchers outside FDA can evaluate the utility of the corpus to aid their own work. The creation of this standard went through four phases: pre-training, pre-production, production-clinical feature annotation, and production-temporal annotation. The pre-production phase used a double annotation followed by adjudication strategy to refine and finalize the annotation model while the production phases followed a single annotation strategy to maximize the number of reports in the corpus. An analysis of 30 reports randomly selected as part of a quality control assessment yielded accuracies of 0.97, 0.96, and 0.83 for clinical features, temporal features, and clinical-temporal associations, respectively and speaks to the quality of the corpus. Copyright © 2018 Elsevier Ltd. All rights reserved.

A UIMA wrapper for the NCBO annotator

PubMed Central

Roeder, Christophe; Jonquet, Clement; Shah, Nigam H.; Baumgartner, William A.; Verspoor, Karin; Hunter, Lawrence

2010-01-01

Summary: The Unstructured Information Management Architecture (UIMA) framework and web services are emerging as useful tools for integrating biomedical text mining tools. This note describes our work, which wraps the National Center for Biomedical Ontology (NCBO) Annotator—an ontology-based annotation service—to make it available as a component in UIMA workflows. Availability: This wrapper is freely available on the web at http://bionlp-uima.sourceforge.net/ as part of the UIMA tools distribution from the Center for Computational Pharmacology (CCP) at the University of Colorado School of Medicine. It has been implemented in Java for support on Mac OS X, Linux and MS Windows. Contact: chris.roeder@ucdenver.edu PMID:20505005

THE EFFECTS OF RADIATION AND RADIOISOTOPES ON THE LIFE PROCESSES. An Annotated Bibliography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pierce, C.M. comp.

Issued in two books and an Index. A total of 11,944 annotated references is presented to report and published literature concerning the effects of radiation on biological systems. Abstracts are included for many references. Author and subject indexes are included. (C.H.)

Annotated A.B.E. Bibliography.

ERIC Educational Resources Information Center

Anderson, Ethel E., Comp.

Based on lists submitted by practitioners working in the 0 to grade 9 levels of English-speaking adult basic education (ABE) in Canada, this annotated bibliography is composed of 283 items currently in use. Six categories are included: (1) reading, which covers reading systems, instructional material, phonics, and independent reading; (2) language…

Concept annotation in the CRAFT corpus.

PubMed

Bada, Michael; Eckert, Miriam; Evans, Donald; Garcia, Kristin; Shipley, Krista; Sitnikov, Dmitry; Baumgartner, William A; Cohen, K Bretonnel; Verspoor, Karin; Blake, Judith A; Hunter, Lawrence E

2012-07-09

Manually annotated corpora are critical for the training and evaluation of automated methods to identify concepts in biomedical text. This paper presents the concept annotations of the Colorado Richly Annotated Full-Text (CRAFT) Corpus, a collection of 97 full-length, open-access biomedical journal articles that have been annotated both semantically and syntactically to serve as a research resource for the biomedical natural-language-processing (NLP) community. CRAFT identifies all mentions of nearly all concepts from nine prominent biomedical ontologies and terminologies: the Cell Type Ontology, the Chemical Entities of Biological Interest ontology, the NCBI Taxonomy, the Protein Ontology, the Sequence Ontology, the entries of the Entrez Gene database, and the three subontologies of the Gene Ontology. The first public release includes the annotations for 67 of the 97 articles, reserving two sets of 15 articles for future text-mining competitions (after which these too will be released). Concept annotations were created based on a single set of guidelines, which has enabled us to achieve consistently high interannotator agreement. As the initial 67-article release contains more than 560,000 tokens (and the full set more than 790,000 tokens), our corpus is among the largest gold-standard annotated biomedical corpora. Unlike most others, the journal articles that comprise the corpus are drawn from diverse biomedical disciplines and are marked up in their entirety. Additionally, with a concept-annotation count of nearly 100,000 in the 67-article subset (and more than 140,000 in the full collection), the scale of conceptual markup is also among the largest of comparable corpora. The concept annotations of the CRAFT Corpus have the potential to significantly advance biomedical text mining by providing a high-quality gold standard for NLP systems. The corpus, annotation guidelines, and other associated resources are freely available at http://bionlp-corpora.sourceforge.net/CRAFT/index.shtml.

Concept annotation in the CRAFT corpus

PubMed Central

2012-01-01

Background Manually annotated corpora are critical for the training and evaluation of automated methods to identify concepts in biomedical text. Results This paper presents the concept annotations of the Colorado Richly Annotated Full-Text (CRAFT) Corpus, a collection of 97 full-length, open-access biomedical journal articles that have been annotated both semantically and syntactically to serve as a research resource for the biomedical natural-language-processing (NLP) community. CRAFT identifies all mentions of nearly all concepts from nine prominent biomedical ontologies and terminologies: the Cell Type Ontology, the Chemical Entities of Biological Interest ontology, the NCBI Taxonomy, the Protein Ontology, the Sequence Ontology, the entries of the Entrez Gene database, and the three subontologies of the Gene Ontology. The first public release includes the annotations for 67 of the 97 articles, reserving two sets of 15 articles for future text-mining competitions (after which these too will be released). Concept annotations were created based on a single set of guidelines, which has enabled us to achieve consistently high interannotator agreement. Conclusions As the initial 67-article release contains more than 560,000 tokens (and the full set more than 790,000 tokens), our corpus is among the largest gold-standard annotated biomedical corpora. Unlike most others, the journal articles that comprise the corpus are drawn from diverse biomedical disciplines and are marked up in their entirety. Additionally, with a concept-annotation count of nearly 100,000 in the 67-article subset (and more than 140,000 in the full collection), the scale of conceptual markup is also among the largest of comparable corpora. The concept annotations of the CRAFT Corpus have the potential to significantly advance biomedical text mining by providing a high-quality gold standard for NLP systems. The corpus, annotation guidelines, and other associated resources are freely available at http://bionlp-corpora.sourceforge.net/CRAFT/index.shtml. PMID:22776079

The Informatics Challenges Facing Biobanks: A Perspective from a United Kingdom Biobanking Network

PubMed Central

Groves, Martin; Jordan, Lee B.; Stobart, Hilary; Purdie, Colin A.; Thompson, Alastair M

2015-01-01

The challenges facing biobanks are changing from simple collections of materials to quality-assured fit-for-purpose clinically annotated samples. As a result, informatics awareness and capabilities of a biobank are now intrinsically related to quality. A biobank may be considered a data repository, in the form of raw data (the unprocessed samples), data surrounding the samples (processing and storage conditions), supplementary data (such as clinical annotations), and an increasing ethical requirement for biobanks to have a mechanism for researchers to return their data. The informatics capabilities of a biobank are no longer simply knowing sample locations; instead the capabilities will become a distinguishing factor in the ability of a biobank to provide appropriate samples. There is an increasing requirement for biobanking systems (whether in-house or commercially sourced) to ensure the informatics systems stay apace with the changes being experienced by the biobanking community. In turn, there is a requirement for the biobanks to have a clear informatics policy and directive that is embedded into the wider decision making process. As an example, the Breast Cancer Campaign Tissue Bank in the UK was a collaboration between four individual and diverse biobanks in the UK, and an informatics platform has been developed to address the challenges of running a distributed network. From developing such a system there are key observations about what can or cannot be achieved by informatics in isolation. This article will highlight some of the lessons learned during this development process. PMID:26418270

Knowledge Annotations in Scientific Workflows: An Implementation in Kepler

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gandara, Aida G.; Chin, George; Pinheiro Da Silva, Paulo

2011-07-20

Abstract. Scientic research products are the result of long-term collaborations between teams. Scientic workfows are capable of helping scientists in many ways including the collection of information as to howresearch was conducted, e.g. scientic workfow tools often collect and manage information about datasets used and data transformations. However,knowledge about why data was collected is rarely documented in scientic workflows. In this paper we describe a prototype system built to support the collection of scientic expertise that infuences scientic analysis. Through evaluating a scientic research eort underway at Pacific Northwest National Laboratory, we identied features that would most benefit PNNL scientistsmore » in documenting how and why they conduct their research making this information available to the entire team. The prototype system was built by enhancing the Kepler Scientic Work-flow System to create knowledge-annotated scientic workfows and topublish them as semantic annotations.« less

A Geo-Distributed System Architecture for Different Domains

NASA Astrophysics Data System (ADS)

Moßgraber, Jürgen; Middleton, Stuart; Tao, Ran

2013-04-01

The presentation will describe work on the system-of-systems (SoS) architecture that is being developed in the EU FP7 project TRIDEC on "Collaborative, Complex and Critical Decision-Support in Evolving Crises". In this project we deal with two use-cases: Natural Crisis Management (e.g. Tsunami Early Warning) and Industrial Subsurface Development (e.g. drilling for oil). These use-cases seem to be quite different at first sight but share a lot of similarities, like managing and looking up available sensors, extracting data from them and annotate it semantically, intelligently manage the data (big data problem), run mathematical analysis algorithms on the data and finally provide decision support on this basis. The main challenge was to create a generic architecture which fits both use-cases. The requirements to the architecture are manifold and the whole spectrum of a modern, geo-distributed and collaborative system comes into play. Obviously, one cannot expect to tackle these challenges adequately with a monolithic system or with a single technology. Therefore, a system architecture providing the blueprints to implement the system-of-systems approach has to combine multiple technologies and architectural styles. The most important architectural challenges we needed to address are 1. Build a scalable communication layer for a System-of-sytems 2. Build a resilient communication layer for a System-of-sytems 3. Efficiently publish large volumes of semantically rich sensor data 4. Scalable and high performance storage of large distributed datasets 5. Handling federated multi-domain heterogeneous data 6. Discovery of resources in a geo-distributed SoS 7. Coordination of work between geo-distributed systems The design decisions made for each of them will be presented. These developed concepts are also applicable to the requirements of the Future Internet (FI) and Internet of Things (IoT) which will provide services like smart grids, smart metering, logistics and environmental monitoring.

NSF Workshop Report: Discovering General Principles of Nervous System Organization by Comparing Brain Maps across Species

PubMed Central

Striedter, Georg F.; Belgard, T. Grant; Chen, Chun-Chun; Davis, Fred P.; Finlay, Barbara L.; Güntürkün, Onur; Hale, Melina E.; Harris, Julie A.; Hecht, Erin E.; Hof, Patrick R.; Hofmann, Hans A.; Holland, Linda Z.; Iwaniuk, Andrew N.; Jarvis, Erich D.; Karten, Harvey J.; Katz, Paul S.; Kristan, William B.; Macagno, Eduardo R.; Mitra, Partha P.; Moroz, Leonid L.; Preuss, Todd M.; Ragsdale, Clifton W.; Sherwood, Chet C.; Stevens, Charles F.; Stüttgen, Maik C.; Tsumoto, Tadaharu; Wilczynski, Walter

2014-01-01

Efforts to understand nervous system structure and function have received new impetus from the federal Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative. Comparative analyses can contribute to this effort by leading to the discovery of general principles of neural circuit design, information processing, and gene-structure-function relationships that are not apparent from studies on single species. We here propose to extend the comparative approach to nervous system ‘maps’ comprising molecular, anatomical, and physiological data. This research will identify which neural features are likely to generalize across species, and which are unlikely to be broadly conserved. It will also suggest causal relationships between genes, development, adult anatomy, physiology, and, ultimately, behavior. These causal hypotheses can then be tested experimentally. Finally, insights from comparative research can inspire and guide technological development. To promote this research agenda, we recommend that teams of investigators coalesce around specific research questions and select a set of ‘reference species’ to anchor their comparative analyses. These reference species should be chosen not just for practical advantages, but also with regard for their phylogenetic position, behavioral repertoire, well-annotated genome, or other strategic reasons. We envision that the nervous systems of these reference species will be mapped in more detail than those of other species. The collected data may range from the molecular to the behavioral, depending on the research question. To integrate across levels of analysis and across species, standards for data collection, annotation, archiving, and distribution must be developed and respected. To that end, it will help to form networks or consortia of researchers and centers for science, technology, and education that focus on organized data collection, distribution, and training. These activities could be supported, at least in part, through existing mechanisms at NSF, NIH, and other agencies. It will also be important to develop new integrated software and database systems for cross-species data analyses. Multidisciplinary efforts to develop such analytical tools should be supported financially. Finally, training opportunities should be created to stimulate multidisciplinary, integrative research into brain structure, function, and evolution. PMID:24603302

SNPit: a federated data integration system for the purpose of functional SNP annotation

PubMed Central

Shen, Terry H; Carlson, Christopher S; Tarczy-Hornoch, Peter

2009-01-01

Genome wide association studies can potentially identify the genetic causes behind the majority of human diseases. With the advent of more advanced genotyping techniques, there is now an explosion of data gathered on single nucleotide polymorphisms (SNPs). The need exists for an integrated system that can provide up-to-date functional annotation information on SNPs. We have developed the SNP Integration Tool (SNPit) system to address this need. Built upon a federated data integration system, SNPit provides current information on a comprehensive list of SNP data sources. Additional logical inference analysis was included through an inference engine plug in. The SNPit web servlet is available online for use. SNPit allows users to go to one source for up-to-date information on the functional annotation of SNPs. A tool that can help to integrate and analyze the potential functional significance of SNPs is important for understanding the results from genome wide association studies. PMID:19327864

Making adjustments to event annotations for improved biological event extraction.

PubMed

Baek, Seung-Cheol; Park, Jong C

2016-09-16

Current state-of-the-art approaches to biological event extraction train statistical models in a supervised manner on corpora annotated with event triggers and event-argument relations. Inspecting such corpora, we observe that there is ambiguity in the span of event triggers (e.g., "transcriptional activity" vs. 'transcriptional'), leading to inconsistencies across event trigger annotations. Such inconsistencies make it quite likely that similar phrases are annotated with different spans of event triggers, suggesting the possibility that a statistical learning algorithm misses an opportunity for generalizing from such event triggers. We anticipate that adjustments to the span of event triggers to reduce these inconsistencies would meaningfully improve the present performance of event extraction systems. In this study, we look into this possibility with the corpora provided by the 2009 BioNLP shared task as a proof of concept. We propose an Informed Expectation-Maximization (EM) algorithm, which trains models using the EM algorithm with a posterior regularization technique, which consults the gold-standard event trigger annotations in a form of constraints. We further propose four constraints on the possible event trigger annotations to be explored by the EM algorithm. The algorithm is shown to outperform the state-of-the-art algorithm on the development corpus in a statistically significant manner and on the test corpus by a narrow margin. The analysis of the annotations generated by the algorithm shows that there are various types of ambiguity in event annotations, even though they could be small in number.

Challenges and Insights in Using HIPAA Privacy Rule for Clinical Text Annotation.

PubMed

Kayaalp, Mehmet; Browne, Allen C; Sagan, Pamela; McGee, Tyne; McDonald, Clement J

2015-01-01

The Privacy Rule of Health Insurance Portability and Accountability Act (HIPAA) requires that clinical documents be stripped of personally identifying information before they can be released to researchers and others. We have been manually annotating clinical text since 2008 in order to test and evaluate an algorithmic clinical text de-identification tool, NLM Scrubber, which we have been developing in parallel. Although HIPAA provides some guidance about what must be de-identified, translating those guidelines into practice is not as straightforward, especially when one deals with free text. As a result we have changed our manual annotation labels and methods six times. This paper explains why we have made those annotation choices, which have been evolved throughout seven years of practice on this field. The aim of this paper is to start a community discussion towards developing standards for clinical text annotation with the end goal of studying and comparing clinical text de-identification systems more accurately.

PathFinder: reconstruction and dynamic visualization of metabolic pathways.

PubMed

Goesmann, Alexander; Haubrock, Martin; Meyer, Folker; Kalinowski, Jörn; Giegerich, Robert

2002-01-01

Beyond methods for a gene-wise annotation and analysis of sequenced genomes new automated methods for functional analysis on a higher level are needed. The identification of realized metabolic pathways provides valuable information on gene expression and regulation. Detection of incomplete pathways helps to improve a constantly evolving genome annotation or discover alternative biochemical pathways. To utilize automated genome analysis on the level of metabolic pathways new methods for the dynamic representation and visualization of pathways are needed. PathFinder is a tool for the dynamic visualization of metabolic pathways based on annotation data. Pathways are represented as directed acyclic graphs, graph layout algorithms accomplish the dynamic drawing and visualization of the metabolic maps. A more detailed analysis of the input data on the level of biochemical pathways helps to identify genes and detect improper parts of annotations. As an Relational Database Management System (RDBMS) based internet application PathFinder reads a list of EC-numbers or a given annotation in EMBL- or Genbank-format and dynamically generates pathway graphs.

Hymenoptera Genome Database: integrating genome annotations in HymenopteraMine.

PubMed

Elsik, Christine G; Tayal, Aditi; Diesh, Colin M; Unni, Deepak R; Emery, Marianne L; Nguyen, Hung N; Hagen, Darren E

2016-01-04

We report an update of the Hymenoptera Genome Database (HGD) (http://HymenopteraGenome.org), a model organism database for insect species of the order Hymenoptera (ants, bees and wasps). HGD maintains genomic data for 9 bee species, 10 ant species and 1 wasp, including the versions of genome and annotation data sets published by the genome sequencing consortiums and those provided by NCBI. A new data-mining warehouse, HymenopteraMine, based on the InterMine data warehousing system, integrates the genome data with data from external sources and facilitates cross-species analyses based on orthology. New genome browsers and annotation tools based on JBrowse/WebApollo provide easy genome navigation, and viewing of high throughput sequence data sets and can be used for collaborative genome annotation. All of the genomes and annotation data sets are combined into a single BLAST server that allows users to select and combine sequence data sets to search. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

A methodology to annotate systems biology markup language models with the synthetic biology open language.

PubMed

Roehner, Nicholas; Myers, Chris J

2014-02-21

Recently, we have begun to witness the potential of synthetic biology, noted here in the form of bacteria and yeast that have been genetically engineered to produce biofuels, manufacture drug precursors, and even invade tumor cells. The success of these projects, however, has often failed in translation and application to new projects, a problem exacerbated by a lack of engineering standards that combine descriptions of the structure and function of DNA. To address this need, this paper describes a methodology to connect the systems biology markup language (SBML) to the synthetic biology open language (SBOL), existing standards that describe biochemical models and DNA components, respectively. Our methodology involves first annotating SBML model elements such as species and reactions with SBOL DNA components. A graph is then constructed from the model, with vertices corresponding to elements within the model and edges corresponding to the cause-and-effect relationships between these elements. Lastly, the graph is traversed to assemble the annotating DNA components into a composite DNA component, which is used to annotate the model itself and can be referenced by other composite models and DNA components. In this way, our methodology can be used to build up a hierarchical library of models annotated with DNA components. Such a library is a useful input to any future genetic technology mapping algorithm that would automate the process of composing DNA components to satisfy a behavioral specification. Our methodology for SBML-to-SBOL annotation is implemented in the latest version of our genetic design automation (GDA) software tool, iBioSim.

CHEMICAL STRUCTURE INDEXING OF TOXICITY DATA ON ...

EPA Pesticide Factsheets

Standardized chemical structure annotation of public toxicity databases and information resources is playing an increasingly important role in the 'flattening' and integration of diverse sets of biological activity data on the Internet. This review discusses public initiatives that are accelerating the pace of this transformation, with particular reference to toxicology-related chemical information. Chemical content annotators, structure locator services, large structure/data aggregator web sites, structure browsers, International Union of Pure and Applied Chemistry (IUPAC) International Chemical Identifier (InChI) codes, toxicity data models and public chemical/biological activity profiling initiatives are all playing a role in overcoming barriers to the integration of toxicity data, and are bringing researchers closer to the reality of a mineable chemical Semantic Web. An example of this integration of data is provided by the collaboration among researchers involved with the Distributed Structure-Searchable Toxicity (DSSTox) project, the Carcinogenic Potency Project, projects at the National Cancer Institute and the PubChem database. Standardizing chemical structure annotation of public toxicity databases

Unsupervised Biomedical Named Entity Recognition: Experiments with Clinical and Biological Texts

PubMed Central

Zhang, Shaodian; Elhadad, Nóemie

2013-01-01

Named entity recognition is a crucial component of biomedical natural language processing, enabling information extraction and ultimately reasoning over and knowledge discovery from text. Much progress has been made in the design of rule-based and supervised tools, but they are often genre and task dependent. As such, adapting them to different genres of text or identifying new types of entities requires major effort in re-annotation or rule development. In this paper, we propose an unsupervised approach to extracting named entities from biomedical text. We describe a stepwise solution to tackle the challenges of entity boundary detection and entity type classification without relying on any handcrafted rules, heuristics, or annotated data. A noun phrase chunker followed by a filter based on inverse document frequency extracts candidate entities from free text. Classification of candidate entities into categories of interest is carried out by leveraging principles from distributional semantics. Experiments show that our system, especially the entity classification step, yields competitive results on two popular biomedical datasets of clinical notes and biological literature, and outperforms a baseline dictionary match approach. Detailed error analysis provides a road map for future work. PMID:23954592

Annotation of Tutorial Dialogue Goals for Natural Language Generation

ERIC Educational Resources Information Center

Kim, Jung Hee; Freedman, Reva; Glass, Michael; Evens, Martha W.

2006-01-01

We annotated transcripts of human tutoring dialogue for the purpose of constructing a dialogue-based intelligent tutoring system, CIRCSIM-Tutor. The tutors were professors of physiology who were also expert tutors. The students were 1st year medical students who communicated with the tutors using typed communication from separate rooms. The tutors…

Overcoming Risk: An Annotated Bibliography of Publications Developed by ERIC Clearinghouses.

ERIC Educational Resources Information Center

Schwartz, Wendy, Ed.; Howley, Craig, Ed.

This publication contains two essays and an annotated bibliography of publications about risk. The entries in the bibliography were produced by the various clearinghouses in the ERIC system. The first essay, "Who Is at Risk? Definitions, Demographics, and Decisions," by Aaron M. Pallas, categorizes personal, family, and environmental factors that…

Curriculum Materials for "Trade and Industrial Occupations." Annotated Listing of Materials Available from Public Education Agencies.

ERIC Educational Resources Information Center

Bureau of Adult, Vocational, and Technical Education (DHEW/OE), Washington, DC.

This annotated listing of curriculum materials for Trade and Industrial Occupations provides planners, administrators, vocational educators, and others with information as to available curriculum materials developed by the various States. The materials are identified with the instructional titles and codes from the classification system of the…

Curriculum Materials for "Health Occupations Education." Annotated Listing of Materials Available from Public Education Agencies

ERIC Educational Resources Information Center

Bureau of Adult, Vocational, and Technical Education (DHEW/OE), Washington, DC.

This annotated listing of curriculum materials for Health Occupations Education provides planners, administrators, vocational educators, and others with information as to available curriculum materials developed by the various States. The materials are identified with the instructional titles and codes from the classification system of the Office…

Handbook of Reference Sources. Second Edition.

ERIC Educational Resources Information Center

Nichols, Margaret Irby

Designed to serve as a selection aid for small public libraries and to list important sources available through the major resource centers of the Texas Library System, this bibliography lists 804 annotated reference sources with 126 additional works named within the annotations. The books are grouped into 18 major areas, each of which is broken…

A multi-ontology approach to annotate scientific documents based on a modularization technique.

PubMed

Gomes, Priscilla Corrêa E Castro; Moura, Ana Maria de Carvalho; Cavalcanti, Maria Cláudia

2015-12-01

Scientific text annotation has become an important task for biomedical scientists. Nowadays, there is an increasing need for the development of intelligent systems to support new scientific findings. Public databases available on the Web provide useful data, but much more useful information is only accessible in scientific texts. Text annotation may help as it relies on the use of ontologies to maintain annotations based on a uniform vocabulary. However, it is difficult to use an ontology, especially those that cover a large domain. In addition, since scientific texts explore multiple domains, which are covered by distinct ontologies, it becomes even more difficult to deal with such task. Moreover, there are dozens of ontologies in the biomedical area, and they are usually big in terms of the number of concepts. It is in this context that ontology modularization can be useful. This work presents an approach to annotate scientific documents using modules of different ontologies, which are built according to a module extraction technique. The main idea is to analyze a set of single-ontology annotations on a text to find out the user interests. Based on these annotations a set of modules are extracted from a set of distinct ontologies, and are made available for the user, for complementary annotation. The reduced size and focus of the extracted modules tend to facilitate the annotation task. An experiment was conducted to evaluate this approach, with the participation of a bioinformatician specialist of the Laboratory of Peptides and Proteins of the IOC/Fiocruz, who was interested in discovering new drug targets aiming at the combat of tropical diseases. Copyright © 2015 Elsevier Inc. All rights reserved.

High Precision Prediction of Functional Sites in Protein Structures

PubMed Central

Buturovic, Ljubomir; Wong, Mike; Tang, Grace W.; Altman, Russ B.; Petkovic, Dragutin

2014-01-01

We address the problem of assigning biological function to solved protein structures. Computational tools play a critical role in identifying potential active sites and informing screening decisions for further lab analysis. A critical parameter in the practical application of computational methods is the precision, or positive predictive value. Precision measures the level of confidence the user should have in a particular computed functional assignment. Low precision annotations lead to futile laboratory investigations and waste scarce research resources. In this paper we describe an advanced version of the protein function annotation system FEATURE, which achieved 99% precision and average recall of 95% across 20 representative functional sites. The system uses a Support Vector Machine classifier operating on the microenvironment of physicochemical features around an amino acid. We also compared performance of our method with state-of-the-art sequence-level annotator Pfam in terms of precision, recall and localization. To our knowledge, no other functional site annotator has been rigorously evaluated against these key criteria. The software and predictive models are incorporated into the WebFEATURE service at http://feature.stanford.edu/wf4.0-beta. PMID:24632601

Information extraction from Italian medical reports: An ontology-driven approach.

PubMed

Viani, Natalia; Larizza, Cristiana; Tibollo, Valentina; Napolitano, Carlo; Priori, Silvia G; Bellazzi, Riccardo; Sacchi, Lucia

2018-03-01

In this work, we propose an ontology-driven approach to identify events and their attributes from episodes of care included in medical reports written in Italian. For this language, shared resources for clinical information extraction are not easily accessible. The corpus considered in this work includes 5432 non-annotated medical reports belonging to patients with rare arrhythmias. To guide the information extraction process, we built a domain-specific ontology that includes the events and the attributes to be extracted, with related regular expressions. The ontology and the annotation system were constructed on a development set, while the performance was evaluated on an independent test set. As a gold standard, we considered a manually curated hospital database named TRIAD, which stores most of the information written in reports. The proposed approach performs well on the considered Italian medical corpus, with a percentage of correct annotations above 90% for most considered clinical events. We also assessed the possibility to adapt the system to the analysis of another language (i.e., English), with promising results. Our annotation system relies on a domain ontology to extract and link information in clinical text. We developed an ontology that can be easily enriched and translated, and the system performs well on the considered task. In the future, it could be successfully used to automatically populate the TRIAD database. Copyright © 2017 Elsevier B.V. All rights reserved.

TogoTable: cross-database annotation system using the Resource Description Framework (RDF) data model.

PubMed

Kawano, Shin; Watanabe, Tsutomu; Mizuguchi, Sohei; Araki, Norie; Katayama, Toshiaki; Yamaguchi, Atsuko

2014-07-01

TogoTable (http://togotable.dbcls.jp/) is a web tool that adds user-specified annotations to a table that a user uploads. Annotations are drawn from several biological databases that use the Resource Description Framework (RDF) data model. TogoTable uses database identifiers (IDs) in the table as a query key for searching. RDF data, which form a network called Linked Open Data (LOD), can be searched from SPARQL endpoints using a SPARQL query language. Because TogoTable uses RDF, it can integrate annotations from not only the reference database to which the IDs originally belong, but also externally linked databases via the LOD network. For example, annotations in the Protein Data Bank can be retrieved using GeneID through links provided by the UniProt RDF. Because RDF has been standardized by the World Wide Web Consortium, any database with annotations based on the RDF data model can be easily incorporated into this tool. We believe that TogoTable is a valuable Web tool, particularly for experimental biologists who need to process huge amounts of data such as high-throughput experimental output. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

Hierarchy-associated semantic-rule inference framework for classifying indoor scenes

NASA Astrophysics Data System (ADS)

Yu, Dan; Liu, Peng; Ye, Zhipeng; Tang, Xianglong; Zhao, Wei

2016-03-01

Typically, the initial task of classifying indoor scenes is challenging, because the spatial layout and decoration of a scene can vary considerably. Recent efforts at classifying object relationships commonly depend on the results of scene annotation and predefined rules, making classification inflexible. Furthermore, annotation results are easily affected by external factors. Inspired by human cognition, a scene-classification framework was proposed using the empirically based annotation (EBA) and a match-over rule-based (MRB) inference system. The semantic hierarchy of images is exploited by EBA to construct rules empirically for MRB classification. The problem of scene classification is divided into low-level annotation and high-level inference from a macro perspective. Low-level annotation involves detecting the semantic hierarchy and annotating the scene with a deformable-parts model and a bag-of-visual-words model. In high-level inference, hierarchical rules are extracted to train the decision tree for classification. The categories of testing samples are generated from the parts to the whole. Compared with traditional classification strategies, the proposed semantic hierarchy and corresponding rules reduce the effect of a variable background and improve the classification performance. The proposed framework was evaluated on a popular indoor scene dataset, and the experimental results demonstrate its effectiveness.

Functional annotation of regulatory pathways.

PubMed

Pandey, Jayesh; Koyutürk, Mehmet; Kim, Yohan; Szpankowski, Wojciech; Subramaniam, Shankar; Grama, Ananth

2007-07-01

Standardized annotations of biomolecules in interaction networks (e.g. Gene Ontology) provide comprehensive understanding of the function of individual molecules. Extending such annotations to pathways is a critical component of functional characterization of cellular signaling at the systems level. We propose a framework for projecting gene regulatory networks onto the space of functional attributes using multigraph models, with the objective of deriving statistically significant pathway annotations. We first demonstrate that annotations of pairwise interactions do not generalize to indirect relationships between processes. Motivated by this result, we formalize the problem of identifying statistically overrepresented pathways of functional attributes. We establish the hardness of this problem by demonstrating the non-monotonicity of common statistical significance measures. We propose a statistical model that emphasizes the modularity of a pathway, evaluating its significance based on the coupling of its building blocks. We complement the statistical model by an efficient algorithm and software, Narada, for computing significant pathways in large regulatory networks. Comprehensive results from our methods applied to the Escherichia coli transcription network demonstrate that our approach is effective in identifying known, as well as novel biological pathway annotations. Narada is implemented in Java and is available at http://www.cs.purdue.edu/homes/jpandey/narada/.

Annotations of Mexican bullfighting videos for semantic index

NASA Astrophysics Data System (ADS)

Montoya Obeso, Abraham; Oropesa Morales, Lester Arturo; Fernando Vázquez, Luis; Cocolán Almeda, Sara Ivonne; Stoian, Andrei; García Vázquez, Mireya Saraí; Zamudio Fuentes, Luis Miguel; Montiel Perez, Jesús Yalja; de la O Torres, Saul; Ramírez Acosta, Alejandro Alvaro

2015-09-01

The video annotation is important for web indexing and browsing systems. Indeed, in order to evaluate the performance of video query and mining techniques, databases with concept annotations are required. Therefore, it is necessary generate a database with a semantic indexing that represents the digital content of the Mexican bullfighting atmosphere. This paper proposes a scheme to make complex annotations in a video in the frame of multimedia search engine project. Each video is partitioned using our segmentation algorithm that creates shots of different length and different number of frames. In order to make complex annotations about the video, we use ELAN software. The annotations are done in two steps: First, we take note about the whole content in each shot. Second, we describe the actions as parameters of the camera like direction, position and deepness. As a consequence, we obtain a more complete descriptor of every action. In both cases we use the concepts of the TRECVid 2014 dataset. We also propose new concepts. This methodology allows to generate a database with the necessary information to create descriptors and algorithms capable to detect actions to automatically index and classify new bullfighting multimedia content.

Essential Annotation Schema for Ecology (EASE)-A framework supporting the efficient data annotation and faceted navigation in ecology.

PubMed

Pfaff, Claas-Thido; Eichenberg, David; Liebergesell, Mario; König-Ries, Birgitta; Wirth, Christian

2017-01-01

Ecology has become a data intensive science over the last decades which often relies on the reuse of data in cross-experimental analyses. However, finding data which qualifies for the reuse in a specific context can be challenging. It requires good quality metadata and annotations as well as efficient search strategies. To date, full text search (often on the metadata only) is the most widely used search strategy although it is known to be inaccurate. Faceted navigation is providing a filter mechanism which is based on fine granular metadata, categorizing search objects along numeric and categorical parameters relevant for their discovery. Selecting from these parameters during a full text search creates a system of filters which allows to refine and improve the results towards more relevance. We developed a framework for the efficient annotation and faceted navigation in ecology. It consists of an XML schema for storing the annotation of search objects and is accompanied by a vocabulary focused on ecology to support the annotation process. The framework consolidates ideas which originate from widely accepted metadata standards, textbooks, scientific literature, and vocabularies as well as from expert knowledge contributed by researchers from ecology and adjacent disciplines.

