CRIT/workflow

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Here, we use three different datasets as shown.
Here, we use three different datasets as shown.
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[[File:schema.png|200px|thumb|left|Data Input Set up]]
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[[File:schema.png|Data Input Set up]]
These objects are named as follows in the R dataset:
These objects are named as follows in the R dataset:
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(3) G: Gene targets and their associated properties
(3) G: Gene targets and their associated properties
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T and G are both post processed from:
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 +
Y. Xia, E. A. Franzosa, and M. B. Gerstein. Integrated assessment of genomic correlates of protein
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evolutionary rate. PLoS Comput Biol, 5(6):e1000413–e1000413, 2009.
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C is post processed from:
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C. T. Harbison, et al. Transcriptional
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regulatory code of a eukaryotic genome. Nature, 431(7004):99–104, 2004.

Revision as of 16:58, 29 January 2011

Transcription Factor Example

Motivation and Problem Set Up

Cis regulatory elements as a means of regulating gene expression have been extensively studied. However, beyond such motifs, are there inherent properties of the targets themselves that make them more or less likely to be regulated by a given class of transcription factors? As an example, do essential transcription factors preferentially regulate essential targets? Are there genome composition features such as GC or codon bias that influence which targets are regulated by which TFs?

Input Data

Here, we use three different datasets as shown.

Data Input Set up

These objects are named as follows in the R dataset:

(1) T: Transcription factors and their associated properties

(2) C: Connector Matrix matching transcription factors to their associated targets

(3) G: Gene targets and their associated properties

T and G are both post processed from:

Y. Xia, E. A. Franzosa, and M. B. Gerstein. Integrated assessment of genomic correlates of protein evolutionary rate. PLoS Comput Biol, 5(6):e1000413–e1000413, 2009.

C is post processed from:

C. T. Harbison, et al. Transcriptional regulatory code of a eukaryotic genome. Nature, 431(7004):99–104, 2004.

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