ResearchMeeting15April2015
•Download as PPTX, PDF•
1 like•73 views
CV + overview of Bioinformatics work
Recommended
Recommended
More Related Content
Similar to ResearchMeeting15April2015
Similar to ResearchMeeting15April2015 (20)
Recently uploaded
Recently uploaded (20)
ResearchMeeting15April2015
- 1. 1 ELIS – Multimedia Lab Big Data Guy… the story so far
- 2. 2 ELIS – Multimedia Lab I. CV I. Electromagnetics for fusion II. Bioinformatics: iADHoRe & BLSSPeller III. Hadoop@Telenet II. @MMLab III. BLSSpeller: I. Motivation I. Genetics II. Motif Discovery II. Algorithm III. Validation & Future work Outline
- 3. 3 ELIS – Multimedia Lab CV
- 4. 4 ELIS – Multimedia Lab EM Wave solver for Nuclear Fusion • Solving Maxwell’s equations in highly inhomogeneous anisotropic plasma
- 5. 5 ELIS – Multimedia Lab HPC toolbox: Integral Equations MoM FFT FMM CGsolver FEM FDTD Cold plasma solution
- 6. 6 ELIS – Multimedia Lab Collinear regions in GHMs Improved sensitivity by using aligned gene segment profiles iADHoRe 3.0
- 7. 7 ELIS – Multimedia LabVisualization suite
- 8. 8 ELIS – Multimedia Lab C++ MPI Pthreads Alignment Clustering Ensembl Dataset
- 9. 9 ELIS – Multimedia Lab Hox
- 10. 10 ELIS – Multimedia Lab • Parallel algorithm to uncover sequence motifs in DNA sequences of closely related species + OR Current Research: BLSSpeller
- 11. 11 ELIS – Multimedia Lab @Telenet: Data Warehousing in a Hadoop production environment
- 12. 12 ELIS – Multimedia Lab @MMLAB
- 13. 13 ELIS – Multimedia Lab • Status file:///C:/Users/ddewitte/Dropbox/BDS_share/index.html Teaching: BDS course
- 14. 14 ELIS – Multimedia Lab SPARQL Public medical data • Ontoforce: Federated query engine for biomedical datasets: • Virtuoso is currently the bottleneck • Federated Querying • Alternative architectures • Some datasets >> Virtuoso Project work Aggregate s
- 15. 15 ELIS – Multimedia Lab You … Are … The … Greatest … => more powerpoints MMLab Sales…
- 16. 16 ELIS – Multimedia Lab BLSSPELLER: MOTIVATION Exhaustive comparative discovery of conserved cis-regulatory elements
- 17. 17 ELIS – Multimedia Lab • DNA stores the information to build proteins… • Proteins are generated in a two-step process: transcription & translation • Update: there should be more arrows! One slide on genetics
- 18. 18 ELIS – Multimedia Lab • RNA polymerase: DNA to RNA • Recruited by transcription factors which bind to gene promoter => where are the sites? Transcription factor binding sites
- 19. 19 ELIS – Multimedia Lab • Original approach: - Search for motifs in the promoter sequences of sets of coregulated genes - But: Coexpression IS NOT EQUAL TO Coregulation • Phylogenetic approach - Compare promoter sequences of genes which are homologous between similar species - Approach: use genome alignments Which data!?
- 20. 20 ELIS – Multimedia Lab Which binding site model? MM IUPAC PWM
- 21. 21 ELIS – Multimedia Lab Word-based Exact Algorithms Nondeterministic Algorithms: EM, Gibbs Sampling, … Algorithm?
- 22. 22 ELIS – Multimedia Lab Alignment comes with a sacrifice
- 23. 23 ELIS – Multimedia Lab BLSSPELLER ALGORITHM
- 24. 24 ELIS – Multimedia Lab • Comparative motif finding • Exhaustive algorithm => no heuristics • Word-based motif model • Alignment-free (Alignment-based as a bonus) How are we different?
