Microarray databases
This presentation is the property of its rightful owner.
Sponsored Links
1 / 25

Microarray Databases PowerPoint PPT Presentation

  • Uploaded on
  • Presentation posted in: General

Microarray Databases. Ka-Lok Ng Asia University. Microarray Databases. Time series data – yeast cell cycle Stanford Genomic Resources - Stanford Microarray Database (SMD) yeast cell cycle analysis project Yeast database - SGD Human Cell Cycle and Their Expression in Tumors.

Download Presentation

Microarray Databases

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

Microarray databases

Microarray Databases

Ka-Lok Ng

Asia University

Microarray databases1

Microarray Databases

  • Time series data – yeast cell cycle

  • Stanford Genomic Resources - Stanford Microarray Database (SMD)

  • yeast cell cycle analysis project

  • Yeast database - SGD

  • Human Cell Cycle and Their Expression in Tumors

Analysis of cell cycle regulation

Analysis of cell-cycle regulation

  • Yeast cells stopped at different stages of cell cycle

    • G1, S, G2, and M

  • RNA extracted from each stage

  • Control RNA from unsynchronized culture

Results of cell cycle analysis

Results of cell-cycle analysis

  • 800 genes identified whose expression changes during cell cycle

  • Grouped by peak expression

    • M/G1, G1, S, G2, and M

  • Four different treatments used to synchronize cells

    • All gave similar results

  • Results from Spellman et al., 1998; Cho et al., 1998

  • Cell cycle regulated genes










    Brown and Botstein, 1999

    Cell-cycle regulated genes

    • Each gene is a line on the longitudinal axis

    • Treatments in different panels

    • Cell-cycle stages are color coded at top

    • Vertical axis groups genes by stage in which expression peaks

    Time course microarray data

    Time-course microarray data

    • Why study time-course microarray data ?

    • Application of time-course microarray data is to group together genes with similar expression patterns.

    • Genes clustered in the same group are considered to have the potential of conducting similar functions.

    • Model system – yeast cell cycle

    Microarray databases

    Cell cycle

    Checkpoints in Cell-Cycle

    Checkpoint controls function to ensure that chromosomes are intact and that critical stages of the cell cycle are completed before the following stage is initiated.

    Checkpoint that function in response to DNA damage prevent entry into S to Muntil the damage is repaired. Moderate DNA damage activates p53﹐a TF that stimulates expression of p21CIP. This cyclin-kinase inhibitor then binds to and inhibits all Cdk-cyclin complexes, causing arrest in G1 and G2.

    One checkpoint operates during S and G2 to prevent the activation of MPF (Mitosis-promoting factor) before DNA synthesis is complete.

    Another checkpoint operates during early mitosis to prevent the activation of APC (Anaphase-promoting complex) and the initiated of anaphase until the mitotic spindle apparatus is completely assembled and all chromosome kinetochores are properly attached to spindle fibers.

    Microarray databases smd

    Microarray Databases - SMD

    • http://genome-www.stanford.edu/

    • SMD : Advanced Search  Select arrays by by Experimenter, Category, Subcategory and Organism

    • http://genome-www5.stanford.edu/cgi-bin/search/QuerySetup.pl.

    Microarray databases smd1

    Microarray Databases - SMD

    • Spellman, yeast cell cycle time series data

      • Alpha factor block data – every 7 min., 18 time points

      • Cdc15 data – 24 time points

      • Cdc28 data – every 10 min., 17 time points

      • Elu data – every 30 min.,14 time points

    Microarray databases smd2

    Microarray Databases - SMD

    Microarray databases yeast cell cycle analysis project

    Microarray Databases - yeast cell cycle analysis project

    • yeast cell cycle analysis project http://genome-www.stanford.edu/cellcycle/ or http://www.cs.sunysb.edu/~skiena/gene/jizu/

    • The web site provides the alpha factor block, cdc15, cdc28 and elu data set

    • Keyword searching using "regul* on YPD database as of Feb. 2000 yielded 1007 genes.

    • By reviewing the published literature on these 1007 genes, 888 transcriptional regulations were collected, of which 647 were activators and 241 were inhibitions (647+241=888).

