1. Gene Expression in that cell at that time
Expressed genes are those that have been transcribed.
A gene expression profile of a cell is the snapshot of which genes are expressed in that cell at the time the sample was taken.
Knowing which genes are expressed in a cell allows:
identification of new genes or transcripts
comparison of expression profiles between samples
Main motives: disease, development, dynamic responses
in that cell
at that time

2. Differential Gene Expression
Expression profile of genes in one sample vs another. Different cells, tissues, disease states, developmental stages, culture conditions, etc, can be compared. Measure both, subtract the overlap, obtain the difference, interpret it. Pay due attention to controls, negative and positive Pay due attention to range of variability within samples High throughput and higher throughput

3. Differential Gene Expression Workflow
Formulate the biological questions
Experimental design
Which platform, which controls, how many replicates
Run the experiment
Image processing (by machine)
Low-level analysis
Data preprocessing (normalisation step)
High-level analysis
Data analysis
Reach biological conclusions
Interpretation of results

4. High Throughput Methods- Advantages
Fast a lot of data produced quickly
Comprehensive entire genomes in one experiment
Easy submit RNA samples to a core facility
Cost getting cheaper still

5. High Throughput Methods - Disadvantages
Cost - Some researchers can’t afford to do
appropriate numbers of controls, replicates
RNA - The final product of gene expression is a protein
Significance - How do you filter out non-coding RNA, or transcripts that are not translated?
Quality - Artifacts with image analysis and data analysis
Control - Not enough attention to experimental design
- Need more collaboration with computational scientists

6. Measuring Differential Gene Expression
Quantification of mRNA transcripts
EST libraries
SAGE libraries
Microarray technology
High throughput RNA-seq technology
trancriptome sequencing
( quantification of mRNA transcripts)

7. EST Libraries Expressed Sequence Tags
Single sequencing reads from cDNA libraries-
250 (earlier) (later) bases long
usually from 5’ or 3’ end according to cloning strategies
an indication of which mRNA are in that cell at that time
Highly expressed genes = many ESTs
Low expression genes = fewer (or no detectable) ESTs
Can miss very low level transcripts, and some transcript variants
OK for quantification, known inaccuracies inherent in the method
Not OK for discovery of rare transcripts, too much noise from common transcripts
dbEST at NCBI

8. SAGE Libraries Serial analysis of gene expression
14 bp fragment is enough to uniquely identify a transcript.
Make cDNA library, cut it to 14bp fragment per transcript.
Ligate tags into long concatemers separated by a marker, and sequence them.
Output is a quantifiable list of short tags denoting presence of a gene and how much of it is there.
Useful in comparing transcriptomes and in discovery of new genes or transcripts

9. Microarray Technology
Single stranded lawn of DNA probes attached to a membrane on a microarray chip
Usually bp of a unique sequence from a gene. Often have more than one probe representing a gene.
Target (hybridization extract)
Total cDNA extracted from biological sample and labeled with fluorescent dye
Targets hybridise to the probes that have complimentary sequences. Intensity of hybridization is measured as an indication of the presence of that gene in the biological sample.

10. Uses of microarrays
Changes in Gene Expression levels (one or two colours)
Probes are ssDNA (cDNA or oligos),
Target is labeled cDNA derived from mRNA.
Genomic Gains and Losses (two colours)
CGH (Comparative Genomic Hybridization)
Probes are ssDNA (oligos)
Target is labeled DNA derived from genomic DNA
Genomic SNPs (one colour)
Probes are short genomic sequences containing SNPs
The Benefits of GEO and MAML
By storing vast amounts of data on gene expression profiles derived from multiple experiments using varied criteria and conditions, GEO will aid in the study of functional genomics—the development and application of global experimental approaches to assess gene function
GEO will facilitate the cross-validation of data obtained using different techniques and technologies and will help set benchmarks and standards for further gene expression studies
By making the information stored in GEO publicly available, the fields of bioinformatics and functional genomics will be both promoted and advanced
That such experimental data should be freely accessible to all is consistent with NCBI's legislative mandate and mission: to develop new information technologies to aid in the understanding of fundamental molecular and genetic processes that control health and disease

11. Holdus, Stavrum, Petersen and Stansberg 2008

12. Image Processing
This is computerized - you just see the final result in a spreadsheet.
The software scans the array and quantitates the signal values, i.e. converts fluorescence intensity to digital value

13. Data Preprocessing Background subtraction: Eliminates background noise
You choose the parameters, software does the work
Background subtraction: Eliminates background noise
Normalization: This step takes care of
Unequal quantity of starting sample
Difference in labeling efficiency
Difference in detection efficiency
System biases, etc.
Brings all samples into a similar range of distribution
Statistical QC
Removes low quality samples and probesets

14. Detection of Significantly Differentially Expressed Genes
Statistical tests
Student’s t test for two conditions/groups (control vs treated)
(i.e. the comparison of the means and standard deviations of two bell shaped curves, based on a t-statistic, testing the null-hypothesis that both distributions came from the same distribution)
ANOVA analysis (control vs treatment 1 vs treatment 2)
(i.e. ANalysis Of VAriances: Allows to test the null hypothesis that the differences within and between at least 3 groups are the same on average.
Based on F-statistic, the ratio of the variance calculated among the means to the variance within the samples)

15. Detection of Significantly Differentially Expressed Genes
2-way ANOVA (eg 2 cell lines, 2 treatments)
(i.e. The two-way ANOVA compares the mean differences between groups that have been split on two independent variables called factors. The primary purpose of a two-way ANOVA is to understand if there is an interaction between the two independent variables on the dependent variable).
All these methods produce p-values to assess the probability to obtain the result by chance. Problem: What happens if we have many such tests?

