Prof Simon HubbardBSc, PhD

Professor of Computational Biology

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Research and projects

I am a Professor in the Faculty of Life Sciences at the University of Manchester. My scientific career has taken me from a PhD in Biochemistry at UCL, London, via the European Molecular Biology Laboratory, back to Manchester in the UK where I undertook a Wellcome Trust fellowship, before gaining a Lectureship in 1998. My research covers themes in computational and systems biology and bioinformatics. My research applies computational approaches to the study of biological systems and molecules, and my particular areas of interests are detailed below, broadly in the areas of protein and genome bioinformatics including quantitative proteomics, regulation of gene expression (and particularly translation from mRNA to protein), and general bioinformatics.

Applied Quantitative Mass Spectrometry-based Proteomics

Proteomics seeks to characterise the proteins produced in a cell or tissue, exploiting sequenced genomes to identify proteins, usually via mass spectrometry. Bioinformatics plays an important role in this process by bridging the gap between the experimental data (mass spectra) and the databases of protein sequences, to identify (and ideally quantify) proteins via their constituent peptides. We have developed algorithms for this purpose, to improve protein/peptide identification and associated statistics. Alongside this work, we are closely involved in the efforts to develop data standards and databases to store and mine all the data emanating from proteomics experiments. Currently we are interested in quantifying the proteome of a whole organism, yeast, to understand how protein levels vary between different genes - some mRNA:protein ratios are < 10, others are > 250,000. A second major project (a sLoLa funded by the BBSRC) is just starting, seeking to quantify the developmental proteome of the fruit fly, Drosophila, with colleagues in Cambridge and UCL. All of the projects require bionformatic tools both to process the data and derive accurate estimates, as well as tools to analyse the data and look for patterns and trends and improve our understanding of these fundamental processes.

We are also investigating how proteomics can help annotate genome sequences (see below).

Genome and Transcriptome Sequence Analysis, including Proteogenomics

Our group also study DNA and protein sequences to understand biological phenomena. We played the leading role analysing EST and cDNA sequences as part of the international effort to sequence the chicken genome, providing web-based resources for chicken ESTs, cDNAs, SNPs and a proteome database. In addition to the chicken work, we are also analysing transcriptome sequences in yeast in order to understand how these are translationally regulated, looking at the sequences bound (or not) by RNA binding proteins involved in regulating these process. This is part of a collaboration led by Graham Pavitt in a LoLa project funded by the BBSRC. Finally, we also attempt to exploit transcriptome data (such as ESTs or from RNA-seq projects) to predict novel proteome databases from which we can identify novel genes and gene structure via mass spectrometry data - this field is known as proteogenomics, and can add great value to the annotation of genome sequences by finding information that the original annotators had missed.

Current funding: BBSRC


Research and projects

No current projects are available for public display