Session ii g1 overview genomics and gene expression mmc-good
•Télécharger en tant que PPTX, PDF•
1 j'aime•708 vues
Recommandé
Contenu connexe
Tendances
Tendances (20)
En vedette
En vedette (19)
Similaire à Session ii g1 overview genomics and gene expression mmc-good
Similaire à Session ii g1 overview genomics and gene expression mmc-good (20)
Plus de USD Bioinformatics
Plus de USD Bioinformatics (20)
Dernier
Dernier (20)
Session ii g1 overview genomics and gene expression mmc-good
- 1. Microarray Dataset: quick mining and gene profile analysis using online tools Dr. Etienne Z. GNIMPIEBA Sioux Falls, March 2013 Etienne.gnimpieba@usd.edu
- 2. Plan Gene expression measurement Microarray process Gene expression data stores Data mining / quering Data analysis Example: ATP13A2 profile in stress conditions
- 3. Gene expression measurement Higher-plex techniques: SAGE DNA microarray Tiling array RNA-Seq NGS Low-to-mid-plex techniques: Reporter gene Northern blot Western blot Fluorescent in situ hybridization Reverse transcription PCR
- 4. What is a Microarray? “A DNA microarray is a multiplex technology consisting of thousands of oligonucleotide spots, each containing picomoles of a specific DNA sequence.” Used to quantitate mRNA or DNA Many applications: ◦ mRNA or DNA levels ◦ SNP identification ◦ ChIP-on-Chip
- 5. Hypotheses Microarrays are usually hypothesis-generating: ◦ They highlight specific genes or features that are particularly interesting for follow-up experiments ◦ There are many interesting exceptions Biomarkers Pathway analyses This does not reduce the importance of experimental design ◦ the low statistical power of array studies make good design even more important and very challenging
- 6. Microarray process (1/3) • Image analysis (genepix) • Normalization (R) • Pre-treatment • Differential expression • Clustering • Data mining • Annotation
- 8. Microarray process (3/3) High density filters(macroarrays) Glass slides (microarrays) Oligonucleotides chips Detail: Detail: Detail: Size: 12cm x 8cm Size: 5,4cm x 0,9cm Size: 1,28cm x 1,28cm •2400 clones by membrane •radioactive labelling •1 experimental condition by membrane •10000 clones by slide •fluorescent labelling •2 experimental conditions by slide •300000 oligonucleotides by slide •fluorescent labelling •1 experimental condition by slide
- 9. Gene expression data management Database Microarray Experiment Sets Sample Profiles Date Reported ArrayExpress at EBI 24,838 708,914 October 28, 2011 ArrayTrack™ 1,622 50,953 February 11, 2012 caArray at NCI 41 1,741 November 15, 2006 Gene Expression Omnibus - NCBI 25,859 641,770 October 28, 2011 Genevestigator database 2,500 65,000 January 2012 MUSC database ~45 555 April 1, 2007 Stanford Microarray database 82,542 Not reported October 23, 2011 UNC Microarray database ~31 2,093 April 1, 2007 UNC modENCODE Microarray database ~6 180 July 17, 2009 UPenn RAD database ~100 ~2,500 September 1, 2007 UPSC-BASE ~100 Not reported November 15, 2007 SAGE GEO GUDMAP (421) MGI BIOGPS
- 10. Data mining / querying Problem specification Query Extraction Storage Load Pretreat / prepare for analysis
- 11. Data analysis (1/3) Question-Answer ◦ Experimental condition profile: group comparison ◦ Annotation profile: systems biological involved ◦ Clustering profile: co-regulation ◦ Time course profile: time variation ◦ … Descriptive ◦ Boxplot (SD, MEAN, MEDIAN, ) ◦ Scatter plot Predictive / inference (clustering) Modeling (machine learning, simulation)
- 12. Data analysis (2/3) 3 Questions ◦ What is the right dataset (experimental condition)? ◦ Is dataset is ready for analysis (quality)? ◦ What is the expression profile for a given gene? ◦ Significant differential expression in groups comparison Tools ◦ ArrayExpress (EBI) ◦ Boxplot ◦ GEO2R (LIMMA, profile graph,) ◦ ….
- 14. Example: ATP13A2 profile in stress conditions Specification: ATP13A2 profile in stress conditions Data querying: ◦ GEO ◦ Array Express ◦ Gene Atlas Data analysis: ◦ Online: GEO2R, Genospace, … ◦ Desktop: R, ArrayTrack, …
- 15. Significant differential expression !!! Kerry Bemis slides
Notes de l'éditeur
- I can not say that I'm into Statistician 20 min. I give you just a few items to give rapid analysis of microarray.
- The following experimental techniques are used to measure gene expression and are listed in roughly chronological order, starting with the older, more established technologies. They are divided into two groups based on their degree of multiplexity.
- The following experimental techniques are used to measure gene expression and are listed in roughly chronological order, starting with the older, more established technologies. They are divided into two groups based on their degree of multiplexity.
- The following experimental techniques are used to measure gene expression and are listed in roughly chronological order, starting with the older, more established technologies. They are divided into two groups based on their degree of multiplexity.
- ArrayTrack™ provides an integrated solution for managing, analyzing, and interpreting microarray gene expression data. Specifically, ArrayTrack™ is MIAME (Minimum Information About A Microarray Experiment)-supportive for storing both microarray data and experiment parameters associated with a pharmacogenomics or toxicogenomics study. Many statistical and visualization tools are available with ArrayTrack™ which provides a rich collection of functional information about genes, proteins, and pathways for biological interpretation. The primary emphasis of ArrayTrack™ is the direct linking of analysis results with functional information to facilitate the interaction between the choice of analysis methods and the biological relevance of analysis results. Using ArrayTrack™, users can easily select a statistical method applied to stored microarray data to determine a list of differentially expressed genes. The gene list can then be directly linked to pathways and gene ontology for functional analysis.
- Boxplots are useful for determining where the majority of the data lies