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Islamaj, Rezarta, Dongseop Kwon, Sun Kim, and Zhiyong Lu. "TeamTat: a collaborative text annotation tool." Nucleic Acids Research 48, W1 (May 8, 2020): W5—W11. http://dx.doi.org/10.1093/nar/gkaa333.
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Abstract Manually annotated data is key to developing text-mining and information-extraction algorithms. However, human annotation requires considerable time, effort and expertise. Given the rapid growth of biomedical literature, it is paramount to build tools that facilitate speed and maintain expert quality. While existing text annotation tools may provide user-friendly interfaces to domain experts, limited support is available for figure display, project management, and multi-user team annotation. In response, we developed TeamTat (https://www.teamtat.org), a web-based annotation tool (local setup available), equipped to manage team annotation projects engagingly and efficiently. TeamTat is a novel tool for managing multi-user, multi-label document annotation, reflecting the entire production life cycle. Project managers can specify annotation schema for entities and relations and select annotator(s) and distribute documents anonymously to prevent bias. Document input format can be plain text, PDF or BioC (uploaded locally or automatically retrieved from PubMed/PMC), and output format is BioC with inline annotations. TeamTat displays figures from the full text for the annotator's convenience. Multiple users can work on the same document independently in their workspaces, and the team manager can track task completion. TeamTat provides corpus quality assessment via inter-annotator agreement statistics, and a user-friendly interface convenient for annotation review and inter-annotator disagreement resolution to improve corpus quality.
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Mazhoud, Omar, Anis Kalboussi, and Ahmed Hadj Kacem. "Educational Recommender System based on Learner’s Annotative Activity." International Journal of Emerging Technologies in Learning (iJET) 16, no. 10 (May 25, 2021): 108. http://dx.doi.org/10.3991/ijet.v16i10.19955.
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In recent years, Educational Recommender Systems (ERSs) have attracted great attention as a solution towards addressing the problem of information overload in e-learning environments and providing relevant recommendations to online learners. These systems play a key role in helping learners to find educational resources relevant and pertinent to their profiles and context. So, it is necessary to identify information that helps learner’s profile definition and in identifying requests and interests. In this context, we suggest to take advantage of the annotation activity used usually in the learning context for different purposes and which may reflect certain learner’s characteristics useful as input data for the recommendation process. Therefore, we propose an educational recommender system of web services based on learner’s annotative activity to assist him in his learning activity. This process of recommendation is founded on two preparatory phases: the phase of modelling learner’s personality profile through analysis of annotation digital traces in learning environment realized through a profile constructor module and the phase of discovery of web services which can meet the goals of annotations made by learner via the web service discovery module. The evaluation of the developed annotation based recommendation system through empirical studies realized on groups of learners based on the Student’s t-test showed significant results.
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Wang, Han, Xinxiao Wu, and Yunde Jia. "Video Annotation via Image Groups from the Web." IEEE Transactions on Multimedia 16, no. 5 (August 2014): 1282–91. http://dx.doi.org/10.1109/tmm.2014.2312251.
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Ma, Zhigang, Feiping Nie, Yi Yang, Jasper R. R. Uijlings, and Nicu Sebe. "Web Image Annotation Via Subspace-Sparsity Collaborated Feature Selection." IEEE Transactions on Multimedia 14, no. 4 (August 2012): 1021–30. http://dx.doi.org/10.1109/tmm.2012.2187179.
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Wei, Chih-Hsuan, Alexis Allot, Robert Leaman, and Zhiyong Lu. "PubTator central: automated concept annotation for biomedical full text articles." Nucleic Acids Research 47, W1 (May 22, 2019): W587—W593. http://dx.doi.org/10.1093/nar/gkz389.
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AbstractPubTator Central (https://www.ncbi.nlm.nih.gov/research/pubtator/) is a web service for viewing and retrieving bioconcept annotations in full text biomedical articles. PubTator Central (PTC) provides automated annotations from state-of-the-art text mining systems for genes/proteins, genetic variants, diseases, chemicals, species and cell lines, all available for immediate download. PTC annotates PubMed (29 million abstracts) and the PMC Text Mining subset (3 million full text articles). The new PTC web interface allows users to build full text document collections and visualize concept annotations in each document. Annotations are downloadable in multiple formats (XML, JSON and tab delimited) via the online interface, a RESTful web service and bulk FTP. Improved concept identification systems and a new disambiguation module based on deep learning increase annotation accuracy, and the new server-side architecture is significantly faster. PTC is synchronized with PubMed and PubMed Central, with new articles added daily. The original PubTator service has served annotated abstracts for ∼300 million requests, enabling third-party research in use cases such as biocuration support, gene prioritization, genetic disease analysis, and literature-based knowledge discovery. We demonstrate the full text results in PTC significantly increase biomedical concept coverage and anticipate this expansion will both enhance existing downstream applications and enable new use cases.
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Hu, Mengqiu, Yang Yang, Fumin Shen, Luming Zhang, Heng Tao Shen, and Xuelong Li. "Robust Web Image Annotation via Exploring Multi-Facet and Structural Knowledge." IEEE Transactions on Image Processing 26, no. 10 (October 2017): 4871–84. http://dx.doi.org/10.1109/tip.2017.2717185.
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Wang, Han, Xiabi Liu, Xinxiao Wu, and Yunde Jia. "Cross-domain structural model for video event annotation via web images." Multimedia Tools and Applications 74, no. 23 (July 30, 2014): 10439–56. http://dx.doi.org/10.1007/s11042-014-2175-z.
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Lelong, Sebastien, Xinghua Zhou, Cyrus Afrasiabi, Zhongchao Qian, Marco Alvarado Cano, Ginger Tsueng, Jiwen Xin, et al. "BioThings SDK: a toolkit for building high-performance data APIs in biomedical research." Bioinformatics 38, no. 7 (January 10, 2022): 2077–79. http://dx.doi.org/10.1093/bioinformatics/btac017.
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Abstract Summary To meet the increased need of making biomedical resources more accessible and reusable, Web Application Programming Interfaces (APIs) or web services have become a common way to disseminate knowledge sources. The BioThings APIs are a collection of high-performance, scalable, annotation as a service APIs that automate the integration of biological annotations from disparate data sources. This collection of APIs currently includes MyGene.info, MyVariant.info and MyChem.info for integrating annotations on genes, variants and chemical compounds, respectively. These APIs are used by both individual researchers and application developers to simplify the process of annotation retrieval and identifier mapping. Here, we describe the BioThings Software Development Kit (SDK), a generalizable and reusable toolkit for integrating data from multiple disparate data sources and creating high-performance APIs. This toolkit allows users to easily create their own BioThings APIs for any data type of interest to them, as well as keep APIs up-to-date with their underlying data sources. Availability and implementation The BioThings SDK is built in Python and released via PyPI (https://pypi.org/project/biothings/). Its source code is hosted at its github repository (https://github.com/biothings/biothings.api). Supplementary information Supplementary data are available at Bioinformatics online.
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Park, Yeon-Ji, Min-a. Lee, Geun-Je Yang, Soo Jun Park, and Chae-Bong Sohn. "Biomedical Text NER Tagging Tool with Web Interface for Generating BERT-Based Fine-Tuning Dataset." Applied Sciences 12, no. 23 (November 24, 2022): 12012. http://dx.doi.org/10.3390/app122312012.
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In this paper, a tagging tool is developed to streamline the process of locating tags for each term and manually selecting the target term. It directly extracts the terms to be tagged from sentences and displays it to the user. It also increases tagging efficiency by allowing users to reflect candidate categories in untagged terms. It is based on annotations automatically generated using machine learning. Subsequently, this architecture is fine-tuned using Bidirectional Encoder Representations from Transformers (BERT) to enable the tagging of terms that cannot be captured using Named-Entity Recognition (NER). The tagged text data extracted using the proposed tagging tool can be used as an additional training dataset. The tagging tool, which receives and saves new NE annotation input online, is added to the NER and RE web interfaces using BERT. Annotation information downloaded by the user includes the category (e.g., diseases, genes/proteins) and the list of words associated to the named entity selected by the user. The results reveal that the RE and NER results are improved using the proposed web service by collecting more NE annotation data and fine-tuning the model using generated datasets. Our application programming interfaces and demonstrations are available to the public at via the website link provided in this paper.
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Barrett, Kristian, Cameron J. Hunt, Lene Lange, and Anne S. Meyer. "Conserved unique peptide patterns (CUPP) online platform: peptide-based functional annotation of carbohydrate active enzymes." Nucleic Acids Research 48, W1 (May 14, 2020): W110—W115. http://dx.doi.org/10.1093/nar/gkaa375.
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Abstract The CUPP platform includes a web server for functional annotation and sub-grouping of carbohydrate active enzymes (CAZymes) based on a novel peptide-based similarity assessment algorithm, i.e. protein grouping according to Conserved Unique Peptide Patterns (CUPP). This online platform is open to all users and there is no login requirement. The web server allows the user to perform genome-based annotation of carbohydrate active enzymes to CAZy families, CAZy subfamilies, CUPP groups and EC numbers (function) via assessment of peptide-motifs by CUPP. The web server is intended for functional annotation assessment of the CAZy inventory of prokaryotic and eukaryotic organisms from genomic DNA (up to 30MB compressed) or directly from amino acid sequences (up to 10MB compressed). The custom query sequences are assessed using the CUPP annotation algorithm, and the outcome is displayed in interactive summary result pages of CAZymes. The results displayed allow for inspection of members of the individual CUPP groups and include information about experimentally characterized members. The web server and the other resources on the CUPP platform can be accessed from https://cupp.info.
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Lachmann, Alexander, Kaeli A. Rizzo, Alon Bartal, Minji Jeon, Daniel J. B. Clarke, and Avi Ma’ayan. "PrismEXP: gene annotation prediction from stratified gene-gene co-expression matrices." PeerJ 11 (February 27, 2023): e14927. http://dx.doi.org/10.7717/peerj.14927.
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Background Gene-gene co-expression correlations measured by mRNA-sequencing (RNA-seq) can be used to predict gene annotations based on the co-variance structure within these data. In our prior work, we showed that uniformly aligned RNA-seq co-expression data from thousands of diverse studies is highly predictive of both gene annotations and protein-protein interactions. However, the performance of the predictions varies depending on whether the gene annotations and interactions are cell type and tissue specific or agnostic. Tissue and cell type-specific gene-gene co-expression data can be useful for making more accurate predictions because many genes perform their functions in unique ways in different cellular contexts. However, identifying the optimal tissues and cell types to partition the global gene-gene co-expression matrix is challenging. Results Here we introduce and validate an approach called PRediction of gene Insights from Stratified Mammalian gene co-EXPression (PrismEXP) for improved gene annotation predictions based on RNA-seq gene-gene co-expression data. Using uniformly aligned data from ARCHS4, we apply PrismEXP to predict a wide variety of gene annotations including pathway membership, Gene Ontology terms, as well as human and mouse phenotypes. Predictions made with PrismEXP outperform predictions made with the global cross-tissue co-expression correlation matrix approach on all tested domains, and training using one annotation domain can be used to predict annotations in other domains. Conclusions By demonstrating the utility of PrismEXP predictions in multiple use cases we show how PrismEXP can be used to enhance unsupervised machine learning methods to better understand the roles of understudied genes and proteins. To make PrismEXP accessible, it is provided via a user-friendly web interface, a Python package, and an Appyter. AVAILABILITY. The PrismEXP web-based application, with pre-computed PrismEXP predictions, is available from: https://maayanlab.cloud/prismexp; PrismEXP is also available as an Appyter: https://appyters.maayanlab.cloud/PrismEXP/; and as Python package: https://github.com/maayanlab/prismexp.
