Search

Finding relevant information from newly published scientific papers is becoming increasingly difficult due to the pace at which articles are published every year as well as the increasing amount of information per paper. Biocuration and model organism databases provide a map for researchers to navigate through the complex structure of the biomedical literature by distilling knowledge into curated and standardized information. In addition, scientific search engines such as PubMed and text-mining tools such as Textpresso allow researchers to easily search for specific biological aspects from newly published papers, facilitating knowledge transfer. However, digesting the information returned by these systems—often a large number of documents—still requires considerable effort. In this paper, we present Wormicloud, a new tool that summarizes scientific articles in a graphical way through word clouds. This tool is aimed at facilitating the discovery of new experimental results not yet curated by model organism databases and is designed for both researchers and biocurators. Wormicloud is customized for the C aenorhabditis   elegans literature and provides several advantages over existing solutions, including being able to perform full-text searches through Textpresso, which provides more accurate results than other existing literature search engines. Wormicloud is integrated through direct links from gene interaction pages in WormBase. Additionally, it allows analysis on the gene sets obtained from literature searches with other WormBase tools such as SimpleMine and Gene Set Enrichment. Database URL : https://wormicloud.textpressolab.com [ABSTRACT FROM AUTHOR]

Short paragraphs that describe gene function, referred to as gene summaries, are valued by users of biological knowledgebases for the ease with which they convey key aspects of gene function. Manual curation of gene summaries, while desirable, is difficult for knowledgebases to sustain. We developed an algorithm that uses curated, structured gene data at the Alliance of Genome Resources (Alliance; www.alliancegenome.org) to automatically generate gene summaries that simulate natural language. The gene data used for this purpose include curated associations (annotations) to ontology terms from the Gene Ontology, Disease Ontology, model organism knowledgebase (MOK)-specific anatomy ontologies and Alliance orthology data. The method uses sentence templates for each data category included in the gene summary in order to build a natural language sentence from the list of terms associated with each gene. To improve readability of the summaries when numerous gene annotations are present, we developed a new algorithm that traverses ontology graphs in order to group terms by their common ancestors. The algorithm optimizes the coverage of the initial set of terms and limits the length of the final summary, using measures of information content of each ontology term as a criterion for inclusion in the summary. The automated gene summaries are generated with each Alliance release, ensuring that they reflect current data at the Alliance. Our method effectively leverages category-specific curation efforts of the Alliance member databases to create modular, structured and standardized gene summaries for seven member species of the Alliance. These automatically generated gene summaries make cross-species gene function comparisons tenable and increase discoverability of potential models of human disease. In addition to being displayed on Alliance gene pages, these summaries are also included on several MOK gene pages. [ABSTRACT FROM AUTHOR]

Biological knowledgebases rely on expert biocuration of the research literature to maintain up-to-date collections of data organized in machine-readable form. To enter information into knowledgebases, curators need to follow three steps: (i) identify papers containing relevant data, a process called triaging; (ii) recognize named entities; and (iii) extract and curate data in accordance with the underlying data models. WormBase (WB), the authoritative repository for research data on Caenorhabditis elegans and other nematodes, uses text mining (TM) to semi-automate its curation pipeline. In addition, WB engages its community, via an Author First Pass (AFP) system, to help recognize entities and classify data types in their recently published papers. In this paper, we present a new WB AFP system that combines TM and AFP into a single application to enhance community curation. The system employs string-searching algorithms and statistical methods (e.g. support vector machines (SVMs)) to extract biological entities and classify data types, and it presents the results to authors in a web form where they validate the extracted information, rather than enter it de novo as the previous form required. With this new system, we lessen the burden for authors, while at the same time receive valuable feedback on the performance of our TM tools. The new user interface also links out to specific structured data submission forms, e.g. for phenotype or expression pattern data, giving the authors the opportunity to contribute a more detailed curation that can be incorporated into WB with minimal curator review. Our approach is generalizable and could be applied to additional knowledgebases that would like to engage their user community in assisting with the curation. In the five months succeeding the launch of the new system, the response rate has been comparable with that of the previous AFP version, but the quality and quantity of the data received has greatly improved. [ABSTRACT FROM AUTHOR]

We analyze the ego-alter Twitter networks of 300 Italian MPs and 18 European leaders, and of about 14,000 generic users. We find structural properties typical of social environments, meaning that Twitter activity is controlled by constraints that are similar to those shaping conventional social relationships. However, the evolution of ego-alter ties is very dynamic, which suggests that they are not entirely used for social interaction, but for public signaling and self-promotion. From this standpoint, the behavior of EU leaders is much more evident, while Italian MPs are in between them and generic users. We find that politicians - more than generic users - create relationships as a side effect of tweeting on discussion topics, rather than by contacting specific alters. [ABSTRACT FROM AUTHOR]

