Mining gene regulation pathways in transcriptome datasets

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About Mining gene regulation pathways in transcriptome datasets


|free text=}} Bacillus subtilis is one of the best know bacterial model systems and most of its transcription factors have been identified and characterized. This bacterium is the paradigm of Gram-positive bacteria. Despite our detailed knowledge on gene regulation in this organism, there are many questions concerning the interaction between different regulatory pathways. A rich source of information that could change this is the large set of whole genome transcriptome analyses that is available. However currently, nobody has attempted to mine these databases for information on regulatory networks. There is also no web-based analyses tool that can compare transcriptome data with published data. This Masters research project is aimed (i) at filling in this lacuna and provide the (large) Bacillus research community with methods to compare transcriptomes, and (ii) at finding regulation patterns using multiple transcriptome data set to find new regulator pathways and links between know pathways. This work is now feasible since a database of all (known) regulons in B. subtilis is available.

This ambitious project comprises the following activities:

1) Building a web-based tool to visualize and quantify the distribution of (known) regulons on (single) transcriptome data set. This will include the design of useful heat-map presentation and static assessment of most active regulons.

2) Development of automatic collection and ‘alignment’ for comparison of public available transcriptome data sets.

3) Assessment of defined (known) regulons using the multiple transcriptome dataset (from ‘activity 2’), and identification of possible interaction of these regulons.

4) Identification of gene regulation patterns in the multiple transcriptome dataset (from ‘activity 2’) that could define new (unknown) regulons.

5) Building a web-based tool that enables the Bacillus community to compare their transcriptome experiment with published transcriptome data (using experience from ‘activity 3 and 4’).


- Proficiency in a language like R or Python to be able to carry out the analyses and build the tools

- Familiarity with statistical mining techniques


Vrije Universiteit Amsterdam (main basis) and University of Amsterdam