Next Now-Generation Genomics: methods and applications for modern disease research Aaron J. Mackey, Ph.D. Center for Public Health Genomics Wednesday October 7 th, 2009 BIMS 853 Special Topics in Cardiovascular Research

source: Francis Ouellette, OICR “omic” Disease Research

source: Francis Ouellette, OICR

Basics of the “old” technology Clone the DNA. Generate a ladder of labeled (colored) molecules that are different by 1 nucleotide. Separate mixture on some matrix. Detect fluorochrome by laser. Interpret peaks as string of DNA. Strings are 500 to 1,000 letters long 1 machine generates 57,000 nucleotides/run Assemble all strings into a genome. source: Francis Ouellette, OICR

Basics of the “new” technology Get DNA. Attach it to something. Extend and amplify signal with some color scheme. Detect fluorochrome by microscopy. Interpret series of spots as short strings of DNA. Strings are letters long Multiple images are interpreted as 0.4 to 1.2 GB/run (1,200,000,000 letters/day). Map or align strings to one or many genome. source: Francis Ouellette, OICR

Differences between platforms: Nanotechnology used. Resolution of the image analysis. Chemistry and enzymology. Signal to noise detection in the software Software/images/file size/pipeline Cost $$$ source: Francis Ouellette, OICR

Adapted from Richard Wilson, School of Medicine, Washington University, “Sequencing the Cancer Genome” 3 Gb == source: Francis Ouellette, OICR

NGS technologies Roche/454 Life Sciences Illumina (Solexa) ABI SOLiD Helicos Complete Genomics Pacific Biosciences Polonator

Roche/454 pyrosequencing

454 flowgram 454 has difficulty quantizing luminescence of long homopolymers; problem gets worse with homopolymer length

Roche/454 first commercially available NGS platform long reads (most bp; soon 1000bp) paired-end module available relatively expensive runs homopolymer error rate is high common uses: metagenomics, bacterial genome (re)sequencing James Watson’s genome done entirely on 454 UVA Biology Dept. has one (Martin Wu)

Illumina (Solexa) 75 bp reads, PE bp fragments 8 lanes per flowcell ~3 Gbp per lane < 5% error rate available at UVA BRF DNA Core

ABI SOLiD

SOLiD “color space”

ABI SOLiD short reads (~35 bp) cheapest cost/base high fidelity reads (easy to detect errors) Common uses: SNP discovery 1000 genome project with PET libraries, all applications within reach …

Comparing Sequencers Roche (454)IlluminaSOLiD ChemistryPyrosequencingPolymerase-basedLigation-based AmplificationEmulsion PCRBridge AmpEmulsion PCR Paired ends/sepYes/3kbYes/200 bpYes/3 kb Mb/run100 Mb1300 Mb3000 Mb Time/run7 h4 days5 days Read length250 bp32-40 bp35 bp Cost per run (total)$8439$8950$17447 Cost per Mb$84.39$5.97$5.81 source: Stefan Bekiranov, UVA

Other NGS platforms Helicos (Stephen Quake, Stanford) – single molecules on slide – like Illumina, but no PCR, greater density Complete Genomics – sequencing factory – 10K human genomes/year, $10K each Pacific Biosciences – SMRT – DNA polymerase bound to laser/camera hookup – records a movie of DNA replication with fluoroscent dNTPs as single strand moves through nanopore Polonator (Shendure and Church) – homebrew, $200K flowcell+laser machine – allows custom chemistry protocols

NGS applications genome (re)sequencing – de novo genomes: 454 in Bact, small Euks – SNP discovery and genotyping (barcoded pools) – targeted, “deep” gene resequencing – metagenomics structural/copy-number variation – Tumor genome SV/CNV: Illumina/PET epigenomics – last week’s seminar RNA-seq: now-generation transcriptomics ChIP-seq: now-generation DNA-binding

RNA-seq: RNA abundance

RNA-seq: alternative splicing

RNA-seq “unbiased” digital measure of abundance – residual PCR artifacts? Helicos says “yes” larger dynamic range than microarray – depends on sequencing depth  cost ability to see alt./edited transcripts – multiple AS sites confounded; 454? Total RNA vs. cDNA – 3’ end bias of cDNA – non-polyA transcripts in total RNA

ChIP-seq: protein-DNA binding

PET: Paired End Tag libraries

PET applications

some things I didn’t get to talk about much: personal genome sequencing/medicine microbial metagenomics ENCODE/modENCODE projects HapMap project human 1000 Genome Project (1KGP) targeted- and/or deep-resequencing microRNAs, piRNAs, ncRNAs, … SVs and CNVs (cancer) read alignment issues (“mapability”)

Questions?