Whose Culture? Data Researcher (volunteering opportunity) with Rising Arts Agency

Context

Bristol boasts a strong creative and cultural hub. For young people of colour, however, it can be difficult to be in and enjoy cultural spaces where you don’t see yourself represented. For local arts organisations, next-to-no data exists on people of colour’s cultural engagement, meaning there’s a lack of evidence to guide them in engagement work.  

Rising Arts Agency is a micro-social enterprise whose mission is to nurture more diverse participation, staffing and leadership across Bristol’s creative sector by providing young creatives (16-25) with platforms, networks and training to showcase work and influence cultural strategy.

Rising’s ‘Whose Culture’ programme is a youth engagement and creative data mapping project which provides opportunities for young people of colour to ask questions within the sector, exploring what “culture” means to them through consultations, workshops and sharings.

Over eighteen months, Whose Culture will collaborate with young people of colour on some creative tech which measures and records what “culture” means to them, providing data capable of creating a radical shift towards inclusion in the sector.

The Brief

Rising Arts Agency are looking for a post-graduate research student who specialises in data collection and analysis. This is an exciting research opportunity and the student will be working in a voluntary capacity. We are looking for an individual who can devise methods for collecting and analysing data as part of two phases in this project: the Workshops phase and the Creative Technology phase.

Firstly, we need to collect data during the workshop phase, which runs throughout October and November 2018. Over eight weeks, we will run twelve Whose Culture workshops taking place across Bristol. Run with experienced facilitators and young assistants. This is an opportunity for Rising to begin to understand what ‘culture’ means to the participants, young local creatives of colour in our target areas (St Pauls, Lawrence Hill, Whitchurch Park and Southmead).

From December we will begin to work on a piece of creative technology (we envisage this being a digital platform e.g. an app or social media network) that will enable us to gather city wide data about cultural habits and tastes of young creatives of colour. We then want to share this data with the city’s arts organisations and funders to affect cultural change at a strategic level.

We would like to understand what data we can safely, efficiently and effectively collect during these two phases, so that we are in line with GDPR and in line with the project’s needs. We would like to understand how we can collect this data, analyse it and disseminate it.

We foresee this process taking around 18 months in total. This would be a unique research opportunity and you could be involved for either or both of the project phases. We are looking for someone who is confident advising and taking a lead on data collection. You will work alongside the Whose Culture Project Coordinator Roseanna Dias and a Social Media Coordinator Fatima Safana, as well as Rising’s Director Kamina Walton.

Next steps

If you are interested in this opportunity, please contact Roseanna Dias on roseanna@rising.org.uk with a short statement detailing your relevant experience, how you would approach the task and why you would like to be a part of the project. Please get in touch with us before Friday 21 September – we will be contacting people as enquiries come in, meaning we may close this opportunity early. We are keen to get someone on board as soon as possible.

Contact us:  If you have questions about the role please contact Roseanna Dias on roseanna@rising.org.uk

Enabling advanced analytics for all users of the proteomics facility

Schematic of BayesProt, which evaluates protein ‘biomarkers’ that can differentiate between healthy and diseased biological samples such as blood.

 Discovery proteomics 

Research in the life sciences and translational medicine is being driven forward by cutting-edge techniques for studying the molecules acting in cells. We are interested in studying what proteins are present in diseased cells and in what quantities, compared with normal cells, since the identity of the proteins may help us understand the disease process, the search for new drug targets, and act themselves as diagnostic tests for the disease. The technologies used to study proteins on a large scale are collectively called discovery proteomics, and the main method used in proteomics is mass spectrometry (MS). 

Improved and novel data processing for mass spectrometry 

The Dowsey group has been working on improved and novel data processing for MS for over a decade. In collaboration with proteomics laboratories in Manchester and Liverpool, they have developed a Bayesian model called BayesProt which has recently been extended to take in Bristol’s TMT data. BayesProt has proved the cornerstone of several large-scale translational studies. 

BayesProt 

BayesProt is fundamentally novel and enables analyses previously not possible, such as determining the relative protein levels derived from different transcripts of a gene, or the products of in-cell proteolysis. The purpose of this project is to port BayesProt to Bristol’s BlueCrystal, the University of Bristol’s High Performance Computing (HPC) machine, and integrate its functionality into the Proteomics Facilities’ workflow so that all studies passing through will benefit. 