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Wang, Jiyao, Philippe Youkharibache, Dachuan Zhang, Christopher J. Lanczycki, Renata C. Geer, Thomas Madej, Lon Phan, et al. "iCn3D, a web-based 3D viewer for sharing 1D/2D/3D representations of biomolecular structures." Bioinformatics 36, no. 1 (June 20, 2019): 131–35. http://dx.doi.org/10.1093/bioinformatics/btz502.
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Abstract Motivation Build a web-based 3D molecular structure viewer focusing on interactive structural analysis. Results iCn3D (I-see-in-3D) can simultaneously show 3D structure, 2D molecular contacts and 1D protein and nucleotide sequences through an integrated sequence/annotation browser. Pre-defined and arbitrary molecular features can be selected in any of the 1D/2D/3D windows as sets of residues and these selections are synchronized dynamically in all displays. Biological annotations such as protein domains, single nucleotide variations, etc. can be shown as tracks in the 1D sequence/annotation browser. These customized displays can be shared with colleagues or publishers via a simple URL. iCn3D can display structure–structure alignments obtained from NCBI’s VAST+ service. It can also display the alignment of a sequence with a structure as identified by BLAST, and thus relate 3D structure to a large fraction of all known proteins. iCn3D can also display electron density maps or electron microscopy (EM) density maps, and export files for 3D printing. The following example URL exemplifies some of the 1D/2D/3D representations: https://www.ncbi.nlm.nih.gov/Structure/icn3d/full.html?mmdbid=1TUP&showanno=1&show2d=1&showsets=1. Availability and implementation iCn3D is freely available to the public. Its source code is available at https://github.com/ncbi/icn3d. Supplementary information Supplementary data are available at Bioinformatics online.
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König, Matthias. "cy3sabiork: A Cytoscape app for visualizing kinetic data from SABIO-RK." F1000Research 5 (July 18, 2016): 1736. http://dx.doi.org/10.12688/f1000research.9211.1.
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Kinetic data of biochemical reactions are essential for the creation of kinetic models of biochemical networks. One of the main resources of such information is SABIO-RK, a curated database for kinetic data of biochemical reactions and their related information. Despite the importance for computational modelling there has been no simple solution to visualize the kinetic data from SABIO-RK. In this work, I present cy3sabiork, an app for querying and visualization of kinetic data from SABIO-RK in Cytoscape. The kinetic information is accessible via a combination of graph structure and annotations of nodes, with provided information consisting of: (I) reaction details, enzyme and organism; (II) kinetic law, formula, parameters; (III) experimental conditions; (IV) publication; (V) additional annotations. cy3sabiork creates an intuitive visualization of kinetic entries in form of a species-reaction-kinetics graph, which reflects the reaction-centered approach of SABIO-RK. Kinetic entries can be imported in SBML format from either the SABIO-RK web interface or via web service queries. The app allows for easy comparison of kinetic data, visual inspection of the elements involved in the kinetic record and simple access to the annotation information of the kinetic record. I applied cy3sabiork in the computational modelling of galactose metabolism in the human liver.
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Gil-de-la-Fuente, Alberto, Maricruz Mamani-Huanca, María C. Stroe, Sergio Saugar, Alejandra Garcia-Alvarez, Axel A. Brakhage, Coral Barbas, and Abraham Otero. "Aspergillus Metabolome Database for Mass Spectrometry Metabolomics." Journal of Fungi 7, no. 5 (May 15, 2021): 387. http://dx.doi.org/10.3390/jof7050387.
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The Aspergillus Metabolome Database is a free online resource to perform metabolite annotation in mass spectrometry studies devoted to the genus Aspergillus. The database was created by retrieving and curating information on 2811 compounds present in 601 different species and subspecies of the genus Aspergillus. A total of 1514 scientific journals where these metabolites are mentioned were added as meta-information linked to their respective compounds in the database. A web service to query the database based on m/z (mass/charge ratio) searches was added to CEU Mass Mediator; these queries can be performed over the Aspergillus database only, or they can also include a user-selectable set of other general metabolomic databases. This functionality is offered via web applications and via RESTful services. Furthermore, the complete content of the database has been made available in .csv files and as a MySQL database to facilitate its integration into third-party tools. To the best of our knowledge, this is the first database and the first service specifically devoted to Aspergillus metabolite annotation based on m/z searches.
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Backes, Paul G., Kam S. Tso, and Gregory K. Tharp. "The Web Interface for Telescience." Presence: Teleoperators and Virtual Environments 8, no. 5 (October 1999): 531–39. http://dx.doi.org/10.1162/105474699566440.
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The Web Interface for Telescience (WITS) is an Internet-based tool that enables members of geographically distributed science teams to participate in daily planetary lander and rover mission planning. WITS enables the viewing of downlinked images and results in various ways, terrain-feature measurement and annotation, and the planning of daily mission activities. WITS is written in the Java language and is accessible by mission scientists and the general public via a Web browser. The public can use WITS to plan and simulate their own rover missions. WITS was used during the 1997 Mars Pathfinder mission primarily for public outreach and was evaluated as a science operations tool at the Jet Propulsion Laboratory (JPL). WITS will be used as an operations tool in the 1998 lander mission, which will land on Mars in December 1999, and in the 2001 and 2003 lander-rover missions to Mars.
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Ruta, Michele, Floriano Scioscia, Maria Di Summa, Saverio Ieva, Eugenio Di Sciascio, and Marco Sacco. "Semantic Matchmaking for Kinect-Based Posture and Gesture Recognition." International Journal of Semantic Computing 08, no. 04 (December 2014): 491–514. http://dx.doi.org/10.1142/s1793351x14400169.
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Innovative analysis methods applied to data extracted by off-the-shelf peripherals can provide useful results in activity recognition without requiring large computational resources. In this paper a framework is proposed for automated posture and gesture recognition, exploiting depth data provided by a commercial tracking device. The detection problem is handled as a semantic-based resource discovery. A general data model and the corresponding ontology provide the formal underpinning for posture and gesture annotation via standard Semantic Web languages. Hence, a logic-based matchmaking, exploiting non-standard inference services, allows to: (i) detect postures via on-the-fly comparison of the annotations with standard posture descriptions stored as instances of a proper Knowledge Base; (ii) compare subsequent postures in order to recognize gestures. The framework has been implemented in a prototypical tool and experimental tests have been carried out on a reference dataset. Preliminary results indicate the feasibility of the proposed approach.
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Sejdiu, Besmir, Florije Ismaili, and Lule Ahmedi. "Integration of Semantics Into Sensor Data for the IoT." International Journal on Semantic Web and Information Systems 16, no. 4 (October 2020): 1–25. http://dx.doi.org/10.4018/ijswis.2020100101.
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The internet of things (IoT) as an evolving technology represents an active scientific research field in recognizing research challenges associated with its application in various domains, ranging from consumer convenience, smart energy, and resource saving to IoT enterprises. Sensors are crucial components of IoT that relay the collected data in the form of the data stream for further processing. Interoperability of various connected digital resources is a key challenge in IoT environments. The enrichment of raw sensor data with semantic annotations using concept definitions from ontologies enables more expressive data representation that supports knowledge discovery. In this paper, a systematic review of integration of semantics into sensor data for the IoT is provided. The conducted review is focused on analyzing the main solutions of adding semantic annotations to the sensor data, standards that enable all types of sensor data via the web, existing models of stream data annotation, and the IoT trend domains that use semantics.
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Kumagai, Masahiko, Daiki Nishikawa, Yoshihiro Kawahara, Hironobu Wakimoto, Ryutaro Itoh, Norio Tabei, Tsuyoshi Tanaka, and Takeshi Itoh. "TASUKE+: a web-based platform for exploring GWAS results and large-scale resequencing data." DNA Research 26, no. 6 (September 20, 2019): 445–52. http://dx.doi.org/10.1093/dnares/dsz022.
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Abstract Recent revolutionary advancements in sequencing technologies have made it possible to obtain mass quantities of genome-scale sequence data in a cost-effective manner and have drastically altered molecular biological studies. To utilize these sequence data, genome-wide association studies (GWASs) have become increasingly important. Hence, there is an urgent need to develop a visualization tool that enables efficient data retrieval, integration of GWAS results with diverse information and rapid public release of such large-scale genotypic and phenotypic data. We developed a web-based genome browser TASUKE+ (https://tasuke.dna.affrc.go.jp/), which is equipped with the following functions: (i) interactive GWAS results visualization with genome resequencing data and annotation information, (ii) PCR primer design, (iii) phylogenetic tree reconstruction and (iv) data sharing via the web. GWAS results can be displayed in parallel with polymorphism data, read depths and annotation information in an interactive and scalable manner. Users can design PCR primers for polymorphic sites of interest. In addition, a molecular phylogenetic tree of any region can be reconstructed so that the overall relationship among the examined genomes can be understood intuitively at a glance. All functions are implemented through user-friendly web-based interfaces so that researchers can easily share data with collaborators in remote places without extensive bioinformatics knowledge.
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Ausland, Catherine, Jinfang Zheng, Haidong Yi, Bowen Yang, Tang Li, Xuehuan Feng, Bo Zheng, and Yanbin Yin. "dbCAN-PUL: a database of experimentally characterized CAZyme gene clusters and their substrates." Nucleic Acids Research 49, no. D1 (September 17, 2020): D523—D528. http://dx.doi.org/10.1093/nar/gkaa742.
