Bioinformatician

The MGnify resource faciliates the analysis and archiving of microbiome derived sequence data (metabarcoding, metagenomic and metatranscriptome) submitted to the European Nucleotide Archive (ENA). This includes publicly available data, as well as privately submitted datasets, covering a wide range of environments and biomes.

MGnify provides taxonomic, and functional analysis, as well as assembly of shotgun metagenomic and metatranscriptomics dataset as a service. The MGnify resource is produced by the Microbiome Informatics Team, led by Rob Finn, and provides access to one of the largest collections of publicly available analysed microbiome data.

As part of this analysis service role, the MGnify team also undertakes collaborations with the wider research community on specific projects that enable the showcasing of MGnify analyses, the development of new methodological approaches to enhance the existing set of analyses, and/or the integration of different data types.

There are currently four 2-year positions within the MGnify team to take on such activities. More specifically, these focus on the analysis of human, animal and marine microbiomes, with the aim of incorporating metatranscriptomics and metaproteomics data, as well as mining the microbiome proteins with unknown function, the so-called protein “dark matter”. These new functions may be investigated by project partners for biotechnological applications, or for their associations with disease states.

All of the projects involve the development and/or execution of analysis pipelines for metagenomic, metatranscriptomic and metaproteomic datasets. The data analysis may also require the evaluation of tools, basic statistical analyses and/or aggregations, and interpretation of multiple threads of analyses to gain insights into function. Where appropriate, new developments will be propagated to the MGnify production pipelines, which are expressed in workflow description languages (CWL and Nextflow), allowing the combination of third party tools and in-house software (primarily written in Python)

Projects include the development and application of computational approaches for metagenomic, metatranscriptomic and metaproteomic data integration, with a view to elucidating function in the considerable portion of microbe-bourne proteins which are currently proteins of unknown function. A particular focus will be on the analysis of existing multi-omic datasets, for example chronic diseases such as inflammatory bowel disease, type 1 diabetes, and Parkinson’s disease. Another project aims to build both genomic and gene catalogs for animal microbiomes associated with food production. In particular, this will involve the analysis of large, longitudinal datasets provided by project partners. Another project specifically aims to analyse marine metagenomics datasets (metagenomics assembly and functional annotation) for novel examples of enzymes that are important for industrial biotechnology applications. Finally, another project will investigate the repertoire of bacteriocins encoded within microbial populations. Candidates are welcome to highlight projects that particularly appeal or match their skill sets.

The primary direction for the developments and data analyses will come from both the team leader Rob Finn, and the line manager Lorna Richardson. The successful candidate will be responsible for the smooth and timely development, testing and implementation of the tools and pipelines required. They will also be responsible for the ongoing maintenance of these workflows, and the throughput of analysis.