An annotated corpus with nanomedicine and pharmacokinetic parameters

PubMed Central

Lewinski, Nastassja A; Jimenez, Ivan; McInnes, Bridget T

2017-01-01

A vast amount of data on nanomedicines is being generated and published, and natural language processing (NLP) approaches can automate the extraction of unstructured text-based data. Annotated corpora are a key resource for NLP and information extraction methods which employ machine learning. Although corpora are available for pharmaceuticals, resources for nanomedicines and nanotechnology are still limited. To foster nanotechnology text mining (NanoNLP) efforts, we have constructed a corpus of annotated drug product inserts taken from the US Food and Drug Administration’s Drugs@FDA online database. In this work, we present the development of the Engineered Nanomedicine Database corpus to support the evaluation of nanomedicine entity extraction. The data were manually annotated for 21 entity mentions consisting of nanomedicine physicochemical characterization, exposure, and biologic response information of 41 Food and Drug Administration-approved nanomedicines. We evaluate the reliability of the manual annotations and demonstrate the use of the corpus by evaluating two state-of-the-art named entity extraction systems, OpenNLP and Stanford NER. The annotated corpus is available open source and, based on these results, guidelines and suggestions for future development of additional nanomedicine corpora are provided. PMID:29066897

Assessment of disease named entity recognition on a corpus of annotated sentences.

PubMed

Jimeno, Antonio; Jimenez-Ruiz, Ernesto; Lee, Vivian; Gaudan, Sylvain; Berlanga, Rafael; Rebholz-Schuhmann, Dietrich

2008-04-11

In recent years, the recognition of semantic types from the biomedical scientific literature has been focused on named entities like protein and gene names (PGNs) and gene ontology terms (GO terms). Other semantic types like diseases have not received the same level of attention. Different solutions have been proposed to identify disease named entities in the scientific literature. While matching the terminology with language patterns suffers from low recall (e.g., Whatizit) other solutions make use of morpho-syntactic features to better cover the full scope of terminological variability (e.g., MetaMap). Currently, MetaMap that is provided from the National Library of Medicine (NLM) is the state of the art solution for the annotation of concepts from UMLS (Unified Medical Language System) in the literature. Nonetheless, its performance has not yet been assessed on an annotated corpus. In addition, little effort has been invested so far to generate an annotated dataset that links disease entities in text to disease entries in a database, thesaurus or ontology and that could serve as a gold standard to benchmark text mining solutions. As part of our research work, we have taken a corpus that has been delivered in the past for the identification of associations of genes to diseases based on the UMLS Metathesaurus and we have reprocessed and re-annotated the corpus. We have gathered annotations for disease entities from two curators, analyzed their disagreement (0.51 in the kappa-statistic) and composed a single annotated corpus for public use. Thereafter, three solutions for disease named entity recognition including MetaMap have been applied to the corpus to automatically annotate it with UMLS Metathesaurus concepts. The resulting annotations have been benchmarked to compare their performance. The annotated corpus is publicly available at ftp://ftp.ebi.ac.uk/pub/software/textmining/corpora/diseases and can serve as a benchmark to other systems. In addition, we found that dictionary look-up already provides competitive results indicating that the use of disease terminology is highly standardized throughout the terminologies and the literature. MetaMap generates precise results at the expense of insufficient recall while our statistical method obtains better recall at a lower precision rate. Even better results in terms of precision are achieved by combining at least two of the three methods leading, but this approach again lowers recall. Altogether, our analysis gives a better understanding of the complexity of disease annotations in the literature. MetaMap and the dictionary based approach are available through the Whatizit web service infrastructure (Rebholz-Schuhmann D, Arregui M, Gaudan S, Kirsch H, Jimeno A: Text processing through Web services: Calling Whatizit. Bioinformatics 2008, 24:296-298).

Revisiting Criteria for Plant MicroRNA Annotation in the Era of Big Data[OPEN

PubMed Central

2018-01-01

MicroRNAs (miRNAs) are ∼21-nucleotide-long regulatory RNAs that arise from endonucleolytic processing of hairpin precursors. Many function as essential posttranscriptional regulators of target mRNAs and long noncoding RNAs. Alongside miRNAs, plants also produce large numbers of short interfering RNAs (siRNAs), which are distinguished from miRNAs primarily by their biogenesis (typically processed from long double-stranded RNA instead of single-stranded hairpins) and functions (typically via roles in transcriptional regulation instead of posttranscriptional regulation). Next-generation DNA sequencing methods have yielded extensive data sets of plant small RNAs, resulting in many miRNA annotations. However, it has become clear that many miRNA annotations are questionable. The sheer number of endogenous siRNAs compared with miRNAs has been a major factor in the erroneous annotation of siRNAs as miRNAs. Here, we provide updated criteria for the confident annotation of plant miRNAs, suitable for the era of “big data” from DNA sequencing. The updated criteria emphasize replication and the minimization of false positives, and they require next-generation sequencing of small RNAs. We argue that improved annotation systems are needed for miRNAs and all other classes of plant small RNAs. Finally, to illustrate the complexities of miRNA and siRNA annotation, we review the evolution and functions of miRNAs and siRNAs in plants. PMID:29343505

A sentence sliding window approach to extract protein annotations from biomedical articles

PubMed Central

Krallinger, Martin; Padron, Maria; Valencia, Alfonso

2005-01-01

Background Within the emerging field of text mining and statistical natural language processing (NLP) applied to biomedical articles, a broad variety of techniques have been developed during the past years. Nevertheless, there is still a great ned of comparative assessment of the performance of the proposed methods and the development of common evaluation criteria. This issue was addressed by the Critical Assessment of Text Mining Methods in Molecular Biology (BioCreative) contest. The aim of this contest was to assess the performance of text mining systems applied to biomedical texts including tools which recognize named entities such as genes and proteins, and tools which automatically extract protein annotations. Results The "sentence sliding window" approach proposed here was found to efficiently extract text fragments from full text articles containing annotations on proteins, providing the highest number of correctly predicted annotations. Moreover, the number of correct extractions of individual entities (i.e. proteins and GO terms) involved in the relationships used for the annotations was significantly higher than the correct extractions of the complete annotations (protein-function relations). Conclusion We explored the use of averaging sentence sliding windows for information extraction, especially in a context where conventional training data is unavailable. The combination of our approach with more refined statistical estimators and machine learning techniques might be a way to improve annotation extraction for future biomedical text mining applications. PMID:15960831

Gene annotation from scientific literature using mappings between keyword systems.

PubMed

Pérez, Antonio J; Perez-Iratxeta, Carolina; Bork, Peer; Thode, Guillermo; Andrade, Miguel A

2004-09-01

The description of genes in databases by keywords helps the non-specialist to quickly grasp the properties of a gene and increases the efficiency of computational tools that are applied to gene data (e.g. searching a gene database for sequences related to a particular biological process). However, the association of keywords to genes or protein sequences is a difficult process that ultimately implies examination of the literature related to a gene. To support this task, we present a procedure to derive keywords from the set of scientific abstracts related to a gene. Our system is based on the automated extraction of mappings between related terms from different databases using a model of fuzzy associations that can be applied with all generality to any pair of linked databases. We tested the system by annotating genes of the SWISS-PROT database with keywords derived from the abstracts linked to their entries (stored in the MEDLINE database of scientific references). The performance of the annotation procedure was much better for SWISS-PROT keywords (recall of 47%, precision of 68%) than for Gene Ontology terms (recall of 8%, precision of 67%). The algorithm can be publicly accessed and used for the annotation of sequences through a web server at http://www.bork.embl.de/kat

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chauhan, Archana; Layton, Alice; Williams, Daniel W

Pseudomonas fluorescens strain HK44 (DSM 6700) is a genetically engineered lux-based bioluminescent bioreporter. Here we report the draft genome sequence of strain HK44. Annotation of {approx}6.1 Mb sequence indicates that 30% of the traits are unique and distributed over 5 genomic islands, a prophage and two plasmids.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hamdani, Hazrina Yusof, E-mail: hazrina@mfrlab.org; Advanced Medical and Dental Institute, Universiti Sains Malaysia, Bertam, Kepala Batas; Artymiuk, Peter J., E-mail: p.artymiuk@sheffield.ac.uk

A fundamental understanding of the atomic level interactions in ribonucleic acid (RNA) and how they contribute towards RNA architecture is an important knowledge platform to develop through the discovery of motifs from simple arrangements base pairs, to more complex arrangements such as triples and larger patterns involving non-standard interactions. The network of hydrogen bond interactions is important in connecting bases to form potential tertiary motifs. Therefore, there is an urgent need for the development of automated methods for annotating RNA 3D structures based on hydrogen bond interactions. COnnection tables Graphs for Nucleic ACids (COGNAC) is automated annotation system using graphmore » theoretical approaches that has been developed for the identification of RNA 3D motifs. This program searches for patterns in the unbroken networks of hydrogen bonds for RNA structures and capable of annotating base pairs and higher-order base interactions, which ranges from triples to sextuples. COGNAC was able to discover 22 out of 32 quadruples occurrences of the Haloarcula marismortui large ribosomal subunit (PDB ID: 1FFK) and two out of three occurrences of quintuple interaction reported by the non-canonical interactions in RNA (NCIR) database. These and several other interactions of interest will be discussed in this paper. These examples demonstrate that the COGNAC program can serve as an automated annotation system that can be used to annotate conserved base-base interactions and could be added as additional information to established RNA secondary structure prediction methods.« less

Segtor: Rapid Annotation of Genomic Coordinates and Single Nucleotide Variations Using Segment Trees

PubMed Central

Renaud, Gabriel; Neves, Pedro; Folador, Edson Luiz; Ferreira, Carlos Gil; Passetti, Fabio

2011-01-01

Various research projects often involve determining the relative position of genomic coordinates, intervals, single nucleotide variations (SNVs), insertions, deletions and translocations with respect to genes and their potential impact on protein translation. Due to the tremendous increase in throughput brought by the use of next-generation sequencing, investigators are routinely faced with the need to annotate very large datasets. We present Segtor, a tool to annotate large sets of genomic coordinates, intervals, SNVs, indels and translocations. Our tool uses segment trees built using the start and end coordinates of the genomic features the user wishes to use instead of storing them in a database management system. The software also produces annotation statistics to allow users to visualize how many coordinates were found within various portions of genes. Our system currently can be made to work with any species available on the UCSC Genome Browser. Segtor is a suitable tool for groups, especially those with limited access to programmers or with interest to analyze large amounts of individual genomes, who wish to determine the relative position of very large sets of mapped reads and subsequently annotate observed mutations between the reads and the reference. Segtor (http://lbbc.inca.gov.br/segtor/) is an open-source tool that can be freely downloaded for non-profit use. We also provide a web interface for testing purposes. PMID:22069465

A way toward analyzing high-content bioimage data by means of semantic annotation and visual data mining

NASA Astrophysics Data System (ADS)

Herold, Julia; Abouna, Sylvie; Zhou, Luxian; Pelengaris, Stella; Epstein, David B. A.; Khan, Michael; Nattkemper, Tim W.

2009-02-01

In the last years, bioimaging has turned from qualitative measurements towards a high-throughput and highcontent modality, providing multiple variables for each biological sample analyzed. We present a system which combines machine learning based semantic image annotation and visual data mining to analyze such new multivariate bioimage data. Machine learning is employed for automatic semantic annotation of regions of interest. The annotation is the prerequisite for a biological object-oriented exploration of the feature space derived from the image variables. With the aid of visual data mining, the obtained data can be explored simultaneously in the image as well as in the feature domain. Especially when little is known of the underlying data, for example in the case of exploring the effects of a drug treatment, visual data mining can greatly aid the process of data evaluation. We demonstrate how our system is used for image evaluation to obtain information relevant to diabetes study and screening of new anti-diabetes treatments. Cells of the Islet of Langerhans and whole pancreas in pancreas tissue samples are annotated and object specific molecular features are extracted from aligned multichannel fluorescence images. These are interactively evaluated for cell type classification in order to determine the cell number and mass. Only few parameters need to be specified which makes it usable also for non computer experts and allows for high-throughput analysis.

Annotated catalogue of the click-beetle tribe Dimini (Coleoptera: Elateridae: Dendrometrinae).

PubMed

Kundrata, Robin; Musalkova, Marketa; Kubaczkova, Magdalena

2018-04-19

An annotated catalogue of the tribe Dimini (Coleoptera: Elateridae: Dendrometrinae) is presented. Altogether, 273 extant species are classified in 11 genera: Brancuccia Schimmel Platia, 1991 (eight species), Csikia Szombathy, 1910 (eight spp.), Dima Charpentier, 1825 (77 spp.), Neocsikia Ôhira Becker, 1972 (two spp.), Neodima Schimmel Platia, 1992a (two spp.), Paracsikia Schimmel Platia, 1991 (seven spp.), Parapenia Suzuki, 1982b (14 spp.), Penia Laporte, 1838 (108 spp.), Platiana Schimmel, 1993 (34 spp.), Pseudocsikia Schimmel Platia, 1991 (10 spp.), and Sabahdima Schimmel Platia, 1993 (three spp.). The only fossil genus included in this tribe is monotypic Alaodima Dolin, 1980. Dimini are distributed in the Palaearctic and Oriental Regions. For each taxon we provide synonyms, information on types, type localities, distribution, and bibliography. The lectotype of Penia apicalis Fleutiaux, 1936 is here designated in order to fix the identity of the species for future studies. Penia impressifrons Schimmel, 1993 from Vietnam is synonymized with Ceroleptus sulcatus (Fleutiaux, 1903), a representative of Elateridae: Oxynopterinae.

Towards ontology-based decision support systems for complex ultrasound diagnosis in obstetrics and gynecology.

PubMed

Maurice, P; Dhombres, F; Blondiaux, E; Friszer, S; Guilbaud, L; Lelong, N; Khoshnood, B; Charlet, J; Perrot, N; Jauniaux, E; Jurkovic, D; Jouannic, J-M

2017-05-01

We have developed a new knowledge base intelligent system for obstetrics and gynecology ultrasound imaging, based on an ontology and a reference image collection. This study evaluates the new system to support accurate annotations of ultrasound images. We have used the early ultrasound diagnosis of ectopic pregnancies as a model clinical issue. The ectopic pregnancy ontology was derived from medical texts (4260 ultrasound reports of ectopic pregnancy from a specialist center in the UK and 2795 Pubmed abstracts indexed with the MeSH term "Pregnancy, Ectopic") and the reference image collection was built on a selection from 106 publications. We conducted a retrospective analysis of the signs in 35 scans of ectopic pregnancy by six observers using the new system. The resulting ectopic pregnancy ontology consisted of 1395 terms, and 80 images were collected for the reference collection. The observers used the knowledge base intelligent system to provide a total of 1486 sign annotations. The precision, recall and F-measure for the annotations were 0.83, 0.62 and 0.71, respectively. The global proportion of agreement was 40.35% 95% CI [38.64-42.05]. The ontology-based intelligent system provides accurate annotations of ultrasound images and suggests that it may benefit non-expert operators. The precision rate is appropriate for accurate input of a computer-based clinical decision support and could be used to support medical imaging diagnosis of complex conditions in obstetrics and gynecology. Copyright © 2017. Published by Elsevier Masson SAS.

Structural and functional annotation of the porcine immunome

PubMed Central

2013-01-01

Background The domestic pig is known as an excellent model for human immunology and the two species share many pathogens. Susceptibility to infectious disease is one of the major constraints on swine performance, yet the structure and function of genes comprising the pig immunome are not well-characterized. The completion of the pig genome provides the opportunity to annotate the pig immunome, and compare and contrast pig and human immune systems. Results The Immune Response Annotation Group (IRAG) used computational curation and manual annotation of the swine genome assembly 10.2 (Sscrofa10.2) to refine the currently available automated annotation of 1,369 immunity-related genes through sequence-based comparison to genes in other species. Within these genes, we annotated 3,472 transcripts. Annotation provided evidence for gene expansions in several immune response families, and identified artiodactyl-specific expansions in the cathelicidin and type 1 Interferon families. We found gene duplications for 18 genes, including 13 immune response genes and five non-immune response genes discovered in the annotation process. Manual annotation provided evidence for many new alternative splice variants and 8 gene duplications. Over 1,100 transcripts without porcine sequence evidence were detected using cross-species annotation. We used a functional approach to discover and accurately annotate porcine immune response genes. A co-expression clustering analysis of transcriptomic data from selected experimental infections or immune stimulations of blood, macrophages or lymph nodes identified a large cluster of genes that exhibited a correlated positive response upon infection across multiple pathogens or immune stimuli. Interestingly, this gene cluster (cluster 4) is enriched for known general human immune response genes, yet contains many un-annotated porcine genes. A phylogenetic analysis of the encoded proteins of cluster 4 genes showed that 15% exhibited an accelerated evolution as compared to 4.1% across the entire genome. Conclusions This extensive annotation dramatically extends the genome-based knowledge of the molecular genetics and structure of a major portion of the porcine immunome. Our complementary functional approach using co-expression during immune response has provided new putative immune response annotation for over 500 porcine genes. Our phylogenetic analysis of this core immunome cluster confirms rapid evolutionary change in this set of genes, and that, as in other species, such genes are important components of the pig’s adaptation to pathogen challenge over evolutionary time. These comprehensive and integrated analyses increase the value of the porcine genome sequence and provide important tools for global analyses and data-mining of the porcine immune response. PMID:23676093

A 2D numerical approach to predict sedimentary deposits of submarine gravity flows based on a Saint-Venant model with density variation effects. Example of Annot Basin (SE, France)

NASA Astrophysics Data System (ADS)

Le Solleuz, A.; Golfier, F.; Verdon, N.

2010-12-01

Submarine gravity flows, so called hyperpycnal currents, are very fast and can be induced by a major river flood or submarine slope instability. Sedimentary deposits, due to the stacking of these events (2 or 3 per year) during millions years can constitute a very good reservoir. However, predicting the evolution of such a sedimentary filling over geological time scales is a tremendous task. Especially, the ability to predict the starting of avalanches and the knowledge of mechanisms which drive erosion and sedimentary deposits are very poor. We focus in this study on the Annot sandstones system in the Alps (SE of France) which developed a very large tertiary deep sea fan well exposed and well studied in a sedimentary point of view (tectonics, sources, facies distribution, duration, etc.). We propose here to simulate the spatial distribution of these high-concentrated submarine gravity flows taking into account density variations of the sediment-water mixture. The main difficulty of our approach consists in simulating thousands events in a reasonable computational time. The ultimate goal is to apply this numerical model to the configuration of Annot Basin and to compare our results to the different existing deposits. To understand the physical processes that drive these hyperpycnal flows (high concentrated turbidites), many researchers focused on an accurate description of the phenomenon, for example by solving the 3D Navier-Stokes equations coupled with a mass transport equation. But, if such approaches are well-suited for the description of a single event, they are too computationally expensive to predict the sedimentary deposit over millions of years, i.e. over millions of events. We propose here an adapted version of the multilayer Saint-Venant model. It allows obtaining results with a low computational time (i.e. well-suited for millions of flows). Given the difference of sediment concentration between the head and the tail of a turbidite, we have derived a new version of these classical equations based on the variation of the fluid density. The resulting system, which takes into account the variation of density along the flow direction, is thus coupled to a density transport equation. Moreover, due to the high density stratification in the fluid flow along the vertical direction, a multilayer approach is adopted. In essence, it consists in discretizing the flow thickness into different layers and hence, considering a Saint-Venant formulation associated to each vertical layer. Numerical implementation of the model is discussed and the above-mentioned theoretical extension of the classical Saint-Venant model is validated through a comparison with benchmarks. The impact of the variable density assumption is investigated. And finally, we apply this 2D model to a realistic geological configuration, the Annot Basin.

OntoMaton: a bioportal powered ontology widget for Google Spreadsheets.

PubMed

Maguire, Eamonn; González-Beltrán, Alejandra; Whetzel, Patricia L; Sansone, Susanna-Assunta; Rocca-Serra, Philippe

2013-02-15

Data collection in spreadsheets is ubiquitous, but current solutions lack support for collaborative semantic annotation that would promote shared and interdisciplinary annotation practices, supporting geographically distributed players. OntoMaton is an open source solution that brings ontology lookup and tagging capabilities into a cloud-based collaborative editing environment, harnessing Google Spreadsheets and the NCBO Web services. It is a general purpose, format-agnostic tool that may serve as a component of the ISA software suite. OntoMaton can also be used to assist the ontology development process. OntoMaton is freely available from Google widgets under the CPAL open source license; documentation and examples at: https://github.com/ISA-tools/OntoMaton.

Annotated checklist of the recent and extinct pythons (Serpentes, Pythonidae), with notes on nomenclature, taxonomy, and distribution

PubMed Central

Schleip, Wulf D.; O’Shea, Mark

2010-01-01

Abstract McDiarmid et al. (1999) published the first part of their planned taxonomic catalog of the snakes of the world. Since then, several new python taxa have been described in both the scientific literature and non-peer-reviewed publications. This checklist evaluates the nomenclatural status of the names and discusses the taxonomic status of the new taxa, and aims to continue the work of McDiarmid et al. (1999) for the family Pythonidae, covering the period 1999 to 2010. Numerous new taxa are listed, and where appropriate recent synonymies are included and annotations are made. A checklist and a taxonomic identification key of valid taxa are provided. PMID:21594030

An annotated checklist of the Chilopoda and Diplopoda (Myriapoda) of the Abrau Peninsula, northwestern Caucasus, Russia

PubMed Central

Semenyuk, Irina I.; Tuf, Ivan H.

2016-01-01

Abstract Background The Abrau Peninsula is located in northwestern Caucasus between the cities of Novorossiysk and Anapa, Krasnodar Province, Russia. This paper contains an annotated checklist of the Chilopoda and Diplopoda inhabiting the Abrau Peninsula. New information The fauna of the Abrau Peninsula comprises 17 centipede (4 orders) and 16 millipede (6 orders) species. Henia taurica, hitherto known only from the Crimea, has now been reported from several localities in the studied region. The study also reveals two possibly new millipede species. Statistical analyses showed that habitat preferences of myriapod species within the Abrau Peninsula are caused by species geographic distribution pattern and microbiotope preferences. PMID:27346949

Grasshoppers, Crickets and Katydids (Insecta: Orthoptera) of Cuba: an annotated checklist.

PubMed

Yong, Sheyla; Perez-Gelabert, Daniel E

2014-07-07

An annotated list of the Cuban fauna of Orthoptera is presented. For each species we include details of valid names, synonyms, type specimens (type category, sex, locality and depository), geographic distribution and bibliographic references. Clarifying notes are added, as well as comments on the species considered doubtful. A total of 140 species included in 62 genera, 31 subfamilies and 12 families make up the known Cuban fauna of Orthoptera. The family Episactidae, the acridid subfamily Ommatolampidinae with 3 unknown genera, 3 unknown genera of Tettigoniidae (Conocephalinae) and 1 undescribed new genus of Tetrigidae (Cladonotinae) are here recorded for the first time from Cuba. Syntypes are designated for Hygronemobius histrionicus Zayas.

VIDANN: a video annotation system.

PubMed

De Clercq, A; Buysse, A; Roeyers, H; Ickes, W; Ponnet, K; Verhofstadt, L

2001-05-01

VIDANN is a computer program that allows participants to watch a video on a standard TV and to write their annotations (thought/feeling entries) on paper attached to a writing tablet. The system is designed as a Microsoft ActiveX module. It can be further adapted by the individual researcher through the use of a VBScript. All data, including the participant's handwriting, are stored in an XML database. An accompanying Wizard has been designed that enables researchers to generate VBScripts for standard configurations.

Phylotastic! Making tree-of-life knowledge accessible, reusable and convenient

PubMed Central

2013-01-01

Background Scientists rarely reuse expert knowledge of phylogeny, in spite of years of effort to assemble a great “Tree of Life” (ToL). A notable exception involves the use of Phylomatic, which provides tools to generate custom phylogenies from a large, pre-computed, expert phylogeny of plant taxa. This suggests great potential for a more generalized system that, starting with a query consisting of a list of any known species, would rectify non-standard names, identify expert phylogenies containing the implicated taxa, prune away unneeded parts, and supply branch lengths and annotations, resulting in a custom phylogeny suited to the user’s needs. Such a system could become a sustainable community resource if implemented as a distributed system of loosely coupled parts that interact through clearly defined interfaces. Results With the aim of building such a “phylotastic” system, the NESCent Hackathons, Interoperability, Phylogenies (HIP) working group recruited 2 dozen scientist-programmers to a weeklong programming hackathon in June 2012. During the hackathon (and a three-month follow-up period), 5 teams produced designs, implementations, documentation, presentations, and tests including: (1) a generalized scheme for integrating components; (2) proof-of-concept pruners and controllers; (3) a meta-API for taxonomic name resolution services; (4) a system for storing, finding, and retrieving phylogenies using semantic web technologies for data exchange, storage, and querying; (5) an innovative new service, DateLife.org, which synthesizes pre-computed, time-calibrated phylogenies to assign ages to nodes; and (6) demonstration projects. These outcomes are accessible via a public code repository (GitHub.com), a website (http://www.phylotastic.org), and a server image. Conclusions Approximately 9 person-months of effort (centered on a software development hackathon) resulted in the design and implementation of proof-of-concept software for 4 core phylotastic components, 3 controllers, and 3 end-user demonstration tools. While these products have substantial limitations, they suggest considerable potential for a distributed system that makes phylogenetic knowledge readily accessible in computable form. Widespread use of phylotastic systems will create an electronic marketplace for sharing phylogenetic knowledge that will spur innovation in other areas of the ToL enterprise, such as annotation of sources and methods and third-party methods of quality assessment. PMID:23668630

Discovering gene annotations in biomedical text databases

PubMed Central

Cakmak, Ali; Ozsoyoglu, Gultekin

2008-01-01

Background Genes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data. Results In this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO) concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products. In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general. Conclusion GEANN is useful for two distinct purposes: (i) automating the annotation of genomic entities with Gene Ontology concepts, and (ii) providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate pattern occurrences with similar semantics. Relatively low recall performance of our pattern-based approach may be enhanced either by employing a probabilistic annotation framework based on the annotation neighbourhoods in textual data, or, alternatively, the statistical enrichment threshold may be adjusted to lower values for applications that put more value on achieving higher recall values. PMID:18325104

Discovering gene annotations in biomedical text databases.

PubMed

Cakmak, Ali; Ozsoyoglu, Gultekin

2008-03-06

Genes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data. In this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO) concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products. In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general. GEANN is useful for two distinct purposes: (i) automating the annotation of genomic entities with Gene Ontology concepts, and (ii) providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate pattern occurrences with similar semantics. Relatively low recall performance of our pattern-based approach may be enhanced either by employing a probabilistic annotation framework based on the annotation neighbourhoods in textual data, or, alternatively, the statistical enrichment threshold may be adjusted to lower values for applications that put more value on achieving higher recall values.

Semantically Interoperable XML Data

PubMed Central

Vergara-Niedermayr, Cristobal; Wang, Fusheng; Pan, Tony; Kurc, Tahsin; Saltz, Joel

2013-01-01

XML is ubiquitously used as an information exchange platform for web-based applications in healthcare, life sciences, and many other domains. Proliferating XML data are now managed through latest native XML database technologies. XML data sources conforming to common XML schemas could be shared and integrated with syntactic interoperability. Semantic interoperability can be achieved through semantic annotations of data models using common data elements linked to concepts from ontologies. In this paper, we present a framework and software system to support the development of semantic interoperable XML based data sources that can be shared through a Grid infrastructure. We also present our work on supporting semantic validated XML data through semantic annotations for XML Schema, semantic validation and semantic authoring of XML data. We demonstrate the use of the system for a biomedical database of medical image annotations and markups. PMID:25298789

CROSS DRIVE: A New Interactive and Immersive Approach for Exploring 3D Time-Dependent Mars Atmospheric Data in Distributed Teams

NASA Astrophysics Data System (ADS)

Gerndt, Andreas M.; Engelke, Wito; Giuranna, Marco; Vandaele, Ann C.; Neary, Lori; Aoki, Shohei; Kasaba, Yasumasa; Garcia, Arturo; Fernando, Terrence; Roberts, David; CROSS DRIVE Team

2016-10-01

Atmospheric phenomena of Mars can be highly dynamic and have daily and seasonal variations. Planetary-scale wavelike disturbances, for example, are frequently observed in Mars' polar winter atmosphere. Possible sources of the wave activity were suggested to be dynamical instabilities and quasi-stationary planetary waves, i.e. waves that arise predominantly via zonally asymmetric surface properties. For a comprehensive understanding of these phenomena, single layers of altitude have to be analyzed carefully and relations between different atmospheric quantities and interaction with the surface of Mars have to be considered. The CROSS DRIVE project tries to address the presentation of those data with a global view by means of virtual reality techniques. Complex orbiter data from spectrometer and observation data from Earth are combined with global circulation models and high-resolution terrain data and images available from Mars Express or MRO instruments. Scientists can interactively extract features from those dataset and can change visualization parameters in real-time in order to emphasize findings. Stereoscopic views allow for perception of the actual 3D behavior of Mars's atmosphere. A very important feature of the visualization system is the possibility to connect distributed workspaces together. This enables discussions between distributed working groups. The workspace can scale from virtual reality systems to expert desktop applications to web-based project portals. If multiple virtual environments are connected, the 3D position of each individual user is captured and used to depict the scientist as an avatar in the virtual world. The appearance of the avatar can also scale from simple annotations to complex avatars using tele-presence technology to reconstruct the users in 3D. Any change of the feature set (annotations, cutplanes, volume rendering, etc.) within the VR is immediately exchanged between all connected users. This allows that everybody is always aware of what is visible and discussed. The discussion is supported by audio and interaction is controlled by a moderator managing turn-taking presentations. A use case execution proved a success and showed the potential of this immersive approach.

ERTS data user investigation to develop a multistage forest sampling inventory system

NASA Technical Reports Server (NTRS)

Langley, P. G.; Vanroessel, J. W. (Principal Investigator); Wert, S. L.

1973-01-01

The author has identified the following significant results. A system to provide precision annotation of predetermined forest inventory sampling units on the ERTS-1 MSS images was developed. In addition, an annotation system for high altitude U2 photographs was completed. MSS bulk image accuracy is good enough to allow the use of one square mile sampling units. IMANCO image analyzer interpretation work for small scale images demonstrated the need for much additional analyses. Continuing image interpretation work for the next reporting period is concentrated on manual image interpretation work as well as digital interpretation system development using the computer compatible tapes.

SAS- Semantic Annotation Service for Geoscience resources on the web

NASA Astrophysics Data System (ADS)

Elag, M.; Kumar, P.; Marini, L.; Li, R.; Jiang, P.

2015-12-01

There is a growing need for increased integration across the data and model resources that are disseminated on the web to advance their reuse across different earth science applications. Meaningful reuse of resources requires semantic metadata to realize the semantic web vision for allowing pragmatic linkage and integration among resources. Semantic metadata associates standard metadata with resources to turn them into semantically-enabled resources on the web. However, the lack of a common standardized metadata framework as well as the uncoordinated use of metadata fields across different geo-information systems, has led to a situation in which standards and related Standard Names abound. To address this need, we have designed SAS to provide a bridge between the core ontologies required to annotate resources and information systems in order to enable queries and analysis over annotation from a single environment (web). SAS is one of the services that are provided by the Geosematnic framework, which is a decentralized semantic framework to support the integration between models and data and allow semantically heterogeneous to interact with minimum human intervention. Here we present the design of SAS and demonstrate its application for annotating data and models. First we describe how predicates and their attributes are extracted from standards and ingested in the knowledge-base of the Geosemantic framework. Then we illustrate the application of SAS in annotating data managed by SEAD and annotating simulation models that have web interface. SAS is a step in a broader approach to raise the quality of geoscience data and models that are published on the web and allow users to better search, access, and use of the existing resources based on standard vocabularies that are encoded and published using semantic technologies.

Multi-dimensional classification of biomedical text: Toward automated, practical provision of high-utility text to diverse users

PubMed Central

Shatkay, Hagit; Pan, Fengxia; Rzhetsky, Andrey; Wilbur, W. John

2008-01-01

Motivation: Much current research in biomedical text mining is concerned with serving biologists by extracting certain information from scientific text. We note that there is no ‘average biologist’ client; different users have distinct needs. For instance, as noted in past evaluation efforts (BioCreative, TREC, KDD) database curators are often interested in sentences showing experimental evidence and methods. Conversely, lab scientists searching for known information about a protein may seek facts, typically stated with high confidence. Text-mining systems can target specific end-users and become more effective, if the system can first identify text regions rich in the type of scientific content that is of interest to the user, retrieve documents that have many such regions, and focus on fact extraction from these regions. Here, we study the ability to characterize and classify such text automatically. We have recently introduced a multi-dimensional categorization and annotation scheme, developed to be applicable to a wide variety of biomedical documents and scientific statements, while intended to support specific biomedical retrieval and extraction tasks. Results: The annotation scheme was applied to a large corpus in a controlled effort by eight independent annotators, where three individual annotators independently tagged each sentence. We then trained and tested machine learning classifiers to automatically categorize sentence fragments based on the annotation. We discuss here the issues involved in this task, and present an overview of the results. The latter strongly suggest that automatic annotation along most of the dimensions is highly feasible, and that this new framework for scientific sentence categorization is applicable in practice. Contact: shatkay@cs.queensu.ca PMID:18718948

New Funding Opportunity: Biospecimen Core Resource | Office of Cancer Clinical Proteomics Research

Cancer.gov

The purpose of this notice is to notify the community that the National Cancer Institute's (NCI’s) Office of Cancer Clinical Proteomics Research (OCCPR) is seeking sources to establish a Biospecimen Core Resource (BCR), capable of receiving, qualifying, processing, and distributing annotated biospecimens.

