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StemBase

StemBase. Stem Cell Network Microarray Course, Unit 6 June 2007. Sections. Introduction to StemBase Using StemBase Introduction to Biomarker server Using the Biomarker server. Introduction to StemBase. http://www.stembase.ca. The Stem Cell Genomics Project.

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StemBase

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  1. StemBase Stem Cell Network Microarray Course, Unit 6 June 2007

  2. Sections • Introduction to StemBase • Using StemBase • Introduction to Biomarker server • Using the Biomarker server

  3. Introduction to StemBase http://www.stembase.ca

  4. The Stem Cell Genomics Project • Objective: acquire a complete understanding of the genetic factors that: • specify stem cell identity and function; and • regulate commitment and differentiation • Rationale: • Stem cells play an essential role in the human body as they provide the starting material for every organ and tissue • Knowledge of regulatory genes acting in and on stem cells is necessary to exploit their full therapeutic potential

  5. STEM CELL NETWORK +20 GROUPS samples OHRI – GENOMICS PLATFORM DNA microarray / SAGE / Proteomics data Bioinformatics Group data StemBase PUBLIC

  6. Lynn Megeney James Piret Derrick Rancourt Janet Rossant Michael Rudnicki Luc Sabourin JP Tremblay T. Michael Underhill Valerie Wallace Peter Zandstra SCN Sample Contributors • Jane Aubin • Mick Bhatia • John Dick • Connie Eaves • Jacques Galipeau • Alain Garnier • Marina Gertsentein • John Hassell • Keith Humphries • Norman Iscove • Michael McBurney

  7. StemBase Database of gene expression data in mouse and human stem cells Study genes important for stem cell function Affymetrix DNA microarray data (225 samples) and SAGE (6 samples) Perez-Iratxeta, C., G. Palidwor, C.J. Porter, N.A. Sanche, M.R. Huska, B.P. Suomela, E.M. Muro, P. Krzyzanowski, E. Hughes, P.A. Campbell, M.A. Rudnicki and M.A. Andrade (2005) Study of stem cell function using microarray experiments. FEBS Letters.579, 1795-1801.

  8. Species Tissue Types Mouse 184 Retina, 3 Adipose tissue, 1 Human Blastocyst, 20 44 Blood, 5 Muscle, 42 3 Rat Bone marrow, 31 Mammary, 14 Kidney, 1 Brain, 7 Fibroblast, 4 Calvaria, 3 Cervical epith., 3 Ciliary margin eye, 4 Fetal cells, 38 Cordblood, 11 Dermis, 1 Embryonic, 42

  9. GO Pvalue GO as name GO:0030529 1.83E-66 ribonucleoprotein complex GO:0003723 1.40E-42 RNA binding GO:0005739 9.43E-31 mitochondrion GO:0006412 3.03E-28 protein biosynthesis GO:0044237 4.31E-27 cellular metabolism GO:0008380 1.79E-22 RNA splicing GO:0045184 3.50E-22 establishment of protein localization GO:0016526 3.40E-11 G-protein coupled receptor activity, unknown ligand GO:0005246 0.00362 calcium channel regulator activity GO:0007416 0.0134 synaptogenesis GO:0016358 0.0204 dendrite morphogenesis GO:0007076 0.0348 mitotic chromosome condensation Genes expressed in: 80%-100% of all samples 0%-20% of all samples

  10. Mouse / MOE430 R1 D4A V6.5 Embyoid bodies C2D C2A C2E J1 Embyoid bodies D4E Cancer R1 serum6999 D4D Myoblasts Embyoid bodies Cancer R1 serum64 Cancer Neurospheres Retinal primary Dim2 Mammospheres Retinal first passage Mammospheres undifferenciated Dermis Osteoblasts Neural Adipose Muscle Bone marrow Bone marrow Bone marrow Myospheres Bone marrow Dim1

  11. Human / HGU133 Cord blood Bone marrow M-O7e Smad7 Cord blood M-O7e Dim2 Peripheral M-O7e I6 Fetal Cord blood Peripheral Bone marrow Kidney Hela Hela I6 Retinal primary Myoblasts Myoblasts differentiated Retinal first passage Dim1

  12. Public web server http://www.scgp.ca:8080/StemBase/

  13. Marker detection Use microarray data to identify probe sets that can act as markers. What and how they mark is a separate issue. Krzyzanowski and Andrade (2007) Identification of novel stem cell markers using gap analysis of gene expression data. Submitted.

  14. Method Overview • Identification • Find probe sets which appear to divide samples into two groups  binary classifications

  15. Method Overview • Identification • Find probe sets which appear to divide samples into two groups  binary classifications • Cluster expansion • Identify clusters of probe sets which support binary classifications

  16. Receiver Operator Characteristic (ROC) curves are used to generate clusters of probe sets with expression patterns which can reproduce each binary classification. Proposed markers for “Pattern X” Probe set Score 1402235_at 1.00000 1409748_at 1.00000 1430293_x_at 0.99302 … 1427392_a_at 0.90021 1402235_at “Pattern X” 1101100101101110

  17. osteoblast fibroblast embryonic P19 spheres hematopoietic Five fold enrichment on 71 stem cell markers. From 71 in about 30,000 genes to 49 in 4,449 genes

  18. http://www.ogic.ca/projects/markerserver/enter.php

  19. Pointers • StemBase. http://www.stembase.ca • Biomarker server. http://www.ogic.ca/projects/markerserver • Thanks for your attention!

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