Bioinformatician in O'Garra Lab
- Professor Anne O’Garra, Principle Group Leader
- This is a full-time, fixed term (3 years with the possibility of extension) position on Crick terms and conditions of employment.
The research group:
We seek a talented and motivated Bioinformatician/Computational Biologist to join Professor Anne O’Garra’s laboratory. The lab works on the regulation of mucosal immune responses in infection and inflammatory diseases in experimental models and human cohorts. An overview of the laboratory can be found at: https://www.crick.ac.uk/research/a-z-researchers/researchers-k-o/anne-o’garra/
The successful candidate will join the team lead by Prof Anne O’Garra at the Francis Crick Institute. The group is interested in studying pathways of protection and pathogenesis in infection and inflammation using bioinformatics analysis and annotation to analyse complex bulk RNA-seq and ATAC-Seq data, as well as Single Cell-RNA-Seq in human disease and mouse models. The candidate will perform such analyses on samples from the airways and blood of cohorts of human tuberculosis (TB) patients and their contacts, a small number of whom only will develop TB. The aim is to establish gene signatures on the early response to Mycobacterium tuberculosis in order to understand the early events in contacts of TB patients who go on to develop TB or not, as compared to those in active TB patients. In addition, the candidate will also perform analysis of data from (i) airway and blood samples from mouse models of TB, and (ii) intestinal samples from mouse models of inflammatory bowel disease.
The successful candidate will work with scientists from the group to analyse large-scale genomic, RNA-seq, single cell RNA-seq and ATAC-seq data. Activities will include quality control and transcription profiling and integration of genomic datasets with public and commercial biomedical data sources for annotation and interpretation, and development, maintenance and optimization of analysis pipelines and procedures. The ability to make strategic contributions to experimental design and advise scientists in the group in the analysis of transcriptional data from human disease and experimental models is key. This will involve assisting scientists with data interpretation, data mining for interesting patterns and signatures in addition to conducting their own analyses are key requirements of the post.
This follows work and methods published in:
Olivier Tabone*, Raman Verma*, Akul Singhania, Probir Chakravarty, William J. Branchett, Christine M. Graham, Jo Lee, Tran Trang, Frederic Reynier, Philippe Leissner, Karine Kaiser, Marc Rodrigue, Gerrit Woltmann, Pranabashis Haldar* and Anne O’Garra*. 2021. Blood transcriptomics reveal the evolution and resolution of the immune response in tuberculosis, Journal of Experimental Medicine 218(10):e20210915. doi: 10.1084/jem.20210915
Moreira-Teixeira, L*., Tabone, *O., Graham, C.M*., Singhania, A, Stavropoulos, E., Redford, P.S., Chakravarty, P., Priestnall, S., Suarez-Bonnet, A., Herbert, E., Mayer-Barber, K.D., Sher, A., Fonseca, K.L., Sousa, J., Cá, B., Verma, R., Haldar, P., Saraiva, M., and O’Garra, A. 2020. Mouse transcriptome reveals potential signatures of protection and pathogenesis in human tuberculosis. Nature Immunol. 21(4):464-476. doi.org/10.1038/s41467-020-19412-6
Singhania A, Verma R, Graham CM, Lee J, Tran T, Richardson M, Lecine P, Leissner P, Berry MPR, Wilkinson RJ, Kaiser K, Rodrigue M, Woltmann G, Haldar P, O'Garra A. (2018). A modular transcriptional signature identifies phenotypic heterogeneity of human tuberculosis infection. Nat Commun. 19;9(1):2308. doi: 10.1038. PMID: http://pmid.us/29921861
Berry, M.P., Graham, C.M., McNab, F.W., Xu, Z., Bloch, S.A., Oni, T., Wilkinson, K.A., Banchereau, R., Skinner, J., Wilkinson, R.J., Quinn, C., Blankenship, D., Dhawan, R., Cush, J.J., Mejias, A., Ramilo, O., Kon, O.M., Pascual, V., Banchereau, J., Chaussabel, D., and O’Garra, A.(2010). An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis. Nature 466, 973-977. PMID: http://pmid.us/20725040
Gabryšová, L., Alvarez-Martinez, M., Luisier, R., Cox, L.S., Sodenkamp, J., Hosking, C., Pérez-Mazliah, D., Whicher, C., Kannan,Y., Potempa, K., Wu, X., Bhaw, L., Wende, H., Sieweke, M.H., Elgar, G., Wilson, M., Briscoe, J., Metzis, V., Langhorne, J., Luscombe, N. M. and O’Garra, A. (2018). C-Maf controls immune responses by regulating disease-specific gene networks and repressing IL-2 in CD4+ T cells article. Nature Immunology. 9(5):497-507. PMID: http://pmid.us/29662170
- To provide bioinformatics support in line with group objectives
- Develop an understanding of the analysis objectives for common project types
- Carry out analysis of high-throughput genomic data using a standard set of well-described software packages, biological resources and bioinformatic methods
- Interpret results in the context of the analysis objectives
- Communicate results to lab scientists and group leader via project meetings and analysis reports
- Help lab scientists to interpret their data
- Follow group project management and reporting procedures
- Continue professional development through maintaining awareness of developments in the wider bioinformatics and research communities
- Participate and contribute to group meetings, workshops and seminars
Key experience and competencies
The post holder should embody and demonstrate our core Crick values: bold, open, and collegial, in addition to the following:
The ideal candidate should have a strong background in bioinformatics or computational biology and be looking for a collaborative and dynamic environment and have a proven track record of productivity in the form of authorship(s) on peer reviewed publications demonstrating application of bioinformatics. Knowledge of biomedical science is expected and a background in immunology is desirable. Applicant must have a sound understanding of the application of genome technologies and bioinformatics in the context of genomics and genetics and practical experience with the processing and analysis of large genomic data. The ability to communicate effectively as well as to be highly organized with respect to effective planning, project analysis, and adherence to deadline requirements, and interact effectively with other team members and collaborators in a multi-disciplinary research environment is essential.
- Degree in Bioinformatics or Computational Biology with relevant work experience and a scientific background in biology
- Excellent scientific analysis skills.
- Experience of handling Next Generation sequencing including bulk RNA-Seq and single cell RNA-Seq
- Basic knowledge of molecular biology
- Experience in applying complex bioinformatics and statistical analysis to samples from clinical human cohorts and/or experimental mouse models
- Experience with applying modular and other clustering approaches to transcriptomic data
- Experience with at least one statistical programming language is required (eg. preferably R/Bioconductor and Python)
- Must be familiar with cloud and high-performance computing (HPC) environment - experience in running bioinformatics analysis software on an HPC Linux system
- Experience of annotation of gene clusters using literature, public databases and available software
- Ability to retrieve and analyse published NGS data and integrate with in-house generated data
- The ability to work independently on tasks and organise and prioritise workload within a project management framework
- Ability to interact effectively within a group, with collaborators and with the leader of the team
- Excellent organizational, documentation and communication skills
- Experience with at least one programming language could be of advantage (eg. Perl, Java, or C/C++).
- Experience with machine learning approaches
- Evidence of data presentation at scientific meetings
- Immunology experience is an advantage
- Track record of contributing to papers as evidenced by publications in refereed journals
Bioinformaticians/Computational Biologists will be expected to contribute to different projects on a collaborative basis and work with the project lab members in bioinformatics approaches and data management being applied to their research. The ability to work in a team is essential.