A Short Bibliography on Library/Media Leadership.

ERIC Educational Resources Information Center

Stanford Univ., CA. ERIC Clearinghouse on Information Resources.

Prepared for distribution at the 1975 Annual Convention of the Association for Educational Communications and Technology, this bibliography was assembled from the Current Index to Journals in Education (CIJE) and Resources in Education (RIE) computer files of the Educational Resources Information Center (ERIC). Annotated CIJE and RIE entries…

Marketing Programs: An Annotated Bibliography.

ERIC Educational Resources Information Center

Larson, Roger A.

1968-01-01

This study reviews the literature of marketing programs and classifies it into the following categories: (1) studies related to the General College of the University of Minnesota, (2) studies related to comparing background characteristics of college populations, (3) studies related to post-high school distributive education programs, and (4)…

Film annotation system for a space experiment

NASA Technical Reports Server (NTRS)

Browne, W. R.; Johnson, S. S.

1989-01-01

This microprocessor system was designed to control and annotate a Nikon 35 mm camera for the purpose of obtaining photographs and data at predefined time intervals. The single STD BUSS interface card was designed in such a way as to allow it to be used in either a stand alone application with minimum features or installed in a STD BUSS computer allowing for maximum features. This control system also allows the exposure of twenty eight alpha/numeric characters across the bottom of each photograph. The data contains such information as camera identification, frame count, user defined text, and time to .01 second.

Genome Information Broker (GIB): data retrieval and comparative analysis system for completed microbial genomes and more

PubMed Central

Fumoto, Masaki; Miyazaki, Satoru; Sugawara, Hideaki

2002-01-01

Genome Information Broker (GIB) is a powerful tool for the study of comparative genomics. GIB allows users to retrieve and display partial and/or whole genome sequences together with the relevant biological annotation. GIB has accumulated all the completed microbial genome and has recently been expanded to include Arabidopsis thaliana genome data from DDBJ/EMBL/GenBank. In the near future, hundreds of genome sequences will be determined. In order to handle such huge data, we have enhanced the GIB architecture by using XML, CORBA and distributed RDBs. We introduce the new GIB here. GIB is freely accessible at http://gib.genes.nig.ac.jp/. PMID:11752256

Automatically classifying sentences in full-text biomedical articles into Introduction, Methods, Results and Discussion.

PubMed

Agarwal, Shashank; Yu, Hong

2009-12-01

Biomedical texts can be typically represented by four rhetorical categories: Introduction, Methods, Results and Discussion (IMRAD). Classifying sentences into these categories can benefit many other text-mining tasks. Although many studies have applied different approaches for automatically classifying sentences in MEDLINE abstracts into the IMRAD categories, few have explored the classification of sentences that appear in full-text biomedical articles. We first evaluated whether sentences in full-text biomedical articles could be reliably annotated into the IMRAD format and then explored different approaches for automatically classifying these sentences into the IMRAD categories. Our results show an overall annotation agreement of 82.14% with a Kappa score of 0.756. The best classification system is a multinomial naïve Bayes classifier trained on manually annotated data that achieved 91.95% accuracy and an average F-score of 91.55%, which is significantly higher than baseline systems. A web version of this system is available online at-http://wood.ims.uwm.edu/full_text_classifier/.

PubSearch and PubFetch: a simple management system for semiautomated retrieval and annotation of biological information from the literature.

PubMed

Yoo, Danny; Xu, Iris; Berardini, Tanya Z; Rhee, Seung Yon; Narayanasamy, Vijay; Twigger, Simon

2006-03-01

For most systems in biology, a large body of literature exists that describes the complexity of the system based on experimental results. Manual review of this literature to extract targeted information into biological databases is difficult and time consuming. To address this problem, we developed PubSearch and PubFetch, which store literature, keyword, and gene information in a relational database, index the literature with keywords and gene names, and provide a Web user interface for annotating the genes from experimental data found in the associated literature. A set of protocols is provided in this unit for installing, populating, running, and using PubSearch and PubFetch. In addition, we provide support protocols for performing controlled vocabulary annotations. Intended users of PubSearch and PubFetch are database curators and biology researchers interested in tracking the literature and capturing information about genes of interest in a more effective way than with conventional spreadsheets and lab notebooks.

Management of information in distributed biomedical collaboratories.

PubMed

Keator, David B

2009-01-01

Organizing and annotating biomedical data in structured ways has gained much interest and focus in the last 30 years. Driven by decreases in digital storage costs and advances in genetics sequencing, imaging, electronic data collection, and microarray technologies, data is being collected at an alarming rate. The specialization of fields in biology and medicine demonstrates the need for somewhat different structures for storage and retrieval of data. For biologists, the need for structured information and integration across a number of domains drives development. For clinical researchers and hospitals, the need for a structured medical record accessible to, ideally, any medical practitioner who might require it during the course of research or patient treatment, patient confidentiality, and security are the driving developmental factors. Scientific data management systems generally consist of a few core services: a backend database system, a front-end graphical user interface, and an export/import mechanism or data interchange format to both get data into and out of the database and share data with collaborators. The chapter introduces some existing databases, distributed file systems, and interchange languages used within the biomedical research and clinical communities for scientific data management and exchange.

The GermOnline cross-species systems browser provides comprehensive information on genes and gene products relevant for sexual reproduction.

PubMed

Gattiker, Alexandre; Niederhauser-Wiederkehr, Christa; Moore, James; Hermida, Leandro; Primig, Michael

2007-01-01

We report a novel release of the GermOnline knowledgebase covering genes relevant for the cell cycle, gametogenesis and fertility. GermOnline was extended into a cross-species systems browser including information on DNA sequence annotation, gene expression and the function of gene products. The database covers eight model organisms and Homo sapiens, for which complete genome annotation data are available. The database is now built around a sophisticated genome browser (Ensembl), our own microarray information management and annotation system (MIMAS) used to extensively describe experimental data obtained with high-density oligonucleotide microarrays (GeneChips) and a comprehensive system for online editing of database entries (MediaWiki). The RNA data include results from classical microarrays as well as tiling arrays that yield information on RNA expression levels, transcript start sites and lengths as well as exon composition. Members of the research community are solicited to help GermOnline curators keep database entries on genes and gene products complete and accurate. The database is accessible at http://www.germonline.org/.

Essential Annotation Schema for Ecology (EASE)—A framework supporting the efficient data annotation and faceted navigation in ecology

PubMed Central

Eichenberg, David; Liebergesell, Mario; König-Ries, Birgitta; Wirth, Christian

2017-01-01

Ecology has become a data intensive science over the last decades which often relies on the reuse of data in cross-experimental analyses. However, finding data which qualifies for the reuse in a specific context can be challenging. It requires good quality metadata and annotations as well as efficient search strategies. To date, full text search (often on the metadata only) is the most widely used search strategy although it is known to be inaccurate. Faceted navigation is providing a filter mechanism which is based on fine granular metadata, categorizing search objects along numeric and categorical parameters relevant for their discovery. Selecting from these parameters during a full text search creates a system of filters which allows to refine and improve the results towards more relevance. We developed a framework for the efficient annotation and faceted navigation in ecology. It consists of an XML schema for storing the annotation of search objects and is accompanied by a vocabulary focused on ecology to support the annotation process. The framework consolidates ideas which originate from widely accepted metadata standards, textbooks, scientific literature, and vocabularies as well as from expert knowledge contributed by researchers from ecology and adjacent disciplines. PMID:29023519

Genome re-annotation of the wild strawberry Fragaria vesca using extensive Illumina- and SMRT-based RNA-seq datasets

PubMed Central

Li, Yongping; Wei, Wei; Feng, Jia; Luo, Huifeng; Pi, Mengting; Liu, Zhongchi; Kang, Chunying

2018-01-01

Abstract The genome of the wild diploid strawberry species Fragaria vesca, an ideal model system of cultivated strawberry (Fragaria × ananassa, octoploid) and other Rosaceae family crops, was first published in 2011 and followed by a new assembly (Fvb). However, the annotation for Fvb mainly relied on ab initio predictions and included only predicted coding sequences, therefore an improved annotation is highly desirable. Here, a new annotation version named v2.0.a2 was created for the Fvb genome by a pipeline utilizing one PacBio library, 90 Illumina RNA-seq libraries, and 9 small RNA-seq libraries. Altogether, 18,641 genes (55.6% out of 33,538 genes) were augmented with information on the 5′ and/or 3′ UTRs, 13,168 (39.3%) protein-coding genes were modified or newly identified, and 7,370 genes were found to possess alternative isoforms. In addition, 1,938 long non-coding RNAs, 171 miRNAs, and 51,714 small RNA clusters were integrated into the annotation. This new annotation of F. vesca is substantially improved in both accuracy and integrity of gene predictions, beneficial to the gene functional studies in strawberry and to the comparative genomic analysis of other horticultural crops in Rosaceae family. PMID:29036429

WebMedSA: a web-based framework for segmenting and annotating medical images using biomedical ontologies

NASA Astrophysics Data System (ADS)

Vega, Francisco; Pérez, Wilson; Tello, Andrés.; Saquicela, Victor; Espinoza, Mauricio; Solano-Quinde, Lizandro; Vidal, Maria-Esther; La Cruz, Alexandra

2015-12-01

Advances in medical imaging have fostered medical diagnosis based on digital images. Consequently, the number of studies by medical images diagnosis increases, thus, collaborative work and tele-radiology systems are required to effectively scale up to this diagnosis trend. We tackle the problem of the collaborative access of medical images, and present WebMedSA, a framework to manage large datasets of medical images. WebMedSA relies on a PACS and supports the ontological annotation, as well as segmentation and visualization of the images based on their semantic description. Ontological annotations can be performed directly on the volumetric image or at different image planes (e.g., axial, coronal, or sagittal); furthermore, annotations can be complemented after applying a segmentation technique. WebMedSA is based on three main steps: (1) RDF-ization process for extracting, anonymizing, and serializing metadata comprised in DICOM medical images into RDF/XML; (2) Integration of different biomedical ontologies (using L-MOM library), making this approach ontology independent; and (3) segmentation and visualization of annotated data which is further used to generate new annotations according to expert knowledge, and validation. Initial user evaluations suggest that WebMedSA facilitates the exchange of knowledge between radiologists, and provides the basis for collaborative work among them.

The future of transposable element annotation and their classification in the light of functional genomics - what we can learn from the fables of Jean de la Fontaine?

PubMed

Arensburger, Peter; Piégu, Benoît; Bigot, Yves

2016-01-01

Transposable element (TE) science has been significantly influenced by the pioneering ideas of David Finnegan near the end of the last century, as well as by the classification systems that were subsequently developed. Today, whole genome TE annotation is mostly done using tools that were developed to aid gene annotation rather than to specifically study TEs. We argue that further progress in the TE field is impeded both by current TE classification schemes and by a failure to recognize that TE biology is fundamentally different from that of multicellular organisms. Novel genome wide TE annotation methods are helping to redefine our understanding of TE sequence origins and evolution. We briefly discuss some of these new methods as well as ideas for possible alternative classification schemes. Our hope is to encourage the formation of a society to organize a larger debate on these questions and to promote the adoption of standards for annotation and an improved TE classification.

Enabling comparative modeling of closely related genomes: Example genus Brucella

DOE PAGES

Faria, José P.; Edirisinghe, Janaka N.; Davis, James J.; ...

2014-03-08

For many scientific applications, it is highly desirable to be able to compare metabolic models of closely related genomes. In this study, we attempt to raise awareness to the fact that taking annotated genomes from public repositories and using them for metabolic model reconstructions is far from being trivial due to annotation inconsistencies. We are proposing a protocol for comparative analysis of metabolic models on closely related genomes, using fifteen strains of genus Brucella, which contains pathogens of both humans and livestock. This study lead to the identification and subsequent correction of inconsistent annotations in the SEED database, as wellmore » as the identification of 31 biochemical reactions that are common to Brucella, which are not originally identified by automated metabolic reconstructions. We are currently implementing this protocol for improving automated annotations within the SEED database and these improvements have been propagated into PATRIC, Model-SEED, KBase and RAST. This method is an enabling step for the future creation of consistent annotation systems and high-quality model reconstructions that will support in predicting accurate phenotypes such as pathogenicity, media requirements or type of respiration.« less

Enabling comparative modeling of closely related genomes: Example genus Brucella

DOE Office of Scientific and Technical Information (OSTI.GOV)

Faria, José P.; Edirisinghe, Janaka N.; Davis, James J.

For many scientific applications, it is highly desirable to be able to compare metabolic models of closely related genomes. In this study, we attempt to raise awareness to the fact that taking annotated genomes from public repositories and using them for metabolic model reconstructions is far from being trivial due to annotation inconsistencies. We are proposing a protocol for comparative analysis of metabolic models on closely related genomes, using fifteen strains of genus Brucella, which contains pathogens of both humans and livestock. This study lead to the identification and subsequent correction of inconsistent annotations in the SEED database, as wellmore » as the identification of 31 biochemical reactions that are common to Brucella, which are not originally identified by automated metabolic reconstructions. We are currently implementing this protocol for improving automated annotations within the SEED database and these improvements have been propagated into PATRIC, Model-SEED, KBase and RAST. This method is an enabling step for the future creation of consistent annotation systems and high-quality model reconstructions that will support in predicting accurate phenotypes such as pathogenicity, media requirements or type of respiration.« less

Influenza Virus Database (IVDB): an integrated information resource and analysis platform for influenza virus research.

PubMed

Chang, Suhua; Zhang, Jiajie; Liao, Xiaoyun; Zhu, Xinxing; Wang, Dahai; Zhu, Jiang; Feng, Tao; Zhu, Baoli; Gao, George F; Wang, Jian; Yang, Huanming; Yu, Jun; Wang, Jing

2007-01-01

Frequent outbreaks of highly pathogenic avian influenza and the increasing data available for comparative analysis require a central database specialized in influenza viruses (IVs). We have established the Influenza Virus Database (IVDB) to integrate information and create an analysis platform for genetic, genomic, and phylogenetic studies of the virus. IVDB hosts complete genome sequences of influenza A virus generated by Beijing Institute of Genomics (BIG) and curates all other published IV sequences after expert annotation. Our Q-Filter system classifies and ranks all nucleotide sequences into seven categories according to sequence content and integrity. IVDB provides a series of tools and viewers for comparative analysis of the viral genomes, genes, genetic polymorphisms and phylogenetic relationships. A search system has been developed for users to retrieve a combination of different data types by setting search options. To facilitate analysis of global viral transmission and evolution, the IV Sequence Distribution Tool (IVDT) has been developed to display the worldwide geographic distribution of chosen viral genotypes and to couple genomic data with epidemiological data. The BLAST, multiple sequence alignment and phylogenetic analysis tools were integrated for online data analysis. Furthermore, IVDB offers instant access to pre-computed alignments and polymorphisms of IV genes and proteins, and presents the results as SNP distribution plots and minor allele distributions. IVDB is publicly available at http://influenza.genomics.org.cn.

Saint: a lightweight integration environment for model annotation.

PubMed

Lister, Allyson L; Pocock, Matthew; Taschuk, Morgan; Wipat, Anil

2009-11-15

Saint is a web application which provides a lightweight annotation integration environment for quantitative biological models. The system enables modellers to rapidly mark up models with biological information derived from a range of data sources. Saint is freely available for use on the web at http://www.cisban.ac.uk/saint. The web application is implemented in Google Web Toolkit and Tomcat, with all major browsers supported. The Java source code is freely available for download at http://saint-annotate.sourceforge.net. The Saint web server requires an installation of libSBML and has been tested on Linux (32-bit Ubuntu 8.10 and 9.04).

NeuroTransDB: highly curated and structured transcriptomic metadata for neurodegenerative diseases.

PubMed

Bagewadi, Shweta; Adhikari, Subash; Dhrangadhariya, Anjani; Irin, Afroza Khanam; Ebeling, Christian; Namasivayam, Aishwarya Alex; Page, Matthew; Hofmann-Apitius, Martin; Senger, Philipp

2015-01-01

Neurodegenerative diseases are chronic debilitating conditions, characterized by progressive loss of neurons that represent a significant health care burden as the global elderly population continues to grow. Over the past decade, high-throughput technologies such as the Affymetrix GeneChip microarrays have provided new perspectives into the pathomechanisms underlying neurodegeneration. Public transcriptomic data repositories, namely Gene Expression Omnibus and curated ArrayExpress, enable researchers to conduct integrative meta-analysis; increasing the power to detect differentially regulated genes in disease and explore patterns of gene dysregulation across biologically related studies. The reliability of retrospective, large-scale integrative analyses depends on an appropriate combination of related datasets, in turn requiring detailed meta-annotations capturing the experimental setup. In most cases, we observe huge variation in compliance to defined standards for submitted metadata in public databases. Much of the information to complete, or refine meta-annotations are distributed in the associated publications. For example, tissue preparation or comorbidity information is frequently described in an article's supplementary tables. Several value-added databases have employed additional manual efforts to overcome this limitation. However, none of these databases explicate annotations that distinguish human and animal models in neurodegeneration context. Therefore, adopting a more specific disease focus, in combination with dedicated disease ontologies, will better empower the selection of comparable studies with refined annotations to address the research question at hand. In this article, we describe the detailed development of NeuroTransDB, a manually curated database containing metadata annotations for neurodegenerative studies. The database contains more than 20 dimensions of metadata annotations within 31 mouse, 5 rat and 45 human studies, defined in collaboration with domain disease experts. We elucidate the step-by-step guidelines used to critically prioritize studies from public archives and their metadata curation and discuss the key challenges encountered. Curated metadata for Alzheimer's disease gene expression studies are available for download. Database URL: www.scai.fraunhofer.de/NeuroTransDB.html. © The Author(s) 2015. Published by Oxford University Press.

NeuroTransDB: highly curated and structured transcriptomic metadata for neurodegenerative diseases

PubMed Central

Bagewadi, Shweta; Adhikari, Subash; Dhrangadhariya, Anjani; Irin, Afroza Khanam; Ebeling, Christian; Namasivayam, Aishwarya Alex; Page, Matthew; Hofmann-Apitius, Martin

2015-01-01

Neurodegenerative diseases are chronic debilitating conditions, characterized by progressive loss of neurons that represent a significant health care burden as the global elderly population continues to grow. Over the past decade, high-throughput technologies such as the Affymetrix GeneChip microarrays have provided new perspectives into the pathomechanisms underlying neurodegeneration. Public transcriptomic data repositories, namely Gene Expression Omnibus and curated ArrayExpress, enable researchers to conduct integrative meta-analysis; increasing the power to detect differentially regulated genes in disease and explore patterns of gene dysregulation across biologically related studies. The reliability of retrospective, large-scale integrative analyses depends on an appropriate combination of related datasets, in turn requiring detailed meta-annotations capturing the experimental setup. In most cases, we observe huge variation in compliance to defined standards for submitted metadata in public databases. Much of the information to complete, or refine meta-annotations are distributed in the associated publications. For example, tissue preparation or comorbidity information is frequently described in an article’s supplementary tables. Several value-added databases have employed additional manual efforts to overcome this limitation. However, none of these databases explicate annotations that distinguish human and animal models in neurodegeneration context. Therefore, adopting a more specific disease focus, in combination with dedicated disease ontologies, will better empower the selection of comparable studies with refined annotations to address the research question at hand. In this article, we describe the detailed development of NeuroTransDB, a manually curated database containing metadata annotations for neurodegenerative studies. The database contains more than 20 dimensions of metadata annotations within 31 mouse, 5 rat and 45 human studies, defined in collaboration with domain disease experts. We elucidate the step-by-step guidelines used to critically prioritize studies from public archives and their metadata curation and discuss the key challenges encountered. Curated metadata for Alzheimer’s disease gene expression studies are available for download. Database URL: www.scai.fraunhofer.de/NeuroTransDB.html PMID:26475471

EuroPineDB: a high-coverage web database for maritime pine transcriptome

PubMed Central

2011-01-01

Background Pinus pinaster is an economically and ecologically important species that is becoming a woody gymnosperm model. Its enormous genome size makes whole-genome sequencing approaches are hard to apply. Therefore, the expressed portion of the genome has to be characterised and the results and annotations have to be stored in dedicated databases. Description EuroPineDB is the largest sequence collection available for a single pine species, Pinus pinaster (maritime pine), since it comprises 951 641 raw sequence reads obtained from non-normalised cDNA libraries and high-throughput sequencing from adult (xylem, phloem, roots, stem, needles, cones, strobili) and embryonic (germinated embryos, buds, callus) maritime pine tissues. Using open-source tools, sequences were optimally pre-processed, assembled, and extensively annotated (GO, EC and KEGG terms, descriptions, SNPs, SSRs, ORFs and InterPro codes). As a result, a 10.5× P. pinaster genome was covered and assembled in 55 322 UniGenes. A total of 32 919 (59.5%) of P. pinaster UniGenes were annotated with at least one description, revealing at least 18 466 different genes. The complete database, which is designed to be scalable, maintainable, and expandable, is freely available at: http://www.scbi.uma.es/pindb/. It can be retrieved by gene libraries, pine species, annotations, UniGenes and microarrays (i.e., the sequences are distributed in two-colour microarrays; this is the only conifer database that provides this information) and will be periodically updated. Small assemblies can be viewed using a dedicated visualisation tool that connects them with SNPs. Any sequence or annotation set shown on-screen can be downloaded. Retrieval mechanisms for sequences and gene annotations are provided. Conclusions The EuroPineDB with its integrated information can be used to reveal new knowledge, offers an easy-to-use collection of information to directly support experimental work (including microarray hybridisation), and provides deeper knowledge on the maritime pine transcriptome. PMID:21762488

Sharing and reusing multimedia multilingual educational resources in medicine.

PubMed

Zdrahal, Zdenek; Knoth, Petr; Mulholland, Paul; Collins, Trevor

2013-01-01

The paper describes the Eurogene portal for sharing and reusing multilingual multimedia educational resources in human genetics. The content is annotated using concepts of two ontologies and a topic hierarchy. The ontology annotation is used to guide search and for calculating semantically similar content. Educational resources can be aggregated into learning packages. The system is in routine use since 2009.

New records and checklist of corticioid Basidiomycota from Uruguay

Treesearch

Sebastian Martinez; Karen K. Nakasone

2010-01-01

Twenty-eight corticioid basidiomycete species are reported from Uruguay for the first time. An annotated checklist with 110 species of corticioid Basidiomycota recorded from Uruguay is presented based on these new records and an intensive literature search. These species are distributed in 49 genera and 10 orders. The order Polyporales...

DSSTox chemical-index files for exposure-related experiments in ArrayExpress and Gene Expression Omnibus: enabling toxico-chemogenomics data linkages

EPA Science Inventory

The Distributed Structure-Searchable Toxicity (DSSTox) ARYEXP and GEOGSE files are newly published, structure-annotated files of the chemical-associated and chemical exposure-related summary experimental content contained in the ArrayExpress Repository and Gene Expression Omnibus...

Transcriptome de novo assembly from next-generation sequencing and comparative analyses in the hexaploid salt marsh species Spartina maritima and Spartina alterniflora (Poaceae)

PubMed Central

Ferreira de Carvalho, J; Poulain, J; Da Silva, C; Wincker, P; Michon-Coudouel, S; Dheilly, A; Naquin, D; Boutte, J; Salmon, A; Ainouche, M

2013-01-01

Spartina species have a critical ecological role in salt marshes and represent an excellent system to investigate recurrent polyploid speciation. Using the 454 GS-FLX pyrosequencer, we assembled and annotated the first reference transcriptome (from roots and leaves) for two related hexaploid Spartina species that hybridize in Western Europe, the East American invasive Spartina alterniflora and the Euro-African S. maritima. The de novo read assembly generated 38 478 consensus sequences and 99% found an annotation using Poaceae databases, representing a total of 16 753 non-redundant genes. Spartina expressed sequence tags were mapped onto the Sorghum bicolor genome, where they were distributed among the subtelomeric arms of the 10 S. bicolor chromosomes, with high gene density correlation. Normalization of the complementary DNA library improved the number of annotated genes. Ecologically relevant genes were identified among GO biological function categories in salt and heavy metal stress response, C4 photosynthesis and in lignin and cellulose metabolism. Expression of some of these genes had been found to be altered by hybridization and genome duplication in a previous microarray-based study in Spartina. As these species are hexaploid, up to three duplicated homoeologs may be expected per locus. When analyzing sequence polymorphism at four different loci in S. maritima and S. alterniflora, we found up to four haplotypes per locus, suggesting the presence of two expressed homoeologous sequences with one or two allelic variants each. This reference transcriptome will allow analysis of specific Spartina genes of ecological or evolutionary interest, estimation of homoeologous gene expression variation using RNA-seq and further gene expression evolution analyses in natural populations. PMID:23149455

A corpus of full-text journal articles is a robust evaluation tool for revealing differences in performance of biomedical natural language processing tools

PubMed Central

2012-01-01

Background We introduce the linguistic annotation of a corpus of 97 full-text biomedical publications, known as the Colorado Richly Annotated Full Text (CRAFT) corpus. We further assess the performance of existing tools for performing sentence splitting, tokenization, syntactic parsing, and named entity recognition on this corpus. Results Many biomedical natural language processing systems demonstrated large differences between their previously published results and their performance on the CRAFT corpus when tested with the publicly available models or rule sets. Trainable systems differed widely with respect to their ability to build high-performing models based on this data. Conclusions The finding that some systems were able to train high-performing models based on this corpus is additional evidence, beyond high inter-annotator agreement, that the quality of the CRAFT corpus is high. The overall poor performance of various systems indicates that considerable work needs to be done to enable natural language processing systems to work well when the input is full-text journal articles. The CRAFT corpus provides a valuable resource to the biomedical natural language processing community for evaluation and training of new models for biomedical full text publications. PMID:22901054

Automatically Recognizing Medication and Adverse Event Information From Food and Drug Administration’s Adverse Event Reporting System Narratives

PubMed Central

Polepalli Ramesh, Balaji; Belknap, Steven M; Li, Zuofeng; Frid, Nadya; West, Dennis P

2014-01-01

Background The Food and Drug Administration’s (FDA) Adverse Event Reporting System (FAERS) is a repository of spontaneously-reported adverse drug events (ADEs) for FDA-approved prescription drugs. FAERS reports include both structured reports and unstructured narratives. The narratives often include essential information for evaluation of the severity, causality, and description of ADEs that are not present in the structured data. The timely identification of unknown toxicities of prescription drugs is an important, unsolved problem. Objective The objective of this study was to develop an annotated corpus of FAERS narratives and biomedical named entity tagger to automatically identify ADE related information in the FAERS narratives. Methods We developed an annotation guideline and annotate medication information and adverse event related entities on 122 FAERS narratives comprising approximately 23,000 word tokens. A named entity tagger using supervised machine learning approaches was built for detecting medication information and adverse event entities using various categories of features. Results The annotated corpus had an agreement of over .9 Cohen’s kappa for medication and adverse event entities. The best performing tagger achieves an overall performance of 0.73 F1 score for detection of medication, adverse event and other named entities. Conclusions In this study, we developed an annotated corpus of FAERS narratives and machine learning based models for automatically extracting medication and adverse event information from the FAERS narratives. Our study is an important step towards enriching the FAERS data for postmarketing pharmacovigilance. PMID:25600332

An Augmented Reality-Based Approach for Surgical Telementoring in Austere Environments.

PubMed

Andersen, Dan; Popescu, Voicu; Cabrera, Maria Eugenia; Shanghavi, Aditya; Mullis, Brian; Marley, Sherri; Gomez, Gerardo; Wachs, Juan P

2017-03-01

Telementoring can improve treatment of combat trauma injuries by connecting remote experienced surgeons with local less-experienced surgeons in an austere environment. Current surgical telementoring systems force the local surgeon to regularly shift focus away from the operating field to receive expert guidance, which can lead to surgery delays or even errors. The System for Telementoring with Augmented Reality (STAR) integrates expert-created annotations directly into the local surgeon's field of view. The local surgeon views the operating field by looking at a tablet display suspended between the patient and the surgeon that captures video of the surgical field. The remote surgeon remotely adds graphical annotations to the video. The annotations are sent back and displayed to the local surgeon while being automatically anchored to the operating field elements they describe. A technical evaluation demonstrates that STAR robustly anchors annotations despite tablet repositioning and occlusions. In a user study, participants used either STAR or a conventional telementoring system to precisely mark locations on a surgical simulator under a remote surgeon's guidance. Participants who used STAR completed the task with fewer focus shifts and with greater accuracy. The STAR reduces the local surgeon's need to shift attention during surgery, allowing him or her to continuously work while looking "through" the tablet screen. Reprint & Copyright © 2017 Association of Military Surgeons of the U.S.

Assisted annotation of medical free text using RapTAT

PubMed Central

Gobbel, Glenn T; Garvin, Jennifer; Reeves, Ruth; Cronin, Robert M; Heavirland, Julia; Williams, Jenifer; Weaver, Allison; Jayaramaraja, Shrimalini; Giuse, Dario; Speroff, Theodore; Brown, Steven H; Xu, Hua; Matheny, Michael E

2014-01-01

Objective To determine whether assisted annotation using interactive training can reduce the time required to annotate a clinical document corpus without introducing bias. Materials and methods A tool, RapTAT, was designed to assist annotation by iteratively pre-annotating probable phrases of interest within a document, presenting the annotations to a reviewer for correction, and then using the corrected annotations for further machine learning-based training before pre-annotating subsequent documents. Annotators reviewed 404 clinical notes either manually or using RapTAT assistance for concepts related to quality of care during heart failure treatment. Notes were divided into 20 batches of 19–21 documents for iterative annotation and training. Results The number of correct RapTAT pre-annotations increased significantly and annotation time per batch decreased by ∼50% over the course of annotation. Annotation rate increased from batch to batch for assisted but not manual reviewers. Pre-annotation F-measure increased from 0.5 to 0.6 to >0.80 (relative to both assisted reviewer and reference annotations) over the first three batches and more slowly thereafter. Overall inter-annotator agreement was significantly higher between RapTAT-assisted reviewers (0.89) than between manual reviewers (0.85). Discussion The tool reduced workload by decreasing the number of annotations needing to be added and helping reviewers to annotate at an increased rate. Agreement between the pre-annotations and reference standard, and agreement between the pre-annotations and assisted annotations, were similar throughout the annotation process, which suggests that pre-annotation did not introduce bias. Conclusions Pre-annotations generated by a tool capable of interactive training can reduce the time required to create an annotated document corpus by up to 50%. PMID:24431336

Linking DICOM pixel data with radiology reports using automatic semantic annotation

NASA Astrophysics Data System (ADS)

Pathak, Sayan D.; Kim, Woojin; Munasinghe, Indeera; Criminisi, Antonio; White, Steve; Siddiqui, Khan

2012-02-01

Improved access to DICOM studies to both physicians and patients is changing the ways medical imaging studies are visualized and interpreted beyond the confines of radiologists' PACS workstations. While radiologists are trained for viewing and image interpretation, a non-radiologist physician relies on the radiologists' reports. Consequently, patients historically have been typically informed about their imaging findings via oral communication with their physicians, even though clinical studies have shown that patients respond to physician's advice significantly better when the individual patients are shown their own actual data. Our previous work on automated semantic annotation of DICOM Computed Tomography (CT) images allows us to further link radiology report with the corresponding images, enabling us to bridge the gap between image data with the human interpreted textual description of the corresponding imaging studies. The mapping of radiology text is facilitated by natural language processing (NLP) based search application. When combined with our automated semantic annotation of images, it enables navigation in large DICOM studies by clicking hyperlinked text in the radiology reports. An added advantage of using semantic annotation is the ability to render the organs to their default window level setting thus eliminating another barrier to image sharing and distribution. We believe such approaches would potentially enable the consumer to have access to their imaging data and navigate them in an informed manner.