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Abstract PULs (polysaccharide utilization loci) are discrete gene clusters of CAZymes (Carbohydrate Active EnZymes) and other genes that work together to digest and utilize carbohydrate substrates. While PULs have been extensively characterized in Bacteroidetes, there exist PULs from other bacterial phyla, as well as archaea and metagenomes, that remain to be catalogued in a database for efficient retrieval. We have developed an online database dbCAN-PUL (http://bcb.unl.edu/dbCAN_PUL/) to display experimentally verified CAZyme-containing PULs from literature with pertinent metadata, sequences, and annotation. Compared to other online CAZyme and PUL resources, dbCAN-PUL has the following new features: (i) Batch download of PUL data by target substrate, species/genome, genus, or experimental characterization method; (ii) Annotation for each PUL that displays associated metadata such as substrate(s), experimental characterization method(s) and protein sequence information, (iii) Links to external annotation pages for CAZymes (CAZy), transporters (UniProt) and other genes, (iv) Display of homologous gene clusters in GenBank sequences via integrated MultiGeneBlast tool and (v) An integrated BLASTX service available for users to query their sequences against PUL proteins in dbCAN-PUL. With these features, dbCAN-PUL will be an important repository for CAZyme and PUL research, complementing our other web servers and databases (dbCAN2, dbCAN-seq).
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Čermák, František, and Alexandr Rosen. "The case of InterCorp, a multilingual parallel corpus." International Journal of Corpus Linguistics 17, no. 3 (December 31, 2012): 411–27. http://dx.doi.org/10.1075/ijcl.17.3.05cer.
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This paper introduces InterCorp, a parallel corpus including texts in Czech and 27 other languages, available for online searches via a web interface. After discussing some issues and merits of a multilingual resource we argue that it has an important role especially for languages with fewer native speakers, supporting both comparative research and studies of the language from the perspective of other languages. We proceed with an overview of the corpus — the strategy and criteria for including new texts, the representation of available languages and text types, linguistic annotation, and a sketch of pre-processing issues. Finally, we present the search interface and suggest some research opportunities.
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Anastasiadi, M., E. Bragin, P. Biojoux, A. Ahamed, J. Burgin, K. de Castro Cogle, S. Llaneza-Lago, et al. "CRAMER: a lightweight, highly customizable web-based genome browser supporting multiple visualization instances." Bioinformatics 36, no. 11 (February 28, 2020): 3556–57. http://dx.doi.org/10.1093/bioinformatics/btaa146.
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Abstract Summary In recent years, the ability to generate genomic data has increased dramatically along with the demand for easily personalized and customizable genome browsers for effective visualization of diverse types of data. Despite the large number of web-based genome browsers available nowadays, none of the existing tools provides means for creating multiple visualization instances without manual set up on the deployment server side. The Cranfield Genome Browser (CRAMER) is an open-source, lightweight and highly customizable web application for interactive visualization of genomic data. Once deployed, CRAMER supports seamless creation of multiple visualization instances in parallel while allowing users to control and customize multiple tracks. The application is deployed on a Node.js server and is supported by a MongoDB database which stored all customizations made by the users allowing quick navigation between instances. Currently, the browser supports visualizing a large number of file formats for genome annotation, variant calling, reads coverage and gene expression. Additionally, the browser supports direct Javascript coding for personalized tracks, providing a whole new level of customization both functionally and visually. Tracks can be added via direct file upload or processed in real-time via links to files stored remotely on an FTP repository. Furthermore, additional tracks can be added by users via simple drag and drop to an existing visualization instance. Availability and implementation CRAMER is implemented in JavaScript and is publicly available on GitHub on https://github.com/FadyMohareb/cramer. The application is released under an MIT licence and can be deployed on any server running Linux or Mac OS. Contact f.mohareb@cranfield.ac.uk Supplementary information Supplementary data are available at Bioinformatics online.
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Cooper, Sinclair, Elizabeth S. Wadsworth, Torsten Ochsenreiter, Alasdair Ivens, Nicholas J. Savill, and Achim Schnaufer. "Assembly and annotation of the mitochondrial minicircle genome of a differentiation-competent strain of Trypanosoma brucei." Nucleic Acids Research 47, no. 21 (October 30, 2019): 11304–25. http://dx.doi.org/10.1093/nar/gkz928.
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Abstract Kinetoplastids are protists defined by one of the most complex mitochondrial genomes in nature, the kinetoplast. In the sleeping sickness parasite Trypanosoma brucei, the kinetoplast is a chain mail-like network of two types of interlocked DNA molecules: a few dozen ∼23-kb maxicircles (homologs of the mitochondrial genome of other eukaryotes) and thousands of ∼1-kb minicircles. Maxicircles encode components of respiratory chain complexes and the mitoribosome. Several maxicircle-encoded mRNAs undergo extensive post-transcriptional RNA editing via addition and deletion of uridines. The process is mediated by hundreds of species of minicircle-encoded guide RNAs (gRNAs), but the precise number of minicircle classes and gRNA genes was unknown. Here we present the first essentially complete assembly and annotation of the kinetoplast genome of T. brucei. We have identified 391 minicircles, encoding not only ∼930 predicted ‘canonical’ gRNA genes that cover nearly all known editing events (accessible via the web at http://hank.bio.ed.ac.uk), but also ∼370 ‘non-canonical’ gRNA genes of unknown function. Small RNA transcriptome data confirmed expression of the majority of both categories of gRNAs. Finally, we have used our data set to refine definitions for minicircle structure and to explore dynamics of minicircle copy numbers.
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Papadopoulou, Maria, Christophe Roche, and Eleni-Melina Tamiolaki. "The LACRIMALit Ontology of Crisis: An Event-Centric Model for Digital History." Information 13, no. 8 (August 22, 2022): 398. http://dx.doi.org/10.3390/info13080398.
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The article presents the building of an event-centric model for the computational representation of crisis events using an ontology encoded in the Web Ontology Language (OWL). The work presented here is done in collaboration with the Leaders and Crisis Management in Ancient Literature. A Comparative Approach (LACRIMALit) project, (2022–2025) hosted at the Institute for Mediterranean Studies/Foundation for Research and Technology (IMS-FORTH). A key outcome of the project is the LACRIMALit ontology that aims principally at the semantic annotation of ancient Greek historiographical texts in open access via Perseus Digital Library. The ontology will facilitate reasoning on and across these documents and enable their semantic querying. The tagset of annotations, concepts, relations, and terms of the ontology will be both human and machine readable, extensible and reusable. The annotated corpus of texts to be produced will be available for sophisticated queries based on the concepts and relations, defined by the ontologies. This will considerably improve the string-based querying of the texts in their present digital format. This article presents the principles of conceptualization of the domain in the three dimensions: domain knowledge (mainly classes illustrated with some individuals), linguistic dimension (terms, proper names, definite descriptions), and references.
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Kapoor, Muskan, Christopher K. Tuggle, Tony Burdett, Timothy Tickle, Peter Harrison, Christine Elsik, Nicholas Provart, et al. "PSII-6 Computational Tools and Resources for Analysis and Exploration of Single-Cell Rnaseq Data in Agriculture." Journal of Animal Science 101, Supplement_2 (October 28, 2023): 267–68. http://dx.doi.org/10.1093/jas/skad341.303.
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Abstract The agriculture genomics community has numerous data submission standards available but little experience describing and storing single-cell (e.g., scRNAseq) data. Other single-cell genomics infrastructure efforts, such as the Human Cell Atlas Data Coordination Platform (HCA DCP), have resources that could benefit our community. For example, the HCA DCP is integrated with Terra, a cloud-native workbench for computational biology developed by Broad, Verily, and Microsoft that houses tools for scGenomics analysis. We will describe a pilot-scale project to determine if our current metadata standards for livestock and crops can be used to ingest scRNAseq datasets in a manner consistent with HCA DCP standards and if established resources (e.g., Terra) can be used to analyze the ingested data. Currently, the most comprehensive data ingestion portal for high throughput sequencing datasets from plants, fungi, protists, and animals/humans is Annotare (located at EMBL-European Bioinformatics Institute), ensures that sufficient metadata are collected to enable re-analysis and dissemination via the Single Cell Expression Atlas (SCEA). Annotare supports user-directed annotation and processing of their data, as well as search tools via the SCEA and transferred to the Galaxy analysis space. For animal datasets, another EMBL-EBI portal, the FAANG portal, has been developed that provides bulk and scRNAseq data access. scRNAseq data/metadata can be submitted to FAANG using a semi-automated process. We have extended this tool for scRNAseq data so that files can be validated using the HCA DCP metadata and data validation service. These files are also incorporated using EMBL-EBI’s HCA DCP ingestion service and transferred to Terra for further analysis. Once incorporated, datasets will augment the DCP resource for the scientific community. In an extension of these efforts, we have also created a Shiny-based web application, called Shiny-PIGGI, for the single cell-level transcriptomic study of pig immune tissues and peripheral blood mononuclear cells, which will be an important resource for improved annotation of porcine immune genes and cell types. The Shiny-PIGGI (https://shinypiggi.ansci.iastate.edu) is implemented completely in R, runs on any modern web browser, and requires no programming. This tool thus increases accessibility through eliminating technical training requirements for using Seurat object and related R packages commonly used in scRNAseq analysis. Our main goal was to develop an interactive web application that allows users such as animal scientists and immunologists to visualize and analyze biological datasets. This tool will be demonstrated at the meeting. We intend to further build upon these existing tools to construct a scientist-friendly data resource and analytical ecosystem to facilitate single cell-level genomic analysis through data ingestion, storage, retrieval, re-use, visualization, and comparative annotation across agricultural species.
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Lemos, Daniela Lucas da Silva, Dalton Lopes Martins, Asla Medeiros e. Sá, Luciana Conrado Martins, and Danielle do Carmo. "A Proposal in Creating a Semantic Repository for Digital 3D Replicas: The Case of Modernist Sculptures in Public Spaces of Rio De Janeiro." KNOWLEDGE ORGANIZATION 49, no. 3 (2022): 151–71. http://dx.doi.org/10.5771/0943-7444-2022-3-151.
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The demand for integrating and sharing heterogeneous data online has attracted the interest of cultural institutions in making information access and retrieval more effective via Semantic Web technologies. The present study proposes a digital repository for 3D scans of modernist sculptures in public spaces in the city of Rio de Janeiro, Brazil, with a view to ensuring access, use, reuse and preservation of this information. This is a qualitative exploratory experimental study based on the scientific literature and specific empirical material. It presents the analysis results of vocabularies for physical artifact documents and their digital counterparts on the Semantic Web and a discussion on how these align with the nature of the metadata determined here, as well as a metadata modeling prototype implemented on the Tainacan platform and aimed at cataloging digital 3D replicas. We claim that the proposed model for documenting cultural heritage assets on Tainacan is easy to implement, in that it uses accessible technology with a wide internet user base, highly expressive in its descriptions of 3D and multimedia content and based on well-established metadata and ontology standards recommended by regulatory bodies and communities such as the World Wide Web Consortium and International Organization for Standardization. Keywords: networked heritage documents, digital repositories, 3D digitization, semantic annotation
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Cordonnier-Pratt, Marie-Michèle, Chun Liang, Haiming Wang, Dmitri S. Kolychev, Feng Sun, Robert Freeman, Robert Sullivan, and Lee H. Pratt. "MAGIC Database and Interfaces: An Integrated Package for Gene Discovery and Expression." Comparative and Functional Genomics 5, no. 3 (2004): 268–75. http://dx.doi.org/10.1002/cfg.399.
