Presentation is loading. Please wait.

Presentation is loading. Please wait. Biomedical Informatics Hub.

Similar presentations

Presentation on theme: " Biomedical Informatics Hub."— Presentation transcript:

1 Biomedical Informatics Hub


3 Contents  Wellcome Trust ISSF Background  Seed Corn Fund Aims  Biomedical Informatics Hub Purpose Expertise Resources Training Seminars Project booking/Prioritisation Sustainability

4 Institutional Strategic Support Fund A £1.5 million Wellcome Trust award match funded by the University  to enhance institutional strategies for the biomedical sciences  to support scientific progress, translation and interdisciplinary collaboration  to encourage greater efficiency, effectiveness and accountability in the stewardship of Wellcome Trust funding

5 University of Exeter ISSF project  Create a virtual Biomedical Informatics Hub to maximise output from emerging technologies and large datasets  Establish an ISSF Seed Corn Fund to support highly-talented researchers to generate preliminary data in support of independent fellowship and grant applications 8 projects supported in 2012  University match-funding = new academic posts that will utilise the Hub to enable major advances in interdisciplinary biomedical research

6 Seed Corn Fund

7 Round 3: Who?  outstanding postdoctoral researchers - to generate preliminary data to support independent Fellowship applications  early career academics - to generate preliminary data in support of research grant applications  mid-career and senior academics seeking pump priming support for new activity that will lead to a Wellcome Trust application (first time applicants to the Trust particularly encouraged)

8 Round 3: What?  Must be in the Wellcome Trust’s remit  5 to 7 projects (up to 12 months)  Up to £20k (£30k for senior researchers)  Closing date = 1st Feb 2013  Assessment criteria track record proposed project outputs  Contact Allison McCaig

9 Biomedical Informatics Hub

10 Purpose 1. Supply informatics expertise in bioinformatics, mass-spectrometry, imaging and statistics 2. Train researchers through existing and newly developed programs to make best use of their data 3. Supply PhD students, postdocs and junior researchers with the tools and expertise to produce 4* research 4. Provide leadership, governance and coordination to leverage and extend Exeter's IT research infrastructure 5. Support researchers with access to centralised computational infrastructure and equipment 6. Act as a central point of contact for initial research ideas

11 Expertise 1.Bioinformatics Medical - Marcus Tuke, Anna Murray & Mike Weedon (Prof. Tim Frayling) Bioscience - Dr Christine Sambles (Sequencing Service) 2.Metabolomics/Proteomics Dr Venura Perera (Dr Hannah Florance) 3.Image analysis Dr Jeremy Metz (starting November) (Prof. Rob Beardmore) 4.Tomography and EM TBC (Prof. Gero Steinberg) 5.Statistics TBC (Prof. Tim Frayling) 6.Computational modelling/sensor-based networks Dr Mahmood Javaid (Prof. Ed Watkins)

12 Resources  Sequencing Service (Konrad Paszkiewicz, Karen Moore) Illumina HiSeq 2000  Mass-spectrometry Service (Hannah Florance) Agilent QQQ Agilent QTOF Metabolomics/Proteomics  Imaging (Martin Schuster, Gero Steinberg) Confocal, EM Tomography, SEM, TEM  Computational Resources Zeus cluster

13 Resources  Zeus Systems Biology cluster  19 nodes with 144 cores  Nodes have between 24Gb and 192Gb RAM  150TB of storage  Designed for serial jobs, not MPI/OpenMP jobs  Upcoming £150k upgrade  Contact Konrad for access

14 Hub Training Courses Training courses currently offered: 1.Amazon EC2 cloud computing 2.Unix & Perl 3.Short read genomics 4.Phylogenetics 5.Use of R statistical package 6.RNA-seq New courses in 2013

15 6. Seminars Monthly seminars/workshop meetings Scope: bioinformatics, statistics, image analysis, programming

16 Submitting requests  You can approach Hub members informally for initial discussions about projects  If you’re unsure – me

17 Submitting requests  BUT before they undertake any work for you:  You must apply via  Await approval by the head of discipline (no more than a few days)  You need to specify up-front outcomes in terms of grant applications and/or publications and when you expect to achieve these  Allison McCaig will review outputs. Users who use Hub time and do not publish or apply for grants outcomes will be penalised in future hub applications.