Bioinformatics resource manager v2.3: an integrated software environment for systems biology with microRNA and cross-species analysis tools

PubMed Central

2012-01-01

Background MicroRNAs (miRNAs) are noncoding RNAs that direct post-transcriptional regulation of protein coding genes. Recent studies have shown miRNAs are important for controlling many biological processes, including nervous system development, and are highly conserved across species. Given their importance, computational tools are necessary for analysis, interpretation and integration of high-throughput (HTP) miRNA data in an increasing number of model species. The Bioinformatics Resource Manager (BRM) v2.3 is a software environment for data management, mining, integration and functional annotation of HTP biological data. In this study, we report recent updates to BRM for miRNA data analysis and cross-species comparisons across datasets. Results BRM v2.3 has the capability to query predicted miRNA targets from multiple databases, retrieve potential regulatory miRNAs for known genes, integrate experimentally derived miRNA and mRNA datasets, perform ortholog mapping across species, and retrieve annotation and cross-reference identifiers for an expanded number of species. Here we use BRM to show that developmental exposure of zebrafish to 30 uM nicotine from 6–48 hours post fertilization (hpf) results in behavioral hyperactivity in larval zebrafish and alteration of putative miRNA gene targets in whole embryos at developmental stages that encompass early neurogenesis. We show typical workflows for using BRM to integrate experimental zebrafish miRNA and mRNA microarray datasets with example retrievals for zebrafish, including pathway annotation and mapping to human ortholog. Functional analysis of differentially regulated (p<0.05) gene targets in BRM indicates that nicotine exposure disrupts genes involved in neurogenesis, possibly through misregulation of nicotine-sensitive miRNAs. Conclusions BRM provides the ability to mine complex data for identification of candidate miRNAs or pathways that drive phenotypic outcome and, therefore, is a useful hypothesis generation tool for systems biology. The miRNA workflow in BRM allows for efficient processing of multiple miRNA and mRNA datasets in a single software environment with the added capability to interact with public data sources and visual analytic tools for HTP data analysis at a systems level. BRM is developed using Java™ and other open-source technologies for free distribution (http://www.sysbio.org/dataresources/brm.stm). PMID:23174015

A Bioacoustic Record of a Conservancy in the Mount Kenya Ecosystem

PubMed Central

Muchiri, David; Njoroge, Peter

2016-01-01

Abstract Background Environmental degradation is a major threat facing ecosystems around the world. In order to determine ecosystems in need of conservation interventions, we must monitor the biodiversity of these ecosystems effectively. Bioacoustic approaches offer a means to monitor ecosystems of interest in a sustainable manner. In this work we show how a bioacoustic record from the Dedan Kimathi University wildlife conservancy, a conservancy in the Mount Kenya ecosystem, was obtained in a cost effective manner. A subset of the dataset was annotated with the identities of bird species present since they serve as useful indicator species. These data reveal the spatial distribution of species within the conservancy and also point to the effects of major highways on bird populations. This dataset will provide data to train automatic species recognition systems for birds found within the Mount Kenya ecosystem. Such systems are necessary if bioacoustic approaches are to be employed at the large scales necessary to influence wildlife conservation measures. New information We provide acoustic recordings from the Dedan Kimathi University wildlife conservancy, a conservancy in the Mount Kenya ecosystem, obtained using a low cost acoustic recorder. A total of 2701 minute long recordings are provided including both daytime and nighttime recordings. We present an annotation of a subset of the daytime recordings indicating the bird species present in the recordings. The dataset contains recordings of at least 36 bird species. In addition, the presence of a few nocturnal species within the conservancy is also confirmed. PMID:27932917

A Bioacoustic Record of a Conservancy in the Mount Kenya Ecosystem.

PubMed

Wa Maina, Ciira; Muchiri, David; Njoroge, Peter

2016-01-01

Environmental degradation is a major threat facing ecosystems around the world. In order to determine ecosystems in need of conservation interventions, we must monitor the biodiversity of these ecosystems effectively. Bioacoustic approaches offer a means to monitor ecosystems of interest in a sustainable manner. In this work we show how a bioacoustic record from the Dedan Kimathi University wildlife conservancy, a conservancy in the Mount Kenya ecosystem, was obtained in a cost effective manner. A subset of the dataset was annotated with the identities of bird species present since they serve as useful indicator species. These data reveal the spatial distribution of species within the conservancy and also point to the effects of major highways on bird populations. This dataset will provide data to train automatic species recognition systems for birds found within the Mount Kenya ecosystem. Such systems are necessary if bioacoustic approaches are to be employed at the large scales necessary to influence wildlife conservation measures. We provide acoustic recordings from the Dedan Kimathi University wildlife conservancy, a conservancy in the Mount Kenya ecosystem, obtained using a low cost acoustic recorder. A total of 2701 minute long recordings are provided including both daytime and nighttime recordings. We present an annotation of a subset of the daytime recordings indicating the bird species present in the recordings. The dataset contains recordings of at least 36 bird species. In addition, the presence of a few nocturnal species within the conservancy is also confirmed.

TnpPred: A Web Service for the Robust Prediction of Prokaryotic Transposases

PubMed Central

Riadi, Gonzalo; Medina-Moenne, Cristobal; Holmes, David S.

2012-01-01

Transposases (Tnps) are enzymes that participate in the movement of insertion sequences (ISs) within and between genomes. Genes that encode Tnps are amongst the most abundant and widely distributed genes in nature. However, they are difficult to predict bioinformatically and given the increasing availability of prokaryotic genomes and metagenomes, it is incumbent to develop rapid, high quality automatic annotation of ISs. This need prompted us to develop a web service, termed TnpPred for Tnp discovery. It provides better sensitivity and specificity for Tnp predictions than given by currently available programs as determined by ROC analysis. TnpPred should be useful for improving genome annotation. The TnpPred web service is freely available for noncommercial use. PMID:23251097

MalaCards: an integrated compendium for diseases and their annotation

PubMed Central

Rappaport, Noa; Nativ, Noam; Stelzer, Gil; Twik, Michal; Guan-Golan, Yaron; Iny Stein, Tsippi; Bahir, Iris; Belinky, Frida; Morrey, C. Paul; Safran, Marilyn; Lancet, Doron

2013-01-01

Comprehensive disease classification, integration and annotation are crucial for biomedical discovery. At present, disease compilation is incomplete, heterogeneous and often lacking systematic inquiry mechanisms. We introduce MalaCards, an integrated database of human maladies and their annotations, modeled on the architecture and strategy of the GeneCards database of human genes. MalaCards mines and merges 44 data sources to generate a computerized card for each of 16 919 human diseases. Each MalaCard contains disease-specific prioritized annotations, as well as inter-disease connections, empowered by the GeneCards relational database, its searches and GeneDecks set analyses. First, we generate a disease list from 15 ranked sources, using disease-name unification heuristics. Next, we use four schemes to populate MalaCards sections: (i) directly interrogating disease resources, to establish integrated disease names, synonyms, summaries, drugs/therapeutics, clinical features, genetic tests and anatomical context; (ii) searching GeneCards for related publications, and for associated genes with corresponding relevance scores; (iii) analyzing disease-associated gene sets in GeneDecks to yield affiliated pathways, phenotypes, compounds and GO terms, sorted by a composite relevance score and presented with GeneCards links; and (iv) searching within MalaCards itself, e.g. for additional related diseases and anatomical context. The latter forms the basis for the construction of a disease network, based on shared MalaCards annotations, embodying associations based on etiology, clinical features and clinical conditions. This broadly disposed network has a power-law degree distribution, suggesting that this might be an inherent property of such networks. Work in progress includes hierarchical malady classification, ontological mapping and disease set analyses, striving to make MalaCards an even more effective tool for biomedical research. Database URL: http://www.malacards.org/ PMID:23584832

Deep landscape update of dispersed and tandem repeats in the genome model of the red jungle fowl, Gallus gallus, using a series of de novo investigating tools.

PubMed

Guizard, Sébastien; Piégu, Benoît; Arensburger, Peter; Guillou, Florian; Bigot, Yves

2016-08-19

The program RepeatMasker and the database Repbase-ISB are part of the most widely used strategy for annotating repeats in animal genomes. They have been used to show that avian genomes have a lower repeat content (8-12 %) than the sequenced genomes of many vertebrate species (30-55 %). However, the efficiency of such a library-based strategies is dependent on the quality and completeness of the sequences in the database that is used. An alternative to these library based methods are methods that identify repeats de novo. These alternative methods have existed for a least a decade and may be more powerful than the library based methods. We have used an annotation strategy involving several complementary de novo tools to determine the repeat content of the model genome galGal4 (1.04 Gbp), including identifying simple sequence repeats (SSRs), tandem repeats and transposable elements (TEs). We annotated over one Gbp. of the galGal4 genome and showed that it is composed of approximately 19 % SSRs and TEs repeats. Furthermore, we estimate that the actual genome of the red jungle fowl contains about 31-35 % repeats. We find that library-based methods tend to overestimate TE diversity. These results have a major impact on the current understanding of repeats distributions throughout chromosomes in the red jungle fowl. Our results are a proof of concept of the reliability of using de novo tools to annotate repeats in large animal genomes. They have also revealed issues that will need to be resolved in order to develop gold-standard methodologies for annotating repeats in eukaryote genomes.

Small molecule annotation for the Protein Data Bank

PubMed Central

Sen, Sanchayita; Young, Jasmine; Berrisford, John M.; Chen, Minyu; Conroy, Matthew J.; Dutta, Shuchismita; Di Costanzo, Luigi; Gao, Guanghua; Ghosh, Sutapa; Hudson, Brian P.; Igarashi, Reiko; Kengaku, Yumiko; Liang, Yuhe; Peisach, Ezra; Persikova, Irina; Mukhopadhyay, Abhik; Narayanan, Buvaneswari Coimbatore; Sahni, Gaurav; Sato, Junko; Sekharan, Monica; Shao, Chenghua; Tan, Lihua; Zhuravleva, Marina A.

2014-01-01

The Protein Data Bank (PDB) is the single global repository for three-dimensional structures of biological macromolecules and their complexes, and its more than 100 000 structures contain more than 20 000 distinct ligands or small molecules bound to proteins and nucleic acids. Information about these small molecules and their interactions with proteins and nucleic acids is crucial for our understanding of biochemical processes and vital for structure-based drug design. Small molecules present in a deposited structure may be attached to a polymer or may occur as a separate, non-covalently linked ligand. During curation of a newly deposited structure by wwPDB annotation staff, each molecule is cross-referenced to the PDB Chemical Component Dictionary (CCD). If the molecule is new to the PDB, a dictionary description is created for it. The information about all small molecule components found in the PDB is distributed via the ftp archive as an external reference file. Small molecule annotation in the PDB also includes information about ligand-binding sites and about covalent and other linkages between ligands and macromolecules. During the remediation of the peptide-like antibiotics and inhibitors present in the PDB archive in 2011, it became clear that additional annotation was required for consistent representation of these molecules, which are quite often composed of several sequential subcomponents including modified amino acids and other chemical groups. The connectivity information of the modified amino acids is necessary for correct representation of these biologically interesting molecules. The combined information is made available via a new resource called the Biologically Interesting molecules Reference Dictionary, which is complementary to the CCD and is now routinely used for annotation of peptide-like antibiotics and inhibitors. PMID:25425036

A study of the effectiveness of machine learning methods for classification of clinical interview fragments into a large number of categories.

PubMed

Hasan, Mehedi; Kotov, Alexander; Carcone, April; Dong, Ming; Naar, Sylvie; Hartlieb, Kathryn Brogan

2016-08-01

This study examines the effectiveness of state-of-the-art supervised machine learning methods in conjunction with different feature types for the task of automatic annotation of fragments of clinical text based on codebooks with a large number of categories. We used a collection of motivational interview transcripts consisting of 11,353 utterances, which were manually annotated by two human coders as the gold standard, and experimented with state-of-art classifiers, including Naïve Bayes, J48 Decision Tree, Support Vector Machine (SVM), Random Forest (RF), AdaBoost, DiscLDA, Conditional Random Fields (CRF) and Convolutional Neural Network (CNN) in conjunction with lexical, contextual (label of the previous utterance) and semantic (distribution of words in the utterance across the Linguistic Inquiry and Word Count dictionaries) features. We found out that, when the number of classes is large, the performance of CNN and CRF is inferior to SVM. When only lexical features were used, interview transcripts were automatically annotated by SVM with the highest classification accuracy among all classifiers of 70.8%, 61% and 53.7% based on the codebooks consisting of 17, 20 and 41 codes, respectively. Using contextual and semantic features, as well as their combination, in addition to lexical ones, improved the accuracy of SVM for annotation of utterances in motivational interview transcripts with a codebook consisting of 17 classes to 71.5%, 74.2%, and 75.1%, respectively. Our results demonstrate the potential of using machine learning methods in conjunction with lexical, semantic and contextual features for automatic annotation of clinical interview transcripts with near-human accuracy. Copyright © 2016 Elsevier Inc. All rights reserved.

Small molecule annotation for the Protein Data Bank.

PubMed

Sen, Sanchayita; Young, Jasmine; Berrisford, John M; Chen, Minyu; Conroy, Matthew J; Dutta, Shuchismita; Di Costanzo, Luigi; Gao, Guanghua; Ghosh, Sutapa; Hudson, Brian P; Igarashi, Reiko; Kengaku, Yumiko; Liang, Yuhe; Peisach, Ezra; Persikova, Irina; Mukhopadhyay, Abhik; Narayanan, Buvaneswari Coimbatore; Sahni, Gaurav; Sato, Junko; Sekharan, Monica; Shao, Chenghua; Tan, Lihua; Zhuravleva, Marina A

2014-01-01

The Protein Data Bank (PDB) is the single global repository for three-dimensional structures of biological macromolecules and their complexes, and its more than 100,000 structures contain more than 20,000 distinct ligands or small molecules bound to proteins and nucleic acids. Information about these small molecules and their interactions with proteins and nucleic acids is crucial for our understanding of biochemical processes and vital for structure-based drug design. Small molecules present in a deposited structure may be attached to a polymer or may occur as a separate, non-covalently linked ligand. During curation of a newly deposited structure by wwPDB annotation staff, each molecule is cross-referenced to the PDB Chemical Component Dictionary (CCD). If the molecule is new to the PDB, a dictionary description is created for it. The information about all small molecule components found in the PDB is distributed via the ftp archive as an external reference file. Small molecule annotation in the PDB also includes information about ligand-binding sites and about covalent and other linkages between ligands and macromolecules. During the remediation of the peptide-like antibiotics and inhibitors present in the PDB archive in 2011, it became clear that additional annotation was required for consistent representation of these molecules, which are quite often composed of several sequential subcomponents including modified amino acids and other chemical groups. The connectivity information of the modified amino acids is necessary for correct representation of these biologically interesting molecules. The combined information is made available via a new resource called the Biologically Interesting molecules Reference Dictionary, which is complementary to the CCD and is now routinely used for annotation of peptide-like antibiotics and inhibitors. © The Author(s) 2014. Published by Oxford University Press.

Enabling phenotypic big data with PheNorm.

PubMed

Yu, Sheng; Ma, Yumeng; Gronsbell, Jessica; Cai, Tianrun; Ananthakrishnan, Ashwin N; Gainer, Vivian S; Churchill, Susanne E; Szolovits, Peter; Murphy, Shawn N; Kohane, Isaac S; Liao, Katherine P; Cai, Tianxi

2018-01-01

Electronic health record (EHR)-based phenotyping infers whether a patient has a disease based on the information in his or her EHR. A human-annotated training set with gold-standard disease status labels is usually required to build an algorithm for phenotyping based on a set of predictive features. The time intensiveness of annotation and feature curation severely limits the ability to achieve high-throughput phenotyping. While previous studies have successfully automated feature curation, annotation remains a major bottleneck. In this paper, we present PheNorm, a phenotyping algorithm that does not require expert-labeled samples for training. The most predictive features, such as the number of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes or mentions of the target phenotype, are normalized to resemble a normal mixture distribution with high area under the receiver operating curve (AUC) for prediction. The transformed features are then denoised and combined into a score for accurate disease classification. We validated the accuracy of PheNorm with 4 phenotypes: coronary artery disease, rheumatoid arthritis, Crohn's disease, and ulcerative colitis. The AUCs of the PheNorm score reached 0.90, 0.94, 0.95, and 0.94 for the 4 phenotypes, respectively, which were comparable to the accuracy of supervised algorithms trained with sample sizes of 100-300, with no statistically significant difference. The accuracy of the PheNorm algorithms is on par with algorithms trained with annotated samples. PheNorm fully automates the generation of accurate phenotyping algorithms and demonstrates the capacity for EHR-driven annotations to scale to the next level - phenotypic big data. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Automated Visual Event Detection, Tracking, and Data Management System for Cabled- Observatory Video

NASA Astrophysics Data System (ADS)

Edgington, D. R.; Cline, D. E.; Schlining, B.; Raymond, E.

2008-12-01

Ocean observatories and underwater video surveys have the potential to unlock important discoveries with new and existing camera systems. Yet the burden of video management and analysis often requires reducing the amount of video recorded through time-lapse video or similar methods. It's unknown how many digitized video data sets exist in the oceanographic community, but we suspect that many remain under analyzed due to lack of good tools or human resources to analyze the video. To help address this problem, the Automated Visual Event Detection (AVED) software and The Video Annotation and Reference System (VARS) have been under development at MBARI. For detecting interesting events in the video, the AVED software has been developed over the last 5 years. AVED is based on a neuromorphic-selective attention algorithm, modeled on the human vision system. Frames are decomposed into specific feature maps that are combined into a unique saliency map. This saliency map is then scanned to determine the most salient locations. The candidate salient locations are then segmented from the scene using algorithms suitable for the low, non-uniform light and marine snow typical of deep underwater video. For managing the AVED descriptions of the video, the VARS system provides an interface and database for describing, viewing, and cataloging the video. VARS was developed by the MBARI for annotating deep-sea video data and is currently being used to describe over 3000 dives by our remotely operated vehicles (ROV), making it well suited to this deepwater observatory application with only a few modifications. To meet the compute and data intensive job of video processing, a distributed heterogeneous network of computers is managed using the Condor workload management system. This system manages data storage, video transcoding, and AVED processing. Looking to the future, we see high-speed networks and Grid technology as an important element in addressing the problem of processing and accessing large video data sets.

The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience.

PubMed

Burns, Randal; Roncal, William Gray; Kleissas, Dean; Lillaney, Kunal; Manavalan, Priya; Perlman, Eric; Berger, Daniel R; Bock, Davi D; Chung, Kwanghun; Grosenick, Logan; Kasthuri, Narayanan; Weiler, Nicholas C; Deisseroth, Karl; Kazhdan, Michael; Lichtman, Jeff; Reid, R Clay; Smith, Stephen J; Szalay, Alexander S; Vogelstein, Joshua T; Vogelstein, R Jacob

2013-01-01

We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes - neural connectivity maps of the brain-using the parallel execution of computer vision algorithms on high-performance compute clusters. These services and open-science data sets are publicly available at openconnecto.me. The system design inherits much from NoSQL scale-out and data-intensive computing architectures. We distribute data to cluster nodes by partitioning a spatial index. We direct I/O to different systems-reads to parallel disk arrays and writes to solid-state storage-to avoid I/O interference and maximize throughput. All programming interfaces are RESTful Web services, which are simple and stateless, improving scalability and usability. We include a performance evaluation of the production system, highlighting the effec-tiveness of spatial data organization.

Titles for Technology: An Annotated Bibliography. Compiled at the 1967 Summer Institute of Technology for Children (Marlton, N.J.)

ERIC Educational Resources Information Center

New Jersey State Dept. of Education, Trenton. Div. of Vocational Education.

This annotated bibliography includes about 400 books which are suitable for use in elementary industrial arts. These books, available in the state library system of New Jersey, are organized under 50 topics such as: (1) Automation, (2) Graphic Arts, (3) Machines, (4) Space Travel, and (5) Tools and Measuring. Most of the citations are children's…

An annotated bibliography of Pyrrone and BBB publications

NASA Technical Reports Server (NTRS)

Burks, H. D.

1972-01-01

This annotated bibliography covers the research and development of two closely related classes of high-temperature polymers, polyimidazopyrrolones (Pyrrone) and bisbenzimidazo-benzophenanthrolines (BBB), from their inception in 1965 through 1971. This compilation of available reference information is not inclusive, but it is sufficiently complete to aid the polymer chemist and materials engineer in the research and development of these two high temperature stable polymeric systems.

Video Annotation Software Application for Thorough Collaborative Assessment of and Feedback on Microteaching Lessons in Geography Education

ERIC Educational Resources Information Center

van der Westhuizen, Christo P.; Golightly, Aubrey

2015-01-01

This article discusses the process and findings of a study in which video annotation (VideoANT) and a learning management system (LMS) were implemented together in the microteaching lessons of fourth-year geography student teachers. The aim was to ensure adequate assessment of and feedback for each student, since these aspects are, in general, a…

Host Specific Diversity in Lactobacillus johnsonii as Evidenced by a Major Chromosomal Inversion and Phage Resistance Mechanisms

PubMed Central

Guinane, Caitriona M.; Kent, Robert M.; Norberg, Sarah; Hill, Colin; Fitzgerald, Gerald F.; Stanton, Catherine; Ross, R. Paul

2011-01-01

Genetic diversity and genomic rearrangements are a driving force in bacterial evolution and niche adaptation. We sequenced and annotated the genome of Lactobacillus johnsonii DPC6026, a strain isolated from the porcine intestinal tract. Although the genome of DPC6026 is similar in size (1.97mbp) and GC content (34.8%) to the sequenced human isolate L. johnsonii NCC 533, a large symmetrical inversion of approximately 750 kb differentiated the two strains. Comparative analysis among 12 other strains of L. johnsonii including 8 porcine, 3 human and 1 poultry isolate indicated that the genome architecture found in DPC6026 is more common within the species than that of NCC 533. Furthermore a number of unique features were annotated in DPC6026, some of which are likely to have been acquired by horizontal gene transfer (HGT) and contribute to protection against phage infection. A putative type III restriction-modification system was identified, as were novel Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) elements. Interestingly, these particular elements are not widely distributed among L. johnsonii strains. Taken together these data suggest intra-species genomic rearrangements and significant genetic diversity within the L. johnsonii species and indicate towards a host-specific divergence of L. johnsonii strains with respect to genome inversion and phage exposure. PMID:21533100

Host specific diversity in Lactobacillus johnsonii as evidenced by a major chromosomal inversion and phage resistance mechanisms.

PubMed

Guinane, Caitriona M; Kent, Robert M; Norberg, Sarah; Hill, Colin; Fitzgerald, Gerald F; Stanton, Catherine; Ross, R Paul

2011-04-20

Genetic diversity and genomic rearrangements are a driving force in bacterial evolution and niche adaptation. We sequenced and annotated the genome of Lactobacillus johnsonii DPC6026, a strain isolated from the porcine intestinal tract. Although the genome of DPC6026 is similar in size (1.97 mbp) and GC content (34.8%) to the sequenced human isolate L. johnsonii NCC 533, a large symmetrical inversion of approximately 750 kb differentiated the two strains. Comparative analysis among 12 other strains of L. johnsonii including 8 porcine, 3 human and 1 poultry isolate indicated that the genome architecture found in DPC6026 is more common within the species than that of NCC 533. Furthermore a number of unique features were annotated in DPC6026, some of which are likely to have been acquired by horizontal gene transfer (HGT) and contribute to protection against phage infection. A putative type III restriction-modification system was identified, as were novel Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) elements. Interestingly, these particular elements are not widely distributed among L. johnsonii strains. Taken together these data suggest intra-species genomic rearrangements and significant genetic diversity within the L. johnsonii species and indicate towards a host-specific divergence of L. johnsonii strains with respect to genome inversion and phage exposure.

Using text mining techniques to extract phenotypic information from the PhenoCHF corpus

PubMed Central

2015-01-01

Background Phenotypic information locked away in unstructured narrative text presents significant barriers to information accessibility, both for clinical practitioners and for computerised applications used for clinical research purposes. Text mining (TM) techniques have previously been applied successfully to extract different types of information from text in the biomedical domain. They have the potential to be extended to allow the extraction of information relating to phenotypes from free text. Methods To stimulate the development of TM systems that are able to extract phenotypic information from text, we have created a new corpus (PhenoCHF) that is annotated by domain experts with several types of phenotypic information relating to congestive heart failure. To ensure that systems developed using the corpus are robust to multiple text types, it integrates text from heterogeneous sources, i.e., electronic health records (EHRs) and scientific articles from the literature. We have developed several different phenotype extraction methods to demonstrate the utility of the corpus, and tested these methods on a further corpus, i.e., ShARe/CLEF 2013. Results Evaluation of our automated methods showed that PhenoCHF can facilitate the training of reliable phenotype extraction systems, which are robust to variations in text type. These results have been reinforced by evaluating our trained systems on the ShARe/CLEF corpus, which contains clinical records of various types. Like other studies within the biomedical domain, we found that solutions based on conditional random fields produced the best results, when coupled with a rich feature set. Conclusions PhenoCHF is the first annotated corpus aimed at encoding detailed phenotypic information. The unique heterogeneous composition of the corpus has been shown to be advantageous in the training of systems that can accurately extract phenotypic information from a range of different text types. Although the scope of our annotation is currently limited to a single disease, the promising results achieved can stimulate further work into the extraction of phenotypic information for other diseases. The PhenoCHF annotation guidelines and annotations are publicly available at https://code.google.com/p/phenochf-corpus. PMID:26099853

Using text mining techniques to extract phenotypic information from the PhenoCHF corpus.

PubMed

Alnazzawi, Noha; Thompson, Paul; Batista-Navarro, Riza; Ananiadou, Sophia

2015-01-01

Phenotypic information locked away in unstructured narrative text presents significant barriers to information accessibility, both for clinical practitioners and for computerised applications used for clinical research purposes. Text mining (TM) techniques have previously been applied successfully to extract different types of information from text in the biomedical domain. They have the potential to be extended to allow the extraction of information relating to phenotypes from free text. To stimulate the development of TM systems that are able to extract phenotypic information from text, we have created a new corpus (PhenoCHF) that is annotated by domain experts with several types of phenotypic information relating to congestive heart failure. To ensure that systems developed using the corpus are robust to multiple text types, it integrates text from heterogeneous sources, i.e., electronic health records (EHRs) and scientific articles from the literature. We have developed several different phenotype extraction methods to demonstrate the utility of the corpus, and tested these methods on a further corpus, i.e., ShARe/CLEF 2013. Evaluation of our automated methods showed that PhenoCHF can facilitate the training of reliable phenotype extraction systems, which are robust to variations in text type. These results have been reinforced by evaluating our trained systems on the ShARe/CLEF corpus, which contains clinical records of various types. Like other studies within the biomedical domain, we found that solutions based on conditional random fields produced the best results, when coupled with a rich feature set. PhenoCHF is the first annotated corpus aimed at encoding detailed phenotypic information. The unique heterogeneous composition of the corpus has been shown to be advantageous in the training of systems that can accurately extract phenotypic information from a range of different text types. Although the scope of our annotation is currently limited to a single disease, the promising results achieved can stimulate further work into the extraction of phenotypic information for other diseases. The PhenoCHF annotation guidelines and annotations are publicly available at https://code.google.com/p/phenochf-corpus.

A Guide to the Recorded Distribution of Endangered, Threatened and Rare Species in Michigan. Providing a Bibliographic Discussion of the Subject, Annotated List of Reference Sources and Directory of Local Nature Associations, Centers, Agencies, and University Field Stations.

ERIC Educational Resources Information Center

Yakes, Nancy A.

This document is a guide to sources of information on endangered species distribution in Michigan. It was prepared for CETA (Comprehensive Employment and Training Act) students who will collect the documents. The guide is divided into three major sections. The first section includes an introduction (briefly discussing endangered, rare, threatened…

LS-SNP/PDB: annotated non-synonymous SNPs mapped to Protein Data Bank structures.

PubMed

Ryan, Michael; Diekhans, Mark; Lien, Stephanie; Liu, Yun; Karchin, Rachel

2009-06-01

LS-SNP/PDB is a new WWW resource for genome-wide annotation of human non-synonymous (amino acid changing) SNPs. It serves high-quality protein graphics rendered with UCSF Chimera molecular visualization software. The system is kept up-to-date by an automated, high-throughput build pipeline that systematically maps human nsSNPs onto Protein Data Bank structures and annotates several biologically relevant features. LS-SNP/PDB is available at (http://ls-snp.icm.jhu.edu/ls-snp-pdb) and via links from protein data bank (PDB) biology and chemistry tabs, UCSC Genome Browser Gene Details and SNP Details pages and PharmGKB Gene Variants Downloads/Cross-References pages.

Automated Patent Categorization and Guided Patent Search using IPC as Inspired by MeSH and PubMed.

PubMed

Eisinger, Daniel; Tsatsaronis, George; Bundschus, Markus; Wieneke, Ulrich; Schroeder, Michael

2013-04-15

Document search on PubMed, the pre-eminent database for biomedical literature, relies on the annotation of its documents with relevant terms from the Medical Subject Headings ontology (MeSH) for improving recall through query expansion. Patent documents are another important information source, though they are considerably less accessible. One option to expand patent search beyond pure keywords is the inclusion of classification information: Since every patent is assigned at least one class code, it should be possible for these assignments to be automatically used in a similar way as the MeSH annotations in PubMed. In order to develop a system for this task, it is necessary to have a good understanding of the properties of both classification systems. This report describes our comparative analysis of MeSH and the main patent classification system, the International Patent Classification (IPC). We investigate the hierarchical structures as well as the properties of the terms/classes respectively, and we compare the assignment of IPC codes to patents with the annotation of PubMed documents with MeSH terms.Our analysis shows a strong structural similarity of the hierarchies, but significant differences of terms and annotations. The low number of IPC class assignments and the lack of occurrences of class labels in patent texts imply that current patent search is severely limited. To overcome these limits, we evaluate a method for the automated assignment of additional classes to patent documents, and we propose a system for guided patent search based on the use of class co-occurrence information and external resources.

Automated Patent Categorization and Guided Patent Search using IPC as Inspired by MeSH and PubMed

PubMed Central

2013-01-01

Document search on PubMed, the pre-eminent database for biomedical literature, relies on the annotation of its documents with relevant terms from the Medical Subject Headings ontology (MeSH) for improving recall through query expansion. Patent documents are another important information source, though they are considerably less accessible. One option to expand patent search beyond pure keywords is the inclusion of classification information: Since every patent is assigned at least one class code, it should be possible for these assignments to be automatically used in a similar way as the MeSH annotations in PubMed. In order to develop a system for this task, it is necessary to have a good understanding of the properties of both classification systems. This report describes our comparative analysis of MeSH and the main patent classification system, the International Patent Classification (IPC). We investigate the hierarchical structures as well as the properties of the terms/classes respectively, and we compare the assignment of IPC codes to patents with the annotation of PubMed documents with MeSH terms. Our analysis shows a strong structural similarity of the hierarchies, but significant differences of terms and annotations. The low number of IPC class assignments and the lack of occurrences of class labels in patent texts imply that current patent search is severely limited. To overcome these limits, we evaluate a method for the automated assignment of additional classes to patent documents, and we propose a system for guided patent search based on the use of class co-occurrence information and external resources. PMID:23734562

Linking Disparate Datasets of the Earth Sciences with the SemantEco Annotator

NASA Astrophysics Data System (ADS)

Seyed, P.; Chastain, K.; McGuinness, D. L.

2013-12-01

Use of Semantic Web technologies for data management in the Earth sciences (and beyond) has great potential but is still in its early stages, since the challenges of translating data into a more explicit or semantic form for immediate use within applications has not been fully addressed. In this abstract we help address this challenge by introducing the SemantEco Annotator, which enables anyone, regardless of expertise, to semantically annotate tabular Earth Science data and translate it into linked data format, while applying the logic inherent in community-standard vocabularies to guide the process. The Annotator was conceived under a desire to unify dataset content from a variety of sources under common vocabularies, for use in semantically-enabled web applications. Our current use case employs linked data generated by the Annotator for use in the SemantEco environment, which utilizes semantics to help users explore, search, and visualize water or air quality measurement and species occurrence data through a map-based interface. The generated data can also be used immediately to facilitate discovery and search capabilities within 'big data' environments. The Annotator provides a method for taking information about a dataset, that may only be known to its maintainers, and making it explicit, in a uniform and machine-readable fashion, such that a person or information system can more easily interpret the underlying structure and meaning. Its primary mechanism is to enable a user to formally describe how columns of a tabular dataset relate and/or describe entities. For example, if a user identifies columns for latitude and longitude coordinates, we can infer the data refers to a point that can be plotted on a map. Further, it can be made explicit that measurements of 'nitrate' and 'NO3-' are of the same entity through vocabulary assignments, thus more easily utilizing data sets that use different nomenclatures. The Annotator provides an extensive and searchable library of vocabularies to assist the user in locating terms to describe observed entities, their properties, and relationships. The Annotator leverages vocabulary definitions of these concepts to guide the user in describing data in a logically consistent manner. The vocabularies made available through the Annotator are open, as is the Annotator itself. We have taken a step towards making semantic annotation/translation of data more accessible. Our vision for the Annotator is as a tool that can be integrated into a semantic data 'workbench' environment, which would allow semantic annotation of a variety of data formats, using standard vocabularies. These vocabularies involved enable search for similar datasets, and integration with any semantically-enabled applications for analysis and visualization.

An Annotated Bibliography of DoD-Funded Reports Concerning Psychological Stress: 1950-1978.

DTIC Science & Technology

1979-09-01

Approved for public release, distribution unlimited. Reproduction in whole or in part is permitted for any, purpose of the U.S. Government. I7...in the physiological measures and the least is found in the performance measures. (Suggestions offered while subject was hypnotized .) 84 307 DeLorge

deFUME: Dynamic exploration of functional metagenomic sequencing data.

