Verna Vu & Timothy Abreo

δC-doublecortin-like kinase (Dclk1)

Transgenic mice express a constitutively active splice variant of doublecortin-like kinase 1 (Dclk1) gene.

Goals:Study the transcriptome of wild type vs transgenic mice

Compare microarray with DGE

Study the transcriptome of transgenic mice vs wild type

http://pubs.niaaa.nih.gov/http://www.lintoninst.co.uk

Microarray

Problems: • high back ground• measures only relative abundances of transcripts• only predefined sequences can be detected• transcripts expressed at low levels can’t be detected

5 different microarray• Affymetrix• Agilent • Illumina • Applied Biosystems• Home spotted

dnachips-microarrays.wikispaces.com/

Quantitative PCR analysis

• Cycle Based• Sybr Green: Binds to all ds PCR products• Gold standard: used to validate

Problems• Can’t Multiplex• Many cycles to detect low frequency RNA transcripts, can be lost in background noise• Can’t use on all transcripts

http://www.thermoscientificbio.com

Helpful Nomenclature

• Canonical sequence: sequences that reflects the most common choice of base at each position

• Noncanonical sequence: tags in the genome that map to any known exon either strand

• Transcripts per million: t.p.m.

Origins of Digital Gene Expression (DGE): SAGE

Problems:• Laborious• Sequencing steps• 100 k canonical tags would take up to a year • Considerable financial investment needed

www.sagenet.org/

Digital Gene Expression (DGE) tag profiling

Procedure

ENSEMBL transcripts

To enable comparison, all canonical sequence tags and microarray

probe sequences were put in FASTA format and then aligned to

the ENSEMBL transcript database.

Same samples used for DGE and microarray.

http://en.wikipedia.org

• Transcripts with different 3’ ends that are separated by at least one restriction site can be differentiated with DGE

• 47% of detected ENSEMBL transcripts were discovered by more than one tag, most likely a result

of alternative polyadenylation in 3’UTR

• 29% estimated previously based on EST sequences

Alternative Polyadenylation

http://www.nature.com

Antisense Transcription

• Found evidence of bidirectional transcription in 51% of gene clusters when considering canonical and non-canonical tags of abundance >2 t.p.m.

• Antisense transcripts were expressed at high levels, although sense transcripts were still most abundant

http://www.nature.com

Differentially Expressed Genes

● Sequencing of pooled samples caused problems; blood contamination in one sample

● Used Bayesian statistics to attempt to exclude genes that were not truly differentially expressed

Biological Implications

● Used Gene Ontology consortium and DGE data to identify differentially regulated gene sets

● Most affected pathway:○ Disturbances of microtubule guided transport of SNARE

containing synaptic vesicles due to changes in gene expression

● CaMK pathway was the second most affected pathway, possibly via feedback mechanisms○ involved in behavioral pathways in brain

http://eferrari.blogs.lincoln.ac.uk

Dynamic Range

● The ratio between the largest and smallest possible values of a changeable quantity

● 3 to 4 orders of magnitude

● Lowest frequency but most consistently detected was 2 t.p.m. (0.3 copies per cell)

The effect of sequencing depth on detection of differentially expressed genes

● Simulated SAGE: randomly took 1/60 of DGE reads.○ Detected a decrease in 15 fold from 3179 to 200 reads

● Simulated SAGE: ○ lowest: 91 t.p.m.

● DGE:○ average: >2 t.p.m. ○ median: 4 t.p.m○ lowest: 0.8 t.pm.

● Microarray platforms○ median: 106 t.p.m ○ Affymetrix had the most transcripts

in common with DGE■ 11 different probes per transcript

Comparison to SAGE and Microarray

Validation with qPCR

● 29 significant genes from DGE, 33 from microarray ● 43/62 showed some sort of variation between the wt and transgenic same

direction● only 5 were considered to be significant by both platforms● To test for accuracy

The correlation between the normalized number of counts from the summed individual samples in their laboratory and the pool analyzed in the other laboratory were

● 0.98 wild-type

● 0.96 transgenic

To test for precision

Technique allows collaboration with other labs.

False discovery rate was 8.5%

Reproducibility

1. unbiased view of the transcriptome2. detects high levels of differential polyadenylation and antisense

transcription 3. data are more precise & accurate than microarray data4. data analysis requires a lower number of preprocessing facilitates

interlaboratory comparisons5. high interlaboratory comparability of DGE data, probably due to the

avoidance of hybridization processes (notoriously difficult to standardize) 6. more sensitive in the detection of low-abundant transcripts and of small

changes in gene expression. ▪ Absence of background signal and saturation effects

Why use DGE over Microarrays?

Implications:

● Enhancements in sequencing depth to improve accuracy in particular for low abundant transcripts

● Improvements in sensitivity, resolution, interlaboratory consistency○ Boost field of expression profiling

● Basic research and comparative genomics fields will benefit from major improvement of data portability○ Once held back by extensive and lengthy standardization issues

Deep sequencing→ robustness, comparability and richness of expression profiling data.

Will boost collaborative, comparative and integrative genomics studies

RNAseq

What the Future has in Store

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Questions? Criticism?

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