18 Some screenshots.....

19 Project proposal submission

20 Project proposal submission

21 Project proposal tracking

22 Prioritisation  Wellcome Trust remit projects have priority  “to achieve extraordinary improvements in human and animal health we support the brightest minds in biomedical research and the medical humanities.” See Research Challenges at 1.Seed Corn Fund projects 2.Existing Wellcome Trust funded projects 3.Wellcome Trust remit research 4.Other research

23 Sustainability  Funding via the Wellcome Trust ISSF for up to three years (annual approval)  To continue the Hub, we need to ensure members are costed into grant applications now  Any projects which use Hub expertise and subsequently apply for a grant need to cost relevant members

24 Hub team presentations

25 Ven Perera

26 Who Am I  Dr Venura Perera  Located Room: M01 Mezzanine Floor Biosciences College of Life and Environmental Sciences Geoffrey Pope  Background:  PhD in Systems biology/Bioinformatics Using mathematical methods for analysis of untargeted mass profiling data sets.  Applied mathematics Use of numerical methods for forecasting and predictive modeling of non-linear systems Data drive techniques for parameter optimization Ordinary and partial differential equations for system modeling

27 My skills  Applied Mathematics  Use of Numerical models for data modeling  Development of dynamic models using numerical methods  Statistical Modeling using non-parametric methods  Large Data Sets Analysis  Focus on Untargeted Metabolite data New methods to solve any question …  Metabolite Pipeline ExSpec  Development of metabolite centers untargeted pipeline  Programming  Java  Data Modeling  R  Mainly for data visualization  Matlab  Data Modeling and Visualization  HTML, PHP, MySQL and basic Java Script

28  Further the use of MS techniques  Numerical methods for data modelling  Using probabilistic methods for data driven coupled systems  Development of techniques for the MS fingerprinting  Using MS methods for polygenetic tree reconstruction My research interests

29 Targeted Targeted / Quantitative Analysis Multiple Reaction Monitorin g Statistical analysis, Modelling and ClusteringSample Comparis on Quantificatio n Non-targeted Exploratory Non-targeted Analysis Extract Data Molecular Feature Extraction (MFE) and data pre- processing My Work Small Molecule analysis: Untargeted profiling and Targeted quantification

30 Targeted Targeted / Quantitative Analysis Multiple Reaction Monitoring Statistical analysis, Modelling and ClusteringSampleComparisonQuantification Non-targeted Exploratory Non-targeted Analysis Extract Data Molecular Feature Extraction (MFE) and data pre- processing

31 Example Project Two groups of patient types : Healthy Cancer Three patients Project Aim: Using a variety of techniques both targeted analysis as well as untargeted profiling to determine key components which are effected by the cancer

32 Example Project

33 Patient 24 had a large amount of inflammation causing the control and cancer tissue to share a great deal of features similarities.

34 Nick Smirnoff (Director of Mass Spectrometry) Hannah Florance (MS Facility Manager) Venura Perera (Bioinformatics and Mathematical Support)

35 Numerical Dynamic Modeling Reaction scheme: A K : known substrate of B (can be removed if un-mapped A U : Unknown substrate of B B : Substrate to model E : Enzyme(s) controlling the reaction C : Known product of B C U : Unknown product of B B U : Unknown substrate of C AkAk AkAk AUAU AUAU B B CUCU CUCU C C E E BUBU BUBU λ λ α α β β μ μ γ γ

36 Example of Substrate-Product combo Substrate-Product combo: Switch profiles show delayed affect indicating possible reaction scheme Profiles visually illustrate similarity AkAk AkAk AUAU AUAU B B CUCU CUCU C C E E BUBU BUBU λ λ α α β β μ μ γ γ

37 Anna Murray & Mike Weedon

38 Mike Weedon, PhD Lecturer in Bioinformatics and Statistics, Exeter Medical School, St Luke’s Campus Graduate in Biochemistry and Molecular Biology PhD in molecular genetics of type 2 diabetes, Peninsula Medical School Postdoc on genetics of complex traits, Peninsula Medical School Current research interests – genome-wide investigations of monogenic and polygenic traits Anna Murray, PhD Senior Lecturer in Human Genetics, Exeter Medical School, St Luke’s Campus Graduate in Biology PhD in molecular biology of T cell receptor rearrangement in coeliac disease in Southampton Postdoc on population genetics of Fragile X syndrome in Salisbury/Southampton Current research interests – Genetics of female reproductive lifespan


40 Whole exome/genome sequencing ENCODE and other projects to annotate non- coding genome Genome-wide SNP and expression array studies Biological pathway analysis Statistical analysis of large datasets

41 Marcus Tuke

42 Background Computer Science Graduate University of Exeter Medical School Supercomputer systems administrator 8 months working on projects at the Wellcome Trust Centre for Human Genetics MSc Bioinformatics

43 Expertise I can offer Human Next Generation Sequence data processing and analysis Human RNA-Seq processing and analysis Computational expertise

44 Expertise I can offer Human RNA-Seq processing and analysis Human Next Generation Sequence data processing and analysis Computational expertise

45 Help with projects that could benefit from expertise in: Programming/scripting Database/web development Linux command line and cluster computing

46 Expertise I can offer Human RNA-Seq processing and analysis Computational expertise Human Next Generation Sequence data processing and analysis

47 Align Next Generation Sequencing reads to reference genome Pipelines to process and call variation in aligned genome including: Pipelines to filter false positives and QC analysis Reference Genome g c c g g g c DeletionSNP g>c heterozygote Realign reads mis-aligned in genome due to indels

48 Expertise I can offer Computational expertise Human Next Generation Sequence data processing and analysis Human RNA-Seq processing and analysis