PubMed

van der Helm, Eric; Geertz-Hansen, Henrik Marcus; Genee, Hans Jasper; Malla, Sailesh; Sommer, Morten Otto Alexander

2015-07-31

Functional metagenomic selections represent a powerful technique that is widely applied for identification of novel genes from complex metagenomic sources. However, whereas hundreds to thousands of clones can be easily generated and sequenced over a few days of experiments, analyzing the data is time consuming and constitutes a major bottleneck for experimental researchers in the field. Here we present the deFUME web server, an easy-to-use web-based interface for processing, annotation and visualization of functional metagenomics sequencing data, tailored to meet the requirements of non-bioinformaticians. The web-server integrates multiple analysis steps into one single workflow: read assembly, open reading frame prediction, and annotation with BLAST, InterPro and GO classifiers. Analysis results are visualized in an online dynamic web-interface. The deFUME webserver provides a fast track from raw sequence to a comprehensive visual data overview that facilitates effortless inspection of gene function, clustering and distribution. The webserver is available at cbs.dtu.dk/services/deFUME/and the source code is distributed at github.com/EvdH0/deFUME.

ChemScreener: A Distributed Computing Tool for Scaffold based Virtual Screening.

PubMed

Karthikeyan, Muthukumarasamy; Pandit, Deepak; Vyas, Renu

2015-01-01

In this work we present ChemScreener, a Java-based application to perform virtual library generation combined with virtual screening in a platform-independent distributed computing environment. ChemScreener comprises a scaffold identifier, a distinct scaffold extractor, an interactive virtual library generator as well as a virtual screening module for subsequently selecting putative bioactive molecules. The virtual libraries are annotated with chemophore-, pharmacophore- and toxicophore-based information for compound prioritization. The hits selected can then be further processed using QSAR, docking and other in silico approaches which can all be interfaced within the ChemScreener framework. As a sample application, in this work scaffold selectivity, diversity, connectivity and promiscuity towards six important therapeutic classes have been studied. In order to illustrate the computational power of the application, 55 scaffolds extracted from 161 anti-psychotic compounds were enumerated to produce a virtual library comprising 118 million compounds (17 GB) and annotated with chemophore, pharmacophore and toxicophore based features in a single step which would be non-trivial to perform with many standard software tools today on libraries of this size.

SciFlo: Semantically-Enabled Grid Workflow for Collaborative Science

NASA Astrophysics Data System (ADS)

Yunck, T.; Wilson, B. D.; Raskin, R.; Manipon, G.

2005-12-01

SciFlo is a system for Scientific Knowledge Creation on the Grid using a Semantically-Enabled Dataflow Execution Environment. SciFlo leverages Simple Object Access Protocol (SOAP) Web Services and the Grid Computing standards (WS-* standards and the Globus Alliance toolkits), and enables scientists to do multi-instrument Earth Science by assembling reusable SOAP Services, native executables, local command-line scripts, and python codes into a distributed computing flow (a graph of operators). SciFlo's XML dataflow documents can be a mixture of concrete operators (fully bound operations) and abstract template operators (late binding via semantic lookup). All data objects and operators can be both simply typed (simple and complex types in XML schema) and semantically typed using controlled vocabularies (linked to OWL ontologies such as SWEET). By exploiting ontology-enhanced search and inference, one can discover (and automatically invoke) Web Services and operators that have been semantically labeled as performing the desired transformation, and adapt a particular invocation to the proper interface (number, types, and meaning of inputs and outputs). The SciFlo client & server engines optimize the execution of such distributed data flows and allow the user to transparently find and use datasets and operators without worrying about the actual location of the Grid resources. The scientist injects a distributed computation into the Grid by simply filling out an HTML form or directly authoring the underlying XML dataflow document, and results are returned directly to the scientist's desktop. A Visual Programming tool is also being developed, but it is not required. Once an analysis has been specified for a granule or day of data, it can be easily repeated with different control parameters and over months or years of data. SciFlo uses and preserves semantics, and also generates and infers new semantic annotations. Specifically, the SciFlo engine uses semantic metadata to understand (infer) what it is doing and potentially improve the data flow; preserves semantics by saving links to the semantics of (metadata describing) the input datasets, related datasets, and the data transformations (algorithms) used to generate downstream products; generates new metadata by allowing the user to add semantic annotations to the generated data products (or simply accept automatically generated provenance annotations); and infers new semantic metadata by understanding and applying logic to the semantics of the data and the transformations performed. Much ontology development still needs to be done but, nevertheless, SciFlo documents provide a substrate for using and preserving more semantics as ontologies develop. We will give a live demonstration of the growing SciFlo network using an example dataflow in which atmospheric temperature and water vapor profiles from three Earth Observing System (EOS) instruments are retrieved using SOAP (geo-location query & data access) services, co-registered, and visually & statistically compared on demand (see http://sciflo.jpl.nasa.gov for more information).

DeTEXT: A Database for Evaluating Text Extraction from Biomedical Literature Figures

PubMed Central

Yin, Xu-Cheng; Yang, Chun; Pei, Wei-Yi; Man, Haixia; Zhang, Jun; Learned-Miller, Erik; Yu, Hong

2015-01-01

Hundreds of millions of figures are available in biomedical literature, representing important biomedical experimental evidence. Since text is a rich source of information in figures, automatically extracting such text may assist in the task of mining figure information. A high-quality ground truth standard can greatly facilitate the development of an automated system. This article describes DeTEXT: A database for evaluating text extraction from biomedical literature figures. It is the first publicly available, human-annotated, high quality, and large-scale figure-text dataset with 288 full-text articles, 500 biomedical figures, and 9308 text regions. This article describes how figures were selected from open-access full-text biomedical articles and how annotation guidelines and annotation tools were developed. We also discuss the inter-annotator agreement and the reliability of the annotations. We summarize the statistics of the DeTEXT data and make available evaluation protocols for DeTEXT. Finally we lay out challenges we observed in the automated detection and recognition of figure text and discuss research directions in this area. DeTEXT is publicly available for downloading at http://prir.ustb.edu.cn/DeTEXT/. PMID:25951377

Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus.

PubMed

Stubbs, Amber; Uzuner, Özlem

2015-12-01

The 2014 i2b2/UTHealth natural language processing shared task featured a track focused on the de-identification of longitudinal medical records. For this track, we de-identified a set of 1304 longitudinal medical records describing 296 patients. This corpus was de-identified under a broad interpretation of the HIPAA guidelines using double-annotation followed by arbitration, rounds of sanity checking, and proof reading. The average token-based F1 measure for the annotators compared to the gold standard was 0.927. The resulting annotations were used both to de-identify the data and to set the gold standard for the de-identification track of the 2014 i2b2/UTHealth shared task. All annotated private health information were replaced with realistic surrogates automatically and then read over and corrected manually. The resulting corpus is the first of its kind made available for de-identification research. This corpus was first used for the 2014 i2b2/UTHealth shared task, during which the systems achieved a mean F-measure of 0.872 and a maximum F-measure of 0.964 using entity-based micro-averaged evaluations. Copyright © 2015 Elsevier Inc. All rights reserved.

The Co-regulation Data Harvester: Automating gene annotation starting from a transcriptome database

NASA Astrophysics Data System (ADS)

Tsypin, Lev M.; Turkewitz, Aaron P.

Identifying co-regulated genes provides a useful approach for defining pathway-specific machinery in an organism. To be efficient, this approach relies on thorough genome annotation, a process much slower than genome sequencing per se. Tetrahymena thermophila, a unicellular eukaryote, has been a useful model organism and has a fully sequenced but sparsely annotated genome. One important resource for studying this organism has been an online transcriptomic database. We have developed an automated approach to gene annotation in the context of transcriptome data in T. thermophila, called the Co-regulation Data Harvester (CDH). Beginning with a gene of interest, the CDH identifies co-regulated genes by accessing the Tetrahymena transcriptome database. It then identifies their closely related genes (orthologs) in other organisms by using reciprocal BLAST searches. Finally, it collates the annotations of those orthologs' functions, which provides the user with information to help predict the cellular role of the initial query. The CDH, which is freely available, represents a powerful new tool for analyzing cell biological pathways in Tetrahymena. Moreover, to the extent that genes and pathways are conserved between organisms, the inferences obtained via the CDH should be relevant, and can be explored, in many other systems.

GI-POP: a combinational annotation and genomic island prediction pipeline for ongoing microbial genome projects.

PubMed

Lee, Chi-Ching; Chen, Yi-Ping Phoebe; Yao, Tzu-Jung; Ma, Cheng-Yu; Lo, Wei-Cheng; Lyu, Ping-Chiang; Tang, Chuan Yi

2013-04-10

Sequencing of microbial genomes is important because of microbial-carrying antibiotic and pathogenetic activities. However, even with the help of new assembling software, finishing a whole genome is a time-consuming task. In most bacteria, pathogenetic or antibiotic genes are carried in genomic islands. Therefore, a quick genomic island (GI) prediction method is useful for ongoing sequencing genomes. In this work, we built a Web server called GI-POP (http://gipop.life.nthu.edu.tw) which integrates a sequence assembling tool, a functional annotation pipeline, and a high-performance GI predicting module, in a support vector machine (SVM)-based method called genomic island genomic profile scanning (GI-GPS). The draft genomes of the ongoing genome projects in contigs or scaffolds can be submitted to our Web server, and it provides the functional annotation and highly probable GI-predicting results. GI-POP is a comprehensive annotation Web server designed for ongoing genome project analysis. Researchers can perform annotation and obtain pre-analytic information include possible GIs, coding/non-coding sequences and functional analysis from their draft genomes. This pre-analytic system can provide useful information for finishing a genome sequencing project. Copyright © 2012 Elsevier B.V. All rights reserved.

ExpTreeDB: web-based query and visualization of manually annotated gene expression profiling experiments of human and mouse from GEO.

PubMed

Ni, Ming; Ye, Fuqiang; Zhu, Juanjuan; Li, Zongwei; Yang, Shuai; Yang, Bite; Han, Lu; Wu, Yongge; Chen, Ying; Li, Fei; Wang, Shengqi; Bo, Xiaochen

2014-12-01

Numerous public microarray datasets are valuable resources for the scientific communities. Several online tools have made great steps to use these data by querying related datasets with users' own gene signatures or expression profiles. However, dataset annotation and result exhibition still need to be improved. ExpTreeDB is a database that allows for queries on human and mouse microarray experiments from Gene Expression Omnibus with gene signatures or profiles. Compared with similar applications, ExpTreeDB pays more attention to dataset annotations and result visualization. We introduced a multiple-level annotation system to depict and organize original experiments. For example, a tamoxifen-treated cell line experiment is hierarchically annotated as 'agent→drug→estrogen receptor antagonist→tamoxifen'. Consequently, retrieved results are exhibited by an interactive tree-structured graphics, which provide an overview for related experiments and might enlighten users on key items of interest. The database is freely available at http://biotech.bmi.ac.cn/ExpTreeDB. Web site is implemented in Perl, PHP, R, MySQL and Apache. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Determining Semantically Related Significant Genes.

PubMed

Taha, Kamal

2014-01-01

GO relation embodies some aspects of existence dependency. If GO term xis existence-dependent on GO term y, the presence of y implies the presence of x. Therefore, the genes annotated with the function of the GO term y are usually functionally and semantically related to the genes annotated with the function of the GO term x. A large number of gene set enrichment analysis methods have been developed in recent years for analyzing gene sets enrichment. However, most of these methods overlook the structural dependencies between GO terms in GO graph by not considering the concept of existence dependency. We propose in this paper a biological search engine called RSGSearch that identifies enriched sets of genes annotated with different functions using the concept of existence dependency. We observe that GO term xcannot be existence-dependent on GO term y, if x- and y- have the same specificity (biological characteristics). After encoding into a numeric format the contributions of GO terms annotating target genes to the semantics of their lowest common ancestors (LCAs), RSGSearch uses microarray experiment to identify the most significant LCA that annotates the result genes. We evaluated RSGSearch experimentally and compared it with five gene set enrichment systems. Results showed marked improvement.

eClims: An Extensible and Dynamic Integration Framework for Biomedical Information Systems.

PubMed

Savonnet, Marinette; Leclercq, Eric; Naubourg, Pierre

2016-11-01

Biomedical information systems (BIS) require consideration of three types of variability: data variability induced by new high throughput technologies, schema or model variability induced by large scale studies or new fields of research, and knowledge variability resulting from new discoveries. Beyond data heterogeneity, managing variabilities in the context of BIS requires extensible and dynamic integration process. In this paper, we focus on data and schema variabilities and we propose an integration framework based on ontologies, master data, and semantic annotations. The framework addresses issues related to: 1) collaborative work through a dynamic integration process; 2) variability among studies using an annotation mechanism; and 3) quality control over data and semantic annotations. Our approach relies on two levels of knowledge: BIS-related knowledge is modeled using an application ontology coupled with UML models that allow controlling data completeness and consistency, and domain knowledge is described by a domain ontology, which ensures data coherence. A system build with the eClims framework has been implemented and evaluated in the context of a proteomic platform.

Djeen (Database for Joomla!'s Extensible Engine): a research information management system for flexible multi-technology project administration.

PubMed

Stahl, Olivier; Duvergey, Hugo; Guille, Arnaud; Blondin, Fanny; Vecchio, Alexandre Del; Finetti, Pascal; Granjeaud, Samuel; Vigy, Oana; Bidaut, Ghislain

2013-06-06

With the advance of post-genomic technologies, the need for tools to manage large scale data in biology becomes more pressing. This involves annotating and storing data securely, as well as granting permissions flexibly with several technologies (all array types, flow cytometry, proteomics) for collaborative work and data sharing. This task is not easily achieved with most systems available today. We developed Djeen (Database for Joomla!'s Extensible Engine), a new Research Information Management System (RIMS) for collaborative projects. Djeen is a user-friendly application, designed to streamline data storage and annotation collaboratively. Its database model, kept simple, is compliant with most technologies and allows storing and managing of heterogeneous data with the same system. Advanced permissions are managed through different roles. Templates allow Minimum Information (MI) compliance. Djeen allows managing project associated with heterogeneous data types while enforcing annotation integrity and minimum information. Projects are managed within a hierarchy and user permissions are finely-grained for each project, user and group.Djeen Component source code (version 1.5.1) and installation documentation are available under CeCILL license from http://sourceforge.net/projects/djeen/files and supplementary material.

Djeen (Database for Joomla!’s Extensible Engine): a research information management system for flexible multi-technology project administration

PubMed Central

2013-01-01

Background With the advance of post-genomic technologies, the need for tools to manage large scale data in biology becomes more pressing. This involves annotating and storing data securely, as well as granting permissions flexibly with several technologies (all array types, flow cytometry, proteomics) for collaborative work and data sharing. This task is not easily achieved with most systems available today. Findings We developed Djeen (Database for Joomla!’s Extensible Engine), a new Research Information Management System (RIMS) for collaborative projects. Djeen is a user-friendly application, designed to streamline data storage and annotation collaboratively. Its database model, kept simple, is compliant with most technologies and allows storing and managing of heterogeneous data with the same system. Advanced permissions are managed through different roles. Templates allow Minimum Information (MI) compliance. Conclusion Djeen allows managing project associated with heterogeneous data types while enforcing annotation integrity and minimum information. Projects are managed within a hierarchy and user permissions are finely-grained for each project, user and group. Djeen Component source code (version 1.5.1) and installation documentation are available under CeCILL license from http://sourceforge.net/projects/djeen/files and supplementary material. PMID:23742665

Consistency of VDJ Rearrangement and Substitution Parameters Enables Accurate B Cell Receptor Sequence Annotation.

PubMed

Ralph, Duncan K; Matsen, Frederick A

2016-01-01

VDJ rearrangement and somatic hypermutation work together to produce antibody-coding B cell receptor (BCR) sequences for a remarkable diversity of antigens. It is now possible to sequence these BCRs in high throughput; analysis of these sequences is bringing new insight into how antibodies develop, in particular for broadly-neutralizing antibodies against HIV and influenza. A fundamental step in such sequence analysis is to annotate each base as coming from a specific one of the V, D, or J genes, or from an N-addition (a.k.a. non-templated insertion). Previous work has used simple parametric distributions to model transitions from state to state in a hidden Markov model (HMM) of VDJ recombination, and assumed that mutations occur via the same process across sites. However, codon frame and other effects have been observed to violate these parametric assumptions for such coding sequences, suggesting that a non-parametric approach to modeling the recombination process could be useful. In our paper, we find that indeed large modern data sets suggest a model using parameter-rich per-allele categorical distributions for HMM transition probabilities and per-allele-per-position mutation probabilities, and that using such a model for inference leads to significantly improved results. We present an accurate and efficient BCR sequence annotation software package using a novel HMM "factorization" strategy. This package, called partis (https://github.com/psathyrella/partis/), is built on a new general-purpose HMM compiler that can perform efficient inference given a simple text description of an HMM.

Management and analysis of genomic functional and phenotypic controlled annotations to support biomedical investigation and practice.

PubMed

Masseroli, Marco

2007-07-01

The growing available genomic information provides new opportunities for novel research approaches and original biomedical applications that can provide effective data management and analysis support. In fact, integration and comprehensive evaluation of available controlled data can highlight information patterns leading to unveil new biomedical knowledge. Here, we describe Genome Function INtegrated Discover (GFINDer), a Web-accessible three-tier multidatabase system we developed to automatically enrich lists of user-classified genes with several functional and phenotypic controlled annotations, and to statistically evaluate them in order to identify annotation categories significantly over- or underrepresented in each considered gene class. Genomic controlled annotations from Gene Ontology (GO), KEGG, Pfam, InterPro, and Online Mendelian Inheritance in Man (OMIM) were integrated in GFINDer and several categorical tests were implemented for their analysis. A controlled vocabulary of inherited disorder phenotypes was obtained by normalizing and hierarchically structuring disease accompanying signs and symptoms from OMIM Clinical Synopsis sections. GFINDer modular architecture is well suited for further system expansion and for sustaining increasing workload. Testing results showed that GFINDer analyses can highlight gene functional and phenotypic characteristics and differences, demonstrating its value in supporting genomic biomedical approaches aiming at understanding the complex biomolecular mechanisms underlying patho-physiological phenotypes, and in helping the transfer of genomic results to medical practice.

DWARF – a data warehouse system for analyzing protein families

PubMed Central

Fischer, Markus; Thai, Quan K; Grieb, Melanie; Pleiss, Jürgen

2006-01-01

Background The emerging field of integrative bioinformatics provides the tools to organize and systematically analyze vast amounts of highly diverse biological data and thus allows to gain a novel understanding of complex biological systems. The data warehouse DWARF applies integrative bioinformatics approaches to the analysis of large protein families. Description The data warehouse system DWARF integrates data on sequence, structure, and functional annotation for protein fold families. The underlying relational data model consists of three major sections representing entities related to the protein (biochemical function, source organism, classification to homologous families and superfamilies), the protein sequence (position-specific annotation, mutant information), and the protein structure (secondary structure information, superimposed tertiary structure). Tools for extracting, transforming and loading data from public available resources (ExPDB, GenBank, DSSP) are provided to populate the database. The data can be accessed by an interface for searching and browsing, and by analysis tools that operate on annotation, sequence, or structure. We applied DWARF to the family of α/β-hydrolases to host the Lipase Engineering database. Release 2.3 contains 6138 sequences and 167 experimentally determined protein structures, which are assigned to 37 superfamilies 103 homologous families. Conclusion DWARF has been designed for constructing databases of large structurally related protein families and for evaluating their sequence-structure-function relationships by a systematic analysis of sequence, structure and functional annotation. It has been applied to predict biochemical properties from sequence, and serves as a valuable tool for protein engineering. PMID:17094801

Active learning reduces annotation time for clinical concept extraction.

PubMed

Kholghi, Mahnoosh; Sitbon, Laurianne; Zuccon, Guido; Nguyen, Anthony

2017-10-01

To investigate: (1) the annotation time savings by various active learning query strategies compared to supervised learning and a random sampling baseline, and (2) the benefits of active learning-assisted pre-annotations in accelerating the manual annotation process compared to de novo annotation. There are 73 and 120 discharge summary reports provided by Beth Israel institute in the train and test sets of the concept extraction task in the i2b2/VA 2010 challenge, respectively. The 73 reports were used in user study experiments for manual annotation. First, all sequences within the 73 reports were manually annotated from scratch. Next, active learning models were built to generate pre-annotations for the sequences selected by a query strategy. The annotation/reviewing time per sequence was recorded. The 120 test reports were used to measure the effectiveness of the active learning models. When annotating from scratch, active learning reduced the annotation time up to 35% and 28% compared to a fully supervised approach and a random sampling baseline, respectively. Reviewing active learning-assisted pre-annotations resulted in 20% further reduction of the annotation time when compared to de novo annotation. The number of concepts that require manual annotation is a good indicator of the annotation time for various active learning approaches as demonstrated by high correlation between time rate and concept annotation rate. Active learning has a key role in reducing the time required to manually annotate domain concepts from clinical free text, either when annotating from scratch or reviewing active learning-assisted pre-annotations. Copyright © 2017 Elsevier B.V. All rights reserved.

Corpus annotation for mining biomedical events from literature

PubMed Central

Kim, Jin-Dong; Ohta, Tomoko; Tsujii, Jun'ichi

2008-01-01

Background Advanced Text Mining (TM) such as semantic enrichment of papers, event or relation extraction, and intelligent Question Answering have increasingly attracted attention in the bio-medical domain. For such attempts to succeed, text annotation from the biological point of view is indispensable. However, due to the complexity of the task, semantic annotation has never been tried on a large scale, apart from relatively simple term annotation. Results We have completed a new type of semantic annotation, event annotation, which is an addition to the existing annotations in the GENIA corpus. The corpus has already been annotated with POS (Parts of Speech), syntactic trees, terms, etc. The new annotation was made on half of the GENIA corpus, consisting of 1,000 Medline abstracts. It contains 9,372 sentences in which 36,114 events are identified. The major challenges during event annotation were (1) to design a scheme of annotation which meets specific requirements of text annotation, (2) to achieve biology-oriented annotation which reflect biologists' interpretation of text, and (3) to ensure the homogeneity of annotation quality across annotators. To meet these challenges, we introduced new concepts such as Single-facet Annotation and Semantic Typing, which have collectively contributed to successful completion of a large scale annotation. Conclusion The resulting event-annotated corpus is the largest and one of the best in quality among similar annotation efforts. We expect it to become a valuable resource for NLP (Natural Language Processing)-based TM in the bio-medical domain. PMID:18182099

A comprehensive clinical research database based on CDISC ODM and i2b2.

PubMed

Meineke, Frank A; Stäubert, Sebastian; Löbe, Matthias; Winter, Alfred

2014-01-01

We present a working approach for a clinical research database as part of an archival information system. The CDISC ODM standard is target for clinical study and research relevant routine data, thus decoupling the data ingest process from the access layer. The presented research database is comprehensive as it covers annotating, mapping and curation of poorly annotated source data. Besides a conventional relational database the medical data warehouse i2b2 serves as main frontend for end-users. The system we developed is suitable to support patient recruitment, cohort identification and quality assurance in daily routine.

pGenN, a gene normalization tool for plant genes and proteins in scientific literature.

PubMed

Ding, Ruoyao; Arighi, Cecilia N; Lee, Jung-Youn; Wu, Cathy H; Vijay-Shanker, K

2015-01-01

Automatically detecting gene/protein names in the literature and connecting them to databases records, also known as gene normalization, provides a means to structure the information buried in free-text literature. Gene normalization is critical for improving the coverage of annotation in the databases, and is an essential component of many text mining systems and database curation pipelines. In this manuscript, we describe a gene normalization system specifically tailored for plant species, called pGenN (pivot-based Gene Normalization). The system consists of three steps: dictionary-based gene mention detection, species assignment, and intra species normalization. We have developed new heuristics to improve each of these phases. We evaluated the performance of pGenN on an in-house expertly annotated corpus consisting of 104 plant relevant abstracts. Our system achieved an F-value of 88.9% (Precision 90.9% and Recall 87.2%) on this corpus, outperforming state-of-art systems presented in BioCreative III. We have processed over 440,000 plant-related Medline abstracts using pGenN. The gene normalization results are stored in a local database for direct query from the pGenN web interface (proteininformationresource.org/pgenn/). The annotated literature corpus is also publicly available through the PIR text mining portal (proteininformationresource.org/iprolink/).

What Information Does Your EHR Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW) to Support Identification and Data Access Within Distributed Clinical Research Networks.

PubMed

Bruland, Philipp; Doods, Justin; Storck, Michael; Dugas, Martin

2017-01-01

Data dictionaries provide structural meta-information about data definitions in health information technology (HIT) systems. In this regard, reusing healthcare data for secondary purposes offers several advantages (e.g. reduce documentation times or increased data quality). Prerequisites for data reuse are its quality, availability and identical meaning of data. In diverse projects, research data warehouses serve as core components between heterogeneous clinical databases and various research applications. Given the complexity (high number of data elements) and dynamics (regular updates) of electronic health record (EHR) data structures, we propose a clinical metadata warehouse (CMDW) based on a metadata registry standard. Metadata of two large hospitals were automatically inserted into two CMDWs containing 16,230 forms and 310,519 data elements. Automatic updates of metadata are possible as well as semantic annotations. A CMDW allows metadata discovery, data quality assessment and similarity analyses. Common data models for distributed research networks can be established based on similarity analyses.

Metabolite signal identification in accurate mass metabolomics data with MZedDB, an interactive m/z annotation tool utilising predicted ionisation behaviour 'rules'

PubMed Central

Draper, John; Enot, David P; Parker, David; Beckmann, Manfred; Snowdon, Stuart; Lin, Wanchang; Zubair, Hassan

2009-01-01

Background Metabolomics experiments using Mass Spectrometry (MS) technology measure the mass to charge ratio (m/z) and intensity of ionised molecules in crude extracts of complex biological samples to generate high dimensional metabolite 'fingerprint' or metabolite 'profile' data. High resolution MS instruments perform routinely with a mass accuracy of < 5 ppm (parts per million) thus providing potentially a direct method for signal putative annotation using databases containing metabolite mass information. Most database interfaces support only simple queries with the default assumption that molecules either gain or lose a single proton when ionised. In reality the annotation process is confounded by the fact that many ionisation products will be not only molecular isotopes but also salt/solvent adducts and neutral loss fragments of original metabolites. This report describes an annotation strategy that will allow searching based on all potential ionisation products predicted to form during electrospray ionisation (ESI). Results Metabolite 'structures' harvested from publicly accessible databases were converted into a common format to generate a comprehensive archive in MZedDB. 'Rules' were derived from chemical information that allowed MZedDB to generate a list of adducts and neutral loss fragments putatively able to form for each structure and calculate, on the fly, the exact molecular weight of every potential ionisation product to provide targets for annotation searches based on accurate mass. We demonstrate that data matrices representing populations of ionisation products generated from different biological matrices contain a large proportion (sometimes > 50%) of molecular isotopes, salt adducts and neutral loss fragments. Correlation analysis of ESI-MS data features confirmed the predicted relationships of m/z signals. An integrated isotope enumerator in MZedDB allowed verification of exact isotopic pattern distributions to corroborate experimental data. Conclusion We conclude that although ultra-high accurate mass instruments provide major insight into the chemical diversity of biological extracts, the facile annotation of a large proportion of signals is not possible by simple, automated query of current databases using computed molecular formulae. Parameterising MZedDB to take into account predicted ionisation behaviour and the biological source of any sample improves greatly both the frequency and accuracy of potential annotation 'hits' in ESI-MS data. PMID:19622150

Enhancing clinical concept extraction with distributional semantics

PubMed Central

Cohen, Trevor; Wu, Stephen; Gonzalez, Graciela

2011-01-01

Extracting concepts (such as drugs, symptoms, and diagnoses) from clinical narratives constitutes a basic enabling technology to unlock the knowledge within and support more advanced reasoning applications such as diagnosis explanation, disease progression modeling, and intelligent analysis of the effectiveness of treatment. The recent release of annotated training sets of de-identified clinical narratives has contributed to the development and refinement of concept extraction methods. However, as the annotation process is labor-intensive, training data are necessarily limited in the concepts and concept patterns covered, which impacts the performance of supervised machine learning applications trained with these data. This paper proposes an approach to minimize this limitation by combining supervised machine learning with empirical learning of semantic relatedness from the distribution of the relevant words in additional unannotated text. The approach uses a sequential discriminative classifier (Conditional Random Fields) to extract the mentions of medical problems, treatments and tests from clinical narratives. It takes advantage of all Medline abstracts indexed as being of the publication type “clinical trials” to estimate the relatedness between words in the i2b2/VA training and testing corpora. In addition to the traditional features such as dictionary matching, pattern matching and part-of-speech tags, we also used as a feature words that appear in similar contexts to the word in question (that is, words that have a similar vector representation measured with the commonly used cosine metric, where vector representations are derived using methods of distributional semantics). To the best of our knowledge, this is the first effort exploring the use of distributional semantics, the semantics derived empirically from unannotated text often using vector space models, for a sequence classification task such as concept extraction. Therefore, we first experimented with different sliding window models and found the model with parameters that led to best performance in a preliminary sequence labeling task. The evaluation of this approach, performed against the i2b2/VA concept extraction corpus, showed that incorporating features based on the distribution of words across a large unannotated corpus significantly aids concept extraction. Compared to a supervised-only approach as a baseline, the micro-averaged f-measure for exact match increased from 80.3% to 82.3% and the micro-averaged f-measure based on inexact match increased from 89.7% to 91.3%. These improvements are highly significant according to the bootstrap resampling method and also considering the performance of other systems. Thus, distributional semantic features significantly improve the performance of concept extraction from clinical narratives by taking advantage of word distribution information obtained from unannotated data. PMID:22085698

GenomeGraphs: integrated genomic data visualization with R.

PubMed

Durinck, Steffen; Bullard, James; Spellman, Paul T; Dudoit, Sandrine

2009-01-06

Biological studies involve a growing number of distinct high-throughput experiments to characterize samples of interest. There is a lack of methods to visualize these different genomic datasets in a versatile manner. In addition, genomic data analysis requires integrated visualization of experimental data along with constantly changing genomic annotation and statistical analyses. We developed GenomeGraphs, as an add-on software package for the statistical programming environment R, to facilitate integrated visualization of genomic datasets. GenomeGraphs uses the biomaRt package to perform on-line annotation queries to Ensembl and translates these to gene/transcript structures in viewports of the grid graphics package. This allows genomic annotation to be plotted together with experimental data. GenomeGraphs can also be used to plot custom annotation tracks in combination with different experimental data types together in one plot using the same genomic coordinate system. GenomeGraphs is a flexible and extensible software package which can be used to visualize a multitude of genomic datasets within the statistical programming environment R.

MyWEST: my Web Extraction Software Tool for effective mining of annotations from web-based databanks.

PubMed

Masseroli, Marco; Stella, Andrea; Meani, Natalia; Alcalay, Myriam; Pinciroli, Francesco

2004-12-12

High-throughput technologies create the necessity to mine large amounts of gene annotations from diverse databanks, and to integrate the resulting data. Most databanks can be interrogated only via Web, for a single gene at a time, and query results are generally available only in the HTML format. Although some databanks provide batch retrieval of data via FTP, this requires expertise and resources for locally reimplementing the databank. We developed MyWEST, a tool aimed at researchers without extensive informatics skills or resources, which exploits user-defined templates to easily mine selected annotations from different Web-interfaced databanks, and aggregates and structures results in an automatically updated database. Using microarray results from a model system of retinoic acid-induced differentiation, MyWEST effectively gathered relevant annotations from various biomolecular databanks, highlighted significant biological characteristics and supported a global approach to the understanding of complex cellular mechanisms. MyWEST is freely available for non-profit use at http://www.medinfopoli.polimi.it/MyWEST/

Annotation analysis for testing drug safety signals using unstructured clinical notes

PubMed Central

2012-01-01

Background The electronic surveillance for adverse drug events is largely based upon the analysis of coded data from reporting systems. Yet, the vast majority of electronic health data lies embedded within the free text of clinical notes and is not gathered into centralized repositories. With the increasing access to large volumes of electronic medical data—in particular the clinical notes—it may be possible to computationally encode and to test drug safety signals in an active manner. Results We describe the application of simple annotation tools on clinical text and the mining of the resulting annotations to compute the risk of getting a myocardial infarction for patients with rheumatoid arthritis that take Vioxx. Our analysis clearly reveals elevated risks for myocardial infarction in rheumatoid arthritis patients taking Vioxx (odds ratio 2.06) before 2005. Conclusions Our results show that it is possible to apply annotation analysis methods for testing hypotheses about drug safety using electronic medical records. PMID:22541596

The Evidence and Conclusion Ontology (ECO): Supporting GO Annotations.