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The rapidly increasing rate at which biological data is being produced requires a corresponding growth in relational databases and associated tools that can help laboratories contend with that data. With this need in mind, we describe here a Modular Approach to a Genomic, Integrated and Comprehensive (MAGIC) Database. This Oracle 9i database derives from an initial focus in our laboratory on gene discovery via production and analysis of expressed sequence tags (ESTs), and subsequently on gene expression as assessed by both EST clustering and microarrays. The MAGIC Gene Discovery portion of the database focuses on information derived from DNA sequences and on its biological relevance. In addition to MAGIC SEQ-LIMS, which is designed to support activities in the laboratory, it contains several additional subschemas. The latter include MAGIC Admin for database administration, MAGIC Sequence for sequence processing as well as sequence and clone attributes, MAGIC Cluster for the results of EST clustering, MAGIC Polymorphism in support of microsatellite and single-nucleotide-polymorphism discovery, and MAGIC Annotation for electronic annotation by BLAST and BLAT. The MAGIC Microarray portion is a MIAME-compliant database with two components at present. These are MAGIC Array-LIMS, which makes possible remote entry of all information into the database, and MAGIC Array Analysis, which provides data mining and visualization. Because all aspects of interaction with the MAGIC Database are via a web browser, it is ideally suited not only for individual research laboratories but also for core facilities that serve clients at any distance.
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Avram, Oren, Dana Rapoport, Shir Portugez, and Tal Pupko. "M1CR0B1AL1Z3R—a user-friendly web server for the analysis of large-scale microbial genomics data." Nucleic Acids Research 47, W1 (May 22, 2019): W88—W92. http://dx.doi.org/10.1093/nar/gkz423.
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Abstract Large-scale mining and analysis of bacterial datasets contribute to the comprehensive characterization of complex microbial dynamics within a microbiome and among different bacterial strains, e.g., during disease outbreaks. The study of large-scale bacterial evolutionary dynamics poses many challenges. These include data-mining steps, such as gene annotation, ortholog detection, sequence alignment and phylogeny reconstruction. These steps require the use of multiple bioinformatics tools and ad-hoc programming scripts, making the entire process cumbersome, tedious and error-prone due to manual handling. This motivated us to develop the M1CR0B1AL1Z3R web server, a ‘one-stop shop’ for conducting microbial genomics data analyses via a simple graphical user interface. Some of the features implemented in M1CR0B1AL1Z3R are: (i) extracting putative open reading frames and comparative genomics analysis of gene content; (ii) extracting orthologous sets and analyzing their size distribution; (iii) analyzing gene presence–absence patterns; (iv) reconstructing a phylogenetic tree based on the extracted orthologous set; (v) inferring GC-content variation among lineages. M1CR0B1AL1Z3R facilitates the mining and analysis of dozens of bacterial genomes using advanced techniques, with the click of a button. M1CR0B1AL1Z3R is freely available at https://microbializer.tau.ac.il/.
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Ramzi, Ahmad Bazli, Muhammad Lutfi Che Me, Ummul Syafiqah Ruslan, Syarul Nataqain Baharum, and Nor Azlan Nor Muhammad. "Insight into plant cell wall degradation and pathogenesis of Ganoderma boninense via comparative genome analysis." PeerJ 7 (December 18, 2019): e8065. http://dx.doi.org/10.7717/peerj.8065.
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Background G. boninense is a hemibiotrophic fungus that infects oil palms (Elaeis guineensis Jacq.) causing basal stem rot (BSR) disease and consequent massive economic losses to the oil palm industry. The pathogenicity of this white-rot fungus has been associated with cell wall degrading enzymes (CWDEs) released during saprophytic and necrotrophic stage of infection of the oil palm host. However, there is a lack of information available on the essentiality of CWDEs in wood-decaying process and pathogenesis of this oil palm pathogen especially at molecular and genome levels. Methods In this study, comparative genome analysis was carried out using the G. boninense NJ3 genome to identify and characterize carbohydrate-active enzyme (CAZymes) including CWDE in the fungal genome. Augustus pipeline was employed for gene identification in G. boninense NJ3 and the produced protein sequences were analyzed via dbCAN pipeline and PhiBase 4.5 database annotation for CAZymes and plant-host interaction (PHI) gene analysis, respectively. Comparison of CAZymes from G. boninense NJ3 was made against G. lucidum, a well-studied model Ganoderma sp. and five selected pathogenic fungi for CAZymes characterization. Functional annotation of PHI genes was carried out using Web Gene Ontology Annotation Plot (WEGO) and was used for selecting candidate PHI genes related to cell wall degradation of G. boninense NJ3. Results G. boninense was enriched with CAZymes and CWDEs in a similar fashion to G. lucidum that corroborate with the lignocellulolytic abilities of both closely-related fungal strains. The role of polysaccharide and cell wall degrading enzymes in the hemibiotrophic mode of infection of G. boninense was investigated by analyzing the fungal CAZymes with necrotrophic Armillaria solidipes, A. mellea, biotrophic Ustilago maydis, Melampsora larici-populina and hemibiotrophic Moniliophthora perniciosa. Profiles of the selected pathogenic fungi demonstrated that necrotizing pathogens including G. boninense NJ3 exhibited an extensive set of CAZymes as compared to the more CAZymes-limited biotrophic pathogens. Following PHI analysis, several candidate genes including polygalacturonase, endo β-1,3-xylanase, β-glucanase and laccase were identified as potential CWDEs that contribute to the plant host interaction and pathogenesis. Discussion This study employed bioinformatics tools for providing a greater understanding of the biological mechanisms underlying the production of CAZymes in G. boninense NJ3. Identification and profiling of the fungal polysaccharide- and lignocellulosic-degrading enzymes would further facilitate in elucidating the infection mechanisms through the production of CWDEs by G. boninense. Identification of CAZymes and CWDE-related PHI genes in G. boninense would serve as the basis for functional studies of genes associated with the fungal virulence and pathogenicity using systems biology and genetic engineering approaches.
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Abdul Baqi, Huda Abdulaali, Ghazali Sulong, Siti Zaiton Mohd Hashim, and Zinah S.Abdul jabar. "Innovative Sketch Board Mining for Online image Retrieval." Modern Applied Science 11, no. 3 (November 22, 2016): 13. http://dx.doi.org/10.5539/mas.v11n3p13.
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Developing an accurate and efficient Sketch-Based Image Retrieval (SBIR) method in determining the resemblances between the user's query and image stream has been a never-ending quest in digital data communication era. The main challenge is to overcome the asymmetry between a binary sketch and a full-color image. We introduce a unique sketch board mining method to recover the online web images. This image conceptual retrieval is performed by matching the sketch query with the relevant terminology of selected images. A systematic sequence is followed, including the sketch drawing by the user in interpreting its geometrical shape of the conceptual form based on annotation metadata matching technique achieved automatically from Google engines, indexing and clustering the selected images via data mining. The sketch mining board being built in dynamic drawing state used a set of features to generalize sketch board conceptualization in semantic level. Images from the global repository are retrieved via a semantic match of the user's sketch query with them. Excellent retrieval of hand-drawn sketches is found to achieve the recall rate within 0.1 to 0.8 and a precision rate is 0.7 to 0.98. The proposed technique solved many problems that stat-of-art suffered from SBIR (e.g. scaling, transport, imperfect) sketch. Furthermore, it is demonstrated that the proposed technique allowed us to exploit high-level features to search the web effectively and may constitute a basis for efficient and precise image recovery tool.
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Grzegorzewski, Jan, Janosch Brandhorst, Kathleen Green, Dimitra Eleftheriadou, Yannick Duport, Florian Barthorscht, Adrian Köller, Danny Yu Jia Ke, Sara De Angelis, and Matthias König. "PK-DB: pharmacokinetics database for individualized and stratified computational modeling." Nucleic Acids Research 49, no. D1 (November 5, 2020): D1358—D1364. http://dx.doi.org/10.1093/nar/gkaa990.
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Abstract A multitude of pharmacokinetics studies have been published. However, due to the lack of an open database, pharmacokinetics data, as well as the corresponding meta-information, have been difficult to access. We present PK-DB (https://pk-db.com), an open database for pharmacokinetics information from clinical trials. PK-DB provides curated information on (i) characteristics of studied patient cohorts and subjects (e.g. age, bodyweight, smoking status, genetic variants); (ii) applied interventions (e.g. dosing, substance, route of application); (iii) pharmacokinetic parameters (e.g. clearance, half-life, area under the curve) and (iv) measured pharmacokinetic time-courses. Key features are the representation of experimental errors, the normalization of measurement units, annotation of information to biological ontologies, calculation of pharmacokinetic parameters from concentration-time profiles, a workflow for collaborative data curation, strong validation rules on the data, computational access via a REST API as well as human access via a web interface. PK-DB enables meta-analysis based on data from multiple studies and data integration with computational models. A special focus lies on meta-data relevant for individualized and stratified computational modeling with methods like physiologically based pharmacokinetic (PBPK), pharmacokinetic/pharmacodynamic (PK/PD), or population pharmacokinetic (pop PK) modeling.
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Wiley, Emily A., and Nicholas A. Stover. "Immediate Dissemination of Student Discoveries to a Model Organism Database Enhances Classroom-Based Research Experiences." CBE—Life Sciences Education 13, no. 1 (March 2014): 131–38. http://dx.doi.org/10.1187/cbe.13-07-0140.
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Use of inquiry-based research modules in the classroom has soared over recent years, largely in response to national calls for teaching that provides experience with scientific processes and methodologies. To increase the visibility of in-class studies among interested researchers and to strengthen their impact on student learning, we have extended the typical model of inquiry-based labs to include a means for targeted dissemination of student-generated discoveries. This initiative required: 1) creating a set of research-based lab activities with the potential to yield results that a particular scientific community would find useful and 2) developing a means for immediate sharing of student-generated results. Working toward these goals, we designed guides for course-based research aimed to fulfill the need for functional annotation of the Tetrahymena thermophila genome, and developed an interactive Web database that links directly to the official Tetrahymena Genome Database for immediate, targeted dissemination of student discoveries. This combination of research via the course modules and the opportunity for students to immediately “publish” their novel results on a Web database actively used by outside scientists culminated in a motivational tool that enhanced students’ efforts to engage the scientific process and pursue additional research opportunities beyond the course.
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Jain, Neha, Kathleen F. Mittendorf, Marilyn Holt, Michele Lenoue-Newton, Ian Maurer, Clinton Miller, Matthew Stachowiak, et al. "The My Cancer Genome clinical trial data model and trial curation workflow." Journal of the American Medical Informatics Association 27, no. 7 (June 1, 2020): 1057–66. http://dx.doi.org/10.1093/jamia/ocaa066.
