Angelica Stamegna and Yeon Jin

1. Angelica Stamegna and Yeon Jin

2. Depth, in biology, refers to the number of times a specific nucleotide is used • Sequencing the same region multiple times • Relies on the attachment of randomly fragmented and amplified DNA • In theory, can read billions of nucleotides per reaction { Deep Sequencing:

3. Deep Sequencing vs _______________ • microarrays • SAGE • Allows for analysis of individual biological samples and then pooling the results • Biological question: identification of transcripts differentially expressed in the hippocampus of wildtype and mutant mice • Compared five genome-wide microarrays and compared them • Found very little differences • Used deep-sequencing to detect more subtle differences Introduction/Background

4. { Using the deep-sequence technology, they predicted an ability to distinguish smaller yet significant differences between genotypes, including antisense transcripts and transcripts with 3’UTR, previously not accomplished by microarray technology. Hypothesis

5. Sample • Wild Male C57/BL6j mice • Easy breeding, • Robustness • Availability of congenic strains • Transgenic male over expressing DCLK-short with a C57/BL6j background Materials and Methods

6. RNA extraction • Overexpressing DCLK-short changes Hippocampus • RNAs from hippocampus to see gene expression difference Materials and Methods cont..

7. Solexa/Illumina deep sequencing (DS) =Sequence tag preparation -Illumina’sDigital Gene expression Tag profiling Kit RNA -> cDNA Nla III to digest 3’CATG Add GEX 1 adapters MmeI to cut 17bp downstream of CATG site. Add GEX 2 adapters PCR with primers complementary to the adapter sequences. Electrophoresis to purify 85bp fragments only Materials and Methods cont..

8. Solexa/Illumina deep sequencing =Sequencing using Solexa/Illumina Whole Genome Sequencer (Cluster Generation) Denatured and hybridized to flow cell Extension/Bridge Amplification Sequencing Primer hybridization Sequencing Obtain sequences of samples. Materials and Methods cont..

9. Materials and Methods cont.. Solexa/Illumina deep sequencing IlluminaDGE tag annotation • Illumina Database to identify each tag • Canonical transcriptomic tags • most tags from known transcripts • Noncanonicaltranscriptomic tags • map to exon • Repeat tags • map to genome more than 100 times

10. Materials and Methods cont.. Microarray Analysis • Differential expressed genes could be found with Microarray Analysis. • For comparison of two method, microarray analysis is also conducted.

11. Materials and Methods cont.. Alignment to Ensembl transcripts • Convert all canonical sequence tags and microarray probe sequences to FASTA format. (like “ATTAGC…….”) • Align them with ENSEMBL cDNA database. • Only consider ENSEMBL transcripts which share with both Illumina Genome Analyzer platform and a certain microarray platform.

12. 4 wildtype vs 4 transgenic mice • 45,550 tags were identified in both groups • Biological variation between samples was not accounted for • Hazards of pool sampling • Sequencing of pooled SAGE libraries was previously the only option, now both affordable and advisable to sequence individual samples • Found 1620 upregulatedtags and 1559 downregulatedcanonical tags General Results

13. Pooled sample variation • Had blood contamination • Lead to a false positive of some of the transcripts • Tried a students t-test but all of the tags had to be normalized and include proper variance stabilization • They could not stabilize the variance and normalize the library size at the same time • Used a Bayesian model Difficulties

14. Volcano plot of canonical tags. Line measures significance. The closer to the top and right of the graph, the more significant the tag (higher average between transgenic and wildtype mice). The most significant tags show small differences in expression but are due to high expression levels. These show very arcuate measurements and therefore display low variation.

15. Only three genes were significant on all three microarrays and were confirmed with qRT-PCR Microarray Pitfalls • Lower number of transcripts gave a number above threshold as compared to DS • Thought to be caused by background due to cross-hybridization • Used 5 microarray chips and Affymetrix had the most in common with the DS • Less abundant transcripts more difficult to detect with microarrays. • No where near as nuanced as the DS

16. Advantage of DGE (DS) over expression Microarray • Unbiased view of the transcriptome • High levels of differential polyadenylation and antisense transcription • More precision • Lower number of preprocessing steps • Interlaboratory comparability of DGE data • More sensitivity in the detection Discussion

17. { “Changes in expression observed by deep sequencing were larger than observed by microarrays and quantitative PCR…Changes in expression observed by deep sequencing provides major advances in robustness, comparability, and richness of expression profiling data and is expected to boost collaborative, comparative, and integrative genomics.” Take Home Message