PubMed

Chibucos, Marcus C; Siegele, Deborah A; Hu, James C; Giglio, Michelle

2017-01-01

The Evidence and Conclusion Ontology (ECO) is a community resource for describing the various types of evidence that are generated during the course of a scientific study and which are typically used to support assertions made by researchers. ECO describes multiple evidence types, including evidence resulting from experimental (i.e., wet lab) techniques, evidence arising from computational methods, statements made by authors (whether or not supported by evidence), and inferences drawn by researchers curating the literature. In addition to summarizing the evidence that supports a particular assertion, ECO also offers a means to document whether a computer or a human performed the process of making the annotation. Incorporating ECO into an annotation system makes it possible to leverage the structure of the ontology such that associated data can be grouped hierarchically, users can select data associated with particular evidence types, and quality control pipelines can be optimized. Today, over 30 resources, including the Gene Ontology, use the Evidence and Conclusion Ontology to represent both evidence and how annotations are made.

Educational Administration.

ERIC Educational Resources Information Center

Haag, Daniel; Munari, Silvio

1979-01-01

Includes 410 annotated citations arranged as follows: systems analysis; management in the public sector; policy, strategy and planning; organization; control and evaluation; costs and financing; information and decision-making systems. (JEG)

Comparative genomics approaches to understanding and manipulating plant metabolism.

PubMed

Bradbury, Louis M T; Niehaus, Tom D; Hanson, Andrew D

2013-04-01

Over 3000 genomes, including numerous plant genomes, are now sequenced. However, their annotation remains problematic as illustrated by the many conserved genes with no assigned function, vague annotations such as 'kinase', or even wrong ones. Around 40% of genes of unknown function that are conserved between plants and microbes are probably metabolic enzymes or transporters; finding functions for these genes is a major challenge. Comparative genomics has correctly predicted functions for many such genes by analyzing genomic context, and gene fusions, distributions and co-expression. Comparative genomics complements genetic and biochemical approaches to dissect metabolism, continues to increase in power and decrease in cost, and has a pivotal role in modeling and engineering by helping identify functions for all metabolic genes. Copyright © 2012 Elsevier Ltd. All rights reserved.

First species of Leptochelia Dana, 1849 (Crustacea: Tanaidacea) from the Eastern Pacific, with an annotated checklist and identification keys for the genus.

PubMed

Jarquín-González, Jani; García-Madrigal, María Del Socorro; Carrera-Parra, Luis Fernando

2015-02-24

Forty three species of leptocheliids are known worldwide. In the American region only eight species have been described from the Western Atlantic, while for the Eastern Pacific none have been described, suggesting that the diversity of this family has been severely underestimated in this region. Here we describe the first species of Leptochelia from the Eastern Pacific, Leptochelia mexicana n. sp., which is characterized by the males having a spiniform seta on the second segment of uropodal endopod, a novel feature for the genus. In addition, the first annotated checklist and a taxonomic key with illustrations for Leptochelia species are included. The list includes the type locality, type depository, distribution, habitat and, in some cases, remarks.

Boosting drug named entity recognition using an aggregate classifier.

PubMed

Korkontzelos, Ioannis; Piliouras, Dimitrios; Dowsey, Andrew W; Ananiadou, Sophia

2015-10-01

Drug named entity recognition (NER) is a critical step for complex biomedical NLP tasks such as the extraction of pharmacogenomic, pharmacodynamic and pharmacokinetic parameters. Large quantities of high quality training data are almost always a prerequisite for employing supervised machine-learning techniques to achieve high classification performance. However, the human labour needed to produce and maintain such resources is a significant limitation. In this study, we improve the performance of drug NER without relying exclusively on manual annotations. We perform drug NER using either a small gold-standard corpus (120 abstracts) or no corpus at all. In our approach, we develop a voting system to combine a number of heterogeneous models, based on dictionary knowledge, gold-standard corpora and silver annotations, to enhance performance. To improve recall, we employed genetic programming to evolve 11 regular-expression patterns that capture common drug suffixes and used them as an extra means for recognition. Our approach uses a dictionary of drug names, i.e. DrugBank, a small manually annotated corpus, i.e. the pharmacokinetic corpus, and a part of the UKPMC database, as raw biomedical text. Gold-standard and silver annotated data are used to train maximum entropy and multinomial logistic regression classifiers. Aggregating drug NER methods, based on gold-standard annotations, dictionary knowledge and patterns, improved the performance on models trained on gold-standard annotations, only, achieving a maximum F-score of 95%. In addition, combining models trained on silver annotations, dictionary knowledge and patterns are shown to achieve comparable performance to models trained exclusively on gold-standard data. The main reason appears to be the morphological similarities shared among drug names. We conclude that gold-standard data are not a hard requirement for drug NER. Combining heterogeneous models build on dictionary knowledge can achieve similar or comparable classification performance with that of the best performing model trained on gold-standard annotations. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Towards comprehensive syntactic and semantic annotations of the clinical narrative

PubMed Central

Albright, Daniel; Lanfranchi, Arrick; Fredriksen, Anwen; Styler, William F; Warner, Colin; Hwang, Jena D; Choi, Jinho D; Dligach, Dmitriy; Nielsen, Rodney D; Martin, James; Ward, Wayne; Palmer, Martha; Savova, Guergana K

2013-01-01

Objective To create annotated clinical narratives with layers of syntactic and semantic labels to facilitate advances in clinical natural language processing (NLP). To develop NLP algorithms and open source components. Methods Manual annotation of a clinical narrative corpus of 127 606 tokens following the Treebank schema for syntactic information, PropBank schema for predicate-argument structures, and the Unified Medical Language System (UMLS) schema for semantic information. NLP components were developed. Results The final corpus consists of 13 091 sentences containing 1772 distinct predicate lemmas. Of the 766 newly created PropBank frames, 74 are verbs. There are 28 539 named entity (NE) annotations spread over 15 UMLS semantic groups, one UMLS semantic type, and the Person semantic category. The most frequent annotations belong to the UMLS semantic groups of Procedures (15.71%), Disorders (14.74%), Concepts and Ideas (15.10%), Anatomy (12.80%), Chemicals and Drugs (7.49%), and the UMLS semantic type of Sign or Symptom (12.46%). Inter-annotator agreement results: Treebank (0.926), PropBank (0.891–0.931), NE (0.697–0.750). The part-of-speech tagger, constituency parser, dependency parser, and semantic role labeler are built from the corpus and released open source. A significant limitation uncovered by this project is the need for the NLP community to develop a widely agreed-upon schema for the annotation of clinical concepts and their relations. Conclusions This project takes a foundational step towards bringing the field of clinical NLP up to par with NLP in the general domain. The corpus creation and NLP components provide a resource for research and application development that would have been previously impossible. PMID:23355458

National Mesothelioma Virtual Bank: a standard based biospecimen and clinical data resource to enhance translational research.

PubMed

Amin, Waqas; Parwani, Anil V; Schmandt, Linda; Mohanty, Sambit K; Farhat, Ghada; Pople, Andrew K; Winters, Sharon B; Whelan, Nancy B; Schneider, Althea M; Milnes, John T; Valdivieso, Federico A; Feldman, Michael; Pass, Harvey I; Dhir, Rajiv; Melamed, Jonathan; Becich, Michael J

2008-08-13

Advances in translational research have led to the need for well characterized biospecimens for research. The National Mesothelioma Virtual Bank is an initiative which collects annotated datasets relevant to human mesothelioma to develop an enterprising biospecimen resource to fulfill researchers' need. The National Mesothelioma Virtual Bank architecture is based on three major components: (a) common data elements (based on College of American Pathologists protocol and National North American Association of Central Cancer Registries standards), (b) clinical and epidemiologic data annotation, and (c) data query tools. These tools work interoperably to standardize the entire process of annotation. The National Mesothelioma Virtual Bank tool is based upon the caTISSUE Clinical Annotation Engine, developed by the University of Pittsburgh in cooperation with the Cancer Biomedical Informatics Grid (caBIG, see http://cabig.nci.nih.gov). This application provides a web-based system for annotating, importing and searching mesothelioma cases. The underlying information model is constructed utilizing Unified Modeling Language class diagrams, hierarchical relationships and Enterprise Architect software. The database provides researchers real-time access to richly annotated specimens and integral information related to mesothelioma. The data disclosed is tightly regulated depending upon users' authorization and depending on the participating institute that is amenable to the local Institutional Review Board and regulation committee reviews. The National Mesothelioma Virtual Bank currently has over 600 annotated cases available for researchers that include paraffin embedded tissues, tissue microarrays, serum and genomic DNA. The National Mesothelioma Virtual Bank is a virtual biospecimen registry with robust translational biomedical informatics support to facilitate basic science, clinical, and translational research. Furthermore, it protects patient privacy by disclosing only de-identified datasets to assure that biospecimens can be made accessible to researchers.

The CHEMDNER corpus of chemicals and drugs and its annotation principles.

PubMed

Krallinger, Martin; Rabal, Obdulia; Leitner, Florian; Vazquez, Miguel; Salgado, David; Lu, Zhiyong; Leaman, Robert; Lu, Yanan; Ji, Donghong; Lowe, Daniel M; Sayle, Roger A; Batista-Navarro, Riza Theresa; Rak, Rafal; Huber, Torsten; Rocktäschel, Tim; Matos, Sérgio; Campos, David; Tang, Buzhou; Xu, Hua; Munkhdalai, Tsendsuren; Ryu, Keun Ho; Ramanan, S V; Nathan, Senthil; Žitnik, Slavko; Bajec, Marko; Weber, Lutz; Irmer, Matthias; Akhondi, Saber A; Kors, Jan A; Xu, Shuo; An, Xin; Sikdar, Utpal Kumar; Ekbal, Asif; Yoshioka, Masaharu; Dieb, Thaer M; Choi, Miji; Verspoor, Karin; Khabsa, Madian; Giles, C Lee; Liu, Hongfang; Ravikumar, Komandur Elayavilli; Lamurias, Andre; Couto, Francisco M; Dai, Hong-Jie; Tsai, Richard Tzong-Han; Ata, Caglar; Can, Tolga; Usié, Anabel; Alves, Rui; Segura-Bedmar, Isabel; Martínez, Paloma; Oyarzabal, Julen; Valencia, Alfonso

2015-01-01

The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/.

The CHEMDNER corpus of chemicals and drugs and its annotation principles

PubMed Central

2015-01-01

The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/ PMID:25810773

Integrating systems biology models and biomedical ontologies

PubMed Central

2011-01-01

Background Systems biology is an approach to biology that emphasizes the structure and dynamic behavior of biological systems and the interactions that occur within them. To succeed, systems biology crucially depends on the accessibility and integration of data across domains and levels of granularity. Biomedical ontologies were developed to facilitate such an integration of data and are often used to annotate biosimulation models in systems biology. Results We provide a framework to integrate representations of in silico systems biology with those of in vivo biology as described by biomedical ontologies and demonstrate this framework using the Systems Biology Markup Language. We developed the SBML Harvester software that automatically converts annotated SBML models into OWL and we apply our software to those biosimulation models that are contained in the BioModels Database. We utilize the resulting knowledge base for complex biological queries that can bridge levels of granularity, verify models based on the biological phenomenon they represent and provide a means to establish a basic qualitative layer on which to express the semantics of biosimulation models. Conclusions We establish an information flow between biomedical ontologies and biosimulation models and we demonstrate that the integration of annotated biosimulation models and biomedical ontologies enables the verification of models as well as expressive queries. Establishing a bi-directional information flow between systems biology and biomedical ontologies has the potential to enable large-scale analyses of biological systems that span levels of granularity from molecules to organisms. PMID:21835028

The coffee genome hub: a resource for coffee genomes

PubMed Central

Dereeper, Alexis; Bocs, Stéphanie; Rouard, Mathieu; Guignon, Valentin; Ravel, Sébastien; Tranchant-Dubreuil, Christine; Poncet, Valérie; Garsmeur, Olivier; Lashermes, Philippe; Droc, Gaëtan

2015-01-01

The whole genome sequence of Coffea canephora, the perennial diploid species known as Robusta, has been recently released. In the context of the C. canephora genome sequencing project and to support post-genomics efforts, we developed the Coffee Genome Hub (http://coffee-genome.org/), an integrative genome information system that allows centralized access to genomics and genetics data and analysis tools to facilitate translational and applied research in coffee. We provide the complete genome sequence of C. canephora along with gene structure, gene product information, metabolism, gene families, transcriptomics, syntenic blocks, genetic markers and genetic maps. The hub relies on generic software (e.g. GMOD tools) for easy querying, visualizing and downloading research data. It includes a Genome Browser enhanced by a Community Annotation System, enabling the improvement of automatic gene annotation through an annotation editor. In addition, the hub aims at developing interoperability among other existing South Green tools managing coffee data (phylogenomics resources, SNPs) and/or supporting data analyses with the Galaxy workflow manager. PMID:25392413

An informatics model for tissue banks--lessons learned from the Cooperative Prostate Cancer Tissue Resource.

PubMed

Patel, Ashokkumar A; Gilbertson, John R; Parwani, Anil V; Dhir, Rajiv; Datta, Milton W; Gupta, Rajnish; Berman, Jules J; Melamed, Jonathan; Kajdacsy-Balla, Andre; Orenstein, Jan; Becich, Michael J

2006-05-05

Advances in molecular biology and growing requirements from biomarker validation studies have generated a need for tissue banks to provide quality-controlled tissue samples with standardized clinical annotation. The NCI Cooperative Prostate Cancer Tissue Resource (CPCTR) is a distributed tissue bank that comprises four academic centers and provides thousands of clinically annotated prostate cancer specimens to researchers. Here we describe the CPCTR information management system architecture, common data element (CDE) development, query interfaces, data curation, and quality control. Data managers review the medical records to collect and continuously update information for the 145 clinical, pathological and inventorial CDEs that the Resource maintains for each case. An Access-based data entry tool provides de-identification and a standard communication mechanism between each group and a central CPCTR database. Standardized automated quality control audits have been implemented. Centrally, an Oracle database has web interfaces allowing multiple user-types, including the general public, to mine de-identified information from all of the sites with three levels of specificity and granularity as well as to request tissues through a formal letter of intent. Since July 2003, CPCTR has offered over 6,000 cases (38,000 blocks) of highly characterized prostate cancer biospecimens, including several tissue microarrays (TMA). The Resource developed a website with interfaces for the general public as well as researchers and internal members. These user groups have utilized the web-tools for public query of summary data on the cases that were available, to prepare requests, and to receive tissues. As of December 2005, the Resource received over 130 tissue requests, of which 45 have been reviewed, approved and filled. Additionally, the Resource implemented the TMA Data Exchange Specification in its TMA program and created a computer program for calculating PSA recurrence. Building a biorepository infrastructure that meets today's research needs involves time and input of many individuals from diverse disciplines. The CPCTR can provide large volumes of carefully annotated prostate tissue for research initiatives such as Specialized Programs of Research Excellence (SPOREs) and for biomarker validation studies and its experience can help development of collaborative, large scale, virtual tissue banks in other organ systems.

Interactive Tree Of Life v2: online annotation and display of phylogenetic trees made easy.

PubMed

Letunic, Ivica; Bork, Peer

2011-07-01

Interactive Tree Of Life (http://itol.embl.de) is a web-based tool for the display, manipulation and annotation of phylogenetic trees. It is freely available and open to everyone. In addition to classical tree viewer functions, iTOL offers many novel ways of annotating trees with various additional data. Current version introduces numerous new features and greatly expands the number of supported data set types. Trees can be interactively manipulated and edited. A free personal account system is available, providing management and sharing of trees in user defined workspaces and projects. Export to various bitmap and vector graphics formats is supported. Batch access interface is available for programmatic access or inclusion of interactive trees into other web services.

Modeling loosely annotated images using both given and imagined annotations

NASA Astrophysics Data System (ADS)

Tang, Hong; Boujemaa, Nozha; Chen, Yunhao; Deng, Lei

2011-12-01

In this paper, we present an approach to learn latent semantic analysis models from loosely annotated images for automatic image annotation and indexing. The given annotation in training images is loose due to: 1. ambiguous correspondences between visual features and annotated keywords; 2. incomplete lists of annotated keywords. The second reason motivates us to enrich the incomplete annotation in a simple way before learning a topic model. In particular, some ``imagined'' keywords are poured into the incomplete annotation through measuring similarity between keywords in terms of their co-occurrence. Then, both given and imagined annotations are employed to learn probabilistic topic models for automatically annotating new images. We conduct experiments on two image databases (i.e., Corel and ESP) coupled with their loose annotations, and compare the proposed method with state-of-the-art discrete annotation methods. The proposed method improves word-driven probability latent semantic analysis (PLSA-words) up to a comparable performance with the best discrete annotation method, while a merit of PLSA-words is still kept, i.e., a wider semantic range.

KID Project: an internet-based digital video atlas of capsule endoscopy for research purposes.

PubMed

Koulaouzidis, Anastasios; Iakovidis, Dimitris K; Yung, Diana E; Rondonotti, Emanuele; Kopylov, Uri; Plevris, John N; Toth, Ervin; Eliakim, Abraham; Wurm Johansson, Gabrielle; Marlicz, Wojciech; Mavrogenis, Georgios; Nemeth, Artur; Thorlacius, Henrik; Tontini, Gian Eugenio

2017-06-01

 Capsule endoscopy (CE) has revolutionized small-bowel (SB) investigation. Computational methods can enhance diagnostic yield (DY); however, incorporating machine learning algorithms (MLAs) into CE reading is difficult as large amounts of image annotations are required for training. Current databases lack graphic annotations of pathologies and cannot be used. A novel database, KID, aims to provide a reference for research and development of medical decision support systems (MDSS) for CE.  Open-source software was used for the KID database. Clinicians contribute anonymized, annotated CE images and videos. Graphic annotations are supported by an open-access annotation tool (Ratsnake). We detail an experiment based on the KID database, examining differences in SB lesion measurement between human readers and a MLA. The Jaccard Index (JI) was used to evaluate similarity between annotations by the MLA and human readers.  The MLA performed best in measuring lymphangiectasias with a JI of 81 ± 6 %. The other lesion types were: angioectasias (JI 64 ± 11 %), aphthae (JI 64 ± 8 %), chylous cysts (JI 70 ± 14 %), polypoid lesions (JI 75 ± 21 %), and ulcers (JI 56 ± 9 %).  MLA can perform as well as human readers in the measurement of SB angioectasias in white light (WL). Automated lesion measurement is therefore feasible. KID is currently the only open-source CE database developed specifically to aid development of MDSS. Our experiment demonstrates this potential.

Global View of Mars Topography

NASA Technical Reports Server (NTRS)

2007-01-01

[figure removed for brevity, see original site] Annotated Version

This global map of Mars is based on topographical information collected by the Mars Orbiter Laser Altimeter instrument on NASA's Mars Global Surveyor orbiter. Illumination is from the upper right. The image width is approximately 18,000 kilometers (11,185 miles). Candor Chasma forms part of the large Martian canyon system named Valles Marineris. The location of Southwest Candor Chasma is indicated in the annotated version.

Enhancement of the Shared Graphics Workspace.

DTIC Science & Technology

1987-12-31

participants to share videodisc images and computer graphics displayed in color and text and facsimile information displayed in black on amber. They...could annotate the information in up to five * colors and print the annotated version at both sites, using a standard fax machine. The SGWS also used a fax...system to display a document, whether text or photo, the camera scans the document, digitizes the data, and sends it via direct memory access (DMA) to

Genome-wide analysis of tandem repeats in plants and green algae

Treesearch

Zhixin Zhao; Cheng Guo; Sreeskandarajan Sutharzan; Pei Li; Craig Echt; Jie Zhang; Chun Liang

2014-01-01

Tandem repeats (TRs) extensively exist in the genomes of prokaryotes and eukaryotes. Based on the sequenced genomes and gene annotations of 31 plant and algal species in Phytozome version 8.0 (http://www.phytozome.net/), we examined TRs in a genome-wide scale, characterized their distributions and motif features, and explored their putative biological functions. Among...

The diversity of sequence and chromosomal distribution of new transposable element-related segments in the rye genome revealed by FISH and lineage annotation

USDA-ARS?s Scientific Manuscript database

The rye genome features a high percentage of repetitive elements, especially transposable elements (TEs). However, studies about the constitution and organization of TEs on rye chromosomes are limited. In this study, 97 unique TE segments were isolated and characterized; 50 TE segmemts showed varyin...

Community Development Resources Handbook. Inventory of Recently-Produced Resources on C.D.

ERIC Educational Resources Information Center

Roeschley, Sheila, Comp.; And Others

This guide is an annotated listing of 121 resources which are not yet available through the usual distribution channels and which might be useful to Community Development Society (CDS) members and those involved in community development. Largely produced over the last 5 years, the materials include books, chapters of books, journal articles,…

Assessing computerized eye tracking technology for gaining insight into expert interpretation of the 12-lead electrocardiogram: an objective quantitative approach.

PubMed

Bond, R R; Zhu, T; Finlay, D D; Drew, B; Kligfield, P D; Guldenring, D; Breen, C; Gallagher, A G; Daly, M J; Clifford, G D

2014-01-01

It is well known that accurate interpretation of the 12-lead electrocardiogram (ECG) requires a high degree of skill. There is also a moderate degree of variability among those who interpret the ECG. While this is the case, there are no best practice guidelines for the actual ECG interpretation process. Hence, this study adopts computerized eye tracking technology to investigate whether eye-gaze can be used to gain a deeper insight into how expert annotators interpret the ECG. Annotators were recruited in San Jose, California at the 2013 International Society of Computerised Electrocardiology (ISCE). Each annotator was recruited to interpret a number of 12-lead ECGs (N=12) while their eye gaze was recorded using a Tobii X60 eye tracker. The device is based on corneal reflection and is non-intrusive. With a sampling rate of 60Hz, eye gaze coordinates were acquired every 16.7ms. Fixations were determined using a predefined computerized classification algorithm, which was then used to generate heat maps of where the annotators looked. The ECGs used in this study form four groups (3=ST elevation myocardial infarction [STEMI], 3=hypertrophy, 3=arrhythmias and 3=exhibiting unique artefacts). There was also an equal distribution of difficulty levels (3=easy to interpret, 3=average and 3=difficult). ECGs were displayed using the 4x3+1 display format and computerized annotations were concealed. Precisely 252 expert ECG interpretations (21 annotators×12 ECGs) were recorded. Average duration for ECG interpretation was 58s (SD=23). Fleiss' generalized kappa coefficient (Pa=0.56) indicated a moderate inter-rater reliability among the annotators. There was a 79% inter-rater agreement for STEMI cases, 71% agreement for arrhythmia cases, 65% for the lead misplacement and dextrocardia cases and only 37% agreement for the hypertrophy cases. In analyzing the total fixation duration, it was found that on average annotators study lead V1 the most (4.29s), followed by leads V2 (3.83s), the rhythm strip (3.47s), II (2.74s), V3 (2.63s), I (2.53s), aVL (2.45s), V5 (2.27s), aVF (1.74s), aVR (1.63s), V6 (1.39s), III (1.32s) and V4 (1.19s). It was also found that on average the annotator spends an equal amount of time studying leads in the frontal plane (15.89s) when compared to leads in the transverse plane (15.70s). It was found that on average the annotators fixated on lead I first followed by leads V2, aVL, V1, II, aVR, V3, rhythm strip, III, aVF, V5, V4 and V6. We found a strong correlation (r=0.67) between time to first fixation on a lead and the total fixation duration on each lead. This indicates that leads studied first are studied the longest. There was a weak negative correlation between duration and accuracy (r=-0.2) and a strong correlation between age and accuracy (r=0.67). Eye tracking facilitated a deeper insight into how expert annotators interpret the 12-lead ECG. As a result, the authors recommend ECG annotators to adopt an initial first impression/pattern recognition approach followed by a conventional systematic protocol to ECG interpretation. This recommendation is based on observing misdiagnoses given due to first impression only. In summary, this research presents eye gaze results from expert ECG annotators and provides scope for future work that involves exploiting computerized eye tracking technology to further the science of ECG interpretation. Copyright © 2014 Elsevier Inc. All rights reserved.

The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience

PubMed Central

Burns, Randal; Roncal, William Gray; Kleissas, Dean; Lillaney, Kunal; Manavalan, Priya; Perlman, Eric; Berger, Daniel R.; Bock, Davi D.; Chung, Kwanghun; Grosenick, Logan; Kasthuri, Narayanan; Weiler, Nicholas C.; Deisseroth, Karl; Kazhdan, Michael; Lichtman, Jeff; Reid, R. Clay; Smith, Stephen J.; Szalay, Alexander S.; Vogelstein, Joshua T.; Vogelstein, R. Jacob

2013-01-01

We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes— neural connectivity maps of the brain—using the parallel execution of computer vision algorithms on high-performance compute clusters. These services and open-science data sets are publicly available at openconnecto.me. The system design inherits much from NoSQL scale-out and data-intensive computing architectures. We distribute data to cluster nodes by partitioning a spatial index. We direct I/O to different systems—reads to parallel disk arrays and writes to solid-state storage—to avoid I/O interference and maximize throughput. All programming interfaces are RESTful Web services, which are simple and stateless, improving scalability and usability. We include a performance evaluation of the production system, highlighting the effec-tiveness of spatial data organization. PMID:24401992

TriAnnot: A Versatile and High Performance Pipeline for the Automated Annotation of Plant Genomes

PubMed Central

Leroy, Philippe; Guilhot, Nicolas; Sakai, Hiroaki; Bernard, Aurélien; Choulet, Frédéric; Theil, Sébastien; Reboux, Sébastien; Amano, Naoki; Flutre, Timothée; Pelegrin, Céline; Ohyanagi, Hajime; Seidel, Michael; Giacomoni, Franck; Reichstadt, Mathieu; Alaux, Michael; Gicquello, Emmanuelle; Legeai, Fabrice; Cerutti, Lorenzo; Numa, Hisataka; Tanaka, Tsuyoshi; Mayer, Klaus; Itoh, Takeshi; Quesneville, Hadi; Feuillet, Catherine

2012-01-01

In support of the international effort to obtain a reference sequence of the bread wheat genome and to provide plant communities dealing with large and complex genomes with a versatile, easy-to-use online automated tool for annotation, we have developed the TriAnnot pipeline. Its modular architecture allows for the annotation and masking of transposable elements, the structural, and functional annotation of protein-coding genes with an evidence-based quality indexing, and the identification of conserved non-coding sequences and molecular markers. The TriAnnot pipeline is parallelized on a 712 CPU computing cluster that can run a 1-Gb sequence annotation in less than 5 days. It is accessible through a web interface for small scale analyses or through a server for large scale annotations. The performance of TriAnnot was evaluated in terms of sensitivity, specificity, and general fitness using curated reference sequence sets from rice and wheat. In less than 8 h, TriAnnot was able to predict more than 83% of the 3,748 CDS from rice chromosome 1 with a fitness of 67.4%. On a set of 12 reference Mb-sized contigs from wheat chromosome 3B, TriAnnot predicted and annotated 93.3% of the genes among which 54% were perfectly identified in accordance with the reference annotation. It also allowed the curation of 12 genes based on new biological evidences, increasing the percentage of perfect gene prediction to 63%. TriAnnot systematically showed a higher fitness than other annotation pipelines that are not improved for wheat. As it is easily adaptable to the annotation of other plant genomes, TriAnnot should become a useful resource for the annotation of large and complex genomes in the future. PMID:22645565

GenColors: annotation and comparative genomics of prokaryotes made easy.

PubMed

Romualdi, Alessandro; Felder, Marius; Rose, Dominic; Gausmann, Ulrike; Schilhabel, Markus; Glöckner, Gernot; Platzer, Matthias; Sühnel, Jürgen

2007-01-01

GenColors (gencolors.fli-leibniz.de) is a new web-based software/database system aimed at an improved and accelerated annotation of prokaryotic genomes considering information on related genomes and making extensive use of genome comparison. It offers a seamless integration of data from ongoing sequencing projects and annotated genomic sequences obtained from GenBank. A variety of export/import filters manages an effective data flow from sequence assembly and manipulation programs (e.g., GAP4) to GenColors and back as well as to standard GenBank file(s). The genome comparison tools include best bidirectional hits, gene conservation, syntenies, and gene core sets. Precomputed UniProt matches allow annotation and analysis in an effective manner. In addition to these analysis options, base-specific quality data (coverage and confidence) can also be handled if available. The GenColors system can be used both for annotation purposes in ongoing genome projects and as an analysis tool for finished genomes. GenColors comes in two types, as dedicated genome browsers and as the Jena Prokaryotic Genome Viewer (JPGV). Dedicated genome browsers contain genomic information on a set of related genomes and offer a large number of options for genome comparison. The system has been efficiently used in the genomic sequencing of Borrelia garinii and is currently applied to various ongoing genome projects on Borrelia, Legionella, Escherichia, and Pseudomonas genomes. One of these dedicated browsers, the Spirochetes Genome Browser (sgb.fli-leibniz.de) with Borrelia, Leptospira, and Treponema genomes, is freely accessible. The others will be released after finalization of the corresponding genome projects. JPGV (jpgv.fli-leibniz.de) offers information on almost all finished bacterial genomes, as compared to the dedicated browsers with reduced genome comparison functionality, however. As of January 2006, this viewer includes 632 genomic elements (e.g., chromosomes and plasmids) of 293 species. The system provides versatile quick and advanced search options for all currently known prokaryotic genomes and generates circular and linear genome plots. Gene information sheets contain basic gene information, database search options, and links to external databases. GenColors is also available on request for local installation.

Methods for eliciting, annotating, and analyzing databases for child speech development.

PubMed

Beckman, Mary E; Plummer, Andrew R; Munson, Benjamin; Reidy, Patrick F

2017-09-01

Methods from automatic speech recognition (ASR), such as segmentation and forced alignment, have facilitated the rapid annotation and analysis of very large adult speech databases and databases of caregiver-infant interaction, enabling advances in speech science that were unimaginable just a few decades ago. This paper centers on two main problems that must be addressed in order to have analogous resources for developing and exploiting databases of young children's speech. The first problem is to understand and appreciate the differences between adult and child speech that cause ASR models developed for adult speech to fail when applied to child speech. These differences include the fact that children's vocal tracts are smaller than those of adult males and also changing rapidly in size and shape over the course of development, leading to between-talker variability across age groups that dwarfs the between-talker differences between adult men and women. Moreover, children do not achieve fully adult-like speech motor control until they are young adults, and their vocabularies and phonological proficiency are developing as well, leading to considerably more within-talker variability as well as more between-talker variability. The second problem then is to determine what annotation schemas and analysis techniques can most usefully capture relevant aspects of this variability. Indeed, standard acoustic characterizations applied to child speech reveal that adult-centered annotation schemas fail to capture phenomena such as the emergence of covert contrasts in children's developing phonological systems, while also revealing children's nonuniform progression toward community speech norms as they acquire the phonological systems of their native languages. Both problems point to the need for more basic research into the growth and development of the articulatory system (as well as of the lexicon and phonological system) that is oriented explicitly toward the construction of age-appropriate computational models.

Use of Annotations for Component and Framework Interoperability

NASA Astrophysics Data System (ADS)

David, O.; Lloyd, W.; Carlson, J.; Leavesley, G. H.; Geter, F.

2009-12-01

The popular programming languages Java and C# provide annotations, a form of meta-data construct. Software frameworks for web integration, web services, database access, and unit testing now take advantage of annotations to reduce the complexity of APIs and the quantity of integration code between the application and framework infrastructure. Adopting annotation features in frameworks has been observed to lead to cleaner and leaner application code. The USDA Object Modeling System (OMS) version 3.0 fully embraces the annotation approach and additionally defines a meta-data standard for components and models. In version 3.0 framework/model integration previously accomplished using API calls is now achieved using descriptive annotations. This enables the framework to provide additional functionality non-invasively such as implicit multithreading, and auto-documenting capabilities while achieving a significant reduction in the size of the model source code. Using a non-invasive methodology leads to models and modeling components with only minimal dependencies on the modeling framework. Since models and modeling components are not directly bound to framework by the use of specific APIs and/or data types they can more easily be reused both within the framework as well as outside of it. To study the effectiveness of an annotation based framework approach with other modeling frameworks, a framework-invasiveness study was conducted to evaluate the effects of framework design on model code quality. A monthly water balance model was implemented across several modeling frameworks and several software metrics were collected. The metrics selected were measures of non-invasive design methods for modeling frameworks from a software engineering perspective. It appears that the use of annotations positively impacts several software quality measures. In a next step, the PRMS model was implemented in OMS 3.0 and is currently being implemented for water supply forecasting in the western United States at the USDA NRCS National Water and Climate Center. PRMS is a component based modular precipitation-runoff model developed to evaluate the impacts of various combinations of precipitation, climate, and land use on streamflow and general basin hydrology. The new OMS 3.0 PRMS model source code is more concise and flexible as a result of using the new framework’s annotation based approach. The fully annotated components are now providing information directly for (i) model assembly and building, (ii) dataflow analysis for implicit multithreading, (iii) automated and comprehensive model documentation of component dependencies, physical data properties, (iv) automated model and component testing, and (v) automated audit-traceability to account for all model resources leading to a particular simulation result. Experience to date has demonstrated the multi-purpose value of using annotations. Annotations are also a feasible and practical method to enable interoperability among models and modeling frameworks. As a prototype example, model code annotations were used to generate binding and mediation code to allow the use of OMS 3.0 model components within the OpenMI context.