49 Align RNA-Seq reads to Human Genome Transcriptome assembly Differential expression analysis

50 Research Interests Understanding the genetic basis of human diseases and traits Genome Wide Association Studies (GWAS) have identified numerous genomic regions associated with several major diseases However, these studies have focussed mostly on common single nucleotide genetic variants How much more disease- associated genetic variation can we discover from 'higher resolution' technologies such as next generation sequencing (NGS)? - Rarer - Structural - Non-autosomal - Sub-groups

51 Whole-genome sequencing in the Inchianti Study Ongoing projects A longitudinal study of aging from the Chianti region of Tuscany, Italy 1453 individuals have been followed up over 4 waves since 1998 Extensive phenotypic and biomedical information collected Hundreds of circulating biomarkers (e.g. Interleukins, sex hormones, vitamins) measured RNA expression profiling in lymphocytes (Illumina 46K array) Methylation profiling (450K array) Already had the Illumina 550K SNP Chip genotyped We are performing low-pass (median 7X) whole genome sequencing on 680 of these individuals RNA-Seq whole-transcriptome assembly and analysis Assemble transcriptomes for 3 primary human microvascular endothelial cells 2 treated with cathepsin L and D respectively, and 1 control Analyse whether there are any differences in expression of RNA between two treatments and control

52 Christine Sambles

53 Christine's prezi

54 Jeremy Metz

55 JEREMY METZ, PHD PhD: Quantum computing - theory & simulations Imperial College London Postdoc:Biological image analysis & cellular modelling Einstein College of Medicine, NY

56 Research Interests Image processing and computer vision applied to biological systems o Object segmentation o Tracking in range of dynamical scenarios o Quantitative analysis Modelling biological systems - analytical and numerical approaches o Monte-carlo simulations o Integration of data and insight from multiple scales of inquiry, e.g. AFM, single cell analysis, high throughput screening

57 Skills and Expertise Python, C++, Java, Matlab Image processing o Matlab - image processing toolkit o Python - Scipy and OpenCV bindings o Java - ImageJ plugins Simulation o C/C++ for fast low-level routines o Python as "glue" code and visualization Linux, shell scripting, Oracle Grid Engine

58 Past Projects - Image processing Object tracking: Developed novel cross-correlation and Bayesian state estimation based object tracker Object segmentation: (In progress) Object detection and segmentation based on Scale-space representation formalism

59 Simulation Biological system: Kinesin diffusion-reaction during mitosis, spindle photo - bleached at t=0. Model using minimalist feature cell: Check for presence of competition for binding sites between diffusing species Quantum system: Noisy (open) atom-cavity system, laser illumination

60 How can we work together? With my expertise in the fields of biological data/image analysis, and mathematical & computational modeling, how can we combine our skills to produce an outstanding research project? Experiments Extract data Model, Simulation Theory

61 Mahmood Javaid

62 Area of expertise: Computational Modelling Sensor-based Networks and Embedded Systems High Performance Computing Software Development MAHMOOD JAVAID PHD COMPUTER SCIENCE EXPERIMENTAL OFFICER- RESEARCH COMPUTING WELLCOME TRUST BIOMEDICAL INFORMATICS HUB

63 Computational Modelling  Agent-based modelling using Agent-based simulation frameworks such as FLAME and SWARM Benefits:  Close association between the model entities and the real-world agents  Heterogeneous agents within an environment  Interaction between the agents through message passing  Potential to uncover emergent behaviours  Possibility of multi-scale modelling

64 Some examples  Prospecting asteroid belt

65 Hypothesis testing

66 Economics and Systems biological applications of ABM Eurace project (Dept. of Computer Science, University of Sheffield.) System Understanding of Microbial Oxygen Responses (SUMO)

67 Sensor-based networks, embedded systems  Involving proximity sensors such as Ultrasonic, Infrared, and Laser-based.  Inertial management units such Accelerometer, Gyroscope, and Magnetometer.  Interfacing between sensors and computation units using interconnects such as Serial ports, Bluetooth, and I2C.  PIC and AVR microcontroller based systems

68 Sensory augmentation device  In order to move around in the world safely and quickly most of us are highly reliant on our visual sense.  When vision is compromised, the problem of safely finding our way becomes much more difficult.  Possibility of augmenting our existing senses with a form of “remote touch” generated by artificial distance sensors and tactile stimulus.

69  Movement is critical to how we use our tactile sense.  We explore objects through touch by controlling the way that we move our sensory surfaces over them— stroking with the fingertips to investigate texture, for instance, or palpating to investigate shape. Active Touch

70 Inspired from biological and biomimetics

71 Physical Components

72 First Prototype

73 High Performance Computing  Agent-based models running on Linux HPC clusters  Administration of Linux HPC cluster  Publishing legacy applications running on Linux clusters using web-services

74 Software Development  Desktop applications for Linux and Windows platforms including Matlab  Web-based applications and publishing legacy systems using web-services  Mobile Operating System applications

75 Online Depression and Mood Disorder Screener



78 Thanks for listening.....

Download ppt " Biomedical Informatics Hub."

Similar presentations

Ads by Google