1 biol6900 bioinformatics chapter 8 bioinformatics approaches to ribonucleic acid
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BIOL6900 BioinformaticsChapter 8
Bioinformatics approaches to ribonucleic acid
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Outline of upcoming lectures
The first part of the course covered sequence analysis,including BLAST (Chapters 1-7).
We begin the next part of the course: functional genomics (Chapters 8-12).
We will study how DNA is transcribed to RNA (i.e. gene expression), and we will discuss microarrays. Then we will study proteins.
We will conclude with a survey of genomes (Ch. 13-20).
32e Fig. 8.1
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Types of RNAs
• tRNA, rRNA - together 95% of total RNA
• mRNA,
• Other non-coding RNA:
small nuclear RNA (snRNA);
small nucleolar RNA (snoRNA);
microRNA (~22 nt); short interfering RNA (siRNA)
52e Fig. 8.3
Rfam
The Rfam family includes alignments and descriptions of RNA families
http://rfam.sanger.ac.uk/
62e Fig. 8.4
Summary of non-coding RNA families in Rfam database that are assigned to the long arm of human chromosome 21.
72e Fig. 8.5
Figure 8.5 Identification of tRNAs using tRNAscan-SE server
82e Fig. 8.5
92e Fig. 8.6
Vienna RNA package
102e Fig. 8.7
Figure 8.7Structure of a eukaryotic ribosomal DNA repeat unit
112e Fig. 8.8
122e Fig. 8.8
132e Fig. 8.9
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• by region (e.g. brain versus kidney)
• in development (e.g. fetal versus adult tissue)
• in dynamic response to environmental signals
(e.g. immediate-early response genes)
• in disease states
• by gene activity
Gene expression is regulated in several basic ways
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Organism Gene expression changes measured...
virus
bacteria
fungi
invertebrates
rodents
human In m
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Fig. 6.1Page 158
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DNA RNA
cDNA
phenotypeprotein
Page 159
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DNA RNA
cDNA
protein DNA RNA
cDNA
protein
UniGene
SAGE
microarray
Fig. 6.2Page 159
(Serial Analysis of Gene Expression)
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DNA RNA
cDNA
phenotypeprotein
[1] Transcription[2] RNA processing (splicing)[3] RNA export[4] RNA surveillance
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19Fig. 6.3Page 161
exon 1 exon 2 exon 3intron intron
transcription
RNA splicing (remove introns)
polyadenylation
Export to cytoplasm
AAAAA 3’5’
5’
5’
5’ 3’5’3’
3’
3’
202e Fig. 8.10
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Relationship of mRNA to genomic DNA for RBP4
~2e Fig. 8.11
222e Fig. 8.11
232e Fig. 8.12
242e Fig. 8.12
exon 2
exon 3
exon 1
252e Fig. 8.12
exon 2
exon 3
exon 1
exon 3
exon 1
query 1: genomic contigNT_037887, nucleotides162875-163708
query 2: cDNA NM_000517
intron
intron
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Analysis of gene expression in cDNA libraries
A fundamental approach to studying gene expressionis through cDNA libraries.
• Isolate RNA (always from a specific organism, region, and time point)
• Convert RNA to complementary DNA
• Subclone into a vector
• Sequence the cDNA inserts. These are expressed sequence tags (ESTs)
2e ~Fig. 8.13
vector
insert
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UniGene: unique genes via ESTs
• Find UniGene at NCBI: from the home page click All databases (on the top bar) then UniGene, or go to: www.ncbi.nlm.nih.gov/UniGene
• UniGene clusters contain many ESTs
• UniGene data come from many cDNA libraries. Thus, when you look up a gene in UniGene you get information on its abundance and its regional distribution.
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Cluster sizes in UniGene
This is a gene with1 EST associated;the cluster size is 1
Page 164& Fig. 2.3,Page 23
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Cluster sizes in UniGene
This is a gene with10 ESTs associated;the cluster size is 10
Page 164
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Cluster sizes in UniGene (human)
Cluster size (ESTs) Number of clusters1 42,8002 6,5003-4 6,5005-8 5,4009-16 4,10017-32 3,300
500-1000 2,1282000-4000 2338000-16,000 2116,000-30,000 8
UniGene build 194, 8/06
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Ten largest human UniGene clusters
Cluster size Gene22,925 eukary. translation EF (Hs. 522463)22,320 eukary. translation EF (Hs. 4395522)16,562 actin, gamma 1 (Hs.514581)16,309 GAPDH (Hs.169476)16,231 actin, beta (Hs.520640)11,076 ribosomal prot. L3 (Hs.119598)10,517 dehydrin (Hs.524390)
10,087 enolase 1 (alpha)(Hs.517145)
9,973 ferritin (Hs.433670)8,966 metastasis associated (Hs.187199)
UniGene build 186, 9/05Table 6.2Page 165
Why ribosomal transcripts are abundantin UniGene
The major types of RNA are:
ribosomal RNA rRNA (~85%)transfer RNA tRNA (~15%)messenger RNA mRNA (~3%)
noncoding RNA ncRNA (<1%)small nuclear RNA snRNAsmall nucleolar RNA snoRNAsmall interfering RNA siRNA
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There are three distinctions of similarity in UniGene:
1. "Highly similar to" means >90% in the aligned region.
