In order to address the question if transcripts are co-regulated with your transcript of interest, you can query CSB.DB for transcript co-responses.
There are various co-response queries that can be made. To get information of your choice for the transcriptional co-response queries use the links listed below.

• tCoR: Single Gene Query (sGQ): allows to pre-define a gene and to retrieve genes associated by co-response
• tCoR: Multiple Gene Query (mGQ) allows pre-definition of a set of genes and returns the co-response between these genes
• tCoR: Intersection Gene Query (isGQ) allows retrieval of genes which exhibit common co-response to at least two pre-defined genes

Transcript Co-Response Query: Single Gene Query (sGQ)

Rank ordered tables of pair wise gene correlations can be obtained using the single gene query (sGQ). The sGQ allows to type in a gene of interest and to retrieve, if this gene available from the set of well measured genes, all genes associated by co-response, which meet pre-set thresholds. Default settings are given and can be changed in the advanced part of this query tool.

The single gene query (sGQ) is the simplest of all co-response queries. It basically answers the question, which transcripts are co-regulated with your gene of interest. Co-regulation is calculated by different correlation coefficients which come from statistical calculation. We recommend you using the Spearman or Kendall coefficient.
Correlation coefficients give you a level how your transcript behaves compared to another transcript and they vary between 1 (totally the same behaviour) and -1 ('negative' behaviour i.e. transcript 1 goes up and transcript 2 goes done and vice versa). You will also be presented with various filters to identify only the best correlated transcripts. Moreover the result set table will be ordered by descending levels of co-regulation. Note however that you can click on the table headers of every column to resort the table according to that column.

Transcript Co-Response Query: Multiple Gene Query (mGQ)

The multiple gene query option (mGQ) allows pre-definition of up to 30 genes of interest and returns only the complete set of available co-responses between these genes. This option may be used to discover interdependencies of genes which are known to contribute to a common function, functional module or pathway. Default settings are given and can be changed in the advanced part of this query tool.

The multiple gene query is a means to answer the question, how a list of transcripts, you might have identified to be involved in a given process, do behave in respect to each other. I.e. you can identify if some transcripts behave similarly or behave strictly differential from other transcripts.
To help in visualization you are presented with a table showing the co-response of the transcripts to one another. Moreover you will be presented with a network view of the transcripts, if you have enabled JAVA version 1.4 or greater. see CoRNetApplet for details

Transcript Co-Response Query: Multiple Gene Query (mGQ)

The intersection gene query tool (isGQ) extracts those genes which exhibit common correlation to at least two (max. three) pre-defined genes of interest. The threshold settings, which are available for the other queries, can also be used for isGQ. The isGQ query may be preferentially used, if genes with a common function are already known and novel genes, which may involve in this function, shall be discovered. Default settings are given and can be changed in the advanced part of this query tool.

The intersection gene query is basically a more refined single gene query. Here you ask the question if you have two or three transcripts, which other transcripts that are available in the matrix, co-respond i.e. are co-regulated with both (or each of the three) transcripts.
A good example would be if you have identified two transcripts which are affected by a transcription factor and you would want to search for other transcripts that might be regulated by the same transcription factor. The advantage of the intersection gene query is that by putting more identified candidate transcripts in, the resulting data set will be reduced.