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Several issues have been reported when running TBrowser with Java 1.7 a new version that correct several bugs was released on 17th october 2012.














Mine molecular interactions using InteractomeBrowser.

Watch the video here .


TBrowser team


The new version of TBrowser (3.0) is available featuring several new annotations (TargetScan, PicTar, TFBSConserved).

TB development team.



Laughing We have just released a new version of TBrowser (V3) available here ! It includes several new species and annotations (TFBS, microRNA, MSigDB,...). Feel free to give us feedbacks. TB development Team


WARNING !  Please note that both plugins and core application of TBrowser have been updated. If you encounter  problems: delete the .tbrowser folder. In case you are launching TBrowser within a terminal, please ensure that you have downloaded the latest version from our FTP site.


The article presenting TBrowser was published in PLoSONE.

Follow this link

TBrowser v2 has been released. This version contains lots of improvements and many new plugins. Try it !!
TranscriptomeBrowser and InteractomeBrowser

TB_icon    VERSION 3.0TranscriptomeBrowser host a large database of transcriptional signatures (TS, n~40 000)  extracted from Gene Expression Omnibus (~4 000 experiments) using the DBF-MCL algorithm.  TBrowser comes with a sophisticated search engine so that users can search for the biological contexts in which several genes were concomitantly regulated. Several examples are provided below and in the article published in PLoSONE . A video tutorial is available here .

InteractomeBrowser is a default plug-in of TranscriptomeBrowser software (a video tutorial is available here). InteractomeBrowser is intented to display interactions networks, given an input list of genes (user-defined or stored in the TranscriptomeBrowser database). The backend database contains the following type of interactions:

  • predicted regulatory interactions (computed by scanning vertebrate alignments with a set of 1,213 position weight matrices),
  • potential regulatory interactions inferred from systematic analysis of ChIP-Seq experiments,
  • predicted post-transcriptional regulation by micro-RNA,
  • regulatory interactions curated from the literature,
  • kinase-substrate interactions,
  • physical protein-protein interactions. 

IBrowser plugin relies on a "cell compartments-based layout" that makes use of a subset of the Gene Ontology (GO Slim) to map gene products onto relevant cell compartments. The current version of IBrowser plug-in support Human and Mouse gene identifiers as input. InteractomeBrowser was developed using the prefuse java library.

More informations here .


Using boolean queries in TranscriptomeBrowser search engine

One can search the  GeneID Field with the following queries. 

  • "ESR1 & GATA3 & FOXA1" -> TS related to breast cancer (containing  ESR1 and GATA3 and FOXA1)
  • "CD3E & CD3D" -> TS containing T-cells (containing  CD3E AND CD3D)
  • "CD3E & CD3D & !CD14 -> TS that contain T-cells markers but not  the monocyte/macrophage marker CD14 (containing  CD3E and CD3D but not CD14)
  • "PCNA & MKI67 & CDC2" -> TS containing cell-cycle related genes (containing PCNA and MKI67 and CDC2)

User can next, ask for genes that are frequently observed in the selected TSs (TBNeighborhood plugin).

One can search the  Annotation Field with the following queries (user may select the q-value):

  •  "CELL CYCLE"[5,12,18] -> TS enriched in genes associated with the functional annotation term "CELL CYCLE". 
  • 6p21.3[4] & 14q32.33[4] & "T CELL ACTIVATION"[5,12] -> TS enriched in genes from 6p21.3 and 14q32.33 cytobands (major histocompatibility complex locus and human immunoglobulin heavy-chain locus respectively) and containing genes related to T-cell activation.


Using large gene list with TranscriptomeBrowser search engine

Simply paste your gene list in the search panel and modify the "%min." argument

Let say you performed a microarray experiment and found 100 genes that best discriminate between your condition A and B. You would like to find the biological contexts in which they were already observed as co-regulated. The probabibility of finding a transcriptional signature containing the whole list of genes is low. In this case it is advisable to directly paste the gene list in the search panel (the line feeds are converted into spaces) (step 1). The "%min." argument controls the proportion of genes falling into a transcriptional signature. For instance, iIn the following example, we have pasted 34 genes symbols in the search panel, the "%min." is set to 50% which means that we are looking for transcriptional signatures containing at least 17 genes out of the list (step 2). Pressing the "search" button  (step 3) allows one to find 16 signatures (result panel). Informations about the signatures are available by pressing the "show" button. They can now be sent to plugins. 



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