2. "Moderately Similar to" means 70-90% similar in the aligned region.
3. "Weakly similar to" means <70% similar in the aligned region.
Page 164
UniGene clusters are often “similar to” a known gene
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Species Canis familiaris (dog) Helianthus annuus (sunflower)Salmo salar (Atlantic salmon)Bombyx mori (domestic silkworm)Apis mellifera (honey bee)Lotus corniculatus (Birdsfoot trefoil)Physcomitrella patens (physco. moss) Lactuca sativa (garden lettuce) Malus x domestica (Apple)Hydra magnipapillata Populus tremula x
Populus tremuloides (aspen) Ovis aries (sheep)
UniGene includes 74 species (as of Aug. 2006), all with many ESTs available. Recent entries include:
Currently: ~130 species
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Identifying protein-coding genes in genomic DNA remains a tremendous challenge. Genes can be predicted “ab initio” (by analyzing genomic DNA for the features of start and stop sites, exons/intron structures, regulatory regions etc.). When EST data are coupled with gene prediction, the accuracy soars.
Thus all ongoing genome sequencing projects include a major component of large-scale EST sequencing. Typically, this is done at different developmental stages (e.g. embryo versus adult), regions (e.g. brain versus gut), and physiological states (e.g. mosquitoes having fed on blood versus sucrose). EST data are deposited in UniGene. (dbEST)
The significance of UniGene’s continued growth
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Digital Differential Display (DDD) in UniGene
• UniGene clusters contain many ESTs
• UniGene data come from many cDNA libraries
• Libraries can be compared electronically
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38Fig. 6.6Page 166
39Fig. 6.6Page 166
40Fig. 6.6Page 166
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UniGene brainlibraries
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UniGene lunglibraries
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n-sec1 up-regulated in brain
CamKII up-regulated
in brain
surfactant up-regulated in lung
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fraction of sequences within the pool that mapped to the cluster shown
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DDD at UniGene
Question: are there individual RNA transcripts that are differentially present in a comparison of EST libraries?
Approach to estimating statistical significance: Fisher’s exact test.
Pages 165
482e Fig. 8.14
492e Fig. 8.14
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DDD at UniGene
Fisher’s exact test is a nonparametric method.
• It does not assume a normal distribution of the observations• It is easy to calculate• It often has less statistical power than parametric tests (such as a t-test)• For nonparametric methods, observations are typically arranged in an array with ranks assigned from 1 to n.
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DDD at UniGene
Fisher’s exact test is related to a chi square (2) test, but is appropriate for small sample sizes.
A 2 test is applied to row x column (rc) contingency tables
Determine whether the observed (O) frequencies of occurrence of a categorical value differ significantly from the expected (E) frequency of occurrence. Is O – E larger than expected by chance? rc
2 = i=1
(Oi – Ei)2
Ei
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Fisher’s exact test provides a p value
Digital differential display (DDD) results in UniGeneare assessed for significance using Fisher’s exact testto generate a p value.
p =
The null hypothesis (that gene 1 is not differentiallyregulated in a comparison of two libraries) is rejectedwhen p is < 0.05/G (where G = the number of UniGeneclusters analyzed).
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NA! NB! c! C!
(NA + NB)! g1A! g1B! (NA – g1A)!(NB – g1B)!
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Pool A
Pool B
total
Gene 1 All other genes total
NA
NB
g1A NA-g1A
c = g1A + g1B
NB-g1Bg1B
C = (NA-g1A) + (NB-g1B)
Fisher’s Exact Test: deriving a p value
Table 6-3Page 167
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Pitfalls in interpreting cDNA library data
• bias in library construction• variable depth of sequencing• library normalization• error rate in sequencing• contamination (chimeric sequences)
Pages 166-168
55Fig. 6.8p. 168-169
http://mgc.nci.nih.gov
Updated 8/06
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Serial analysis of gene expression (SAGE)
• 9 to 11 base “tags” correspond to genes
• measure of gene expression in different biological samples
• SAGE tags can be compared electronically
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Tag 1
Tag 1Tag 2Tag n
Cluster 1Cluster 2Cluster 3
Cluster 1
SAGE tags are mapped to UniGene clusters
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58Fig. 6.10Page 171
59Fig. 6.11Page 171
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61Fig. 6.12Page 171
62Fig. 6.13Page 173
63Fig. 6.14Page 174
64Fig. 6.15Page 175
65Fig. 6.15Page 175
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Microarrays: tools for gene expression
A microarray is a solid support (such as a membraneor glass microscope slide) on which DNA of knownsequence is deposited in a grid-like array.
The most common form of microarray is used to measure gene expression. RNA is isolated from matched samples of interest. The RNA is typically converted to cDNA, labeled with fluorescence (or radioactivity), then hybridized to microarrays in order to measure the expression levelsof thousands of genes.
