Topic
Annotation
About: Annotation is a research topic. Over the lifetime, 6719 publications have been published within this topic receiving 203463 citations. The topic is also known as: note & markup.
Papers published on a yearly basis
Papers
More filters
••
01 Jul 2018TL;DR: Yedda as mentioned in this paper is an open-source tool for text span annotation, which overcomes the low efficiency of traditional text annotation tools by annotating entities through both command line and shortcut keys which are configurable with custom labels.
Abstract: In this paper, we introduce Yedda, a lightweight but efficient and comprehensive open-source tool for text span annotation. Yedda provides a systematic solution for text span annotation, ranging from collaborative user annotation to administrator evaluation and analysis. It overcomes the low efficiency of traditional text annotation tools by annotating entities through both command line and shortcut keys, which are configurable with custom labels. Yedda also gives intelligent recommendations by learning the up-to-date annotated text. An administrator client is developed to evaluate annotation quality of multiple annotators and generate detailed comparison report for each annotator pair. Experiments show that the proposed system can reduce the annotation time by half compared with existing annotation tools. And the annotation time can be further compressed by 16.47% through intelligent recommendation.
58 citations
••
TL;DR: The Microbial Genomic context Viewer (MGcV), an interactive, web-based application tailored to strengthen the practice of manual comparative genome context analysis for bacteria, advances the manual comparative analysis of genes and regulatory elements by providing fast and flexible integration of gene related data combined with straightforward data retrieval.
Abstract: Conserved gene context is used in many types of comparative genome analyses. It is used to provide leads on gene function, to guide the discovery of regulatory sequences, but also to aid in the reconstruction of metabolic networks. We present the Microbial Genomic context Viewer (MGcV), an interactive, web-based application tailored to strengthen the practice of manual comparative genome context analysis for bacteria. MGcV is a versatile, easy-to-use tool that renders a visualization of the genomic context of any set of selected genes, genes within a phylogenetic tree, genomic segments, or regulatory elements. It is tailored to facilitate laborious tasks such as the interactive annotation of gene function, the discovery of regulatory elements, or the sequence-based reconstruction of gene regulatory networks. We illustrate that MGcV can be used in gene function annotation by visually integrating information on prokaryotic genes, like their annotation as available from NCBI with other annotation data such as Pfam domains, sub-cellular location predictions and gene-sequence characteristics such as GC content. We also illustrate the usefulness of the interactive features that allow the graphical selection of genes to facilitate data gathering (e.g. upstream regions, ID’s or annotation), in the analysis and reconstruction of transcription regulation. Moreover, putative regulatory elements and their corresponding scores or data from RNA-seq and microarray experiments can be uploaded, visualized and interpreted in (ranked-) comparative context maps. The ranked maps allow the interpretation of predicted regulatory elements and experimental data in light of each other. MGcV advances the manual comparative analysis of genes and regulatory elements by providing fast and flexible integration of gene related data combined with straightforward data retrieval. MGcV is available at http://mgcv.cmbi.ru.nl
.
58 citations
••
TL;DR: The GO Annotation Quality (GAQ) score is reported, a quantitative measure of GO quality that includes breadth of GO annotation, the level of detail of annotation and the type of evidence used to make the annotation.
Abstract: Functional analysis using the Gene Ontology (GO) is crucial for array analysis, but it is often difficult for researchers to assess the amount and quality of GO annotations associated with different sets of gene products. In many cases the source of the GO annotations and the date the GO annotations were last updated is not apparent, further complicating a researchers’ ability to assess the quality of the GO data provided. Moreover, GO biocurators need to ensure that the GO quality is maintained and optimal for the functional processes that are most relevant for their research community. We report the GO Annotation Quality (GAQ) score, a quantitative measure of GO quality that includes breadth of GO annotation, the level of detail of annotation and the type of evidence used to make the annotation. As a case study, we apply the GAQ scoring method to a set of diverse eukaryotes and demonstrate how the GAQ score can be used to track changes in GO annotations over time and to assess the quality of GO annotations available for specific biological processes. The GAQ score also allows researchers to quantitatively assess the functional data available for their experimental systems (arrays or databases).
58 citations
••
TL;DR: The Annotation and Image Mark-up Project is a standardized semantically interoperable information model with storage and communication formats for image annotation and markup.
Abstract: The Annotation and Image Mark-up Project is a standardized semantically interoperable information model with storage and communication formats for image annotation and markup.
58 citations
01 Dec 2012
TL;DR: The annotation graph framework provides the new software, “LaBB-CAT”, greater flexibility for automatic and manual annotation of corpus data at various independent levels of granularity, and allows more sophisticated annotation structures, opening up new possibilities for corpus mining and conversion between tool formats.
Abstract: “ONZE Miner”, an open-source tool for storing and automatically annotating Transcriber transcripts, has been redeveloped to use “annotation graphs” as its data model. The annotation graph framework provides the new software, “LaBB-CAT”, greater flexibility for automatic and manual annotation of corpus data at various independent levels of granularity, and allows more sophisticated annotation structures, opening up new possibilities for corpus mining and conversion between tool formats.
58 citations