1. Biol 456/656 Molecular Epigenetics Lecture #2 Wed August 26, 2015

2. 5 BIG IDEAS 1. Instead of measuring your favorite gene’s expression levels, measure everything! RNA-seq CHIP-seq Bisulfite Sequencing Gro-seq 3’-seq Ribosome profiling

3. 5 BIG IDEAS 2. RNA: more than a messenger! lncRNA piRNA siRNA circRNA microRNA snoRNA

4. 5 BIG IDEAS 3. Gene Regulation: From bench to bedside @Bench2Bedside

5. 5 BIG IDEAS (covered in 456/656) 4. Lineage Reprogramming

6. 5 BIG IDEAS 5. Breaking the Genome with CRISPR/Cas

7. Gene expression. Transcription basics https://www.youtube.com/watch?v=tMr9XH64rtM Eric Lander, MIT REVIEW OF THE BASICS

9. Gene-centric viewGenome-wide view Biomedical science is undergoing a rapid change

10. Technology changes everything

11. Motorola DynaTAC 8000X $4000 1984 Motorola Moto G $179 2015

12. Sanger Sequencing -400 bases 1977 Illumina Hiseq 2500 -4 billion reads 2014

13. $10,000,000 human genome 2008 $1000 human genome 2015 http://www.illumina.com/systems/hiseq-x- sequencing-system/system.html

14. --$100 million --$10,000 --$1 million 2002 2012 Cost of sequencing a human genome Moore’s Law for computing costs Hayden, Nature 2014 Computing power per $ ~doubles every year

15. Methods to measure 1 RNA at a time – Northern Blot – RNAse protection assay – Quantitative RT-PCR

16. PCR http://www.lci-koeln.de/deutsch/veroeffentlichungen/lci-focus/die-polymerasekettenreaktion-pcr- Reverse Transcription -create cDNA from RNA REVIEW OF THE BASICS

17. Quantitative PCR End Point PCR REVIEW OF THE BASICS

18. mRNA from cells/tissues Fragmentation Convert to cDNA Adapter ligation RNA-seq: quantify the transcriptome

19. cDNA/DNA Single molecule array Sample preparation Cluster growth 5’ 3’ G T C A G T C A G T C A C A G T C A T C A C C T A G C G T A G T 123789456 Image acquisition Base calling T G C T A C G A T … Sequencing Sequencing by synthesis

20. \ 100 Microns Sequencing 100’s Million Clusters Per Flow Cell

21. RNA-seq quantitative gene expression novel transcript discovery

22. RNA-seq data from Fly tissues Visualized using Integrated Genomics Viewer (IGV) 1 read “Stranded” Mei-P26 OVARY

23. How do you deal with so much data? Different skill set required – Non GUI (Graphical User Interface) software – Unix – Various programming languages (Perl, Python) – Statistics/Visualization platform (R) – Visualize sequencing data (IGV, UCSC genome browser)

24. chr3R:18,842,756-18,864,455 http://genome.ucsc.edu/cgi- bin/hgTracks?hgS_doOtherUser=submit&hgS_otherUserName=pedstunite& hgS_otherUserSessionName=brent RNA-seq track of 6-8 hr embryos

25. Become a hybrid scientist! -alternatively become a “genome-informed” health care worker