SELECTED ANNOTATED BIBLIOGRAPHY ON SYSTEMS OF THEORETICAL DEVICES,

DTIC Science & Technology

BIONICS, BIBLIOGRAPHIES), (*BIBLIOGRAPHIES, BIONICS), (*CYBERNETICS, BIBLIOGRAPHIES), MATHEMATICS, COMPUTER LOGIC, NETWORKS, NERVOUS SYSTEM , THEORY , SEQUENCE SWITCHES, SWITCHING CIRCUITS, REDUNDANT COMPONENTS, LEARNING, MATHEMATICAL MODELS, BEHAVIOR, NERVES, SIMULATION, NERVE CELLS

EuCAP, a Eukaryotic Community Annotation Package, and its application to the rice genome

PubMed Central

Thibaud-Nissen, Françoise; Campbell, Matthew; Hamilton, John P; Zhu, Wei; Buell, C Robin

2007-01-01

Background Despite the improvements of tools for automated annotation of genome sequences, manual curation at the structural and functional level can provide an increased level of refinement to genome annotation. The Institute for Genomic Research Rice Genome Annotation (hereafter named the Osa1 Genome Annotation) is the product of an automated pipeline and, for this reason, will benefit from the input of biologists with expertise in rice and/or particular gene families. Leveraging knowledge from a dispersed community of scientists is a demonstrated way of improving a genome annotation. This requires tools that facilitate 1) the submission of gene annotation to an annotation project, 2) the review of the submitted models by project annotators, and 3) the incorporation of the submitted models in the ongoing annotation effort. Results We have developed the Eukaryotic Community Annotation Package (EuCAP), an annotation tool, and have applied it to the rice genome. The primary level of curation by community annotators (CA) has been the annotation of gene families. Annotation can be submitted by email or through the EuCAP Web Tool. The CA models are aligned to the rice pseudomolecules and the coordinates of these alignments, along with functional annotation, are stored in the MySQL EuCAP Gene Model database. Web pages displaying the alignments of the CA models to the Osa1 Genome models are automatically generated from the EuCAP Gene Model database. The alignments are reviewed by the project annotators (PAs) in the context of experimental evidence. Upon approval by the PAs, the CA models, along with the corresponding functional annotations, are integrated into the Osa1 Genome Annotation. The CA annotations, grouped by family, are displayed on the Community Annotation pages of the project website , as well as in the Community Annotation track of the Genome Browser. Conclusion We have applied EuCAP to rice. As of July 2007, the structural and/or functional annotation of 1,094 genes representing 57 families have been deposited and integrated into the current gene set. All of the EuCAP components are open-source, thereby allowing the implementation of EuCAP for the annotation of other genomes. EuCAP is available at . PMID:17961238

Automatic acquisition of motion trajectories: tracking hockey players

NASA Astrophysics Data System (ADS)

Okuma, Kenji; Little, James J.; Lowe, David

2003-12-01

Computer systems that have the capability of analyzing complex and dynamic scenes play an essential role in video annotation. Scenes can be complex in such a way that there are many cluttered objects with different colors, shapes and sizes, and can be dynamic with multiple interacting moving objects and a constantly changing background. In reality, there are many scenes that are complex, dynamic, and challenging enough for computers to describe. These scenes include games of sports, air traffic, car traffic, street intersections, and cloud transformations. Our research is about the challenge of inventing a descriptive computer system that analyzes scenes of hockey games where multiple moving players interact with each other on a constantly moving background due to camera motions. Ultimately, such a computer system should be able to acquire reliable data by extracting the players" motion as their trajectories, querying them by analyzing the descriptive information of data, and predict the motions of some hockey players based on the result of the query. Among these three major aspects of the system, we primarily focus on visual information of the scenes, that is, how to automatically acquire motion trajectories of hockey players from video. More accurately, we automatically analyze the hockey scenes by estimating parameters (i.e., pan, tilt, and zoom) of the broadcast cameras, tracking hockey players in those scenes, and constructing a visual description of the data by displaying trajectories of those players. Many technical problems in vision such as fast and unpredictable players' motions and rapid camera motions make our challenge worth tackling. To the best of our knowledge, there have not been any automatic video annotation systems for hockey developed in the past. Although there are many obstacles to overcome, our efforts and accomplishments would hopefully establish the infrastructure of the automatic hockey annotation system and become a milestone for research in automatic video annotation in this domain.

The grapevine kinome: annotation, classification and expression patterns in developmental processes and stress responses.

PubMed

Zhu, Kaikai; Wang, Xiaolong; Liu, Jinyi; Tang, Jun; Cheng, Qunkang; Chen, Jin-Gui; Cheng, Zong-Ming Max

2018-01-01

Protein kinases (PKs) have evolved as the largest family of molecular switches that regulate protein activities associated with almost all essential cellular functions. Only a fraction of plant PKs, however, have been functionally characterized even in model plant species. In the present study, the entire grapevine kinome was identified and annotated using the most recent version of the grapevine genome. A total of 1168 PK-encoding genes were identified and classified into 20 groups and 121 families, with the RLK-Pelle group being the largest, with 872 members. The 1168 kinase genes were unevenly distributed over all 19 chromosomes, and both tandem and segmental duplications contributed to the expansion of the grapevine kinome, especially of the RLK-Pelle group. Ka/Ks values indicated that most of the tandem and segmental duplication events were under purifying selection. The grapevine kinome families exhibited different expression patterns during plant development and in response to various stress treatments, with many being coexpressed. The comprehensive annotation of grapevine kinase genes, their patterns of expression and coexpression, and the related information facilitate a more complete understanding of the roles of various grapevine kinases in growth and development, responses to abiotic stress, and evolutionary history.

Annotated bibliography of selected publications, through 1996, Cheyenne municipal well field areas, Cheyenne, Wyoming

USGS Publications Warehouse

Ogle, K.M.; Jordan, B.J.

1997-01-01

Annotated bibliographies for 55 hydrology and geology manuscripts pertaining to the Cheyenne municipal wells fields are listed in this report. For each manuscript, a citation is provided, a summaryparagraph is presented, key words are listed, and a location of the report is given. The report lists manuscripts, conference proceedings, and guidebooks published by the U. S. Geological Survey, State of Wyoming, Geological Society of America, Wyoming State Geological Survey,private consultants, and University of Wyoming.Information on geological formations, structural geology, aquifer characteristics, water levels, well- field production, water-demand projections, and water quality is included in the manuscripts. The Cheyenne Board of Public Utilities, the University of Wyoming, and the U. S. Geological Surveycooperatively produced this annotated bibliography to allow easy access and efficient utilization of existing data. The manuscripts were authored between 1910 and 1996, reflecting work completed over a long period of development in the Cheyenne, Wyoming area. Some manuscripts did not receive broad distribution and indexing, thus they have been difficult to locate in the past. By having the references and summaries within one report, time and effort to gather previous study results will be minimized.

Crowdsourcing for bioinformatics

PubMed Central

Good, Benjamin M.; Su, Andrew I.

2013-01-01

Motivation: Bioinformatics is faced with a variety of problems that require human involvement. Tasks like genome annotation, image analysis, knowledge-base population and protein structure determination all benefit from human input. In some cases, people are needed in vast quantities, whereas in others, we need just a few with rare abilities. Crowdsourcing encompasses an emerging collection of approaches for harnessing such distributed human intelligence. Recently, the bioinformatics community has begun to apply crowdsourcing in a variety of contexts, yet few resources are available that describe how these human-powered systems work and how to use them effectively in scientific domains. Results: Here, we provide a framework for understanding and applying several different types of crowdsourcing. The framework considers two broad classes: systems for solving large-volume ‘microtasks’ and systems for solving high-difficulty ‘megatasks’. Within these classes, we discuss system types, including volunteer labor, games with a purpose, microtask markets and open innovation contests. We illustrate each system type with successful examples in bioinformatics and conclude with a guide for matching problems to crowdsourcing solutions that highlights the positives and negatives of different approaches. Contact: bgood@scripps.edu PMID:23782614

Annotated chemical patent corpus: a gold standard for text mining.

PubMed

Akhondi, Saber A; Klenner, Alexander G; Tyrchan, Christian; Manchala, Anil K; Boppana, Kiran; Lowe, Daniel; Zimmermann, Marc; Jagarlapudi, Sarma A R P; Sayle, Roger; Kors, Jan A; Muresan, Sorel

2014-01-01

Exploring the chemical and biological space covered by patent applications is crucial in early-stage medicinal chemistry activities. Patent analysis can provide understanding of compound prior art, novelty checking, validation of biological assays, and identification of new starting points for chemical exploration. Extracting chemical and biological entities from patents through manual extraction by expert curators can take substantial amount of time and resources. Text mining methods can help to ease this process. To validate the performance of such methods, a manually annotated patent corpus is essential. In this study we have produced a large gold standard chemical patent corpus. We developed annotation guidelines and selected 200 full patents from the World Intellectual Property Organization, United States Patent and Trademark Office, and European Patent Office. The patents were pre-annotated automatically and made available to four independent annotator groups each consisting of two to ten annotators. The annotators marked chemicals in different subclasses, diseases, targets, and modes of action. Spelling mistakes and spurious line break due to optical character recognition errors were also annotated. A subset of 47 patents was annotated by at least three annotator groups, from which harmonized annotations and inter-annotator agreement scores were derived. One group annotated the full set. The patent corpus includes 400,125 annotations for the full set and 36,537 annotations for the harmonized set. All patents and annotated entities are publicly available at www.biosemantics.org.

Annotated Chemical Patent Corpus: A Gold Standard for Text Mining

PubMed Central

Akhondi, Saber A.; Klenner, Alexander G.; Tyrchan, Christian; Manchala, Anil K.; Boppana, Kiran; Lowe, Daniel; Zimmermann, Marc; Jagarlapudi, Sarma A. R. P.; Sayle, Roger; Kors, Jan A.; Muresan, Sorel

2014-01-01

Exploring the chemical and biological space covered by patent applications is crucial in early-stage medicinal chemistry activities. Patent analysis can provide understanding of compound prior art, novelty checking, validation of biological assays, and identification of new starting points for chemical exploration. Extracting chemical and biological entities from patents through manual extraction by expert curators can take substantial amount of time and resources. Text mining methods can help to ease this process. To validate the performance of such methods, a manually annotated patent corpus is essential. In this study we have produced a large gold standard chemical patent corpus. We developed annotation guidelines and selected 200 full patents from the World Intellectual Property Organization, United States Patent and Trademark Office, and European Patent Office. The patents were pre-annotated automatically and made available to four independent annotator groups each consisting of two to ten annotators. The annotators marked chemicals in different subclasses, diseases, targets, and modes of action. Spelling mistakes and spurious line break due to optical character recognition errors were also annotated. A subset of 47 patents was annotated by at least three annotator groups, from which harmonized annotations and inter-annotator agreement scores were derived. One group annotated the full set. The patent corpus includes 400,125 annotations for the full set and 36,537 annotations for the harmonized set. All patents and annotated entities are publicly available at www.biosemantics.org. PMID:25268232

Web 2.0-Based Crowdsourcing for High-Quality Gold Standard Development in Clinical Natural Language Processing

PubMed Central

Deleger, Louise; Li, Qi; Kaiser, Megan; Stoutenborough, Laura

2013-01-01

Background A high-quality gold standard is vital for supervised, machine learning-based, clinical natural language processing (NLP) systems. In clinical NLP projects, expert annotators traditionally create the gold standard. However, traditional annotation is expensive and time-consuming. To reduce the cost of annotation, general NLP projects have turned to crowdsourcing based on Web 2.0 technology, which involves submitting smaller subtasks to a coordinated marketplace of workers on the Internet. Many studies have been conducted in the area of crowdsourcing, but only a few have focused on tasks in the general NLP field and only a handful in the biomedical domain, usually based upon very small pilot sample sizes. In addition, the quality of the crowdsourced biomedical NLP corpora were never exceptional when compared to traditionally-developed gold standards. The previously reported results on medical named entity annotation task showed a 0.68 F-measure based agreement between crowdsourced and traditionally-developed corpora. Objective Building upon previous work from the general crowdsourcing research, this study investigated the usability of crowdsourcing in the clinical NLP domain with special emphasis on achieving high agreement between crowdsourced and traditionally-developed corpora. Methods To build the gold standard for evaluating the crowdsourcing workers’ performance, 1042 clinical trial announcements (CTAs) from the ClinicalTrials.gov website were randomly selected and double annotated for medication names, medication types, and linked attributes. For the experiments, we used CrowdFlower, an Amazon Mechanical Turk-based crowdsourcing platform. We calculated sensitivity, precision, and F-measure to evaluate the quality of the crowd’s work and tested the statistical significance (P<.001, chi-square test) to detect differences between the crowdsourced and traditionally-developed annotations. Results The agreement between the crowd’s annotations and the traditionally-generated corpora was high for: (1) annotations (0.87, F-measure for medication names; 0.73, medication types), (2) correction of previous annotations (0.90, medication names; 0.76, medication types), and excellent for (3) linking medications with their attributes (0.96). Simple voting provided the best judgment aggregation approach. There was no statistically significant difference between the crowd and traditionally-generated corpora. Our results showed a 27.9% improvement over previously reported results on medication named entity annotation task. Conclusions This study offers three contributions. First, we proved that crowdsourcing is a feasible, inexpensive, fast, and practical approach to collect high-quality annotations for clinical text (when protected health information was excluded). We believe that well-designed user interfaces and rigorous quality control strategy for entity annotation and linking were critical to the success of this work. Second, as a further contribution to the Internet-based crowdsourcing field, we will publicly release the JavaScript and CrowdFlower Markup Language infrastructure code that is necessary to utilize CrowdFlower’s quality control and crowdsourcing interfaces for named entity annotations. Finally, to spur future research, we will release the CTA annotations that were generated by traditional and crowdsourced approaches. PMID:23548263

A bibliographical surveys of large-scale systems

NASA Technical Reports Server (NTRS)

Corliss, W. R.

1970-01-01

A limited, partly annotated bibliography was prepared on the subject of large-scale system control. Approximately 400 references are divided into thirteen application areas, such as large societal systems and large communication systems. A first-author index is provided.

TSSAR: TSS annotation regime for dRNA-seq data.

PubMed

Amman, Fabian; Wolfinger, Michael T; Lorenz, Ronny; Hofacker, Ivo L; Stadler, Peter F; Findeiß, Sven

2014-03-27

Differential RNA sequencing (dRNA-seq) is a high-throughput screening technique designed to examine the architecture of bacterial operons in general and the precise position of transcription start sites (TSS) in particular. Hitherto, dRNA-seq data were analyzed by visualizing the sequencing reads mapped to the reference genome and manually annotating reliable positions. This is very labor intensive and, due to the subjectivity, biased. Here, we present TSSAR, a tool for automated de novo TSS annotation from dRNA-seq data that respects the statistics of dRNA-seq libraries. TSSAR uses the premise that the number of sequencing reads starting at a certain genomic position within a transcriptional active region follows a Poisson distribution with a parameter that depends on the local strength of expression. The differences of two dRNA-seq library counts thus follow a Skellam distribution. This provides a statistical basis to identify significantly enriched primary transcripts.We assessed the performance by analyzing a publicly available dRNA-seq data set using TSSAR and two simple approaches that utilize user-defined score cutoffs. We evaluated the power of reproducing the manual TSS annotation. Furthermore, the same data set was used to reproduce 74 experimentally validated TSS in H. pylori from reliable techniques such as RACE or primer extension. Both analyses showed that TSSAR outperforms the static cutoff-dependent approaches. Having an automated and efficient tool for analyzing dRNA-seq data facilitates the use of the dRNA-seq technique and promotes its application to more sophisticated analysis. For instance, monitoring the plasticity and dynamics of the transcriptomal architecture triggered by different stimuli and growth conditions becomes possible.The main asset of a novel tool for dRNA-seq analysis that reaches out to a broad user community is usability. As such, we provide TSSAR both as intuitive RESTful Web service ( http://rna.tbi.univie.ac.at/TSSAR) together with a set of post-processing and analysis tools, as well as a stand-alone version for use in high-throughput dRNA-seq data analysis pipelines.

SNPnexus: assessing the functional relevance of genetic variation to facilitate the promise of precision medicine.

PubMed

Dayem Ullah, Abu Z; Oscanoa, Jorge; Wang, Jun; Nagano, Ai; Lemoine, Nicholas R; Chelala, Claude

2018-05-11

Broader functional annotation of genetic variation is a valuable means for prioritising phenotypically-important variants in further disease studies and large-scale genotyping projects. We developed SNPnexus to meet this need by assessing the potential significance of known and novel SNPs on the major transcriptome, proteome, regulatory and structural variation models. Since its previous release in 2012, we have made significant improvements to the annotation categories and updated the query and data viewing systems. The most notable changes include broader functional annotation of noncoding variants and expanding annotations to the most recent human genome assembly GRCh38/hg38. SNPnexus has now integrated rich resources from ENCODE and Roadmap Epigenomics Consortium to map and annotate the noncoding variants onto different classes of regulatory regions and noncoding RNAs as well as providing their predicted functional impact from eight popular non-coding variant scoring algorithms and computational methods. A novel functionality offered now is the support for neo-epitope predictions from leading tools to facilitate its use in immunotherapeutic applications. These updates to SNPnexus are in preparation for its future expansion towards a fully comprehensive computational workflow for disease-associated variant prioritization from sequencing data, placing its users at the forefront of translational research. SNPnexus is freely available at http://www.snp-nexus.org.

dictyBase 2015: Expanding data and annotations in a new software environment.

PubMed

Basu, Siddhartha; Fey, Petra; Jimenez-Morales, David; Dodson, Robert J; Chisholm, Rex L

2015-08-01

dictyBase is the model organism database for the social amoeba Dictyostelium discoideum and related species. The primary mission of dictyBase is to provide the biomedical research community with well-integrated high quality data, and tools that enable original research. Data presented at dictyBase is obtained from sequencing centers, groups performing high throughput experiments such as large-scale mutagenesis studies, and RNAseq data, as well as a growing number of manually added functional gene annotations from the published literature, including Gene Ontology, strain, and phenotype annotations. Through the Dicty Stock Center we provide the community with an impressive amount of annotated strains and plasmids. Recently, dictyBase accomplished a major overhaul to adapt an outdated infrastructure to the current technological advances, thus facilitating the implementation of innovative tools and comparative genomics. It also provides new strategies for high quality annotations that enable bench researchers to benefit from the rapidly increasing volume of available data. dictyBase is highly responsive to its users needs, building a successful relationship that capitalizes on the vast efforts of the Dictyostelium research community. dictyBase has become the trusted data resource for Dictyostelium investigators, other investigators or organizations seeking information about Dictyostelium, as well as educators who use this model system. © 2015 Wiley Periodicals, Inc.

Developing a knowledge base to support the annotation of ultrasound images of ectopic pregnancy.

PubMed

Dhombres, Ferdinand; Maurice, Paul; Friszer, Stéphanie; Guilbaud, Lucie; Lelong, Nathalie; Khoshnood, Babak; Charlet, Jean; Perrot, Nicolas; Jauniaux, Eric; Jurkovic, Davor; Jouannic, Jean-Marie

2017-01-31

Ectopic pregnancy is a frequent early complication of pregnancy associated with significant rates of morbidly and mortality. The positive diagnosis of this condition is established through transvaginal ultrasound scanning. The timing of diagnosis depends on the operator expertise in identifying the signs of ectopic pregnancy, which varies dramatically among medical staff with heterogeneous training. Developing decision support systems in this context is expected to improve the identification of these signs and subsequently improve the quality of care. In this article, we present a new knowledge base for ectopic pregnancy, and we demonstrate its use on the annotation of clinical images. The knowledge base is supported by an application ontology, which provides the taxonomy, the vocabulary and definitions for 24 types and 81 signs of ectopic pregnancy, 484 anatomical structures and 32 technical elements for image acquisition. The knowledge base provides a sign-centric model of the domain, with the relations of signs to ectopic pregnancy types, anatomical structures and the technical elements. The evaluation of the ontology and knowledge base demonstrated a positive feedback from a panel of 17 medical users. Leveraging these semantic resources, we developed an application for the annotation of ultrasound images. Using this application, 6 operators achieved a precision of 0.83 for the identification of signs in 208 ultrasound images corresponding to 35 clinical cases of ectopic pregnancy. We developed a new ectopic pregnancy knowledge base for the annotation of ultrasound images. The use of this knowledge base for the annotation of ultrasound images of ectopic pregnancy showed promising results from the perspective of clinical decision support system development. Other gynecological disorders and fetal anomalies may benefit from our approach.

Evaluating Computational Gene Ontology Annotations.

PubMed

Škunca, Nives; Roberts, Richard J; Steffen, Martin

2017-01-01

Two avenues to understanding gene function are complementary and often overlapping: experimental work and computational prediction. While experimental annotation generally produces high-quality annotations, it is low throughput. Conversely, computational annotations have broad coverage, but the quality of annotations may be variable, and therefore evaluating the quality of computational annotations is a critical concern.In this chapter, we provide an overview of strategies to evaluate the quality of computational annotations. First, we discuss why evaluating quality in this setting is not trivial. We highlight the various issues that threaten to bias the evaluation of computational annotations, most of which stem from the incompleteness of biological databases. Second, we discuss solutions that address these issues, for example, targeted selection of new experimental annotations and leveraging the existing experimental annotations.

Protein location prediction using atomic composition and global features of the amino acid sequence

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cherian, Betsy Sheena, E-mail: betsy.skb@gmail.com; Nair, Achuthsankar S.

2010-01-22

Subcellular location of protein is constructive information in determining its function, screening for drug candidates, vaccine design, annotation of gene products and in selecting relevant proteins for further studies. Computational prediction of subcellular localization deals with predicting the location of a protein from its amino acid sequence. For a computational localization prediction method to be more accurate, it should exploit all possible relevant biological features that contribute to the subcellular localization. In this work, we extracted the biological features from the full length protein sequence to incorporate more biological information. A new biological feature, distribution of atomic composition is effectivelymore » used with, multiple physiochemical properties, amino acid composition, three part amino acid composition, and sequence similarity for predicting the subcellular location of the protein. Support Vector Machines are designed for four modules and prediction is made by a weighted voting system. Our system makes prediction with an accuracy of 100, 82.47, 88.81 for self-consistency test, jackknife test and independent data test respectively. Our results provide evidence that the prediction based on the biological features derived from the full length amino acid sequence gives better accuracy than those derived from N-terminal alone. Considering the features as a distribution within the entire sequence will bring out underlying property distribution to a greater detail to enhance the prediction accuracy.« less

Representing annotation compositionality and provenance for the Semantic Web

PubMed Central

2013-01-01

Background Though the annotation of digital artifacts with metadata has a long history, the bulk of that work focuses on the association of single terms or concepts to single targets. As annotation efforts expand to capture more complex information, annotations will need to be able to refer to knowledge structures formally defined in terms of more atomic knowledge structures. Existing provenance efforts in the Semantic Web domain primarily focus on tracking provenance at the level of whole triples and do not provide enough detail to track how individual triple elements of annotations were derived from triple elements of other annotations. Results We present a task- and domain-independent ontological model for capturing annotations and their linkage to their denoted knowledge representations, which can be singular concepts or more complex sets of assertions. We have implemented this model as an extension of the Information Artifact Ontology in OWL and made it freely available, and we show how it can be integrated with several prominent annotation and provenance models. We present several application areas for the model, ranging from linguistic annotation of text to the annotation of disease-associations in genome sequences. Conclusions With this model, progressively more complex annotations can be composed from other annotations, and the provenance of compositional annotations can be represented at the annotation level or at the level of individual elements of the RDF triples composing the annotations. This in turn allows for progressively richer annotations to be constructed from previous annotation efforts, the precise provenance recording of which facilitates evidence-based inference and error tracking. PMID:24268021

IMG/M: integrated genome and metagenome comparative data analysis system

DOE PAGES

Chen, I-Min A.; Markowitz, Victor M.; Chu, Ken; ...

2016-10-13

The Integrated Microbial Genomes with Microbiome Samples (IMG/M: https://img.jgi.doe.gov/m/) system contains annotated DNA and RNA sequence data of (i) archaeal, bacterial, eukaryotic and viral genomes from cultured organisms, (ii) single cell genomes (SCG) and genomes from metagenomes (GFM) from uncultured archaea, bacteria and viruses and (iii) metagenomes from environmental, host associated and engineered microbiome samples. Sequence data are generated by DOE's Joint Genome Institute (JGI), submitted by individual scientists, or collected from public sequence data archives. Structural and functional annotation is carried out by JGI's genome and metagenome annotation pipelines. A variety of analytical and visualization tools provide support formore » examining and comparing IMG/M's datasets. IMG/M allows open access interactive analysis of publicly available datasets, while manual curation, submission and access to private datasets and computationally intensive workspace-based analysis require login/password access to its expert review(ER) companion system (IMG/M ER: https://img.jgi.doe.gov/ mer/). Since the last report published in the 2014 NAR Database Issue, IMG/M's dataset content has tripled in terms of number of datasets and overall protein coding genes, while its analysis tools have been extended to cope with the rapid growth in the number and size of datasets handled by the system.« less

IMG/M: integrated genome and metagenome comparative data analysis system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, I-Min A.; Markowitz, Victor M.; Chu, Ken

The Integrated Microbial Genomes with Microbiome Samples (IMG/M: https://img.jgi.doe.gov/m/) system contains annotated DNA and RNA sequence data of (i) archaeal, bacterial, eukaryotic and viral genomes from cultured organisms, (ii) single cell genomes (SCG) and genomes from metagenomes (GFM) from uncultured archaea, bacteria and viruses and (iii) metagenomes from environmental, host associated and engineered microbiome samples. Sequence data are generated by DOE's Joint Genome Institute (JGI), submitted by individual scientists, or collected from public sequence data archives. Structural and functional annotation is carried out by JGI's genome and metagenome annotation pipelines. A variety of analytical and visualization tools provide support formore » examining and comparing IMG/M's datasets. IMG/M allows open access interactive analysis of publicly available datasets, while manual curation, submission and access to private datasets and computationally intensive workspace-based analysis require login/password access to its expert review(ER) companion system (IMG/M ER: https://img.jgi.doe.gov/ mer/). Since the last report published in the 2014 NAR Database Issue, IMG/M's dataset content has tripled in terms of number of datasets and overall protein coding genes, while its analysis tools have been extended to cope with the rapid growth in the number and size of datasets handled by the system.« less

IMG/M: integrated genome and metagenome comparative data analysis system

PubMed Central

Chen, I-Min A.; Markowitz, Victor M.; Chu, Ken; Palaniappan, Krishna; Szeto, Ernest; Pillay, Manoj; Ratner, Anna; Huang, Jinghua; Andersen, Evan; Huntemann, Marcel; Varghese, Neha; Hadjithomas, Michalis; Tennessen, Kristin; Nielsen, Torben; Ivanova, Natalia N.; Kyrpides, Nikos C.

2017-01-01

The Integrated Microbial Genomes with Microbiome Samples (IMG/M: https://img.jgi.doe.gov/m/) system contains annotated DNA and RNA sequence data of (i) archaeal, bacterial, eukaryotic and viral genomes from cultured organisms, (ii) single cell genomes (SCG) and genomes from metagenomes (GFM) from uncultured archaea, bacteria and viruses and (iii) metagenomes from environmental, host associated and engineered microbiome samples. Sequence data are generated by DOE's Joint Genome Institute (JGI), submitted by individual scientists, or collected from public sequence data archives. Structural and functional annotation is carried out by JGI's genome and metagenome annotation pipelines. A variety of analytical and visualization tools provide support for examining and comparing IMG/M's datasets. IMG/M allows open access interactive analysis of publicly available datasets, while manual curation, submission and access to private datasets and computationally intensive workspace-based analysis require login/password access to its expert review (ER) companion system (IMG/M ER: https://img.jgi.doe.gov/mer/). Since the last report published in the 2014 NAR Database Issue, IMG/M's dataset content has tripled in terms of number of datasets and overall protein coding genes, while its analysis tools have been extended to cope with the rapid growth in the number and size of datasets handled by the system. PMID:27738135

Collection of annotated data in a clinical validation study for alarm algorithms in intensive care--a methodologic framework.

PubMed

Siebig, Sylvia; Kuhls, Silvia; Imhoff, Michael; Langgartner, Julia; Reng, Michael; Schölmerich, Jürgen; Gather, Ursula; Wrede, Christian E

2010-03-01

Monitoring of physiologic parameters in critically ill patients is currently performed by threshold alarm systems with high sensitivity but low specificity. As a consequence, a multitude of alarms are generated, leading to an impaired clinical value of these alarms due to reduced alertness of the intensive care unit (ICU) staff. To evaluate a new alarm procedure, we currently generate a database of physiologic data and clinical alarm annotations. Data collection is taking place at a 12-bed medical ICU. Patients with monitoring of at least heart rate, invasive arterial blood pressure, and oxygen saturation are included in the study. Numerical physiologic data at 1-second intervals, monitor alarms, and alarm settings are extracted from the surveillance network. Bedside video recordings are performed with network surveillance cameras. Based on the extracted data and the video recordings, alarms are clinically annotated by an experienced physician. The alarms are categorized according to their technical validity and clinical relevance by a taxonomy system that can be broadly applicable. Preliminary results showed that only 17% of the alarms were classified as relevant, and 44% were technically false. The presented system for collecting real-time bedside monitoring data in conjunction with video-assisted annotations of clinically relevant events is the first allowing the assessment of 24-hour periods and reduces the bias usually created by bedside observers in comparable studies. It constitutes the basis for the development and evaluation of "smart" alarm algorithms, which may help to reduce the number of alarms at the ICU, thereby improving patient safety. Copyright 2010 Elsevier Inc. All rights reserved.

Genome-Wide Microsatellite Characterization and Marker Development in the Sequenced Brassica Crop Species

PubMed Central

Shi, Jiaqin; Huang, Shunmou; Zhan, Jiepeng; Yu, Jingyin; Wang, Xinfa; Hua, Wei; Liu, Shengyi; Liu, Guihua; Wang, Hanzhong

2014-01-01

Although much research has been conducted, the pattern of microsatellite distribution has remained ambiguous, and the development/utilization of microsatellite markers has still been limited/inefficient in Brassica, due to the lack of genome sequences. In view of this, we conducted genome-wide microsatellite characterization and marker development in three recently sequenced Brassica crops: Brassica rapa, Brassica oleracea and Brassica napus. The analysed microsatellite characteristics of these Brassica species were highly similar or almost identical, which suggests that the pattern of microsatellite distribution is likely conservative in Brassica. The genomic distribution of microsatellites was highly non-uniform and positively or negatively correlated with genes or transposable elements, respectively. Of the total of 115 869, 185 662 and 356 522 simple sequence repeat (SSR) markers developed with high frequencies (408.2, 343.8 and 356.2 per Mb or one every 2.45, 2.91 and 2.81 kb, respectively), most represented new SSR markers, the majority had determined physical positions, and a large number were genic or putative single-locus SSR markers. We also constructed a comprehensive database for the newly developed SSR markers, which was integrated with public Brassica SSR markers and annotated genome components. The genome-wide SSR markers developed in this study provide a useful tool to extend the annotated genome resources of sequenced Brassica species to genetic study/breeding in different Brassica species. PMID:24130371

Genome-wide microsatellite characterization and marker development in the sequenced Brassica crop species.

PubMed

Shi, Jiaqin; Huang, Shunmou; Zhan, Jiepeng; Yu, Jingyin; Wang, Xinfa; Hua, Wei; Liu, Shengyi; Liu, Guihua; Wang, Hanzhong

2014-02-01

Although much research has been conducted, the pattern of microsatellite distribution has remained ambiguous, and the development/utilization of microsatellite markers has still been limited/inefficient in Brassica, due to the lack of genome sequences. In view of this, we conducted genome-wide microsatellite characterization and marker development in three recently sequenced Brassica crops: Brassica rapa, Brassica oleracea and Brassica napus. The analysed microsatellite characteristics of these Brassica species were highly similar or almost identical, which suggests that the pattern of microsatellite distribution is likely conservative in Brassica. The genomic distribution of microsatellites was highly non-uniform and positively or negatively correlated with genes or transposable elements, respectively. Of the total of 115 869, 185 662 and 356 522 simple sequence repeat (SSR) markers developed with high frequencies (408.2, 343.8 and 356.2 per Mb or one every 2.45, 2.91 and 2.81 kb, respectively), most represented new SSR markers, the majority had determined physical positions, and a large number were genic or putative single-locus SSR markers. We also constructed a comprehensive database for the newly developed SSR markers, which was integrated with public Brassica SSR markers and annotated genome components. The genome-wide SSR markers developed in this study provide a useful tool to extend the annotated genome resources of sequenced Brassica species to genetic study/breeding in different Brassica species.

DeepFruits: A Fruit Detection System Using Deep Neural Networks

PubMed Central

Sa, Inkyu; Ge, Zongyuan; Dayoub, Feras; Upcroft, Ben; Perez, Tristan; McCool, Chris

2016-01-01

This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). We adapt this model, through transfer learning, for the task of fruit detection using imagery obtained from two modalities: colour (RGB) and Near-Infrared (NIR). Early and late fusion methods are explored for combining the multi-modal (RGB and NIR) information. This leads to a novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0.807 to 0.838 for the detection of sweet pepper. In addition to improved accuracy, this approach is also much quicker to deploy for new fruits, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform). The model is retrained to perform the detection of seven fruits, with the entire process taking four hours to annotate and train the new model per fruit. PMID:27527168

DeepFruits: A Fruit Detection System Using Deep Neural Networks.