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Abstract Objective As clinical trials evolve in complexity, clinical trial data models that can capture relevant trial data in meaningful, structured annotations and computable forms are needed to support accrual. Material and Methods We have developed a clinical trial information model, curation information system, and a standard operating procedure for consistent and accurate annotation of cancer clinical trials. Clinical trial documents are pulled into the curation system from publicly available sources. Using a web-based interface, a curator creates structured assertions related to disease-biomarker eligibility criteria, therapeutic context, and treatment cohorts by leveraging our data model features. These structured assertions are published on the My Cancer Genome (MCG) website. Results To date, over 5000 oncology trials have been manually curated. All trial assertion data are available for public view on the MCG website. Querying our structured knowledge base, we performed a landscape analysis to assess the top diseases, biomarker alterations, and drugs featured across all cancer trials. Discussion Beyond curating commonly captured elements, such as disease and biomarker eligibility criteria, we have expanded our model to support the curation of trial interventions and therapeutic context (ie, neoadjuvant, metastatic, etc.), and the respective biomarker-disease treatment cohorts. To the best of our knowledge, this is the first effort to capture these fields in a structured format. Conclusion This paper makes a significant contribution to the field of biomedical informatics and knowledge dissemination for precision oncology via the MCG website. Key words knowledge representation, My Cancer Genome, precision oncology, knowledge curation, cancer informatics, clinical trial data model
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Duhan, Naveen, Simardeep Kaur, and Rakesh Kaundal. "ranchSATdb: A Genome-Wide Simple Sequence Repeat (SSR) Markers Database of Livestock Species for Mutant Germplasm Characterization and Improving Farm Animal Health." Genes 14, no. 7 (July 20, 2023): 1481. http://dx.doi.org/10.3390/genes14071481.
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Microsatellites, also known as simple sequence repeats (SSRs), are polymorphic loci that play an important role in genome research, animal breeding, and disease control. Ranch animals are important components of agricultural landscape. The ranch animal SSR database, ranchSATdb, is a web resource which contains 15,520,263 putative SSR markers. This database provides a comprehensive tool for performing end-to-end marker selection, from SSRs prediction to generating marker primers and their cross-species feasibility, visualization of the resulting markers, and finding similarities between the genomic repeat sequences all in one place without the need to switch between other resources. The user-friendly online interface allows users to browse SSRs by genomic coordinates, repeat motif sequence, chromosome, motif type, motif frequency, and functional annotation. Users may enter their preferred flanking area around the repeat to retrieve the nucleotide sequence, they can investigate SSRs present in the genic or the genes between SSRs, they can generate custom primers, and they can also execute in silico validation of primers using electronic PCR. For customized sequences, an SSR prediction pipeline called miSATminer is also built. New species will be added to this website’s database on a regular basis throughout time. To improve animal health via genomic selection, we hope that ranchSATdb will be a useful tool for mapping quantitative trait loci (QTLs) and marker-assisted selection. The web-resource is freely accessible at https://bioinfo.usu.edu/ranchSATdb/.
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Suehnholz, Sarah P., Moriah Nissan, Hongxin Zhang, Ritika Kundra, Calvin Lu, Amanda Dhaneshwar, Nicole Fernandez, et al. "Abstract 6585: OncoKB, MSK’s precision oncology knowledge base." Cancer Research 83, no. 7_Supplement (April 4, 2023): 6585. http://dx.doi.org/10.1158/1538-7445.am2023-6585.
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Abstract OncoKB, Memorial Sloan Kettering Cancer Center’s (MSK) precision oncology knowledge base (www.oncokb.org), is an FDA-recognized* somatic variant database that contains information about the oncogenic effect and clinical implications of genomic alterations in cancer. Since its 2016 public release, OncoKB has grown to include annotation for >5,770 alterations in ~700 cancer-associated genes. OncoKB data is integrated into the cBioPortal for Cancer Genomics and used to annotate >12,000 MSK patient sequencing reports annually, encompassing both solid tumor and hematological malignancies. Users in academic, commercial and hospital settings outside MSK can programmatically access OncoKB data via its web API with an OncoKB license, which is free for academic research. To date, users from ~ 1400 institutions across >70 countries have licensed access to OncoKB annotations. The OncoKB Therapeutic (Tx) Levels of Evidence assign tumor-type specific clinical actionability to individual mutational events based on data supporting whether an alteration is predictive of response to matched targeted therapies. To date, OncoKB includes 44 Level 1 genes (included in the FDA drug label), 23 Level 2 genes (included in professional guidelines), 33 Level 3A genes (predictive of drug response in well-powered clinical studies), 27 Level 4 genes (predictive of drug response based on compelling biological evidence), and 11 R1/R2 resistance genes. In 2022, several major content additions were made to OncoKB based on key shifts in the precision oncology landscape. For example, OncoKB included 2 new tumor-agnostic FDA drug approvals, dabrafenib + trametinib and selpercatinib for BRAF V600E and RET fusion-positive solid tumors respectively (Level 1), capturing 5 tumor-agnostic FDA drug approvals to date. OncoKB promoted ERBB2 oncogenic mutations and FGFR1 fusions to Level 1 following their inclusion as patient eligibility criteria in FDA drug labels for trastuzumab deruxtecan (NSCLC) and pemigatinib (myeloid/lymphoid neoplasms) respectively. NCCN guidelines for uterine sarcoma and pancreatic cancer listed PARP-inhibition for BRCA-mutant disease, making them Level 2 in these indications. Lastly, previously considered undruggable targets, TP53 Y220C and KRAS G12D, were included in OncoKB based on compelling evidence demonstrating response to allele-targeting drugs, PC14586 and RMC-6263, respectively. In sum, 7 novel clinically actionable biomarkers (Levels 1-4) and 11 follow-on precision oncology therapies for existing leveled biomarkers were added to OncoKB in 2022. Current OncoKB efforts are focused on prioritized high-volume cancer gene curation for annotation of whole exome/genome data, annotation of germline alterations and development of a clinical trials matching system. *FDA recognition of OncoKB is partial and limited to the information clearly marked on www.oncokb.org. Citation Format: Sarah P. Suehnholz, Moriah Nissan, Hongxin Zhang, Ritika Kundra, Calvin Lu, Amanda Dhaneshwar, Nicole Fernandez, Stephanie Carrero, Maria E. Arcila, Marc Ladanyi, Michael F. Berger, Aijazuddin Syed, Rose Brannon, Ross Levine, Ahmet Dogan, Ezra Rosen, Alexander Drilon, David B. Solit, Nikolaus Schultz, Debyani Chakravarty. OncoKB, MSK’s precision oncology knowledge base. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6585.
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O’Connor, Timothy, Charles E. Grant, Mikael Bodén, and Timothy L. Bailey. "T-Gene: improved target gene prediction." Bioinformatics 36, no. 12 (April 4, 2020): 3902–4. http://dx.doi.org/10.1093/bioinformatics/btaa227.
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Abstract Motivation Identifying the genes regulated by a given transcription factor (TF) (its ‘target genes’) is a key step in developing a comprehensive understanding of gene regulation. Previously, we developed a method (CisMapper) for predicting the target genes of a TF based solely on the correlation between a histone modification at the TF’s binding site and the expression of the gene across a set of tissues or cell lines. That approach is limited to organisms for which extensive histone and expression data are available, and does not explicitly incorporate the genomic distance between the TF and the gene. Results We present the T-Gene algorithm, which overcomes these limitations. It can be used to predict which genes are most likely to be regulated by a TF, and which of the TF’s binding sites are most likely involved in regulating particular genes. T-Gene calculates a novel score that combines distance and histone/expression correlation, and we show that this score accurately predicts when a regulatory element bound by a TF is in contact with a gene’s promoter, achieving median precision above 60%. T-Gene is easy to use via its web server or as a command-line tool, and can also make accurate predictions (median precision above 40%) based on distance alone when extensive histone/expression data is not available for the organism. T-Gene provides an estimate of the statistical significance of each of its predictions. Availability and implementation The T-Gene web server, source code, histone/expression data and genome annotation files are provided at http://meme-suite.org. Supplementary information Supplementary data are available at Bioinformatics online.
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Weber, Cédric R., Rahmad Akbar, Alexander Yermanos, Milena Pavlović, Igor Snapkov, Geir K. Sandve, Sai T. Reddy, and Victor Greiff. "immuneSIM: tunable multi-feature simulation of B- and T-cell receptor repertoires for immunoinformatics benchmarking." Bioinformatics 36, no. 11 (April 14, 2020): 3594–96. http://dx.doi.org/10.1093/bioinformatics/btaa158.
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Abstract Summary B- and T-cell receptor repertoires of the adaptive immune system have become a key target for diagnostics and therapeutics research. Consequently, there is a rapidly growing number of bioinformatics tools for immune repertoire analysis. Benchmarking of such tools is crucial for ensuring reproducible and generalizable computational analyses. Currently, however, it remains challenging to create standardized ground truth immune receptor repertoires for immunoinformatics tool benchmarking. Therefore, we developed immuneSIM, an R package that allows the simulation of native-like and aberrant synthetic full-length variable region immune receptor sequences by tuning the following immune receptor features: (i) species and chain type (BCR, TCR, single and paired), (ii) germline gene usage, (iii) occurrence of insertions and deletions, (iv) clonal abundance, (v) somatic hypermutation and (vi) sequence motifs. Each simulated sequence is annotated by the complete set of simulation events that contributed to its in silico generation. immuneSIM permits the benchmarking of key computational tools for immune receptor analysis, such as germline gene annotation, diversity and overlap estimation, sequence similarity, network architecture, clustering analysis and machine learning methods for motif detection. Availability and implementation The package is available via https://github.com/GreiffLab/immuneSIM and on CRAN at https://cran.r-project.org/web/packages/immuneSIM. The documentation is hosted at https://immuneSIM.readthedocs.io. Contact sai.reddy@ethz.ch or victor.greiff@medisin.uio.no Supplementary information Supplementary data are available at Bioinformatics online.
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Suehnholz, Sarah P., Moriah Nissan, Hongxin Zhang, Ritika Kundra, Calvin Lu, Benjamin Xu, Maria E. Arcila, et al. "Abstract 1189: OncoKB, MSK’s precision oncology knowledge base." Cancer Research 82, no. 12_Supplement (June 15, 2022): 1189. http://dx.doi.org/10.1158/1538-7445.am2022-1189.
