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DTSTAMP:20260711T102848Z
UID:4a7aee00-46e5-4910-b9b9-0da77c08bb51
DTSTART:20190624T090000Z
DTEND:20190628T170000Z
DESCRIPTION:This course provides an introduction to the use of bioinformati
 cs in biological research\, giving participants guidance for using bioinfo
 rmatics in their work whilst also providing hands-on training in tools and
  resources appropriate to their research.\n\nParticipants will initially b
 e introduced to bioinformatics theory and practice\, including best practi
 ces for undertaking bioinformatics analysis\, data management and reproduc
 ibility. To enable specific exploration of resources in their particular f
 ield of interest\, participants will be divided into focused groups to wor
 k on a small project set by EMBL-EBI resource and research staff\, ending 
 in a presentation from each group on the final day of the course.\n\nThe c
 ourse includes training and mentoring provided by experts from EMBL-EBI an
 d external institutes.\n\n**Group projects**\n\nA major element of this co
 urse is a group project\, where participants will be placed in small group
 s to work together on a challenge set by trainers from EMBL-EBI data resou
 rce and research teams.  This allows people to explore the bioinformatics
  tools and resources available in their area of interest and to apply thes
 e to a set problem\, providing them with hands-on experience of relevance 
 to their own research. The group work will culminate in a presentation ses
 sion involving all participants on the final day of the course\, giving an
  opportunity for wider discussion on the benefits and challenges of workin
 g with biological data.\n\nGroups are mentored by the trainers who set the
  initial challenge\, but active participation from all group members is ex
 pected.  Groups are pre-organised before the course\, and all group membe
 rs will be sent some short “homework” in preparation for their project
  work prior to the start of the course.\n\nThe basic outline of the projec
 ts on offer this year are given below.  In your application you should in
 dicate your first and second choice of project\, based on your judgement o
 f which would benefit your research most.  Not all projects may be offere
 d\, final decisions on which projects will be run during the course will b
 e made based on the number of applicants per project.\n\nThis year’s pro
 jects are as follows:\n\n**Networks and pathways**\n\nThe project will mak
 e use of gene expression data (RNA-seq) to build protein-protein interacti
 on networks which can be used to explore functional relationships between 
 the (potentially) expressed protein products. You will use Cytoscape to vi
 sualise protein networks\, identify key regulators of biological pathways 
 and explore biological function through network analysis\, integration and
  co-visualisation of additional data\, and ontology/functional enrichment 
 analysis - helping to build a better view of the wider biological context.
 \n\n**Metabolic network engineering using a systems model based approach**
 \n\nYou will work with a model example of metabolic pathways set\, coming 
 from the BioModels database\, and you will learn how to carry out computat
 ional analyses to find common patterns (i.e. set of reactions) in the netw
 ork. These might include computing feasible pathways through the network\,
  and minimal set of reactions to knock out specific metabolic functions. V
 isualisation of results will be achieved with an interactive graphical too
 l available as a web service.\n\n**Modelling cell signalling pathways**\n\
 nCurating models of biological processes is an effective training in compu
 tational systems biology\, where the curators gain an integrative knowledg
 e on biological systems\, modelling and bioinformatics. You will learn to 
 encode models of signalling pathways from a recent publication using COPAS
 I and reproduce the simulation results. Furthermore you will learn to anno
 tate models and learn to re-use pre-existing models from open repositories
  such as BioModels.\n\n**Proteomics (data analysis and functional annotati
 on)**\n\nIn this project\, you will obtain real-life proteomics data from 
 clinical tumour samples. Your task will be to process the raw data\, analy
 se the results\, and eventually interpret them in a wider context using th
 e Open Targets Platform.\n\n**An introduction to deep learning through fun
 ctional annotation of proteins**\n\nAutomatically annotating protein seque
 nces with functional information is vital in a world where sequences are p
 roduced so fast that humans can't keep up. In this project you will explor
 e how deep learning can be used to enrich sequences automatically.\n\n**Si
 ngle cell characterization of cell types and cell development**\n\nThis pr
 oject will make use of single cell RNA Sequencing data (scRNA-Seq) to show
  how to: 1) quality control the sequencing data\; 2) understand the varian
 ces of the data\; 3) cluster the cell types\; 4) understand the cell devel
 opment\; 5) find differential expression genes that determine the cell typ
 es or cell development. You will use data from the Human Cell Atlas and Ta
 bula Muris to understand human and mouse cell types respectively.\n\n**Fin
 ding and extracting meaningful structural data from PDBe**\n\nThis project
  will introduce you to the wealth of data available at PDBe and how this c
 an be extracted to analyse macromolecular structures. You will firstly exp
 lore the search and entry pages at PDBe to identify the type of data avail
 able for analysis. Using this knowledge\, you will then use and adapt temp
 late scripts in order to access this data programmatically and analyse a s
 ubset of your results. This project should give you the foundation of know
 ledge about how to access data through the PDBe API\, and how you can anal
 yse subsets of PDB data related to your field of expertise.\n\n**Exploring
  variation data across human populations**\n\nNatural variation is require
 d to generate the broad range of traits and phenotypes that exist between 
 single individuals and between different populations. In this project you 
 will explore the results of SNP-calling using web-based resources such as 
 Ensembl Variant Effect Predictor. You will predict the functional conseque
 nces of variants between separate human populations and identify the varia
 nt(s) within your samples that have been associated with several interesti
 ng phenotypes.
LOCATION:European Bioinformatics Institute\, Hinxton
SUMMARY:Summer school in bioinformatics
URL;VALUE=URI:https://www.ebi.ac.uk/training/events/summer-school-bioinform
 atics
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