PubMed

Sa, Inkyu; Ge, Zongyuan; Dayoub, Feras; Upcroft, Ben; Perez, Tristan; McCool, Chris

2016-08-03

This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). We adapt this model, through transfer learning, for the task of fruit detection using imagery obtained from two modalities: colour (RGB) and Near-Infrared (NIR). Early and late fusion methods are explored for combining the multi-modal (RGB and NIR) information. This leads to a novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0 . 807 to 0 . 838 for the detection of sweet pepper. In addition to improved accuracy, this approach is also much quicker to deploy for new fruits, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform). The model is retrained to perform the detection of seven fruits, with the entire process taking four hours to annotate and train the new model per fruit.

Multimedia consultation session recording and playback using Java-based browser in global PACS

NASA Astrophysics Data System (ADS)

Martinez, Ralph; Shah, Pinkesh J.; Yu, Yuan-Pin

1998-07-01

The current version of the Global PACS software system uses a Java-based implementation of the Remote Consultation and Diagnosis (RCD) system. The Java RCD includes a multimedia consultation session between physicians that includes text, static image, image annotation, and audio data. The JAVA RCD allows 2-4 physicians to collaborate on a patient case. It allows physicians to join the session via WWW Java-enabled browsers or stand alone RCD application. The RCD system includes a distributed database archive system for archiving and retrieving patient and session data. The RCD system can be used for store and forward scenarios, case reviews, and interactive RCD multimedia sessions. The RCD system operates over the Internet, telephone lines, or in a private Intranet. A multimedia consultation session can be recorded, and then played back at a later time for review, comments, and education. A session can be played back using Java-enabled WWW browsers on any operating system platform. The JAVA RCD system shows that a case diagnosis can be captured digitally and played back with the original real-time temporal relationships between data streams. In this paper, we describe design and implementation of the RCD session playback.

pGenN, a Gene Normalization Tool for Plant Genes and Proteins in Scientific Literature

PubMed Central

Ding, Ruoyao; Arighi, Cecilia N.; Lee, Jung-Youn; Wu, Cathy H.; Vijay-Shanker, K.

2015-01-01

Background Automatically detecting gene/protein names in the literature and connecting them to databases records, also known as gene normalization, provides a means to structure the information buried in free-text literature. Gene normalization is critical for improving the coverage of annotation in the databases, and is an essential component of many text mining systems and database curation pipelines. Methods In this manuscript, we describe a gene normalization system specifically tailored for plant species, called pGenN (pivot-based Gene Normalization). The system consists of three steps: dictionary-based gene mention detection, species assignment, and intra species normalization. We have developed new heuristics to improve each of these phases. Results We evaluated the performance of pGenN on an in-house expertly annotated corpus consisting of 104 plant relevant abstracts. Our system achieved an F-value of 88.9% (Precision 90.9% and Recall 87.2%) on this corpus, outperforming state-of-art systems presented in BioCreative III. We have processed over 440,000 plant-related Medline abstracts using pGenN. The gene normalization results are stored in a local database for direct query from the pGenN web interface (proteininformationresource.org/pgenn/). The annotated literature corpus is also publicly available through the PIR text mining portal (proteininformationresource.org/iprolink/). PMID:26258475

Analysis of student engagement in an online annotation system in the context of a flipped introductory physics class

NASA Astrophysics Data System (ADS)

Miller, Kelly; Zyto, Sacha; Karger, David; Yoo, Junehee; Mazur, Eric

2016-12-01

We discuss student participation in an online social annotation forum over two semesters of a flipped, introductory physics course at Harvard University. We find that students who engage in high-level discussion online, especially by providing answers to their peers' questions, make more gains in conceptual understanding than students who do not. This is true regardless of students' physics background. We find that we can steer online interaction towards more productive and engaging discussion by seeding the discussion and managing the size of the sections. Seeded sections produce higher quality annotations and a greater proportion of generative threads than unseeded sections. Larger sections produce longer threads; however, beyond a certain section size, the quality of the discussion decreases.

Annotated checklist of the Blennidus subgenus Agraphoderus species from Peru with description of B. bombonensis n. sp. and synonymic notes (Coleoptera: Carabidae: Pterostichinae).

PubMed

Allegro, Gianni; Giachino, Pier Mauro

2015-08-13

Forty species belonging to the subgenus Agraphoderus of Blennidus have been recorded so far from Peru. An annotated checklist is provided with information about their type locality, distribution and habitat. The nomenclature of each species is also provided, together with some notes on their systematic status. Blennidus bombonensis n. sp. from Cerro de Pasco is described; Blennidus pseudangularis nomen novum for Ogmopleura angularis Straneo, 1993 (nec Straneo, 1985) is proposed and the following synonymies are stated: Blennidus pseudangularis Allegro & Giachino, nomen novum = Blennidus rectangulus (Straneo, 1993) syn. nov.; Ogmopleura minor Straneo, 1993 = Blennidus rectangulus (Straneo, 1993) syn. nov. Type specimens of most species are illustrated, as well as male genitalia. Finally, a revised key to all the Agraphoderus species from Peru is provided.

Developing eThread pipeline using SAGA-pilot abstraction for large-scale structural bioinformatics.

PubMed

Ragothaman, Anjani; Boddu, Sairam Chowdary; Kim, Nayong; Feinstein, Wei; Brylinski, Michal; Jha, Shantenu; Kim, Joohyun

2014-01-01

While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread--a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure.

Developing eThread Pipeline Using SAGA-Pilot Abstraction for Large-Scale Structural Bioinformatics

PubMed Central

Ragothaman, Anjani; Feinstein, Wei; Jha, Shantenu; Kim, Joohyun

2014-01-01

While most of computational annotation approaches are sequence-based, threading methods are becoming increasingly attractive because of predicted structural information that could uncover the underlying function. However, threading tools are generally compute-intensive and the number of protein sequences from even small genomes such as prokaryotes is large typically containing many thousands, prohibiting their application as a genome-wide structural systems biology tool. To leverage its utility, we have developed a pipeline for eThread—a meta-threading protein structure modeling tool, that can use computational resources efficiently and effectively. We employ a pilot-based approach that supports seamless data and task-level parallelism and manages large variation in workload and computational requirements. Our scalable pipeline is deployed on Amazon EC2 and can efficiently select resources based upon task requirements. We present runtime analysis to characterize computational complexity of eThread and EC2 infrastructure. Based on results, we suggest a pathway to an optimized solution with respect to metrics such as time-to-solution or cost-to-solution. Our eThread pipeline can scale to support a large number of sequences and is expected to be a viable solution for genome-scale structural bioinformatics and structure-based annotation, particularly, amenable for small genomes such as prokaryotes. The developed pipeline is easily extensible to other types of distributed cyberinfrastructure. PMID:24995285

Hypertext Annotation: Effects of Presentation Formats and Learner Proficiency on Reading Comprehension and Vocabulary Learning in Foreign Languages

ERIC Educational Resources Information Center

Chen, I-Jung; Yen, Jung-Chuan

2013-01-01

This study extends current knowledge by exploring the effect of different annotation formats, namely in-text annotation, glossary annotation, and pop-up annotation, on hypertext reading comprehension in a foreign language and vocabulary acquisition across student proficiencies. User attitudes toward the annotation presentation were also…

Ethnicity and the Mass Media in Canada: An Annotated Bibliography = Bibliographie annotee sur les ethnies et les media au Canada.

ERIC Educational Resources Information Center

Karim, Karim H.; Sansom, Gareth

This bibliography lists 135 publications dealing with the portrayal and employment of Canadians of all ethnic backgrounds in mainstream Canadian media. A broad definition of mass media encompassing all widely distributed communicative materials, including educational textbooks and forms of hate material, is used for the purposes of the…

Genome Sequences of Two Listeria monocytogenes Strains from Nectarines Associated with Listeriosis in 2014

PubMed Central

Lu, Chunye; Marjanovic, Olivera; Kiang, David

2017-01-01

ABSTRACT Listeria monocytogenes is an important foodborne pathogen. Here, we present the annotated whole genome of Listeria monocytogenes strains F14M01297-C2 and F14M01297-C4, isolated from nectarines distributed by a packing facility in California during an investigation of listeriosis associated with stone fruit in 2014. PMID:28729255

Transcriptome analysis of Capsicum annuum varieties Mandarin and Blackcluster: assembly, annotation and molecular marker discovery.

PubMed

Ahn, Yul-Kyun; Tripathi, Swati; Kim, Jeong-Ho; Cho, Young-Il; Lee, Hye-Eun; Kim, Do-Sun; Woo, Jong-Gyu; Cho, Myeong-Cheoul

2014-01-10

Next generation sequencing technologies have proven to be a rapid and cost-effective means to assemble and characterize gene content and identify molecular markers in various organisms. Pepper (Capsicum annuum L., Solanaceae) is a major staple vegetable crop, which is economically important and has worldwide distribution. High-throughput transcriptome profiling of two pepper cultivars, Mandarin and Blackcluster, using 454 GS-FLX pyrosequencing yielded 279,221 and 316,357 sequenced reads with a total 120.44 and 142.54Mb of sequence data (average read length of 431 and 450 nucleotides). These reads resulted from 17,525 and 16,341 'isogroups' and were assembled into 19,388 and 18,057 isotigs, and 22,217 and 13,153 singletons for both the cultivars, respectively. Assembled sequences were annotated functionally based on homology to genes in multiple public databases. Detailed sequence variant analysis identified a total of 9701 and 12,741 potential SNPs which eventually resulted in 1025 and 1059 genotype specific SNPs, for both the varieties, respectively, after examining SNP frequency distribution for each mapped unigenes. These markers for pepper will be highly valuable for marker-assisted breeding and other genetic studies. © 2013 Elsevier B.V. All rights reserved.

Annotated checklist of marine fishes from the Sanctuary of Bahía Chamela, Mexico with occurrence and biogeographic data.

PubMed

Galván-Villa, Cristian Moisés; Ríos-Jara, Eduardo; Bastida-Izaguirre, Dafne; Hastings, Philip A; Balart, Eduardo F

2016-01-01

An annotated checklist of marine fishes of the Sanctuary of Islands and Islets of Bahía Chamela in the central Mexican Pacific is presented. Records of fish species were obtained by different methods including visual census, sampling with anesthetics, fisherman-nets, and trawling with a biological dredge. Additional records were obtained from natural history collections and publications. The list comprises 196 species in 64 families and 141 genera. The Carangidae is the most speciose family with 11 species, followed by the Labridae with 10 and the Pomacentridae with nine. Fourteen species are endemic in Mexican Pacific waters, but none is restricted to Bahía Chamela. The most dominant species recorded during underwater surveys were Epinephelus labriformis, Stegastes flavilatus, and Halichoeres dispilus. Most species are of tropical affinities distributed throughout the tropical eastern Pacific (123), eastern Pacific (23), and Mexican Pacific (14). Other species are known from the eastern and Indo-Pacific regions (18), eastern Pacific and western Atlantic oceans (2), and some are circumtropical (9). A new record of the Gulf Brotula Ogilbia ventralis is provided for the Bahía Chamela and its geographical distribution is extended to Mexican central Pacific.

PIGD: a database for intronless genes in the Poaceae.

PubMed

Yan, Hanwei; Jiang, Cuiping; Li, Xiaoyu; Sheng, Lei; Dong, Qing; Peng, Xiaojian; Li, Qian; Zhao, Yang; Jiang, Haiyang; Cheng, Beijiu

2014-10-01

Intronless genes are a feature of prokaryotes; however, they are widespread and unequally distributed among eukaryotes and represent an important resource to study the evolution of gene architecture. Although many databases on exons and introns exist, there is currently no cohesive database that collects intronless genes in plants into a single database. In this study, we present the Poaceae Intronless Genes Database (PIGD), a user-friendly web interface to explore information on intronless genes from different plants. Five Poaceae species, Sorghum bicolor, Zea mays, Setaria italica, Panicum virgatum and Brachypodium distachyon, are included in the current release of PIGD. Gene annotations and sequence data were collected and integrated from different databases. The primary focus of this study was to provide gene descriptions and gene product records. In addition, functional annotations, subcellular localization prediction and taxonomic distribution are reported. PIGD allows users to readily browse, search and download data. BLAST and comparative analyses are also provided through this online database, which is available at http://pigd.ahau.edu.cn/. PIGD provides a solid platform for the collection, integration and analysis of intronless genes in the Poaceae. As such, this database will be useful for subsequent bio-computational analysis in comparative genomics and evolutionary studies.

Evolutionary interrogation of human biology in well-annotated genomic framework of rhesus macaque.

PubMed

Zhang, Shi-Jian; Liu, Chu-Jun; Yu, Peng; Zhong, Xiaoming; Chen, Jia-Yu; Yang, Xinzhuang; Peng, Jiguang; Yan, Shouyu; Wang, Chenqu; Zhu, Xiaotong; Xiong, Jingwei; Zhang, Yong E; Tan, Bertrand Chin-Ming; Li, Chuan-Yun

2014-05-01

With genome sequence and composition highly analogous to human, rhesus macaque represents a unique reference for evolutionary studies of human biology. Here, we developed a comprehensive genomic framework of rhesus macaque, the RhesusBase2, for evolutionary interrogation of human genes and the associated regulations. A total of 1,667 next-generation sequencing (NGS) data sets were processed, integrated, and evaluated, generating 51.2 million new functional annotation records. With extensive NGS annotations, RhesusBase2 refined the fine-scale structures in 30% of the macaque Ensembl transcripts, reporting an accurate, up-to-date set of macaque gene models. On the basis of these annotations and accurate macaque gene models, we further developed an NGS-oriented Molecular Evolution Gateway to access and visualize macaque annotations in reference to human orthologous genes and associated regulations (www.rhesusbase.org/molEvo). We highlighted the application of this well-annotated genomic framework in generating hypothetical link of human-biased regulations to human-specific traits, by using mechanistic characterization of the DIEXF gene as an example that provides novel clues to the understanding of digestive system reduction in human evolution. On a global scale, we also identified a catalog of 9,295 human-biased regulatory events, which may represent novel elements that have a substantial impact on shaping human transcriptome and possibly underpin recent human phenotypic evolution. Taken together, we provide an NGS data-driven, information-rich framework that will broadly benefit genomics research in general and serves as an important resource for in-depth evolutionary studies of human biology.

KID Project: an internet-based digital video atlas of capsule endoscopy for research purposes

PubMed Central

Koulaouzidis, Anastasios; Iakovidis, Dimitris K.; Yung, Diana E.; Rondonotti, Emanuele; Kopylov, Uri; Plevris, John N.; Toth, Ervin; Eliakim, Abraham; Wurm Johansson, Gabrielle; Marlicz, Wojciech; Mavrogenis, Georgios; Nemeth, Artur; Thorlacius, Henrik; Tontini, Gian Eugenio

2017-01-01

Background and aims  Capsule endoscopy (CE) has revolutionized small-bowel (SB) investigation. Computational methods can enhance diagnostic yield (DY); however, incorporating machine learning algorithms (MLAs) into CE reading is difficult as large amounts of image annotations are required for training. Current databases lack graphic annotations of pathologies and cannot be used. A novel database, KID, aims to provide a reference for research and development of medical decision support systems (MDSS) for CE. Methods  Open-source software was used for the KID database. Clinicians contribute anonymized, annotated CE images and videos. Graphic annotations are supported by an open-access annotation tool (Ratsnake). We detail an experiment based on the KID database, examining differences in SB lesion measurement between human readers and a MLA. The Jaccard Index (JI) was used to evaluate similarity between annotations by the MLA and human readers. Results  The MLA performed best in measuring lymphangiectasias with a JI of 81 ± 6 %. The other lesion types were: angioectasias (JI 64 ± 11 %), aphthae (JI 64 ± 8 %), chylous cysts (JI 70 ± 14 %), polypoid lesions (JI 75 ± 21 %), and ulcers (JI 56 ± 9 %). Conclusion  MLA can perform as well as human readers in the measurement of SB angioectasias in white light (WL). Automated lesion measurement is therefore feasible. KID is currently the only open-source CE database developed specifically to aid development of MDSS. Our experiment demonstrates this potential. PMID:28580415

MicroScope in 2017: an expanding and evolving integrated resource for community expertise of microbial genomes

PubMed Central

Vallenet, David; Calteau, Alexandra; Cruveiller, Stéphane; Gachet, Mathieu; Lajus, Aurélie; Josso, Adrien; Mercier, Jonathan; Renaux, Alexandre; Rollin, Johan; Rouy, Zoe; Roche, David; Scarpelli, Claude; Médigue, Claudine

2017-01-01

The annotation of genomes from NGS platforms needs to be automated and fully integrated. However, maintaining consistency and accuracy in genome annotation is a challenging problem because millions of protein database entries are not assigned reliable functions. This shortcoming limits the knowledge that can be extracted from genomes and metabolic models. Launched in 2005, the MicroScope platform (http://www.genoscope.cns.fr/agc/microscope) is an integrative resource that supports systematic and efficient revision of microbial genome annotation, data management and comparative analysis. Effective comparative analysis requires a consistent and complete view of biological data, and therefore, support for reviewing the quality of functional annotation is critical. MicroScope allows users to analyze microbial (meta)genomes together with post-genomic experiment results if any (i.e. transcriptomics, re-sequencing of evolved strains, mutant collections, phenotype data). It combines tools and graphical interfaces to analyze genomes and to perform the expert curation of gene functions in a comparative context. Starting with a short overview of the MicroScope system, this paper focuses on some major improvements of the Web interface, mainly for the submission of genomic data and on original tools and pipelines that have been developed and integrated in the platform: computation of pan-genomes and prediction of biosynthetic gene clusters. Today the resource contains data for more than 6000 microbial genomes, and among the 2700 personal accounts (65% of which are now from foreign countries), 14% of the users are performing expert annotations, on at least a weekly basis, contributing to improve the quality of microbial genome annotations. PMID:27899624

Proposal for a common nomenclature for fragment ions in mass spectra of lipids

PubMed Central

Hartler, Jürgen; Christiansen, Klaus; Gallego, Sandra F.; Peng, Bing; Ahrends, Robert

2017-01-01

Advances in mass spectrometry-based lipidomics have in recent years prompted efforts to standardize the annotation of the vast number of lipid molecules that can be detected in biological systems. These efforts have focused on cataloguing, naming and drawing chemical structures of intact lipid molecules, but have provided no guidelines for annotation of lipid fragment ions detected using tandem and multi-stage mass spectrometry, albeit these fragment ions are mandatory for structural elucidation and high confidence lipid identification, especially in high throughput lipidomics workflows. Here we propose a nomenclature for the annotation of lipid fragment ions, describe its implementation and present a freely available web application, termed ALEX123 lipid calculator, that can be used to query a comprehensive database featuring curated lipid fragmentation information for more than 430,000 potential lipid molecules from 47 lipid classes covering five lipid categories. We note that the nomenclature is generic, extendable to stable isotope-labeled lipid molecules and applicable to automated annotation of fragment ions detected by most contemporary lipidomics platforms, including LC-MS/MS-based routines. PMID:29161304

Proposal for a common nomenclature for fragment ions in mass spectra of lipids.

PubMed

Pauling, Josch K; Hermansson, Martin; Hartler, Jürgen; Christiansen, Klaus; Gallego, Sandra F; Peng, Bing; Ahrends, Robert; Ejsing, Christer S

2017-01-01

Advances in mass spectrometry-based lipidomics have in recent years prompted efforts to standardize the annotation of the vast number of lipid molecules that can be detected in biological systems. These efforts have focused on cataloguing, naming and drawing chemical structures of intact lipid molecules, but have provided no guidelines for annotation of lipid fragment ions detected using tandem and multi-stage mass spectrometry, albeit these fragment ions are mandatory for structural elucidation and high confidence lipid identification, especially in high throughput lipidomics workflows. Here we propose a nomenclature for the annotation of lipid fragment ions, describe its implementation and present a freely available web application, termed ALEX123 lipid calculator, that can be used to query a comprehensive database featuring curated lipid fragmentation information for more than 430,000 potential lipid molecules from 47 lipid classes covering five lipid categories. We note that the nomenclature is generic, extendable to stable isotope-labeled lipid molecules and applicable to automated annotation of fragment ions detected by most contemporary lipidomics platforms, including LC-MS/MS-based routines.

iPad: Semantic annotation and markup of radiological images.

PubMed

Rubin, Daniel L; Rodriguez, Cesar; Shah, Priyanka; Beaulieu, Chris

2008-11-06

Radiological images contain a wealth of information,such as anatomy and pathology, which is often not explicit and computationally accessible. Information schemes are being developed to describe the semantic content of images, but such schemes can be unwieldy to operationalize because there are few tools to enable users to capture structured information easily as part of the routine research workflow. We have created iPad, an open source tool enabling researchers and clinicians to create semantic annotations on radiological images. iPad hides the complexity of the underlying image annotation information model from users, permitting them to describe images and image regions using a graphical interface that maps their descriptions to structured ontologies semi-automatically. Image annotations are saved in a variety of formats,enabling interoperability among medical records systems, image archives in hospitals, and the Semantic Web. Tools such as iPad can help reduce the burden of collecting structured information from images, and it could ultimately enable researchers and physicians to exploit images on a very large scale and glean the biological and physiological significance of image content.

Solar Tutorial and Annotation Resource (STAR)

NASA Astrophysics Data System (ADS)

Showalter, C.; Rex, R.; Hurlburt, N. E.; Zita, E. J.

2009-12-01

We have written a software suite designed to facilitate solar data analysis by scientists, students, and the public, anticipating enormous datasets from future instruments. Our “STAR" suite includes an interactive learning section explaining 15 classes of solar events. Users learn software tools that exploit humans’ superior ability (over computers) to identify many events. Annotation tools include time slice generation to quantify loop oscillations, the interpolation of event shapes using natural cubic splines (for loops, sigmoids, and filaments) and closed cubic splines (for coronal holes). Learning these tools in an environment where examples are provided prepares new users to comfortably utilize annotation software with new data. Upon completion of our tutorial, users are presented with media of various solar events and asked to identify and annotate the images, to test their mastery of the system. Goals of the project include public input into the data analysis of very large datasets from future solar satellites, and increased public interest and knowledge about the Sun. In 2010, the Solar Dynamics Observatory (SDO) will be launched into orbit. SDO’s advancements in solar telescope technology will generate a terabyte per day of high-quality data, requiring innovation in data management. While major projects develop automated feature recognition software, so that computers can complete much of the initial event tagging and analysis, still, that software cannot annotate features such as sigmoids, coronal magnetic loops, coronal dimming, etc., due to large amounts of data concentrated in relatively small areas. Previously, solar physicists manually annotated these features, but with the imminent influx of data it is unrealistic to expect specialized researchers to examine every image that computers cannot fully process. A new approach is needed to efficiently process these data. Providing analysis tools and data access to students and the public have proven efficient in similar astrophysical projects (e.g. the “Galaxy Zoo.”) For “crowdsourcing” to be effective for solar research, the public needs knowledge and skills to recognize and annotate key events on the Sun. Our tutorial can provide this training, with over 200 images and 18 movies showing examples of active regions, coronal dimmings, coronal holes, coronal jets, coronal waves, emerging flux, sigmoids, coronal magnetic loops, filaments, filament eruption, flares, loop oscillation, plage, surges, and sunspots. Annotation tools are provided for many of these events. Many features of the tutorial, such as mouse-over definitions and interactive annotation examples, are designed to assist people without previous experience in solar physics. After completing the tutorial, the user is presented with an interactive quiz: a series of movies and images to identify and annotate. The tutorial teaches the user, with feedback on correct and incorrect answers, until the user develops appropriate confidence and skill. This prepares users to annotate new data, based on their experience with event recognition and annotation tools. Trained users can contribute significantly to our data analysis tasks, even as our training tool contributes to public science literacy and interest in solar physics.

Quality of Computationally Inferred Gene Ontology Annotations

PubMed Central

Škunca, Nives; Altenhoff, Adrian; Dessimoz, Christophe

2012-01-01

Gene Ontology (GO) has established itself as the undisputed standard for protein function annotation. Most annotations are inferred electronically, i.e. without individual curator supervision, but they are widely considered unreliable. At the same time, we crucially depend on those automated annotations, as most newly sequenced genomes are non-model organisms. Here, we introduce a methodology to systematically and quantitatively evaluate electronic annotations. By exploiting changes in successive releases of the UniProt Gene Ontology Annotation database, we assessed the quality of electronic annotations in terms of specificity, reliability, and coverage. Overall, we not only found that electronic annotations have significantly improved in recent years, but also that their reliability now rivals that of annotations inferred by curators when they use evidence other than experiments from primary literature. This work provides the means to identify the subset of electronic annotations that can be relied upon—an important outcome given that >98% of all annotations are inferred without direct curation. PMID:22693439

A Genome-Wide Identification of Basic Helix-Loop-Helix Motifs in Pediculus humanus corporis (Phthiraptera: Pediculidae)

PubMed Central

Wang, Xu-Hua; Wang, Yong; Zhang, De-Bao; Liu, A-Ke; Yao, Qin; Chen, Ke-Ping

2014-01-01

Abstract Basic helix-loop-helix (bHLH) proteins comprise a large superfamily of transcription factors, which are involved in the regulation of various developmental processes. bHLH family members are widely distributed in various eukaryotes including yeast, fruit fly, zebrafish, mouse, and human. In this study, we identified 55 bHLH motifs encoded in genome sequence of the human body louse, Pediculus humanus corporis (Phthiraptera: Pediculidae). Phylogenetic analyses of the identified P. humanus corporis bHLH (PhcbHLH) motifs revealed that there are 23, 11, 9, 1, 10, and 1 member(s) in groups A, B, C, D, E, and F, respectively. Examination to GenBank annotations of the 55 PhcbHLH members indicated that 29 PhcbHLH proteins were annotated in consistence with our analytical result, 8 were annotated different with our analytical result, 12 were merely annotated as hypothetical protein, and the rest 6 were not deposited in GenBank. A comparison on insect bHLH gene composition revealed that human body louse possibly has more hairy and E(spl) genes than other insect species. Because hairy and E(spl) genes have been found to negatively regulate the differentiation of insect preneural cells, it is suggested that the existence of additional hairy and E(spl) genes in human body louse is probably the consequence of its long period adaptation to the relatively dark and stable environment. These data provide good references for further studies on regulatory functions of bHLH proteins in the growth and development of human body louse. PMID:25434030

Reptilian Transcriptomes v2.0: An Extensive Resource for Sauropsida Genomics and Transcriptomics

PubMed Central

Tzika, Athanasia C.; Ullate-Agote, Asier; Grbic, Djordje; Milinkovitch, Michel C.

2015-01-01

Despite the availability of deep-sequencing techniques, genomic and transcriptomic data remain unevenly distributed across phylogenetic groups. For example, reptiles are poorly represented in sequence databases, hindering functional evolutionary and developmental studies in these lineages substantially more diverse than mammals. In addition, different studies use different assembly and annotation protocols, inhibiting meaningful comparisons. Here, we present the “Reptilian Transcriptomes Database 2.0,” which provides extensive annotation of transcriptomes and genomes from species covering the major reptilian lineages. To this end, we sequenced normalized complementary DNA libraries of multiple adult tissues and various embryonic stages of the leopard gecko and the corn snake and gathered published reptilian sequence data sets from representatives of the four extant orders of reptiles: Squamata (snakes and lizards), the tuatara, crocodiles, and turtles. The LANE runner 2.0 software was implemented to annotate all assemblies within a single integrated pipeline. We show that this approach increases the annotation completeness of the assembled transcriptomes/genomes. We then built large concatenated protein alignments of single-copy genes and inferred phylogenetic trees that support the positions of turtles and the tuatara as sister groups of Archosauria and Squamata, respectively. The Reptilian Transcriptomes Database 2.0 resource will be updated to include selected new data sets as they become available, thus making it a reference for differential expression studies, comparative genomics and transcriptomics, linkage mapping, molecular ecology, and phylogenomic analyses involving reptiles. The database is available at www.reptilian-transcriptomes.org and can be enquired using a wwwblast server installed at the University of Geneva. PMID:26133641

Towards building high performance medical image management system for clinical trials

NASA Astrophysics Data System (ADS)

Wang, Fusheng; Lee, Rubao; Zhang, Xiaodong; Saltz, Joel

2011-03-01

Medical image based biomarkers are being established for therapeutic cancer clinical trials, where image assessment is among the essential tasks. Large scale image assessment is often performed by a large group of experts by retrieving images from a centralized image repository to workstations to markup and annotate images. In such environment, it is critical to provide a high performance image management system that supports efficient concurrent image retrievals in a distributed environment. There are several major challenges: high throughput of large scale image data over the Internet from the server for multiple concurrent client users, efficient communication protocols for transporting data, and effective management of versioning of data for audit trails. We study the major bottlenecks for such a system, propose and evaluate a solution by using a hybrid image storage with solid state drives and hard disk drives, RESTfulWeb Services based protocols for exchanging image data, and a database based versioning scheme for efficient archive of image revision history. Our experiments show promising results of our methods, and our work provides a guideline for building enterprise level high performance medical image management systems.

A study of active learning methods for named entity recognition in clinical text.

PubMed

Chen, Yukun; Lasko, Thomas A; Mei, Qiaozhu; Denny, Joshua C; Xu, Hua

2015-12-01

Named entity recognition (NER), a sequential labeling task, is one of the fundamental tasks for building clinical natural language processing (NLP) systems. Machine learning (ML) based approaches can achieve good performance, but they often require large amounts of annotated samples, which are expensive to build due to the requirement of domain experts in annotation. Active learning (AL), a sample selection approach integrated with supervised ML, aims to minimize the annotation cost while maximizing the performance of ML-based models. In this study, our goal was to develop and evaluate both existing and new AL methods for a clinical NER task to identify concepts of medical problems, treatments, and lab tests from the clinical notes. Using the annotated NER corpus from the 2010 i2b2/VA NLP challenge that contained 349 clinical documents with 20,423 unique sentences, we simulated AL experiments using a number of existing and novel algorithms in three different categories including uncertainty-based, diversity-based, and baseline sampling strategies. They were compared with the passive learning that uses random sampling. Learning curves that plot performance of the NER model against the estimated annotation cost (based on number of sentences or words in the training set) were generated to evaluate different active learning and the passive learning methods and the area under the learning curve (ALC) score was computed. Based on the learning curves of F-measure vs. number of sentences, uncertainty sampling algorithms outperformed all other methods in ALC. Most diversity-based methods also performed better than random sampling in ALC. To achieve an F-measure of 0.80, the best method based on uncertainty sampling could save 66% annotations in sentences, as compared to random sampling. For the learning curves of F-measure vs. number of words, uncertainty sampling methods again outperformed all other methods in ALC. To achieve 0.80 in F-measure, in comparison to random sampling, the best uncertainty based method saved 42% annotations in words. But the best diversity based method reduced only 7% annotation effort. In the simulated setting, AL methods, particularly uncertainty-sampling based approaches, seemed to significantly save annotation cost for the clinical NER task. The actual benefit of active learning in clinical NER should be further evaluated in a real-time setting. Copyright © 2015 Elsevier Inc. All rights reserved.

Modular assembly of transposable element arrays by microsatellite targeting in the guayule and rice genomes.

PubMed

Valdes Franco, José A; Wang, Yi; Huo, Naxin; Ponciano, Grisel; Colvin, Howard A; McMahan, Colleen M; Gu, Yong Q; Belknap, William R

2018-04-19

Guayule (Parthenium argentatum A. Gray) is a rubber-producing desert shrub native to Mexico and the United States. Guayule represents an alternative to Hevea brasiliensis as a source for commercial natural rubber. The efficient application of modern molecular/genetic tools to guayule improvement requires characterization of its genome. The 1.6 Gb guayule genome was sequenced, assembled and annotated. The final 1.5 Gb assembly, while fragmented (N 50  = 22 kb), maps > 95% of the shotgun reads and is essentially complete. Approximately 40,000 transcribed, protein encoding genes were annotated on the assembly. Further characterization of this genome revealed 15 families of small, microsatellite-associated, transposable elements (TEs) with unexpected chromosomal distribution profiles. These SaTar (Satellite Targeted) elements, which are non-autonomous Mu-like elements (MULEs), were frequently observed in multimeric linear arrays of unrelated individual elements within which no individual element is interrupted by another. This uniformly non-nested TE multimer architecture has not been previously described in either eukaryotic or prokaryotic genomes. Five families of similarly distributed non-autonomous MULEs (microsatellite associated, modularly assembled) were characterized in the rice genome. Families of TEs with similar structures and distribution profiles were identified in sorghum and citrus. The sequencing and assembly of the guayule genome provides a foundation for application of current crop improvement technologies to this plant. In addition, characterization of this genome revealed SaTar elements with distribution profiles unique among TEs. Satar targeting appears based on an alternative MULE recombination mechanism with the potential to impact gene evolution.