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1. Wordle: Wordle is a toy for generating “word clouds” from text that you provide. The clouds give greater prominence to words that appear more frequently in the source text.

2. Tag Cloud Generator: Using this Tag Cloud Generator you can generate your personal tag cloud. Easy and free.

3. TagCrowd: TagCrowd  is a web application for visualizing word frequencies in any text by creating what is popularly known as a word cloud, text cloud or tag cloud.

4. ToCloud: ToCloud is an online free word cloud generator that uses word frequency as the weight. Hence, the generated word cloud of a page gives a quick understanding of how the page is optimized for certain words. ToCloud is also smart enough to extract phrases and works much better than most other word cloud generators on the web. The word cloud of a blog, news page, feed will give a quick idea about what topics are being discussed.

5. Tagxedo: Tagxedo is another online website that allows for tag clouds to be created. However, unlike other websites, they allow you to create tag clouds from more than just URL links and plain text. Tagxedo is a website that even allows you create tag clouds from Twitter ID, Del.icio.us, and RSS.

6. WordArt: WordArt is an online word art creator that enables you to create amazing and unique word art with ease.

7. GOsummaries: GOsummaries is a novel R package that visualises the GO enrichment results as concise word clouds that can be combined together if the number of gene lists is larger. By also adding the graphs of corresponding raw experimental data, GOsummaries can create informative summary plots for various analyses such as differential expression or clustering (Kolde R, et al., 2015).

8. GeneCodis3: GeneCodis3 is a web server application for functional analysis of gene lists that integrates different sources of information and finds modular patterns of interrelated annotations (Tabas-Madrid D, et al., 2012).

9. REVIGO: REVIGO is a Web server that summarizes long, unintelligible lists of GO terms by finding a representative subset of the terms using a simple clustering algorithm that relies on semantic similarity measures (Supek F, et al., 2011).

10. Genes2WordCloud: Genes2WordCloud is a web application that enables users to quickly identify biological themes from gene lists and research relevant text by constructing and displaying word-clouds (Baroukh C, et al., 2011).

11. WordCloud: The WordCloud App is a Cytoscape App that generates a word tag cloud from a user-defined node selection, summarizing an attribute of choice (Oesper L, et al., 2011).

12. DAVID: DAVID provides a comprehensive set of functional annotation tools for investigators to understand biological meaning behind large list of genes (Dennis G Jr, et al., 2003).

13. FatiGO+: The FatiGO+ tool is the first application for finding significant differences in the distribution of GO terms between groups of genes taking into account the multiple testing nature of the contrast (Al-Shahrour F, et al., 2007).

14. GOrilla: GOrilla is a web-based application that identifies enriched GO terms in ranked lists of genes, without requiring the user to provide explicit target and background sets (Eden E, et al., 2009).

15. Onto-Express: Onto-Tools is a set of four seamlessly integrated databases: Onto-Express, Onto-Compare, Onto-Design and Onto-Translate. Onto-Express is able to automatically translate lists of genes found to be differentially regulated in a given condition into functional profiles characterizing the impact of the condition studied upon various biological processes and pathways (Khatri P, et al., 2002).

16. g:Profiler: g:Profiler is a web server for functional interpretation of gene lists (Reimand J, et al., 2011).

17. KOBAS 3.0: KOBAS 3.0 is a web server for gene/protein functional annotation (Annotate module) and functional gene set enrichment(Enrichment module). For Annotate module, it accepts gene list as input, including IDs or sequences, and generates annotations for each gene based on multiple databases about pathways, diseases, and Gene Ontology. For Enrichment module, it can accept either gene list or gene expression data as input, and generates enriched gene sets, corresponding name, p-value or a probability of enrichment and enrichment score based on results of multiple methods (Xie C, et al., 2011).