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Abstract OncoKB, Memorial Sloan Kettering Cancer Center’s (MSK) precision oncology knowledge base (www.oncokb.org), is a comprehensive database that annotates the oncogenic effects and clinical actionability of somatic alterations in cancer. OncoKB supports variant interpretation by the cBioPortal for Cancer Genomics and is used to annotate >12,000 MSK patient sequencing reports annually. Since its introduction in 2016, OncoKB has expanded to include 5685 alterations in 682 genes, and in October 2021, it became the first somatic knowledge base to be partially recognized by the FDA. The scope of the OncoKB FDA recognition includes clinically actionable variants that map to an FDA level of evidence, the processes of variant curation, and policies regarding database oversight, personnel training and transparency of data sources and operations. This recognition credentials OncoKB as providing accurate, reliable and clinically meaningful information to the medical and scientific communities. The OncoKB Therapeutic (Tx) Levels of Evidence categorize variants based on their tumor type-specific predictive value of sensitivity or resistance to matched standard care or investigational targeted therapies. To date, OncoKB includes 43 Level 1 genes (included in the FDA drug label), 23 Level 2 genes (included in professional guidelines), 25 Level 3A genes (predictive of drug response in well-powered clinical studies), 23 Level 4 genes (predictive of drug response based on compelling biological evidence), and 11 R1 or R2 resistance genes. Initially focused on solid tumors, OncoKB was expanded to include hematologic disease annotation in 2019 and introduced Diagnostic (Dx) and Prognostic (Px) levels of evidence. All three level of evidence systems (Tx, Dx and Px) are consistent with the guidelines for evidence-based categorization of somatic variants published as a joint consensus recommendation by AMP/ASCO/CAP. OncoKB is governed by a Clinical Genomics Annotation Committee, composed of MSK physicians and scientists who ensure that the information captured is accurate and current, and an external advisory board composed of leaders in the clinical oncology and genomics communities who oversee OncoKB updates and progress. OncoKB curation rules and processes are transparent and documented in the OncoKB Curation Standard Operating Procedure, which is publicly available via the website. User feedback to OncoKB content is encouraged via the website and through cBioPortal. Queries or suggestions by OncoKB users are addressed by the OncoKB team within 72 hours. OncoKB offers licenses for academic, commercial and hospital use, with which users can programmatically access the web API. Future work includes coverage of additional cancer-associated genes, annotation of germline alterations that are predictive of drug response and/or associated with increased heritable cancer risk and the development of a clinical trial matching system. Citation Format: Sarah P. Suehnholz, Moriah Nissan, Hongxin Zhang, Ritika Kundra, Calvin Lu, Benjamin Xu, Maria E. Arcila, Marc Ladanyi, Michael F. Berger, Ahmet Zehir, Aijaz Syed, Julia E. Rudolph, Ross L. Levine, Ahmet Dogan, Jianjiong Gao, David B. Solit, Nikolaus Schultz, Debyani Chakravarty. OncoKB, MSK’s precision oncology knowledge base [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1189.
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Han, Ah-Reum, Hae Ran Park, Geum Jin Kim, Bo-Ram Kim, Ye-Ram Kim, Hyeon Hwa Park, Jisu Park, et al. "18:0 Lyso PC Derived by Bioactivity-Based Molecular Networking from Lentil Mutant Lines and Its Effects on High-Fat Diet-Induced Obese Mice." Molecules 26, no. 24 (December 13, 2021): 7547. http://dx.doi.org/10.3390/molecules26247547.
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Lentil (Lens culinaris; Fabaceae), one of the major pulse crops in the world, is an important source of proteins, prebiotics, lipids, and essential minerals as well as functional components such as flavonoids, polyphenols, and phenolic acids. To improve crop nutritional and medicinal traits, hybridization and mutation are widely used in plant breeding research. In this study, mutant lentil populations were generated by γ-irradiation for the development of new cultivars by inducing genetic diversity. Molecular networking via Global Natural Product Social Molecular Networking web platform and dipeptidyl peptide-IV inhibitor screening assay were utilized as tools for structure-based discovery of active components in active mutant lines selected among the lentil population. The bioactivity-based molecular networking analysis resulted in the annotation of the molecular class of phosphatidylcholine (PC) from the most active mutant line. Among PCs, 1-stearoyl-2-hydroxy-sn-glycero-3-phosphocholine (18:0 Lyso PC) was selected for further in vivo study of anti-obesity effect in a high-fat diet (HFD)-induced obese mouse model. The administration of 18:0 Lyso PC not only prevented body weight gain and decreased relative gonadal adipose tissue weight, but also attenuated the levels of total cholesterol, triglycerides, low-density lipoprotein cholesterol, and leptin in the sera of HFD-induced obese mice. Additionally, 18:0 Lyso PC treatment inhibited the increase of adipocyte area and crown-like structures in adipose tissue. Therefore, these results suggest that 18:0 Lyso PC is a potential compound to have protective effects against obesity, improving obese phenotype induced by HFD.
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Driller, Christine, Markus Koch, Marco Schmidt, Claus Weiland, Thomas Hörnschemeyer, Thomas Hickler, Giuseppe Abrami, et al. "Workflow and Current Achievements of BIOfid, an Information Service Mobilizing Biodiversity Data from Literature Sources." Biodiversity Information Science and Standards 2 (April 16, 2018): e25876. http://dx.doi.org/10.3897/biss.2.25876.
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BIOfid is a specialized information service currently being developed to mobilize biodiversity data dormant in printed historical and modern literature and to offer a platform for open access journals on the science of biodiversity. Our team of librarians, computer scientists and biologists produce high-quality text digitizations, develop new text-mining tools and generate detailed ontologies enabling semantic text analysis and semantic search by means of user-specific queries. In a pilot project we focus on German publications on the distribution and ecology of vascular plants, birds, moths and butterflies extending back to the Linnaeus period about 250 years ago. The three organism groups have been selected according to current demands of the relevant research community in Germany. The text corpus defined for this purpose comprises over 400 volumes with more than 100,000 pages to be digitized and will be complemented by journals from other digitization projects, copyright-free and project-related literature. With TextImager (Natural Language Processing & Text Visualization) and TextAnnotator (Discourse Semantic Annotation) we have already extended and launched tools that focus on the text-analytical section of our project. Furthermore, taxonomic and anatomical ontologies elaborated by us for the taxa prioritized by the project’s target group - German institutions and scientists active in biodiversity research - are constantly improved and expanded to maximize scientific data output. Our poster describes the general workflow of our project ranging from literature acquisition via software development, to data availability on the BIOfid web portal (http://biofid.de/), and the implementation into existing platforms which serve to promote global accessibility of biodiversity data.
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Suehnholz, Sarah P., Moriah Nissan, Hongxin Zhang, Ritika Kundra, Calvin Lu, Amanda Dhaneshwar, Nicole Fernandez, et al. "Abstract 3544: OncoKB™, MSK’s precision oncology knowledge base: 2023 updates." Cancer Research 84, no. 6_Supplement (March 22, 2024): 3544. http://dx.doi.org/10.1158/1538-7445.am2024-3544.
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Abstract OncoKBTM is Memorial Sloan Kettering Cancer Center’s (MSK) FDA-recognized precision oncology knowledge base that contains detailed, evidence-based information about the oncogenic effect and therapeutic implications of individual somatic mutations and structural alterations present in patient tumors. Since its public release in 2016, OncoKBTM has expanded to include annotation for >7,500 alterations in ~820 cancer-associated genes. OncoKB supports variant interpretation by the cBioPortal for Cancer Genomics, is used to annotate >15,000 MSK patient sequencing reports annually and its data is publicly available through the website (www.oncokb.org). Programmatic access to OncoKBTM data via its web-based API is freely available to users in an academic setting while users from commercial and hospital settings require a fee-based license. OncoKBTM utilizes its Therapeutic Levels of Evidence system to classify variants based on their tumor type-specific sensitivity or resistance to matched standard care or investigational targeted therapies. To date, OncoKB includes 49 Level 1 genes as well as MSI-H and TMB-H (included in the FDA drug label), 24 Level 2 genes (included in professional guidelines), 35 Level 3A genes (predictive of drug response in well-powered clinical studies), 27 Level 4 genes (predictive of drug response based on compelling biological evidence), and 11 R1/R2 resistance genes. In 2023, OncoKBTM captured the following notable changes in precision oncology drug development: Level 1 annotation of ESR1 ligand-binding domain mutations in breast cancer and promotion from Level 2 to Level 1 of ERBB2 amplification in colorectal cancer following the FDA drug approvals of elacestrant and tucatinib + trastuzumab, respectively. Additionally, KRAS G12C became a Level 2 biomarker in pancreatic and colon cancers with the listing of adagrasib and sotorasib as systemic therapy options for KRAS G12C-mutant disease in the NCCN pancreatic and colon cancer guidelines. IDH1/2 mutations in low grade glioma were annotated as Level 3A based on compelling clinical evidence demonstrating response to the IDH-specific inhibitor vorasidenib. Lastly, noting emerging data with the KRAS G12X-specific inhibitor, RMC-6236, OncoKBTM included all alleles at KRAS position G12 as Level 4. In sum, six novel clinically actionable biomarkers (all Level 1) and 14 follow-on precision oncology therapies for existing leveled biomarkers were added to OncoKBTM in the past year. OncoKBTM’s current focus includes coverage of additional cancer-associated genes, annotation of germline alterations and incorporation of OncoKBTM data into an electronic health record system. Citation Format: Sarah P. Suehnholz, Moriah Nissan, Hongxin Zhang, Ritika Kundra, Calvin Lu, Amanda Dhaneshwar, Nicole Fernandez, Benjamin Preiser, Maria E. Arcila, Marc Ladanyi, Michael F. Berger, Aijazuddin Syed, A. Rose Brannon, Ross Levine, Ahmet Dogan, Alexander Drilon, David B. Solit, Nikolaus Schultz, Debyani Chakravarty. OncoKB™, MSK’s precision oncology knowledge base: 2023 updates [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3544.
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Elfer, Katherine N., Kim Blenman, Sarah N. Dudgeon, Victor Garcia, Anna Ehinger, Xiaoxian Li, Amy Ly, et al. "Abstract 460: Tools for collecting pathologist annotations and understanding interobserver variability." Cancer Research 82, no. 12_Supplement (June 15, 2022): 460. http://dx.doi.org/10.1158/1538-7445.am2022-460.
