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(BBSRC DTP) Integrative statistical inference methods for eukaryotic gene regulation with applications to embryonic stem cell differentiation

Project Description

Embryonic stem cells (ESCs) can differentiate into different cell types through intermediary cell states and deeper understanding of the regulatory control underlying these differentiation stages is a very important topic in the study of mammalian development (Yang et al., 2014 & 2019). This regulatory control involves complex mechanisms including binding of regulatory proteins (transcription factors) to genomic regulatory regions (enhancers), which interact and regulate proximal as well as distal genes via chromatin looping. Available technologies, e.g., ATAC-seq, HiChIP, single-cell RNA-seq, can quantify different aspects of this hierarchical regulatory system. The statistical and computational challenge is to integrate these data in a modeling framework with efficient model inference methods to unravel the regulatory connections in ESCs differentiation process. Bayesian sparse factor models (Dai et al. 2017, Iqbal et al. 2012) are a popular choice for such data integration and modeling and have been successfully applied in prokaryotic systems with relatively simpler model of gene regulation. In this project, these methods will be extended to deal with new data on chromatin opening and genomic architecture as well as single-cell RNA-seq data in order to uncover key regulators governing the cell state transitions in early mouse embryonic stem cell differentiation. The project will provide training and skills development opportunities in advanced statistical and machine learning methods and will involve collaborative work with experimental groups as well as opportunities to interact with wider data science community under the umbrella of University of Manchester Data Science Institute.

Entry Requirements:
Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.

Funding Notes

This project is to be funded under the BBSRC Doctoral Training Partnership. If you are interested in this project, please make direct contact with the Principal Supervisor to arrange to discuss the project further as soon as possible. You MUST also submit an online application form - full details on how to apply can be found on the BBSRC DTP website View Website

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.


Yang, S-H., Andrabi, M., Biss, R., Baker, S.M., Iqbal, M., Sharrocks, A.D. (2019) ZIC3 Controls the Transition from Naive to Primed Pluripotency, Cell Reports 27(11), 3215-3227

Dai, Z., Iqbal, M., Lawrence, N.D. and Rattray, M. (2017) Efficient inference for sparse latent variable models of transcriptional regulation Bioinformatics 33(23), 3776-3783

Iqbal, M., Mast, Y., Amin, R., Hodgson, D. A., The STREAM Consortium, Wohlleben, W., and Burroughs, N. J. (2012). Extracting regulator activity profiles by integration of de novo motifs and expression data: characterizing key regulators of nutrient depletion responses in Streptomyces coelicolor. Nucleic Acids Research, 40(12), 5227–5239.

Yang, S-H., Kalkan, T., Morissroe, C., Marks, H., Stunnenberg, H., Smith, A., and Sharrocks, A.D. (2014) Otx2 and Oct4 drive early enhancer activation during ES cell transition from naïve pluripotency. Cell Reports. 7:1968-81.doi: 10.1016/j.celrep.2014.05.037.

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