- 25. 25 ELIS – Multimedia Lab Preprocessing: gene families
- 26. 26 ELIS – Multimedia Lab Local motif conservation
- 27. 27 ELIS – Multimedia Lab Every candidate gets a BLS score
- 28. 28 ELIS – Multimedia Lab Depth-first exhaustive motif enumeration for 15 character IUPAC alphabet GST can be used for Branch & Bound purposes: Search space reduction Motif discovery with GSTs
- 29. 29 ELIS – Multimedia Lab Genome wide conservation
- 30. 30 ELIS – Multimedia Lab Motif vs Background => Confidence
- 31. 31 ELIS – Multimedia Lab BLSSPELLER VALIDATION
- 32. 32 ELIS – Multimedia Lab • http://bioinformatics.intec.ugent.be/blsspeller/AFABGlobal.html Comparing AF vs AB
- 33. 33 ELIS – Multimedia Lab • http://bioinformatics.intec.ugent.be/blsspeller/AFABHistograms.html Validation: FDR
- 34. 34 ELIS – Multimedia Lab Retrieving a known maize regulator
- 35. 35 ELIS – Multimedia Lab Status?
- 36. 36 ELIS – Multimedia Lab • Extending BLSSpeller: treat it as a reverse index! • Query engine • OCR data • Frontend • Clustering & Visualizations • Alternative paths (decide after Speller review) • Scaling out Sparql / Graph algorithms (Ontoforce) • Stream analytics and querying (IoT) • Medical images (Wesley) New Research
- 37. 37 ELIS – Multimedia Lab Questions!?
Editor's Notes
- Master Thesis: solving maxwell’s equation in a fusion reactor RF antenna is used to pump energy into the plasma, fully ionized gas, needs 10^7 K to ignite and produce energy (like a fire) Clean energy: D, T in => He + n out
- Scattering on a cold plasma cylinder, validation of the algorithm since it was possible to derive an analytical solution here
- Followed Jan Fostier to IBCN: iterative Alignment-based Detection of Homologous Regions GHM: compare two chromosomes, may also be same species or same chromosome Collinear regions = gene order and content conserved Bottomup clustering algorithm using a nonuniform distance (shortest along diagonals) Alignments used as new ‘chromosomes’ for bigger sensitivity (right panel)
- Visualisation suite mainly used to understand the algorithm (green boxes pvalue, red boxes bad pvalue, dots, blue confidence interval for regression line)
- “I just want to make things fast” Joachim VH Bad News: we were able to process the full ensembl dataset of genomes in approximately 8 hours on a single node => no need for 4.0
- Why compare 50 genomes? We managed to find a multiplicon of all species: HOX cluster (=gold standard) known to relate to the body plan of an organism
- After that on my own: started a new research based on a book I read about index structures: find dna motifs, exhaustive algorithm which forced us to look at parallellisation and big computers
- March left: Telenet, Dataretention project => Identify people based on request by the Police
- Met Erik on a train, convinced me to resign my well paid job
- Big Data course: Visualisation, Big Data management (nosql, hadoop, architectures, semantics, spark, IOT streaming), Analytics (deep learning, scalable algorithms)
- Project with Ontoforce: design of a federated query engine. Status: fixing federated engine of MMLab, open question federated querying connected to disqover not feasible So many options possible since virtuoso is only used as a data adapter
- I am often not here, and I often wonder what I am actualy doing but I think you should call it sales. We already have strong connections with IoT en Data Science, on the right a number of Companies we introduced ourselves (companies want to do or are already doing big data)
- More arrows: many different types of RNAs, often an end product, proteins are in a dynamic equilibrium, proteins bind back to DNA to trigger the creation of new proteins If one of the arrows breaks: Cancer Protein networks are often drawn = pathway analysis
- Transcription: molecular machine, recruited by transcription factors (=proteins) Where are the sites: in vitro versus in vivo is not the same!! (chipSeq)
- At first: compare promoters of coregulated genes Next: compare promoters of genes which are related (or mostly WGA)
- IUPAC motif model is the most exact motif model
- Word based algorithms (based on MM), exhaustive,… are better than randomized algorithms but overall most of them are not very good
- Current approaches work mainly with alignments but these biologically identified binding sites are misaligned!!
- Set of 17724 gene families for which we have the promoter sequences (each time >=1 gene from each species)
- Alignment-free!, score phylogenetic conservation (BLS)
- Internal nodes are bitvectors which represent in which sequences a prefix occurs
- Genome wide: a given motif for all gene families => aggregate How many families does a motif occurs with a certain BLS threshold
- Summarized in motif confidence charts