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Abstract Background: The High Throughput Truthing (HTT) project is assessing pathologist agreement estimates of stromal tumor-infiltrating lymphocytes (sTILs) density in hematoxylin and eosin (H&E) stained breast cancer biopsy slides. The HTT project will create a validation dataset for artificial intelligence and machine learning (AI/ML) algorithms in digital pathology fit for a training, proficiency testing, and regulatory purpose. Methods: The pilot study crowdsourced pathologists to estimate sTIL density in 640 regions of interest (ROIs) across 64 slides via two modalities: an optical microscope (eeDAP) and two digital platforms (caMicroscope and PathPresenter). eeDAP is a hardware-software interface that presents the observer with pre-defined fields of view on H&E slides that corresponds to the ROI on a whole slide image. The PathPresenter and caMicroscope web-applications replicate the eeDAP workflow on the whole slide image without microscope hardware. In the workflow, pathologists evaluated the eligibility of an ROI for sTILs content then estimated the densities of tumor-associated stroma and sTILs in the ROI. Inter-pathologist agreement within ROIs was characterized with the root mean-squared difference. Using 72 of the highest variability ROIs selected from the pilot study, seven practicing pathologists participated in a subsequent focus group to improve the clinical training and data-collection workflows. Results: The pilot study collected 7,373 sTIL density estimates from 35 pathologists between February 2020 and May 2021. The focus group provided an additional 411 evaluations on 72 ROIs and in-depth discussions to identify pitfalls, gaps in training, and workflow improvements. Installation of eeDAP for physical data collection guided improvements in documentation and operation capabilities. Updated training materials refine the definition of tumor-associated stroma, provide reference images to differentiate sTILs from other cell types, and provide feedback during training. Digital and microscope platforms benefitted from enforcing registration and training, standardizing workflows, and accelerating eeDAP slide-image registration. Conclusions: The slides, images, and annotations provided by volunteer collaborators and participants for our pilot study led to improvements in data collection tools and crowdsourcing workflows to ensure consistency and minimize annotation variability. Our pilot dataset and analysis methods are available on a public HTT Github repository to allow open access to our methodology and feedback from the digital pathology and statistics communities. These data-collection and analysis methods are applicable to other quantitative biomarkers for validation of AI/ML algorithms. The lessons learned from this work will be applied to the HTT pivotal study and inform future quality data-collection methods of pathologist annotations. Citation Format: Katherine N. Elfer, Kim Blenman, Sarah N. Dudgeon, Victor Garcia, Anna Ehinger, Xiaoxian Li, Amy Ly, Dieter Peeters, Bruce Werness, Matthew Hanna, Roberto Salgado. Tools for collecting pathologist annotations and understanding interobserver variability [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 460.
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Li, Fuyi, Cunshuo Fan, Tatiana T. Marquez-Lago, André Leier, Jerico Revote, Cangzhi Jia, Yan Zhu, et al. "PRISMOID: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact." Briefings in Bioinformatics 21, no. 3 (June 4, 2019): 1069–79. http://dx.doi.org/10.1093/bib/bbz050.
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Abstract Post-translational modifications (PTMs) play very important roles in various cell signaling pathways and biological process. Due to PTMs’ extremely important roles, many major PTMs have been studied, while the functional and mechanical characterization of major PTMs is well documented in several databases. However, most currently available databases mainly focus on protein sequences, while the real 3D structures of PTMs have been largely ignored. Therefore, studies of PTMs 3D structural signatures have been severely limited by the deficiency of the data. Here, we develop PRISMOID, a novel publicly available and free 3D structure database for a wide range of PTMs. PRISMOID represents an up-to-date and interactive online knowledge base with specific focus on 3D structural contexts of PTMs sites and mutations that occur on PTMs and in the close proximity of PTM sites with functional impact. The first version of PRISMOID encompasses 17 145 non-redundant modification sites on 3919 related protein 3D structure entries pertaining to 37 different types of PTMs. Our entry web page is organized in a comprehensive manner, including detailed PTM annotation on the 3D structure and biological information in terms of mutations affecting PTMs, secondary structure features and per-residue solvent accessibility features of PTM sites, domain context, predicted natively disordered regions and sequence alignments. In addition, high-definition JavaScript packages are employed to enhance information visualization in PRISMOID. PRISMOID equips a variety of interactive and customizable search options and data browsing functions; these capabilities allow users to access data via keyword, ID and advanced options combination search in an efficient and user-friendly way. A download page is also provided to enable users to download the SQL file, computational structural features and PTM sites’ data. We anticipate PRISMOID will swiftly become an invaluable online resource, assisting both biologists and bioinformaticians to conduct experiments and develop applications supporting discovery efforts in the sequence–structural–functional relationship of PTMs and providing important insight into mutations and PTM sites interaction mechanisms. The PRISMOID database is freely accessible at http://prismoid.erc.monash.edu/. The database and web interface are implemented in MySQL, JSP, JavaScript and HTML with all major browsers supported.
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Obermayer, Benedikt, Manuel Holtgrewe, Mikko Nieminen, Clemens Messerschmidt, and Dieter Beule. "SCelVis: exploratory single cell data analysis on the desktop and in the cloud." PeerJ 8 (February 19, 2020): e8607. http://dx.doi.org/10.7717/peerj.8607.
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Background Single cell omics technologies present unique opportunities for biomedical and life sciences from lab to clinic, but the high dimensional nature of such data poses challenges for computational analysis and interpretation. Furthermore, FAIR data management as well as data privacy and security become crucial when working with clinical data, especially in cross-institutional and translational settings. Existing solutions are either bound to the desktop of one researcher or come with dependencies on vendor-specific technology for cloud storage or user authentication. Results To facilitate analysis and interpretation of single-cell data by users without bioinformatics expertise, we present SCelVis, a flexible, interactive and user-friendly app for web-based visualization of pre-processed single-cell data. Users can survey multiple interactive visualizations of their single cell expression data and cell annotation, define cell groups by filtering or manual selection and perform differential gene expression, and download raw or processed data for further offline analysis. SCelVis can be run both on the desktop and cloud systems, accepts input from local and various remote sources using standard and open protocols, and allows for hosting data in the cloud and locally. We test and validate our visualization using publicly available scRNA-seq data. Methods SCelVis is implemented in Python using Dash by Plotly. It is available as a standalone application as a Python package, via Conda/Bioconda and as a Docker image. All components are available as open source under the permissive MIT license and are based on open standards and interfaces, enabling further development and integration with third party pipelines and analysis components. The GitHub repository is https://github.com/bihealth/scelvis.
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Lenti, Jacopo, Yelena Mejova, Kyriaki Kalimeri, André Panisson, Daniela Paolotti, Michele Tizzani, and Michele Starnini. "Global Misinformation Spillovers in the Vaccination Debate Before and During the COVID-19 Pandemic: Multilingual Twitter Study." JMIR Infodemiology 3 (May 24, 2023): e44714. http://dx.doi.org/10.2196/44714.
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Background Antivaccination views pervade online social media, fueling distrust in scientific expertise and increasing the number of vaccine-hesitant individuals. Although previous studies focused on specific countries, the COVID-19 pandemic has brought the vaccination discourse worldwide, underpinning the need to tackle low-credible information flows on a global scale to design effective countermeasures. Objective This study aimed to quantify cross-border misinformation flows among users exposed to antivaccination (no-vax) content and the effects of content moderation on vaccine-related misinformation. Methods We collected 316 million vaccine-related Twitter (Twitter, Inc) messages in 18 languages from October 2019 to March 2021. We geolocated users in 28 different countries and reconstructed a retweet network and cosharing network for each country. We identified communities of users exposed to no-vax content by detecting communities in the retweet network via hierarchical clustering and manual annotation. We collected a list of low-credibility domains and quantified the interactions and misinformation flows among no-vax communities of different countries. Results The findings showed that during the pandemic, no-vax communities became more central in the country-specific debates and their cross-border connections strengthened, revealing a global Twitter antivaccination network. US users are central in this network, whereas Russian users also became net exporters of misinformation during vaccination rollout. Interestingly, we found that Twitter’s content moderation efforts, in particular the suspension of users following the January 6 US Capitol attack, had a worldwide impact in reducing the spread of misinformation about vaccines. Conclusions These findings may help public health institutions and social media platforms mitigate the spread of health-related, low-credibility information by revealing vulnerable web-based communities.
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Hermann, K. G., M. Protopopov, A. Serfaty, I. Hmamouchi, F. Sommerfleck, F. Macori, K. Ziegeler, T. Diekhoff, D. Poddubnyy, and J. Sieper. "POS1460 CONTRIBUTING TO THE TRAINING OF IMAGING IN RHEUMATOLOGY BY EXPERTS WORLDWIDE VIA INTERACTIVE MOBILE E-TEACHING: BERLINCASEVIEWER." Annals of the Rheumatic Diseases 81, Suppl 1 (May 23, 2022): 1075.1–1075. http://dx.doi.org/10.1136/annrheumdis-2022-eular.4885.
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BackgroundRheumatology education today can be very diverse, and you can find everything from structured textbooks to YouTube channels to social media accounts. Peer-reviewed content is still recognized as a very high-quality source of information. App-based content has the advantage of bundling information in one place, always available on the go. However, the majority of offerings are only available in English.ObjectivesAn app was to be created to learn about imaging in rheumatology in a very easy to understand way in different languages, with experts being able to create translated content very easily.MethodsUsing mySQL, Java, Objective C and JavaScript, a case database with specific structure and numerous interactive elements was created for academic teaching. Special functions for the annotation of images were provided. The development was initially for devices with the iOS operating system, and later for Android. Rheumatologists and radiologists worldwide were invited to participate via the social media channels LinkedIn, Instagram, Facebook, Twitter, and TikTok.ResultsThe app, called BerlinCaseViewer, was developed for smartphones, tablets and Mac computers. All information is entered and processed in a web-based database. Using XML files and ZIP archives, the relevant data is then transferred to the mobile apps. Case of the month and learning modules on rheumatoid arthritis, psoriatic arthritis, and axial spondyloarthritis are available, many in English, Spanish, French, Italian, Portuguese, German, and other languages (Figure 1). In addition to the medical image data, the patient’s medical history is also presented in an exciting way with the help of multiple-choice questions. Only when all questions are answered, the diagnosis becomes visible. Timeline functions can be used to visualize medical courses as well. Colored overlays are used to annotate images and can be placed with pixel precision. The user can decide whether these should be displayed as aids. Content is peer-reviewed before publication.Figure 1.Multi-lingual presentation of medical training cases.ConclusionBerlinCaseViewer is a new approach not only to train medical professionals, but also to connect colleagues and overcome language barriers. As a platform, BerlinCaseViewer is open to all medical professionals to collaborate, whether to contribute their own cases or translate existing cases for use in the local language.References[1]BerlinCaseViewer home page: https://www.berlincaseviewer.de/Disclosure of Interests:Kay-Geert Hermann Shareholder of: Co-founder of BerlinFlame GmbH, Mikhail Protopopov: None declared, Aline Serfaty: None declared, Ihsane Hmamouchi: None declared, Fernando Sommerfleck: None declared, Fabio Macori: None declared, Katharina Ziegeler: None declared, Torsten Diekhoff: None declared, Denis Poddubnyy Shareholder of: Co-founder of BerlinFlame GmbH, Joachim Sieper Shareholder of: Co-founder of BerlinFlame GmbH
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Echa Oktamiani Maulana. "Deteksi Hunian Di Tempat Parkir (Occupancy Detection In Parking Lot)." Journal Islamic Global Network for Information Technology and Entrepreneurship 2, no. 2 (April 3, 2024): 45–60. http://dx.doi.org/10.59841/ignite.v2i2.1050.
