Simon Andrews

Simon Andrews
Simon Andrews
Simon Andrews
Head of Bioinformatics Facility
Simon Andrews

Simon Andrews did his first degree in Microbiology at the University of Warwick.  After a brief period working for Sandoz pharmaceuticals he went on  to do a PhD in protein engineering a the University of Newcastle with Harry Gilbert.  During his PhD his interests moved from bench work toward the emerging field of bioinformatics, and he decided to follow this direction in his future career.

After completing his PhD Simon worked with the BBSRC IT Services where he developed and then presented a series of bioinformatics training courses in protein structure analysis to the BBSRC institutes.  At one of these courses at Babraham he met John Coadwell who establised the Babraham Bioinformatics group and was then employed as the second member of the bioinformatics team.  Since joining Babraham Simon has seen the group grow from two people to nine as the field has become far more prominent in the biological research community.  He took over the running of the group in 2010.

Latest Publications

Lopes JS, Ivanova E, Ruiz S, Andrews S, Kelsey G, Coy P Epigenetics, Bioinformatics

Controlled ovarian stimulation is a necessary step in some assisted reproductive procedures allowing a higher collection of female gametes. However, consequences of this stimulation for the gamete or the offspring have been shown in several mammals. Most studies used comparisons between oocytes from different donors, which may contribute to different responses. In this work, we use the bovine model in which each animal serves as its own control. DNA methylation profiles were obtained by single-cell whole-genome bisulfite sequencing of oocytes from pre-ovulatory unstimulated follicles compared to oocytes from stimulated follicles. Results show that the global percentage of methylation was similar between groups, but the percentage of methylation was lower for non-stimulated oocytes in the imprinted genes , , and and higher in when compared to stimulated oocytes. Differences were also found in CGI of imprinted genes: higher methylation was found among non-stimulated oocytes in (), , , , and . In another region around , the methylation percentage was lower for non-stimulated oocytes when compared to stimulated oocytes. Data drawn from this study might help to understand the molecular reasons for the appearance of certain syndromes in assisted reproductive technologies-derived offspring.

+view abstract International journal of molecular sciences, PMID: 36555801 18 Dec 2022

Samant RS, Batista S, Larance M, Ozer B, Milton CI, Bludau I, Wu E, Biggins L, Andrews S, Hervieu A, Johnston HE, Al-Lazikhani B, Lamond AI, Clarke PA, Workman P Signalling, Bioinformatics

The molecular chaperone heat shock protein 90 (HSP90) works in concert with co-chaperones to stabilize its client proteins, which include multiple drivers of oncogenesis and malignant progression. Pharmacologic inhibitors of HSP90 have been observed to exert a wide range of effects on the proteome, including depletion of client proteins, induction of heat shock proteins, dissociation of co-chaperones from HSP90, disruption of client protein signaling networks, and recruitment of the protein ubiquitylation and degradation machinery-suggesting widespread remodeling of cellular protein complexes. However, proteomics studies to date have focused on inhibitor-induced changes in total protein levels, often overlooking protein complex alterations. Here, we use size-exclusion chromatography in combination with mass spectrometry (SEC-MS) to characterize the early changes in native protein complexes following treatment with the HSP90 inhibitor tanespimycin (17-AAG) for 8 hours in the HT29 colon adenocarcinoma cell line. After confirming the signature cellular response to HSP90 inhibition (e.g., induction of heat shock proteins, decreased total levels of client proteins), we were surprised to find only modest perturbations to the global distribution of protein elution profiles in inhibitor-treated HT29 cells at this relatively early time-point. Similarly, co-chaperones that co-eluted with HSP90 displayed no clear difference between control and treated conditions. However, two distinct analysis strategies identified multiple inhibitor-induced changes, including known and unknown components of the HSP90-dependent proteome. We validate two of these-the actin-binding protein Anillin, and the mitochondrial isocitrate dehydrogenase 3 (IDH3) complex-as novel HSP90 inhibitor-modulated proteins. We present this dataset as a resource for the HSP90, proteostasis, and cancer communities (https://www.bioinformatics.babraham.ac.uk/shiny/HSP90/SEC-MS/), laying the groundwork for future mechanistic and therapeutic studies related to HSP90 pharmacology. Data are available via ProteomeXchange with identifier PXD033459.

+view abstract Molecular & cellular proteomics : MCP, PMID: 36549590 19 Dec 2022

Ni Z, Wölk M, Jukes G, Mendivelso Espinosa K, Ahrends R, Aimo L, Alvarez-Jarreta J, Andrews S, Andrews R, Bridge A, Clair GC, Conroy MJ, Fahy E, Gaud C, Goracci L, Hartler J, Hoffmann N, Kopczyinki D, Korf A, Lopez-Clavijo AF, Malik A, Ackerman JM, Molenaar MR, O'Donovan C, Pluskal T, Shevchenko A, Slenter D, Siuzdak G, Kutmon M, Tsugawa H, Willighagen EL, Xia J, O'Donnell VB, Fedorova M Signalling, Bioinformatics

Progress in mass spectrometry lipidomics has led to a rapid proliferation of studies across biology and biomedicine. These generate extremely large raw datasets requiring sophisticated solutions to support automated data processing. To address this, numerous software tools have been developed and tailored for specific tasks. However, for researchers, deciding which approach best suits their application relies on ad hoc testing, which is inefficient and time consuming. Here we first review the data processing pipeline, summarizing the scope of available tools. Next, to support researchers, LIPID MAPS provides an interactive online portal listing open-access tools with a graphical user interface. This guides users towards appropriate solutions within major areas in data processing, including (1) lipid-oriented databases, (2) mass spectrometry data repositories, (3) analysis of targeted lipidomics datasets, (4) lipid identification and (5) quantification from untargeted lipidomics datasets, (6) statistical analysis and visualization, and (7) data integration solutions. Detailed descriptions of functions and requirements are provided to guide customized data analysis workflows.

+view abstract Nature methods, PMID: 36543939 21 Dec 2022

Group Members

Simon Andrews

Head of Bioinformatics Facility

Laura Biggins

Core Bioinformatician

Caroline Gaud

LIPID MAPS Web Developer

Sarah Inglesfield

Core Bioinformatician

Jo Montgomery

Biological Training Developer