    • 469/155 known transcription activations/inhibitions in cdc28 data set

    • 343/96 known transcription activations/inhibitions in alpha data set

    • Download the Tab delimited data

    • Use the Tab delimited option in Excel to store the data

    Microarray databases yeast cell cycle analysis project1

    Microarray Databases - yeast cell cycle analysis project

    • the Pearson correlation coefficient of the gene YLR256W and YPL028W in the alpha factor block data set is -0.5094  anti-correlate

    • Spearman correlation coefficient is -0.5698  anti-correlate

    Microarray databases yeast cell cycle analysis project2

    Microarray Databases - yeast cell cycle analysis project

    • time series plot

    Finding similar time series

    Finding Similar Time Series

    • Time series are an important class of complex data objects

    • arise in financial and scientific applications examples include stock price indices, the volume of product sales, telecommunications data, 1D medicalsignals audio data and environmental measurement sequences

    • Two time series seqs. are similar if they exhibit similar behavior for a large subset of their length. The sequences to be comparedcan have

      • Outliers, ie values that are measurement errors and should be omitted whencomparing the sequence against others

      • different scaling factors and baselines the sequences can be due tomeasurements doneusing different devices and the scaling and baseline values canbe different

    • That is if we have a sequence X and modify it to sequence X’ by introducing outliers by scaling and translation and by adding or removing some observations the sequences X and X’ should still be considered reasonably similar


    Gautam Das, Dimitrios Gunopulos, Heikki Mannila (1996), Finding Similar Time Series. Principles of Data Mining and Knowledge Discovery.

    陳延洛,"基因表現時間序列的叢集分析方法與系統實作", 國立成功大學資訊工程研究所碩士論文,2003

    Finding similar time series http www cs sunysb edu skiena gene jizu

    Finding Similar Time Serieshttp://www.cs.sunysb.edu/~skiena/gene/jizu/

    Now HAP1 is 137

    Finding similar time series1

    Finding Similar Time Series

    PCC rank 331 among 343 genes

    Finding similar time series2

    Finding Similar Time Series

    • Remove the last point in YLR256W (GO: HAP1) and the first point in YPL028W (GO: ERG10)

    • pcc changes from -0.5094 to 0.6233  correlate

    • src changes from to -0.5698 0.6169  correlate

    Yeast database sgd

    Yeast database - SGD

    SGD - http://www.yeastgenome.org/

    Cancer gene expression database

    Cancer Gene Expression Database

    • CGED (Cancer Gene Expression Database) is a database of gene expression profile and accompanying clinical information.

    • http://cged.genes.nig.ac.jp/

    • CGED includes data on breast, colorectal, hepatocellular, esophageal, thyroid, and gastric cancers.

    • Follow “Information” to get the flat file data

    Cancer gene expression database1

    Cancer Gene Expression Database

    Liver cancer data

    Human cell cycle genes expression data

    Human Cell Cycle Genes Expression Data

    • Genes Periodically Expressed in the Human Cell Cycle and Their Expression in Tumors http://genome-www.stanford.edu/Human-CellCycle/Hela/

    • Over 850 genes showed periodic variation during the cell cycle

    • Hierarchical clustering of the gene expression patterns revealed co-expressed groups of genes involved in cell cycle processes such as DNA replication, chromosome segregation, and cell adhesion

    • Most of the genes whose expression had previously been reported to correlate with the proliferative state of tumors were found herein also to be periodically expressed during the HeLa cell cycle.

    Human fibroblasts genes expression data

    Human fibroblasts Genes Expression Data

    • http://www-sequence.stanford.edu:16080/human_cell_cycle/

    • Fibroblasts (纖維母細胞) prepared from human skin

    • synchronized in late G1 using a double thymidine-block protocol

    • Cultures were then released from arrest, and cells were collected every 2 hours for 24 hours, covering nearly 2 complete cell cycles.

    • identified approximately 700 genes that display transcriptional fluctuation with a periodicity consistent with that of the cell cycle.


    • Cho et al. (2001) Nature Genetics27, 48 - 54

    Human fibroblasts genes expression data1

    Human fibroblasts Genes Expression Data

    Human fibroblasts genes expression data2

    Human fibroblasts Genes Expression Data

    • functional analysis results from the human cell cycle experiments

    • How to study the biological functions ?

    • Fibroblasts cell expose to the following changes

    • E7 – transfection with the E7 oncoprotein

    • MMS – methyl methane sulfonate

    • UV – ultraviolet light

    Human fibroblasts genes expression data3

    Human fibroblasts Genes Expression Data

    G2 phase

  • Login