BEGIN:VCALENDAR
VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
BEGIN:VEVENT
DTSTAMP:20260711T171612Z
UID:1a9a4e6e-7a8b-40c3-8e6f-5b363b23d7a9
DTSTART:20240617T090000Z
DTEND:20240621T170000Z
DESCRIPTION:This course provides an introduction to the use of bioinformati
 cs in biological research\, giving you guidance for using bioinformatics i
 n your work whilst also providing hands-on training in tools and resources
  appropriate to your research.\n\nYou will initially be introduced to bioi
 nformatics theory and practice\, including best practices for undertaking 
 bioinformatics analysis\, data management\, and reproducibility. \n\nYou 
 will be required to review some pre-recorded material for their group proj
 ect prior to the start of the course.\n\n**Group projects**\n\nA major ele
 ment of this course is a group project\, where you'll be placed in small g
 roups to work together on a challenge set by trainers from EMBL-EBI and ex
 ternal institutes. This allows you to explore the bioinformatics tools and
  resources available in your area of interest and apply them to a set prob
 lem\, providing you with hands-on experience relevant to your own research
 . The group work will culminate in a presentation session involving everyo
 ne on the final day of the course\, giving an opportunity for wider discus
 sion on the benefits and challenges of working with biological data.\n\nGr
 oups are mentored and supported by the trainers who set the initial challe
 nge\, but the groups will be responsible for driving their projects forwar
 d\, with all members expected to take an active role. Groups are pre-organ
 ised before the course\, and all group members will be sent some short “
 homework” in preparation for your project work prior to the start of the
  course.\n\nBasic outlines of the projects on offer this year are given be
 low. In your application\, you must indicate your first and second choice 
 of project\, based on which you think would benefit your research most. No
 t all projects may be offered\, and final decisions on which projects will
  be run during the course will be made based on the number of applicants p
 er project.\n\nMost of the projects cover mammalian data sets\, however\, 
 in many cases\, the methods and approaches taught are transferable to data
  from various species.\n\n**Group project one: Genome variation across hum
 an populations **\n\nNatural variation between individuals or between dif
 ferent human populations is a result of genome mutations throughout evolut
 ionary history. Some mutations may become fixed because of their beneficia
 l effect while most drift among individuals. During this project\, you wil
 l investigate genomic variation between two separate human populations of 
 European and Asian descent. Using sequence data from a number of individua
 ls from each population\, you will use a range of bioinformatics tools to 
 discover variants that exist between them. In the second section of the pr
 oject\, you will attempt to analyse the functional consequences of the var
 iants you have identified\, attempting to find clinical association and li
 nking them to phenotypes.\n\nProject mentor: Anu Shivalikanjli | EMBL-EBI
  \n\n**Group project two: Interpreting functional information from large 
 scale protein structure data**\n\nThis project will introduce you to the w
 ealth of publicly available data in the [Protein Data Bank](https://www.e
 bi.ac.uk/pdbe/) (PDB) and give you the opportunity to investigate how larg
 e subsets of structure data can be used to analyse protein features and de
 termine function. In the project you will learn how to identify relevant p
 rotein structures\, collate and interpret functional information\, and imp
 lement this process programmatically.\n\nProject mentor: Marcus Bage | EMB
 L-EBI and Joseph Ellaway | EMBL-EBI\n\n**Group project three: Modelling ce
 ll signalling pathways**\n\nCurating models of biological processes is an 
 effective training in computational systems biology\, where the curators g
 ain an integrative knowledge of biological systems\, modelling\, and bioin
 formatics. You will learn to encode and simulate ordinary differential equ
 ation models of signalling pathways from a recent publication using user-f
 riendly software such as [COPASI](https://copasi.org/) even without exten
 sive mathematical background. You will learn to perform in-silico experime
 nts\, new predictions\, and develop hypotheses. Furthermore\, you will lea
 rn how to annotate models and re-use pre-existing models from open reposit
 ories such as [BioModels](https://www.ebi.ac.uk/biomodels/).\n\nProject m
 entors: Rahuman Sheriff | EMBL-EBI and Krishna Tiwari | EMBL-EBI \n\n**Gr
 oup project four: Improving AI-based bioimage analysis **\n\nArtificial I
 ntelligence (AI) algorithms outperform classical image analysis methods\, 
 however\, the performance of these models is highly dependent on the quali
 ty of the annotated image datasets used to train them. In this project\, y
 ou will explore the application of AI for biological imaging and the relat
 ionship between model training data and model performance. You will use mo
 dels stored in the [BioImage Model Zoo](https://bioimage.io/#/) and data 
 in the [BioImage Archive](https://www.ebi.ac.uk/bioimage-archive/) to fin
 e-tune and aggregate AI outputs. The aim of this project will be to test\,
  evaluate\, and improve model performance on a diverse set of microscopy i
 mages and annotations within the BioImage Archive. You will learn how to a
 pply\, train\, tune\, and employ the most performant state-of-the-art comp
 uter vision models. This project serves as a valuable demonstration of how
  FAIR (Findable\, Accessible\, Interoperable\, Reusable) data plays an ess
 ential role in the training and enhancement of AI models. \n\nProject men
 tors: Aybuke Kupcu Yoldas | EMBL-EBI and Craig Russel | EMBL-EBI \n\n**Gr
 oup project five: Single-cell RNA-sequencing analysis with Python**\n\nIn 
 this project\, you will learn how to perform single-cell RNA-sequencing da
 ta analysis to investigate cell type heterogeneity and expression differen
 ces across conditions. The analysis will be based on the[ SCANPY](https:/
 /scanpy.readthedocs.io/en/stable/index.html) framework in Python. You will
  start by collecting the raw count matrix and relevant metadata from the[
  Single-cell Expression Atlas](https://www.ebi.ac.uk/gxa/sc/home). After 
 constructing the AnnData objects\, you will perform quality control\, prep
 rocessing\, dimensionality reduction\, cell type annotation\, and differen
 tial expression analysis. We will also explore the batch effect and its co
 rrection. \n\nProject mentors: Yuyao Song | EMBL-EBI and Anna Vathrakokoi
 li-Pournara | EMBL-EBI \n\n**Group project six: Networks and pathways**\n
 \nThis project will cover typical bioinformatics analysis steps needed to 
 put differentially expressed genes into a wider biological context. You wi
 ll start with gene expression data (RNA-seq) to build an initial interacti
 on network. Next\, you will learn to combine public network datasets\, ide
 ntify key regulators of biological pathways\, and explore biological funct
 ion through network analysis. You will get first-hand experience in integr
 ation and co-visualising with additional data and functional enrichment an
 alysis. All this helps to put the initial results into a previously known 
 context and provide hypotheses for potential follow up experiments. We wil
 l use Cytoscape\, Expression Atlas\, g:Profiler\, StringDb\, among other t
 ools. We may also give a few R packages a try.\n\nProject mentor: Priit Ad
 ler | University of Tartu
LOCATION:European Bioinformatics Institute\, Hinxton
SUMMARY:Summer school in bioinformatics
URL;VALUE=URI:https://www.ebi.ac.uk/training/events/summer-school-bioinform
 atics-1
END:VEVENT
END:VCALENDAR