Integration of BayesProt into Galaxy Integrated Omics 

BayesProt has now been ported to PBS and SLURM cluster managers utilised by BlueCrystal Phase 3 and 4. We have also integrated BayesProt into the Galaxy Integrated Omics’(GIO) environment available on BlueCrystal 3. GIO integrates a collection of state-of-the-art tools for genomics and proteomics to enable ‘proteomics informed by transcriptomics’. This system is key to the study of the effects of differential transcription or ‘gene switching’ caused by e.g. viral infection. BayesProt’s new deconvolution functionality will be critical to quantitative understanding of gene switching, and hence bringing this tool into GIO will enable us to demonstrate these possibilities 

Acknowledgements / People involved in this project 

Andrew Dowsey & Ranjeet Bhamber, Population Health Sciences; Kate Heesom, Biochemistry; Andrew Davidson, David Matthews, Christoph Wuelfing, & David Lee, Cellular and Molecular Medicine, all at the University of Bristol.

This project was funded by the Jean Golding Institute Seed Corn Funding Scheme 2018. To find out about other projects supported by this scheme, take a look at the Jean Golding Institute Projects. 

EPIC-KITCHENS 2018: A publicly available dataset

EPIC-KITCHENS is a collaboration with the University of Toronto (Canada) and the University of Catania (Italy), led by the University of Bristol to collect and annotate the largest (over 10 million frames) dataset, capturing 32 individuals in their own homes, over several consecutive days.  

The dataset was collected in 4 different countries and was narrated in 6 languages to assist in vision and language challenges. It offers a series of challenges from object recognition to action prediction and activity modelling in non-scripted realistic daily setting. 

The size of publicly available datasets is crucial to the progress of this field, which is of prime importance to robotics, healthcare and augmented reality. 

For further information please visit Epic Kitchens.

Blog written by Dima Damen 

Acknowledgements: Hazel Doughty, Giovanni M. Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, Michael Wray.

This project was partially funded by the Jean Golding Institute Seed Corn Funding Scheme 2018. To find out about other projects supported by this scheme, take a look at the Jean Golding Institute Projects. 

Developing computational tools for analysis of exome and data in highly phenotyped rare disease cohorts

Building a bridge between bench and computer science

This project begins a new interdisciplinary collaboration between renal biology and engineering mathematics to apply novel machine learning methods to investigate genetic signatures in a highly phenotyped rare kidney disease cohort.

Idiopathic Nephrotic Syndrome (INS) is a rare and one of the most difficult renal diseases in children and adults with the central event being glomerular podocyte injury. Up to 30% of steroid resistant NS cases are caused by a single-gene defect. The rest are considered to be immunologically mediated and caused by an as yet unidentified circulating factor(s) and can present as secondary steroid resistance after initial steroid sensitivity. There are currently no robust or reliable clinical indicators or biomarkers of response meaning that prediction of disease progression or response to medication cannot be clearly defined. The impact of genetic architecture in acquired disease is completely unknown.

Our aim is to understand the genetic architecture of acquired nephrotic syndrome and its impact on the disease course.

Pilot study

This is a pilot project with the aim of developing and using state of the art methods from bioinformatics and machine learning to classify subtypes of INS, potentially with different clinical outcomes, using whole exome and genome sequencing data.

We used exome sequencing data from the deeply phenotype UK cohort of paediatric INS patients. We investigated the differences in the enrichment of single nucleotide variations in different INS phenotypes. Since ~70% of patients are likely to have immunologically mediated cause of INS, we focused on genes not only known to be involved in INS but also B-cell, T-cell and lymphocyte genes.

Key objectives achieved

  • Hierarchical clustering pointed out significant enrichment of certain INS phenotypes in specific clusters which were based on the proportion of genetic variations in the selected genes. Thus indicating a potential corresponding genetic signature for each INS phenotype, which can be now investigated further
  • Results from this preliminary project were used for a KRUK fellowship application which was successful.

Looking ahead

  • Extend our investigation genome-wide (all genes instead of a tiny subset)
  • Tease out the influential genes and/or variants
  • Stratify INS patients into sub-types with corresponding genetic fingerprints/signatures
  • Predict best treatment options based on the patients’ genotype.

Blog written by Agnieszka Bierzynska, Bristol Medical School & Mark Rogers, Engineering Maths at the University of Bristol.

This project was funded by the Jean Golding Institute Seed Corn Funding Scheme 2018. To find out about other projects supported by this scheme, take a look at the Jean Golding Institute Projects.