Nuria Lopez-Bigas Methods and tools in functional genomics (microarrays) BCO17.
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Transcript of Nuria Lopez-Bigas Methods and tools in functional genomics (microarrays) BCO17.
![Page 1: Nuria Lopez-Bigas Methods and tools in functional genomics (microarrays) BCO17.](https://reader035.fdocuments.us/reader035/viewer/2022062405/5697bf731a28abf838c7edc4/html5/thumbnails/1.jpg)
Nuria Lopez-Bigas
Methods and tools in functional genomics
(microarrays)
BCO17
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What are microarrays?
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What are microarrays?
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Microarray data analysis is the step that will allow us to extract biological meaning to high-throughput data generated with the experiment.
Microarray data analysis
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Microarray data analysis
Microarray DATANormalized data Data preprocession and normalization
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Normalization and Noise:
Normalization
• Some kind of normalization is usually required when comparing more
than one microarray experiment.
• Adjust to account for differences in overall brightness of slides
• Normalize relative to housekeeping genes
Noise
• Refers to variability and reproducibility of microarray experiments
• Intra and inter-microarray variations can significantly skew
interpretation of data
• Sample collection is very important. If comparing two conditions you
must control for all variables other than the one you are trying to measure
• Technical noise can result from imperfections in the chip.
• Both biological and technical replicates are required to measure and
control these sources of noise
Microarray data analysis
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Microarray data analysis
Differential expression
Microarray DATANormalized data Data preprocession and normalization
Data
analy
sis
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Microarray data analysis
Differential expression
GO,KEGG…analysis
Microarray DATANormalized data Data preprocession and normalization
Data
analy
sis
![Page 9: Nuria Lopez-Bigas Methods and tools in functional genomics (microarrays) BCO17.](https://reader035.fdocuments.us/reader035/viewer/2022062405/5697bf731a28abf838c7edc4/html5/thumbnails/9.jpg)
http://www.geneontology.org
The Gene Ontology project provides a controlled vocabulary to describe gene and gene product attributes in any organism.
The Ontologies •Cellular component•Biological process•Molecular function
BROWSER::AMIGO
TOOLS
Gene Ontology
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Gene Ontology
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Gene Ontology
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Gene Ontology::Tools
http://www.geneontology.org/GO.tools.shtml
http://www.fatigo.org/
http://www.barleybase.org/funcexpression.php
http://discover.nci.nih.gov/gominer/htgm.jsp
FUNC-EXPRESSION
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KEGG http://www.genome.jp/kegg/
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Microarray data analysis
Differential expression
GO,KEGG…analysis
Classification
Microarray DATANormalized data Data preprocession and normalization
Data
analy
sis
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Classification
Support vectors machines
Desition trees
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Microarray data analysis
Differential expression
GO,KEGG…analysis
Classification
Clustering
Microarray DATANormalized data Data preprocession and normalization
Data
analy
sis
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Supervised versus Unsupervised:
Supervised
• Analysis to determine genes that fit a predetermined pattern
• Usually used to find genes with expression levels that are significantly different between
groups of samples or finding genes that accurately predict a characteristic of the sample
• Two popular supervised techniques would be nearest-neighbour analysis and support
vector machines.
Unsupervised
• Analysis to characterize the components of a data set without a priori input or
knowledge of a training signal
• Try to find internal structure or relationships in data without trying to predict some
‘correct answer’.
• Three classes:
1. Feature determination: Look for genes with interesting patterns
Eg. Principal-components analysis
2. Cluster determination: Determine groups of genes with similar expression patterns
eg. Nearest-neighbour clustering, self-organizing maps, k-means clustering, 2d
hierarchical clustering
3. Network determination: Determine graphs representing gene-gene or gene-phenotype
interactions.
Eg. Boolean networks, Bayesian networks, relevance networks
Clustering & Classification
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Clustering & Classification
Cooper Breast Cancer Res 2001 3:158
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Microarray data analysis
Differential expression
GO,KEGG…analysis
Clustering
Classification
Promoter analysis
Microarray DATANormalized data Data preprocession and normalization
Data
analy
sis
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Promoter analysis::TFBS
TRANSFAC
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Promoter analysis::Tools
http://www.cisreg.ca/
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Microarray data analysis
Differential expression
GO,KEGG…analysis
Clustering
Classification
Promoter analysis
Reverse engineering
Microarray DATANormalized data Data preprocession and normalization
Data
analy
sis
![Page 23: Nuria Lopez-Bigas Methods and tools in functional genomics (microarrays) BCO17.](https://reader035.fdocuments.us/reader035/viewer/2022062405/5697bf731a28abf838c7edc4/html5/thumbnails/23.jpg)
Reverse engineering
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Microarray data analysis
Differential expression
GO,KEGG…analysis
Clustering
Classification
Promoter analysis
Reverse engineering
Microarray DATANormalized data Data preprocession and normalization
Data
analy
sis