scholarly journals Algal Functional Annotation Tool: A Web-Based Analysis Suite to Functionally Interpret Large Gene Lists Using Integrated Annotation and Expression Data

2016 ◽  
pp. 333-352
Keyword(s):  
Functional Annotation ◽  
Annotation Tool ◽  
Expression Data ◽  
Web Based ◽  
Large Gene

scholarly journals Algal Functional Annotation Tool: a web-based analysis suite to functionally interpret large gene lists using integrated annotation and expression data

2011 ◽  
Vol 12 (1) ◽  
pp. 282 ◽  
Author(s):  
David Lopez ◽  
David Casero ◽  
Shawn J Cokus ◽  
Sabeeha S Merchant ◽  
Matteo Pellegrini
Keyword(s):  
Functional Annotation ◽  
Annotation Tool ◽  
Expression Data ◽  
Web Based ◽  
Large Gene

scholarly journals GO FEAT: a rapid web-based functional annotation tool for genomic and transcriptomic data

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Fabricio Almeida Araujo ◽  
Debmalya Barh ◽  
Artur Silva ◽  
Luis Guimarães ◽  
Rommel Thiago Juca Ramos
Keyword(s):  
Functional Annotation ◽  
Annotation Tool ◽  
Web Based ◽  
Transcriptomic Data

scholarly journals INMEX—a web-based tool for integrative meta-analysis of expression data

2013 ◽  
Vol 41 (W1) ◽  
pp. W63-W70 ◽  
Author(s):  
Jianguo Xia ◽  
Christopher D. Fjell ◽  
Matthew L. Mayer ◽  
Olga M. Pena ◽  
David S. Wishart ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Expression Data ◽  
Web Based

Analyzing Large Gene Expression Data Sets

2006 ◽  
Author(s):  
Soumya Raychaudhuri
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Data Sets ◽  
Expression Data ◽  
Clustering Methods ◽  
Biologically Relevant ◽  
Large Gene ◽  
Functional Coherence

The most interesting and challenging gene expression data sets to analyze are large multidimensional data sets that contain expression values for many genes across multiple conditions. In these data sets the use of scientific text can be particularly useful, since there are a myriad of genes examined under vastly different conditions, each of which may induce or repress expression of the same gene for different reasons. There is an enormous complexity to the data that we are examining—each gene is associated with dozens if not hundreds of expression values as well as multiple documents built up from vocabularies consisting of thousands of words. In Section 2.4 we reviewed common gene expression strategies, most of which revolve around defining groups of genes based on common profiles. A limitation of many gene expression analytic approaches is that they do not incorporate comprehensive background knowledge about the genes into the analysis. We present computational methods that leverage the peer-reviewed literature in the automatic analysis of gene expression data sets. Including the literature in gene expression data analysis offers an opportunity to incorporate background functional information about the genes when defining expression clusters. In Chapter 5 we saw how literature- based approaches could help in the analysis of single condition experiments. Here we will apply the strategies introduced in Chapter 6 to assess the coherence of groups of genes to enhance gene expression analysis approaches. The methods proposed here could, in fact, be applied to any multivariate genomics data type. The key concepts discussed in this chapter are listed in the frame box. We begin with a discussion of gene groups and their role in expression analysis; we briefly discuss strategies to assign keywords to groups and strategies to assess their functional coherence. We apply functional coherence measures to gene expression analysis; for examples we focus on a yeast expression data set. We first demonstrate how functional coherence can be used to focus in on the key biologically relevant gene groups derived by clustering methods such as self-organizing maps and k-means clustering.

scholarly journals INDRA-IPM: interactive pathway modeling using natural language with automated assembly

2019 ◽  
Vol 35 (21) ◽  
pp. 4501-4503 ◽  
Author(s):  
Petar V Todorov ◽  
Benjamin M Gyori ◽  
John A Bachman ◽  
Peter K Sorger
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Web Service ◽  
Language Processing ◽  
Source Code ◽  
Supplementary Information ◽  
Expression Data ◽  
Supplementary Data ◽  
Automated Assembly ◽  
Web Based

Abstract Summary INDRA-IPM (Interactive Pathway Map) is a web-based pathway map modeling tool that combines natural language processing with automated model assembly and visualization. INDRA-IPM contextualizes models with expression data and exports them to standard formats. Availability and implementation INDRA-IPM is available at: http://pathwaymap.indra.bio. Source code is available at http://github.com/sorgerlab/indra_pathway_map. The underlying web service API is available at http://api.indra.bio:8000. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals TeamTat: a collaborative text annotation tool

2020 ◽  
Vol 48 (W1) ◽  
pp. W5-W11
Author(s):  
Rezarta Islamaj ◽  
Dongseop Kwon ◽  
Sun Kim ◽  
Zhiyong Lu
Keyword(s):  
Project Managers ◽  
Biomedical Literature ◽  
Task Completion ◽  
Annotation Tool ◽  
Web Based ◽  
Domain Experts ◽  
Plain Text ◽  
Text Annotation ◽  
User Friendly ◽  
Input Format

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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