Commit 403efec9 authored by Jerome Mariette's avatar Jerome Mariette

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......@@ -389,7 +389,7 @@ To simply produce a Venn diagram from identifier lists, jvenn is
available as a Web application at http://bioinfo.genotoul.fr/jvenn/example.html
(Fig. 3).
jvenn's Web application performances depends on the client browser. Using the running version
on a standard Linux computer (one cpu, 4GB of RAM), it displays a six lists
on a standard Linux computer (one cpu, four GB of RAM), it displays a six lists
diagram of 10 000 identifiers in two seconds.
......@@ -401,53 +401,51 @@ data processing. In table two, they have shown the differences between methods
pair-wise. The raw data table provided by the team contains 5,277 lines and
seven columns. The columns correspond to the different methods presented in the
'Differential expression analysis' section of the article. The data in the table
was threshold ($p < 0.05$) to produce the method specific gene name lists. Six
out of seven methods were selected for further processing ; Med was left out.
was filtered ($p < 0.05$) to produce the method specific gene name lists. Six
out of seven lists were selected for further processing ; Med was left out.
The lists were uploaded to the jvenn application and a Venn diagram was
produced. Using the layout selector the diagram was shown in Edwards Venn
format, in which all figures are accessible. This view presents all the lists
overlaps between methods. Considering Fig. 2, the higher values are located in
overlap between methods. Considering Fig. 2, the higher values are located in
central areas of the graph showing that the methods share large portions of gene
lists. The list of 484 gene shared by DESeq, TMM, UQ and FQ has been extracted
lists. The list of 484 genes shared by DESeq, TMM, UQ and FQ has been extracted
by clicking on the corresponding figure. Gene G002562 was sought using the
search box. It was found to be part of the five genes shared by FQ and UQ.
The jvenn statistics show that the different methods produce gene lists with
very different sizes (minimum 417 - maximum 1,249) and the most of the genes are
very different sizes (minimum 417 - maximum 1,249) and that most of the genes are
shared between methods : 1,069 genes out of 1,347 shared by at least four methods.
The same analysis was performed with VENNTURE, the only tool enabling to
The same analysis was performed with VENNTURE, the only other tool able to
generate a six list Edwards Venn diagram. First, the software package was
installed on a computer running under MS-Windows. Then, the six gene lists were loaded
in an MS-Excel spreadsheet. VENNTURE was run using the spreadsheet as input
generating a static MS-PowerPoint file containing the diagram and a MS-Excel
file with all the intersection contents. The names of 484 genes shared by DESeq,
file with all the intersection contents. The 484 genes shared by DESeq,
TMM, UQ and FQ were found in the intersection spreadsheet. The diagram did not
allow to search for gene G002562. Once more it was found in the intersection
spreadsheet.
allow to search for gene G002562. It was found using MS-Excel text search in the
intersection spreadsheet.
\section*{Discussion}
jvenn enables to compare up to six lists and updates the diagram automatically
when modifying the list content. Compared to VENNTURE it does not need any
when modifying the lists content. Compared to VENNTURE it does not need any
installation and gives access to a dynamic diagram providing simple functions to
extract gene lists and perform searches.
jvenns' statistical charts give a simple and quick overview of the sizes of the
different lists and of their overlaps. This enables to compare different Venn diagrams.
different lists and of their overlaps. It permits to compare different Venn diagrams.
These features are not available in the VENNTURE software package.
For biologists using different techniques in their experiment or in their statistical
analysis, jvenn enables to quickly extract the shared identifiers. Being found
using different approaches increases their confidence level.
using different approaches these elements present a higher confidence level.
\section*{Conclusions}
Thanks' to the presented features, jvenn can be used in many cases to compare different
sets of results and easily extract the shared elements. Other examples, such as OTU
comparisons or bioinformatic software benchmarking results,
could complete the previously given ones.
jvenn can help to automate this kind of analysis while embedded in a Web environment as it has
been implemented in WallProtDB \cite{SanClemente}
Thanks to its numerous features, dynamic behavior and graphical layout quality, jvenn can be
efficiently used in many cases to compare different sets of results and easily extract shared
elements. Being a simple JavaScript plugin allows to embed it in any Web environment.
\section*{Availability and requirements}
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