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• Wildtype versus mutant
• Cultured cells +/- drug
• Physiological states (hibernation, cell polarity formation)
• Normal versus diseased tissue (cancer, autism)
Questions addressed using microarrays
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• metazoans: human, mouse, rat, worm, insect
• fungi: yeast
• plants: Arabidopsis
• many other: e.g. bacteria, viruses
Organisms represented on microarrays
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Fast Data on >20,000 genes in several weeks
Comprehensive Entire yeast or mouse genome on a chip
Flexible • As more genomes are sequenced, more arrays can be made. • Custom arrays can be made
to represent genes of interest
Easy Submit RNA samples to a core facility
Cheap? Chip representing 20,000 genes for $350; robotic spotter/scanner cost $100,000
Advantages of microarray experiments
Table 6-4Page 175
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Cost Some researchers can’t afford to doappropriate controls, replicates
RNA The final product of gene expression is proteinsignificance (see pages 174-176 for references)
Quality Impossible to assess elements on array surfacecontrol Artifacts with image analysis
Artifacts with data analysis
Disadvantages of microarray experiments
Table 6-5Page 176
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purify RNA, label
hybridize,wash, image
Biological insight
Sampleacquisition
Dataacquisition
Data analysis
Data confirmation
data storage
experimentaldesign
Fig. 6.16Page 176
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Stage 1: Experimental design
[1] Biological samples: technical and biological replicates
[2] RNA extraction, conversion, labeling, hybridization
[3] Arrangement of array elements on a surface
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Sample 1 Sample 2 Sample 3
Fig. 6.17Page 177
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Samples 1,2 Samples 1,3 Samples 2,3
Sample 1, pool Sample 2, poolSamples 2,1:switch dyes
2e Fig. 8.18
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Stage 2: RNA and probe preparation
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For Affymetrix chips, need total RNA (about 10 ug)
Confirm purity by running agarose gel
Measure a260/a280 to confirm purity, quantity
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Stage 3: hybridization to DNA arrays
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The array consists of cDNA or oligonucleotides
Oligonucleotides can be deposited by photolithography
The sample is converted to cRNA or cDNA
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Microarrays: array surface
Southern et al. (1999) Nature Genetics, microarray supplement 2e Fig. 8.19
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Stage 4: Image analysis
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RNA expression levels are quantitated
Fluorescence intensity is measured with a scanner,or radioactivity with a phosphorimager
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Rett
Control
Differential Gene Expression on a cDNA Microarray
B Crystallin is over-expressed in Rett Syndrome
2e Fig. 8.20
802e Fig. 8.21
81Fig. 8.21
82Fig. 8.21
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Stage 5: Microarray data analysis
• How can arrays be compared? • Which genes are regulated?• Are differences authentic?• What are the criteria for statistical significance?• Are there meaningful patterns in the data (such as groups)?
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preprocessing
inferential statistics
exploratory statistics
Stage 5: Microarray data analysis
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preprocessing
inferential statistics
exploratory statistics
t-tests
global normalizationlocal normalizationscatter plots
clustering
Stage 5: Microarray data analysis
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Matrix of genes versus samples
T-test: • for each gene, calculate the mean expression value in control (C) and experimental (E) samples• Null hypothesis: the mean C and E values are the same• Use a t-test to see whether the null hypothesis can be rejected with a particular cutoff value (e.g. p < 0.05)• Correct the p value for multiple comparisons (e.g. if you measure expression values in 10,000 genes, then 5% (500 genes) might vary by chance alone).
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small p value; ratio large
small p value; ratio unimpressive
Perform a t-test in Excel to compare the mean of two groups,and to compare fold change to probability values
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disease vs normal
Error
t-test to determine statistical significance
difference between mean of disease and normalt statistic = variation due to error
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Error
Error
Tissue type
ANOVA partitions total data variability
variation between DS and normalF ratio = variation due to error
Before partitioning After partitioning
Subjectdisease vs normal
disease vs normal
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Matrix of genes versus samples
Metric (define distance)
supervised,unsupervised
analyses
clusteringtrees(hierarchical,k-means)
self-organizing
maps
principalcomponentsanalysis
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Stage 5: MIAME
In an effort to standardize microarray data presentationand analysis, Alvis Brazma and colleagues at 17institutions introduced Minimum Information About aMicroarray Experiment (MIAME). The MIAME framework standardizes six areas of information:• experimental design• microarray design• sample preparation• hybridization procedures• image analysis• controls for normalization
Visit http://www.mged.org
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Stage 6: Biological confirmation
Microarray experiments can be thought of as“hypothesis-generating” experiments.
The differential up- or down-regulation of specificgenes can be measured using independent assayssuch as
-- Northern blots-- polymerase chain reaction (RT-PCR)-- in situ hybridization
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Stage 7: Microarray databases
There are two main repositories:
Gene expression omnibus (GEO) at NCBI
ArrayExpress at the European Bioinformatics Institute (EBI)
See the URLs on page 184
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Gene expression omnibus (GEO)
NCBI repository for gene expression data
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http://www.dnachip.org
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• Stanford Microarray Database http://www.dnachip.org
• links at http://pevsnerlab.kennedykrieger.org/
Microarrays: web resources