The extremely pervasive nature of mobile technologies, together with the user's need to continuously interact with her personal devices and to be always connected, strengthen the user-centric approach to design and develop new communication and computing solutions. Nowadays users not only represent the final utilizers of the technology, but they actively contribute to its evolution by assuming different roles: they act as humans, by sharing contents and experiences through social networks, and as virtual sensors, by moving freely in the environment with their sensing devices. Smart cities represent an important reference scenario for the active participation of users through mobile technologies. It involves multiple application domains and defines different levels of user engagement. Participatory sensing, opportunistic sensing, and mobile social networks (MSNs) currently represent some of the most promising people-centric paradigms. In addition, their integration can further improve the user involvement through new services and applications. In this article we present SmartCitizen app, an MSN application designed in the framework of a smart city project to stimulate the active participation of citizens in generating and sharing useful contents related to the quality of life in their city. The app has been developed on top of a context- and social-aware middleware platform (CAMEO) able to integrate the main features of people-centric computing paradigms, lightening the app developer's effort. Existing middleware platforms generally focus on a single people-centric paradigm, exporting a limited set of features to mobile applications. CAMEO overcomes these limitations and, through Smart- Citizen, we highlight the advantages of implementing this type of mobile application in a smart city scenario. Experimental results shown in this article can also represent the technical guidelines for the development of heterogeneous people-centric mobile applications embracing different application domains. [ABSTRACT FROM PUBLISHER]

We present two web apps for interactively performing common tasks with single-cell RNA sequencing data: scdefg for differential expression and wormcells-viz for visualization of gene expression. We deployed these tools with public Caenorhabditis elegans datasets curated by WormBase at https://single-cell.wormbase.org. Source code for deploying these tools with other datasets is available at https://github.com/WormBase/scdefg and https://github.com/WormBase/wormcells-viz. Supplementary information Supplementary data are available at Bioinformatics Advances online. [ABSTRACT FROM AUTHOR]

WormBase (www.wormbase.org) is the central repository for the genetics and genomics of the nematode Caenorhabditis elegans.Weprovide the research community with data and tools to facilitate the use of C. elegans and related nematodes as model organisms for studying human health, development, and many aspects of fundamental biology. Throughout our 22-year history, we have continued to evolve to reflect progress and innovation in the science and technologies involved in the study of C. elegans. We strive to incorporate new data types and richer data sets, and to provide integrated displays and services that avail the knowledge generated by the published nematode genetics literature. Here, we provide a broad overview of the current state of WormBase in terms of data type, curation workflows, analysis, and tools, including exciting new advances for analysis of single-cell data, text mining and visualization, and the new community collaboration forum. Concurrently, we continue the integration and harmonization of infrastructure, processes, and tools with the Alliance of Genome Resources, of which WormBase is a founding member. [ABSTRACT FROM AUTHOR]

WormBase has been the major repository and knowledgebase of information about the genome and genetics of Caenorhabditis elegans and other nematodes of experimental interest for over 2 decades. We have 3 goals: to keep current with the fast-paced C. elegans research, to provide better integration with other resources, and to be sustainable. Here, we discuss the current state of WormBase as well as progress and plans for moving core WormBase infrastructure to the Alliance of Genome Resources (the Alliance). As an Alliance member, WormBase will continue to interact with the C. elegans community, develop new features as needed, and curate key information from the literature and large-scale projects. [ABSTRACT FROM AUTHOR]

WormBase and the Alliance of Genome Resources provide several types of gene data including annotations to ontology terms and controlled vocabularies. These are used to automatically generate text summaries to give users a cogent view of gene function. However, automated summaries are not available for genes that lack curated annotations. To increase the genome coverage of the summaries in WormBase, we developed a new software module that generates additional gene summaries for C. elegans and new gene summaries for nine other nematode species: four Caenorhabditis species ( C. brenneri, C. briggsae, C. japonica, C. remanei ), P. pacificus , and four parasitic species ( B. malayi, O. volvulus, S. ratti and T. muris )., Competing Interests: The authors declare that there are no conflicts of interest present., (Copyright: © 2024 by the authors.)

Biological databases collect and standardize data through biocuration. Even though major model organism databases have adopted some automation of curation methods, a large portion of biocuration is still performed manually. To speed up the extraction of the genomic positions of variants, we have developed a hybrid approach that combines regular expressions, Named Entity Recognition based on BERT (Bidirectional Encoder Representations from Transformers) and bag-of-words to extract variant genomic locations from C. elegans papers for WormBase. Our model has a precision of 82.59% for the gene-mutation matches tested on extracted text from 100 papers, and even recovers some data not discovered during manual curation. Code at: https://github.com/WormBase/genomic-info-from-papers., (Copyright: © 2022 by the authors.)

: We present two web apps for interactively performing common tasks with single-cell RNA sequencing data: scdefg for differential expression and wormcells-viz for visualization of gene expression. We deployed these tools with public Caenorhabditis elegans datasets curated by WormBase at https://single-cell.wormbase.org. Source code for deploying these tools with other datasets is available at https://github.com/WormBase/scdefg and https://github.com/WormBase/wormcells-viz., Supplementary Information: Supplementary data are available at Bioinformatics Advances online., (© The Author(s) 2022. Published by Oxford University Press.)