Bioinformatics Research at NYU caBIG Presentation

1. New York University September 2 2003 Bioinformatics Research at NYUcaBIG Presentation ¦ Bud Mishra Professor of Computer Science & Mathematics (Courant Institute, NYU) Professor (Cold Spring Harbor Laboratory) Professor of Human Genetics (Mt Sinai School of Medicine) Professor (Tata Institute of Fundamental Research)

2. Bioinformatics Group • Our Bioinformatics group is a research center with 20 researchers • 5 senior scientists (with PhD's in Math and CS), 4 research scientists and graduate students • This is an interdisciplinary group that places an equal emphasis on computational techniques and biology, and thus, attracts researchers and students from mathematics, statistics, computer science, and biology. • The group’s approach through a close attention to fundamental mathematics is to provide a new paradigm in which to place biological research. • To this end the group has developed a sophisticated set of analytical tools to help biologists pose and answer the questions in the post-genomic age.

3. Bioinformatics Group • The group is engaged in various research projects at the interface of computation and biology: namely, • a software language and environment incorporating a powerful genome browser and library of software for genomic data manipulation, • a microarray database system useful for gene-expression analysis and genome aberration studies (e.g., CGH) and • a systems biology tool capable of modeling, simulating and reasoning about complex biochemical pathways (e.g., RAS pathway, apoptosis, etc.). • These systems have been integrated into an environment that can be used by multiple groups working on a common problem but with varying experimental protocols, platforms and data formats.

4. Objectives, specific aims, and anticipated outcomes: We can bring our own experience in creating our nascent grid-technologies to the caBIG effort. We believe that we have the critical mass to help Cancer Centers in collaboration with NCICB develop enabling tools and systems that could support the varying needs and environments of multiple Cancer Centers

5. Infrastructure Ontologies Middleware Databases Applications Clinical trials support Analytic tools Data mining Data Population Trials Case-control Cohorts Experimental outcome Genomic Microarray Proteomic Sample contributions NYUShrink & Simpathica, Valis & NYUMAD Valis, NYUMAD, NYUPROD, ROMA, SARA, XSSystem & Simpathica, Valis & Simpathica, ROMA

6. caBIG & Computational Biology Valis + NYUMAD NYUMAD Simpathica

8. Valis, NYUMAD & Simpathica

9. VALIS

10. Valis

11. Valis Architecture

12. Valis Screenshot

13. NYUMAD

14. Architecture • The NYUMAD has a three-tier architecture: • Front tier • NYUMAD java applet/application • User's custom-built programs • HTML forms. • Middle tier • Java servlets handle requests and submissions from the front tier • Invisible to the end user. • Algorithms and analytics (C++, C, other languages) • Back tier • Relational database management systems (RDBMS, currently PostgreSQL running on a 6 nodes Linux cluster). • File management system used to store large files such as image files.

15. Middle Tier Back Tier Front Tier HTTP Java Application Relational DB Java Servlets Java Applet DB Web pages Algorithms Custom Application Files Architecture

16. Array & Experiment

17. Cluster Table & Graph

18. NYUSHRINK

19. Eisen et al.’s Approach

20. Shrinking qX = (q0/s02 + x/s12)/(1/s02 + 1/s12) = (s12q0 + s02 x)/(s12 + s02 ) • A simpler form qX = [1 – {s12/(s12 + s02)} {1 - q0/x}] x • The observation x is “shrunk” towards the belief q0. • The estimator swaps “bias” for “variance” x, s12 q0, s02 qx shrinkage

21. Yeast Cell Cycle

22. Yeast Cell Cycle

23. Clusters based onTranscription Activators

24. Clusters based on Eisen et al.

25. Clusters based on Pearson

26. Clusters based on Shrinkage

27. Summary

28. Simpathica//XS-System

29. Biological Model • Standard molecular biology: Construct • A low-copy plasmid encoding the repressilator and • A compatible higher-copy reporter plasmid containing the tet-repressible promoter PLtet01 fused to an intermediate stability variant of gfp.

