Ch10_Clustering

Ch10_Clustering - An Introduction to Bioinformatics...

Info iconThis preview shows pages 1–11. Sign up to view the full content.

View Full Document Right Arrow Icon
    www.bioalgorithms.info An Introduction to Bioinformatics Algorithms Clustering
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Proposed Changes Microarrays – very poor intro – can we find better slides in BIO section?
Background image of page 2
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Outline Microarrays Hierarchical Clustering K-Means Clustering Corrupted Cliques Problem CAST Clustering Algorithm
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Applications of Clustering Viewing and analyzing vast amounts of biological data as a whole set can be perplexing It is easier to interpret the data if they are partitioned into clusters combining similar data points.
Background image of page 4
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Inferring Gene Functionality Researchers want to know the functions of newly sequenced genes Simply comparing the new gene sequences to known DNA sequences often does not give away the function of gene For 40% of sequenced genes, functionality cannot be ascertained by only comparing to sequences of other known genes Microarrays allow biologists to infer gene function even when sequence similarity alone is insufficient to infer function.
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Microarrays and Expression Analysis Microarrays measure the activity (expression level) of the genes under varying conditions/time points Expression level is estimated by measuring the amount of mRNA for that particular gene A gene is active if it is being transcribed More mRNA usually indicates more gene activity
Background image of page 6
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Microarray Experiments Produce cDNA from mRNA (DNA is more stable) Attach phosphor to cDNA to see when a particular gene is expressed Different color phosphors are available to compare many samples at once Hybridize cDNA over the micro array Scan the microarray with a phosphor-illuminating laser Illumination reveals transcribed genes Scan microarray multiple times for the different color phosphor’s
Background image of page 7

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Microarray Experiments (con’t) www.affymetrix.com Phosphors can be added here instead Then instead of staining, laser illumination can be used
Background image of page 8
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Using Microarrays Each box represents one gene’s expression over time Track the sample over a period of time to see gene expression over time Track two different samples under the same conditions to see the difference in gene expressions
Background image of page 9

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info Using Microarrays (cont’d) Green : expressed only from control Red : expressed only from experimental cell Yellow : equally expressed in both samples Black : NOT expressed in either control or experimental cells
Background image of page 10
Image of page 11
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 02/10/2012 for the course CSE 5615 taught by Professor Mitra during the Fall '11 term at FIT.

Page1 / 48

Ch10_Clustering - An Introduction to Bioinformatics...

This preview shows document pages 1 - 11. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online