Changes between Version 1 and Version 2 of Courses/ComputationalMolecularBiologyResearch2016/P5


Ignore:
Timestamp:
2016-01-29T19:11:08+01:00 (9 years ago)
Author:
Pieter Neerincx
Comment:

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  • Courses/ComputationalMolecularBiologyResearch2016/P5

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     1= Improved DNA Motif finding =
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    33== Supervisors ==
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     5Iris Jonkers and Pieter Neerincx
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    67== Introduction ==
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     9Improved detection of regulatory transcription factor binding sites in non-coding DNA by merging sequence (context) with in vivo DNA accessibility data
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    9 == Project 5 - ==
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     12Transcription factors are important regulators of gene expression and bind DNA in a sequence dependent manner. Transcription factor binding can be measured genome-wide and in vivo by performing Chromatin Immune Precipitation -sequencing (ChIP-seq), which uses specific antibodies to pull down the transcription factors of interest together with any DNA that may be cross linked to it, and subsequently sequencing this DNA. This means that each transcription factor of interest requires an individual ChIP-seq experiment, which is highly time-consuming, expensive and often not possible due to a lack of specific antibodies. Alternatively, open chromatin regions can be detected genome-wide as well by methods like DNase I hypersensitivity- (DHS-) or ATAC-sequencing, which uncovers all potential transcription factor binding sites in vivo at once. However, while this method uncovers regulatory regions that are bound by transcription factors, it does not distinguish which transcription factors bind where. To distinguish this, sequences or DNA motifs that are known to bind specific transcription factors can be detected in silica at open chromatin regions. Multiple bioinformatics tools for this are available, of which the MEME suite and Homer are the most commonly used.
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     14== Project 5 ==
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     16In these 6 weeks, you will work with up to 4 of these tools to detect DNA motifs. You will compare the results between tools and will validate results from each with ChIP-seq transcription factor binding data. Data that can be used for this are the transcription factor ChIP-seq, ATAC-seq and DHS-seq data sets of ENCODE that have been generated for the GM12878 B-cell line. Your results will help us determine which tool to implement in our analysis.
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