30. - x1 x2 - x3 x4 - x5 x6 Cascade Model: Repressilator? dx2/dt = a2 X6g26X1g21 - b2 X2h22 dx4/dt = a4 X2g42X3g43 - b4 X4h44 dx6/dt = a6 X4g64X5g65 - b6 X6h66 X1, X3, X5 = const

31. SimPathica System

32. Modal Logic Queries

33. Simpathica Movies ..\..\Simpathica Movies

34. ROMA

35. Amplifications & Deletions

36. RDA by Microarray ROMA, Representational Oligonucleotides Microarray Analysis

37. Human Merscape

38. ROMA.Tumor Vs. Normal

39. Detecting Amplifications & Deletions

40. Optical Maps

41. Optical Mapping • Sizing Error • (Bernoulli labeling, absorption cross-section, PSF) • Partial Digestion • False Optical Sites • Orientation • Spurious molecules, Optical chimerism, Calibration Image of restriction enzyme digestedYAC clone: YAC clone 6H3, derived from human chromosome 11, digested with the restriction endonuclease Eag I and Mlu I, stained with a fluorochrome and imaged by fluorescence microscopy.

42. Mapping the DAZ locus on Y Chromosome

43. Gentig MapDeinococcus radiodurans Nhe I map of D.radiodurans generated by Gentig

44. E. coli Shotgun Map

45. Gentig Maps:Plasmodium falciparum • A. Gap-free consensus BamHI & NheI maps for all 14 chromosomes. • B.BamHI map • C. NheI map • D.NheI map of Chromosome 3 displayed by ConVEx

46. P. Falciparum c14 Alignment

47. Haplotyping:Output of the RFLP Phasing Algorithm

48. Marco Antoniotti: Sr. Res. Scientist (CS, Courant) Simulation System//Simpathica Archisman Rudra: Sr. Res. Scientist (CS, Courant) Genome Grammar//Copy Number Analysis Raoul Daruwala: Sr. Res. Scientist (CS, Courant) Copy Number Analysis// Learning Salvatore Paxia: Sr. Res. Scientist (CS, Courant) Software Environment//Valis Vera Cherepinsky: Sr. Res. Scientist (CS, Courant) Software Environment//Valis Gilad Lerman: Sr. Res. Scientist (Mathematics, Courant) Multi-strip Algorithms// Normalization Paolo Barbano: Sr. Res. Scientist (Mathematics, Courant) Time-Frequency Multiresolution Analysis Saurabh Sinha: Postdoc (Courant & Rockefeller) Detecting CIS elements Marc Rejali: Sr. Res. Scientist (CS, Courant) Microarray Data Analysis//MAD Nadia Ugel: Jr. Res. Scientist (CS, Courant) Simpathica//Stem Cell Models Marina Spivak: Jr. Res. Scientist (Biology & CS, Courant) Simpathica//Stem Cell Models Joe McQuown: Jr. Res. Scientist (Stat, NYU) Statistical Analysis Graduate Students: Joey Zhou (Biology, NYU) Bing Sun (Computer Science, NYU) Jerry Huang (Biology, NYU) Matthias Heyman (Mathematics, NYU) Fang Cheng (Biology, NYU) Ofer Gill (Computer Science, NYU) Iuliana Ionita (Computer Science, NYU) Venkatesh Mysore (Computer Science, NYU) People

49. VISITORS Alberto Policriti Computer Science, University of Udine, Italy Pasquale Cainiello Computer Science, University of L’Aquilla, Italy Haim Wolfson: Computer Science, Tel Aviv University, Israel Chris Wiggins: Physics & Applied Mathematics Columbia University, USA Franz Winkler: Mathematics Johann Kepler University, Austria COLLABORATORS Steve Burakoff, Brian Dynlacht, Alex Schier & Tom Neubert: Skirball Institute Mike Wigler: Cold Spring Harbor Lab Misha Gromov & Ale Carbone: IHES & Courant Amir Pnueli: Minerva Center & Courant Harel Weinstein, Ravi Iyengar & Bob Desnick: Mt Sinai School of Medicine Sanjoy Mitter & Dimitri Beretskas: MIT Charles Cantor & Jim Collins: Boston Univ Mike Seoul: Bioarrays Visitors & Collaborators • VISITORS • Frank Park: • Control Theory • University of Seoul, S. Korea • Naomi Silver: • Computer Science • Marco Isopi: • Applied Mathematics • Italy • Ilya Nemenman: • Physics & Neurosicience • ITP, California • David Harel: • Computer Science • Weizmann Institute, Israel • Carla Piazza: • Computer Science & Mathematics • Universita Ca Foscari di Venezia,

50. The End http://www.cs.nyu.edu/mishra http://bioinformatics.cat.nyu.edu Valis, Gene Grammar, NYU MAD, Cell Simulation,…