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MSIB (Certified Independent Study and Internship) is one of the activity programs at the Merdeka Campus which aims to help students improve their skills and develop themselves. MSIB appointed Orbit Future Academy as one of the partners in the Independent Study program. Founded in 2016 with the aim of improving the quality of life through innovation, education and skills training. In accordance with its mission, namely "We curate and localize international programs and courses for upskilling, re-skilling youth, and the workforce towards jobs of the future". Partners provide opportunities for students to take Artificial Intelligence programs and study online. Learning consists of eight material courses including Python Programming, AI Technology Logic and Concepts, AI Project Cycle, AI Research Methods, ChatGPT, Professional and Company Ethics, Financial Literacy and ending with a Final Project. The final project scope carried out is the Occupancy Detection in Parking Lot project. This project uses the Computer Vision domain with the selection of the YOLO model in detecting objects and pixel segmentation. The project begins with selecting a dataset using roboflow which then goes through data pre-processing for cloning, annotation and augmentation. Then the model is trained using machine learning and deep learning algorithms to understand patterns and characteristics related to parking spaces. Once trained, the AI model will be validated using test data. This aims to ensure that the model truly recognizes the presence of the vehicle. Next, form the application design in creating an informative interface using wireframes. Then enter the deployment stage so that the system can be accessed widely and easily via the web. Lastly, field testing is to find out the performance of the application that has been designed.
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Echa Oktamiani Maulana. "Deteksi Hunian Di Tempat Parkir (Occupancy Detection In Parking Lot)." Journal Islamic Global Network for Information Technology and Entrepreneurship 2, no. 2 (April 6, 2024): 45–61. http://dx.doi.org/10.59841/ignite.v2i2.1058.
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MSIB (Certified Independent Study and Internship) is one of the activity programs at the Merdeka Campus which aims to help students improve their skills and develop themselves. MSIB appointed Orbit Future Academy as one of the partners in the Independent Study program. Founded in 2016 with the aim of improving the quality of life through innovation, education and skills training. In accordance with its mission, namely "We curate and localize international programs and courses for upskilling, re-skilling youth, and the workforce towards jobs of the future". Partners provide opportunities for students to take Artificial Intelligence programs and study online. Learning consists of eight material courses including Python Programming, AI Technology Logic and Concepts, AI Project Cycle, AI Research Methods, ChatGPT, Professional and Company Ethics, Financial Literacy and ending with a Final Project. The final project scope carried out is the Occupancy Detection in Parking Lot project. This project uses the Computer Vision domain with the selection of the YOLO model in detecting objects and pixel segmentation. The project begins with selecting a dataset using roboflow which then goes through data pre-processing for cloning, annotation and augmentation. Then the model is trained using machine learning and deep learning algorithms to understand patterns and characteristics related to parking spaces. Once trained, the AI model will be validated using test data. This aims to ensure that the model truly recognizes the presence of the vehicle. Next, form the application design in creating an informative interface using wireframes. Then enter the deployment stage so that the system can be accessed widely and easily via the web. Lastly, field testing is to find out the performance of the application that has been designed.
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Joey Lee, Jia Ying, Joe Yeong, Li Wen Justina Nadia Lee, Lit-Hsin Loo, and Jiahui Dong. "627 ImmunoAtlas: an online public portal for sharing, visualizing, and referencing multiplex immunohistochemistry/immunofluorescence (mIHC/IF) images and results for immuno-oncology." Journal for ImmunoTherapy of Cancer 9, Suppl 2 (November 2021): A657. http://dx.doi.org/10.1136/jitc-2021-sitc2021.627.
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BackgroundRecent advances in multiplex immunohistochemistry/immunofluorescence (mIHC/IF) technologies have enabled simultaneous measurements of large numbers of markers on the same tissue sections, and more comprehensive views of the cellular compositions and immune responses of the tumor microenvironment. However, the reproducibility and interpretation of the complex staining patterns and analysis results obtained from these markers remain major barriers to more general adoptions of these technologies. Here, we report the availability of an online public portal, “ImmunoAtlas”, which would enable researchers/clinicians to present or share their published mIHC/IF images or results; international workgroups to create and share standard marker panels or assay guidelines; end users to validate antibodies or protocols; or computational scientists to benchmark different analysis methods on standard reference images (figure 1).MethodsImmunoAtlas is based on a HistoPathological image management and Analysis (HPA) software platform developed by us, and hosted in the data center of Bioinformatics Institute. The platform uses the cellXpress software,1 which is written in C++ and supports parallel processing, to efficiently process and manage large numbers of huge mIHC/IF or brightfield images. The web interface of ImmunoAtlas is also completely developed by us in PHP and JavaScript to address the specific needs and requirements in managing these images.ResultsImmunoAtlas is a user-friendly online portal for sharing, visualizing, and referencing original tissue images and analysis results (https://ImmunoAtlas.org). We have completed the first phase of development of the portal. Users can now perform image uploading, annotation, publishing, sharing, and viewing with standard web browsers on desktop computers or mobile devices/phones (figure 2). The portal supports image files from common microscopes and slide scanners, and can process mIHC/IF or brightfield images from selected areas, tissues microarrays, or whole slides. It can handle up to 1000 multiplexed markers, and whole-slide images that are >20GB/image. Several internal and external immuno-oncology studies have deposited and shared their images via ImmunoAtlas. They include a study of multiplexed PD-L1 markers in breast cancers2; the development of a panel of 56 highly-multiplexed markers for cutaneous T cell lymphoma3; and a study of CD38 scoring in hepatocellular carcinoma.4ConclusionsImmunoAtlas promotes open science and collaborations that can accelerate the adoptions of mIHC/IF technologies in immuno-oncology. The next phase of development will focus on adding image searching and comparison functions to the portal. The community is welcome to use and share their images and analysis results via the portal.ReferencesLaksameethanasan D, Tan RZ, Toh GW-L, et al. cellXpress: a fast and user-friendly software platform for profiling cellular phenotypes. BMC Bioinformatics 2013;14:S4. doi:10.1186/1471-2105-14-S16-S4.Yeong J, Tan T, Chow ZL, et al. Multiplex immunohistochemistry/immunofluorescence (mIHC/IF) for PD-L1 testing in triple-negative breast cancer: a translational assay compared with conventional IHC. J Clin Pathol 2020;73:557–62. doi:10.1136/jclinpath-2019-206252.Phillips D, Schürch CM, Khodadoust MS, et al. Highly multiplexed phenotyping of immunoregulatory proteins in the tumor microenvironment by CODEX tissue imaging. Front Immunol 2021;0. doi:10.3389/fimmu.2021.687673.Ng HHM, Lee RY, Goh S, et al. Immunohistochemical scoring of CD38 in the tumor microenvironment predicts responsiveness to anti-PD-1/PD-L1 immunotherapy in hepatocellular carcinoma. J Immunother Cancer 2020;8:e000987. doi:10.1136/jitc-2020-000987Abstract 627 Figure 1Key target users and applications of ImmunoAtlasAbstract 627 Figure 2ImmunoAtlas' web interface for sharing and visualizing mIHC
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Heng, Sobroney, Sawannee Sutheeworapong, Verawat Champreda, Ayaka Uke, Akihiko Kosugi, Patthra Pason, Rattiya Waeonukul, Ruben Michael Ceballos, Khanok Ratanakhanokchai, and Chakrit Tachaapaikoon. "Genomics and cellulolytic, hemicellulolytic, and amylolytic potential of Iocasia fonsfrigidae strain SP3-1 for polysaccharide degradation." PeerJ 10 (October 19, 2022): e14211. http://dx.doi.org/10.7717/peerj.14211.
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Background Cellulolytic, hemicellulolytic, and amylolytic (CHA) enzyme-producing halophiles are understudied. The recently defined taxon Iocasia fonsfrigidae consists of one well-described anaerobic bacterial strain: NS-1T. Prior to characterization of strain NS-1T, an isolate designated Halocella sp. SP3-1 was isolated and its genome was published. Based on physiological and genetic comparisons, it was suggested that Halocella sp. SP3-1 may be another isolate of I. fronsfrigidae. Despite being geographic variants of the same species, data indicate that strain SP3-1 exhibits genetic, genomic, and physiological characteristics that distinguish it from strain NS-1T. In this study, we examine the halophilic and alkaliphilic nature of strain SP3-1 and the genetic substrates underlying phenotypic differences between strains SP3-1 and NS-1T with focus on sugar metabolism and CHA enzyme expression. Methods Standard methods in anaerobic cell culture were used to grow strains SP3-1 as well as other comparator species. Morphological characterization was done via electron microscopy and Schaeffer-Fulton staining. Data for sequence comparisons (e.g., 16S rRNA) were retrieved via BLAST and EzBioCloud. Alignments and phylogenetic trees were generated via CLUTAL_X and neighbor joining functions in MEGA (version 11). Genomes were assembled/annotated via the Prokka annotation pipeline. Clusters of Orthologous Groups (COGs) were defined by eegNOG 4.5. DNA-DNA hybridization calculations were performed by the ANI Calculator web service. Results Cells of strain SP3-1 are rods. SP3-1 cells grow at NaCl concentrations of 5-30% (w/v). Optimal growth occurs at 37 °C, pH 8.0, and 20% NaCl (w/v). Although phylogenetic analysis based on 16S rRNA gene indicates that strain SP3-1 belongs to the genus Iocasia with 99.58% average nucleotide sequence identity to Iocasia fonsfrigida NS-1T, strain SP3-1 is uniquely an extreme haloalkaliphile. Moreover, strain SP3-1 ferments D-glucose to acetate, butyrate, carbon dioxide, hydrogen, ethanol, and butanol and will grow on L-arabinose, D-fructose, D-galactose, D-glucose, D-mannose, D-raffinose, D-xylose, cellobiose, lactose, maltose, sucrose, starch, xylan and phosphoric acid swollen cellulose (PASC). D-rhamnose, alginate, and lignin do not serve as suitable culture substrates for strain SP3-1. Thus, the carbon utilization profile of strain SP3-1 differs from that of I. fronsfrigidae strain NS-1T. Differences between these two strains are also noted in their lipid composition. Genomic data reveal key differences between the genetic profiles of strain SP3-1 and NS-1T that likely account for differences in morphology, sugar metabolism, and CHA-enzyme potential. Important to this study, I. fonsfrigidae SP3-1 produces and extracellularly secretes CHA enzymes at different levels and composition than type strain NS-1T. The high salt tolerance and pH range of SP3-1 makes it an ideal candidate for salt and pH tolerant enzyme discovery.
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El-Hajj, Wassim, Ghassen Ben Brahim, Hazem Hajj, Haidar Safa, and Ralph Adaimy. "Security-by-construction in web applications development via database annotations." Computers & Security 59 (June 2016): 151–65. http://dx.doi.org/10.1016/j.cose.2015.12.004.
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