Worked examples

1 Studying transcription factors in Fanconi Anemia using FatiGO

Fanconi Anemia (FA) is a genetic disease in which signaling is relevant. FA is a rare chromosome instability syndrome characterized by aplastic anemia, cancer and leukemia susceptibility1). A recent study uses gene expression microarrays to identify differences at the transcription level between bone marrow cells from normal volunteers and from children and adults with FA. Eleven normal volunteers and 21 patients were studied. Gene expression datasets for FA can be found at GEO database (GSE16334).

  1. With the downloaded CEL files, we can carrying a differential expression (DE) analysis. For this step we are going to use a user-friendly web tool called Babelomics. The necessary steps to undertake this analysis have been described in this tutorial.
    For this study we are just interested in the genes that are over represented in FA, for this reason we are going to work with the up-regulated list of genes. Here you can find the resulting file for this worked example, containing a list of human up-regulated genes in FA. Save this file to your desktop or local directory and upload it to RENATO as a gene type of data.
  2. Create a new project (e.g. fanconi anemia TFBSs) and set it as active. Start the REANTO form page from the Enrichment Analysis button located at the top left of the page.
  3. Set Human as specie.
  4. Select FatiGO as the functional method to be used.
  5. Browse the uploaded file and select it.
  6. In the database selection, choose TFBS (Transcription Factor Binding Site).
  7. Give a name to the new job (e.g. fa_UPgenes_TF_FatiGO).
  8. Submit the job (press the run button).

The number of significantly enriched TFs is indicated in the section Significant Results. In this case, after multiple testing correction (by FDR) we have obtained 6 significantly enriched TFs. You will get a resume table with information about the enrichment test for each of the significant TFs. The table can be sorted by any of the column fields.

Significant (adjusted p-value < 0.05) results are represented graphically through a network. Red colored nodes correspond to the enriched TFs and blue nodes to the input genes which are targets of the enriched TFs. This regulatory network justifies the observed increase in the gene activity caused by the disease. Six TFs (E2F1, Gabp, Yy1, Nfya, Egr1, Cmyc) can be pointed to be responsible for the abnormal activation of the genes observed in FA. Some of these TFs have already been linked to FA2).


2 Studying microRNAs in Fanconi Anemia using FatiScan and your own annotation

In this worked example we are going to study the enrichment in miRNA of differentially expressed genes in Fanconi Anemia (FA). The dataset is the same that in the previous example and can be downloaded from GEO database (GSE16334).

  1. With the downloaded CEL files, we can carrying a differential expression (DE) analysis. For this step we are going to use a user-friendly web tool called Babelomics. The necessary steps to undertake this analysis have been described in this tutorial.
    We are going to work with the whole gene ranking which results from the differential expression analysis. Here you can find the resulting file for this worked example, containing a ranked list of human genes after a differential expression in FA. Save this file to your desktop or local directory and upload it to RENATO as a ranked type of data.
  2. For this example we are going to test the enrichment against our own annotation. The annotation file can be downloaded here. Save this file to your desktop or local directory and upload it to RENATO as Annotation type of data.
  3. Create a new project (e.g. fanconi anemia miRNAs) and set it as active. Start the REANTO form page from the Enrichment Analysis button located at the top left of the page.
  4. Set Human as specie.
  5. Select FatiScan as the functional method to be used.
  6. Browse the ranked uploaded file and select it.
  7. In the database selection, choose Your annotations and browse the uploaded annotation file.
  8. Give a name to the new job (e.g. fa_ranked_mirna_FatiScan).
  9. Submit the job (press the run button).

The number of significantly enriched miRNAs is indicated in the section Significant Results. In this case, after multiple testing correction (by FDR) we have obtained 7 significantly enriched miRNAs. You will get a resume table with information about the enrichment test for each of the significant miRNAs. The table can be sorted by any of the column fields.

Significant (adjusted p-value < 0.05) results are represented graphically through a network. Red colored nodes correspond to the enriched miRNAs and blue nodes to the input genes which are targets of the enriched miRNAs. These seven miRNAs are not working properly in patients with FA. We must check whether the regulated genes are over- or under-expressed in the differential expression analysis. Significant miRNAs that regulate over-expressed genes would indicate that this miRNA is down, significant miRNAs that regulate under-expressed genes would indicate that this miRNA is activated in comparison with the normal expression.

worked_examples.txt · Last modified: 2012/05/03 15:37 by mbleda
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