16. Detection of Significantly Differentially Expressed Genes
Multiple testing problem:
Say you have a set of hypotheses that you wish to test simultaneously. Let’s, consider a case where you have 20 hypotheses to test, and a significance level of a = What’s the probability of observing at least one significant result just due to chance?
P(at least one significant result) = 1 − P(no significant results)
= 1−(1−0.05)20
≈ 0.64
We have a 64% CHANCE to find one significant result randomly …

17. Detection of Significantly Differentially Expressed Genes
Correction methods:
Bonferroni (very conservative): significance threshold is a/N
FDR (False Discovery Rate): check if the kth ordered p-value is larger than (k × a)/N
q-value:
chance that p-values in this column
are false positives: q-value

18. Detection of Significantly Differentially Expressed Genes
Fold change
Difference in the intensity of a sample vs control or another sample, indicative of difference in level of expression of the gene
Threshold > 2, or > 1.6 in some cases

19. Then clustering

20. Then clustering
In differential gene expression, you are looking for genes that behave differently between one sample and another, either up- or down-
Once you get your DE gene set, you group the genes according to similar expression, and the outliers become more obvious
Clustering methods similar to those of phylogenetics, but without the evolutionary weightings, ie distance matrices
More downstream analysis later in the course

21. RNA-seq Same concept as sequencing ESTs and counting SAGE tags,
but does not stop at short segments and tags.
What is being sequenced is the cDNA from the mRNA component.
Sequencing of whole transcriptome of a sample (NGS), and
comparing it against the whole transcriptome of another sample.
Costly, informative, bioinformatics not yet fully sorted out-
when does a lot of data become too much data?

22. Finding the real transcripts

23. It’s all about the alignment
First, you align your reads to a reference genome or genomic region (or assemble the reads de novo)
BWA, Bowtie2, etc
Then you use a splice-aware aligner, such as TopHat or STAR, to refine the aligments according to coding sequences (exons) using known and/or predicted splice junctions

24. Quantifying reads per gene
Your aim is to count sequence reads per gene
When mapping reads to genome:
Filter out rRNA, tRNA, mitRNA, etc
Filtering out (or in!) non-coding RNA
Deal with alternative splicing
Deal with overlapping genes, pseudogenes
Small reads mean many short overlaps at one end or the other of intron gaps
Allele specific gene expression

25. Some Solutions
Can create a library of transcripts and map reads to transcripts (still have some ambiguity for multiple isoforms)
[limited, few (if any) use this method]
Can create a library of splice-junctions (span intron gaps) [Illumina CASAVA uses this method]
Can predict transcripts from genome mapped RNA-seq reads plus known splice junctions plus predicted splice junctions [TopHat]
Can do de novo assembly of new transcripts from reads [Trinity]
c.f. S. Brown, NYU

26. Normalization Coverage is not exactly the same for each sample
Problem: Need to scale RNA counts per gene to total sample coverage
Solution – divide counts per million reads
Problem: Longer genes have more reads, gives better chance to detect DE
Solution – divide counts by gene length
Result = RPKM and later FRKM
(Reads/Fragments Per KB per Million)
c.f. S. Brown, NYU

27. Better Normalization FPKM assumes:
Total amount of RNA per cell is constant
Most genes do not change expression
FPKM is invalid if there are a few very highly expressed genes that have dramatic change in expression (dominate the pool of reads)
Many now use “Quantile” normalization
New normalization methods currently being published
Different normalization methods give different results
c.f. S. Brown, NYU

28. Better Normalization
quantile normalization: making distributions identical in statistical properties
arrays
genes
rearrange
columns
assign ranks
arrays
genes
rank values
assign
values
c.f. S. Brown, NYU

29. Statistics of Differential Gene Expression
mRNA levels are variable in cells/tissues/organisms over time/treatment/tissue etc.
Need enough replicates to separate biological variability from experimental variability
If there is high experimental variability, then variance within replicates will be high, statistical significance for DE will be difficult to find.
Best methods to discover DE are coupled with sophisticated approaches to normalization
Very low expressing genes are tricky: FPKM<1
c.f. S. Brown, NYU

30. Gene Expression Analysis
Databases: GEO from NCBI ArrayExpress from EBI Commercial software: GeneSpring GX, CLC Bio, many others Free: Mostly R based Not being scared of statistics is an advantage New methods and algorithms continually being published Routine experiments are routine, innovative methods more care The really tricky part is the interpretation of the results

31. https://github.com/ccsstudentmentors/tutorials/wiki/CCS-Student-Mentors---Tutorials

32. Suggested additional reading: