Thanks to: DARPA BioComp DNA&RNA Polonies: Mitra, Shendure, Zhu Protein MS: Jaffe, Leptos Metabolism/Proliferation models : Segre, Vitkup, Badarinarayana.

Thanks to: DARPA BioComp

DNA&RNA Polonies: Mitra, Shendure, Zhu

Protein MS: Jaffe, LeptosMetabolism/Proliferation models: Segre, Vitkup, Badarinarayana

20-Mar-2003

New Methods for Genomic Systems Biology

gggatttagctcagttgggagagcgccagactgaa gatttg gaggtcctgtgttcgatccacagaattcgcacca

Modeling successes:

3D & Sequence alignment

Agenda for March 2015' George Church -- proteomics & polonies20' Daniel Segre – Metabolic modeling10' Matt Wright – 3D & 4D modeling25' Jingdong Tian -- minigenome10' Wayne Rindone – BioSpice

Discussion throughout is welcome.

10’ Financial, etc.

DNA RNA Proteins

Metabolites

Replication rate

Environment

Biosystems Integrating Measures & Models

Microbes Cancer & stem cells Darwinian optimaIn vitro replicationSmall multicellular organisms

RNAiInsertionsSNPs

interactions

Improving Models & Measures

Why model?

“Killer Applications”: Share, Search, Merge, Check, Design

The issue is not speed, but integration.Cost per 99.99% bp : Including Reagents, Personnel, Equipment/5yr, Overhead/sq.m• Sub-mm scale : 1m = femtoliter (10-15)• Instruments $2-50K per CPU

Why improve measurements?

Human genomes (6 billion)2 = 1019 bpImmune & cancer genome changes >1010 bp per time pointRNA ends & splicing: in situ 1012 bits/mm3

Biodiversity: Environmental & lab evolution Compact storage 105 now to 1017 bits/ mm3 eventually

& How? ($1K per genome, 108-1013 bits/$ )

Projected costs determine when biosystems data overdetermination is feasible.

In 1984, pre-HGP (X, pBR322, etc.) 0.1bp/$, would have been $30B per human

genome.

In 2002, (de novo full vs. resequencing ) ABI/Perlegen/Lynx: $300M vs. $3M

103 bp/$ (4 log improvement)

Other data I/O (e.g. video) 1013 bits/$

Steeper than exponential growth

0.001

0.01

0.1

1

10

100

1000

10000

1970 1980 1990 2000 2010

bp/$R2 = 0.985

R2 = 0.992

-5-3-113579

111315

1830 1850 1870 1890 1910 1930 1950 1970 1990 2010

log(IPS/$K)

log(bits/sec transmit)

http://www.faughnan.com/poverty.htmlhttp://www.kurzweilai.net/meme/frame.html?main=/articles/art0184.html

Kurzweil/Moore's law of ICs 1965

New sequencing approaches in commercial R&DMethod liter/bp Length Error Test-set $/device bp/hr

Capil fluidics e-6 600 <0.1% 1e11 350k 80k

ABI, Amersham, GenoMEMS, Caliper*, RTS*

SeqByHyb e-12 1 <5% 1e9 200k 1M

Perlegen-Affymetrix*, Xeotron*

Mass Spectrometry Sequenom, Bruker*

Single molecule >e-24 >>40 ? >80 30k-1M 180k

Pore(Agilent*) Fluor(USGenomics, Solexa) FRET(VisiGen,Mobious)

In vitro DNA-Amplification (e.g. Polonies) -- Multiplex cycles:

Lynx* e-15 20 <3% 1e7 ? 1M

Pyroseq.* e-6 >40 <1% 1e6 100k 5k

HMS* e-13<1% 40 90k >1M?

ParAllele, 454, RTS**GMC has a potential financial interest (or Harvard license)

Why single molecules?

Integration from cells/genomes/RNAs to data

Geometric constraints :Who’s “in cis” on a molecule, complex, or cell.e.g. DNA Haplotypes & RNA splice-forms

Polymerasecolonies

(Polonies) along a DNA

or RNAmolecule

A’

A’A’

A’

A’

A’

B

BB

B

BB

A

Single Molecule From Library

B

BA’

A’

1st Round of PCR

Primer is Extendedby Polymerase

B

A’

BA’

Polymerase colony (polony) PCR in a gel

Primer A has 5’ immobilizing Acrydite

Mitra & Church Nucleic Acids Res. 27: e34

• Hybridize Universal Primer • Add Red (Cy3) dTTP. Wash.• Add Green (FITC) dCTP• Wash; Scan

B B’

3’ 5’

AGT.

TC

B B’

3’ 5’

GCG..

C

Sequence polonies by sequential, fluorescent single-base extensions

Inexpensive, off-the-shelf equipment

MJR in situ Cycler$10K

Automatedslide fluidics

$4K

MicroarrayScanner$26K+

Human Haplotype:CFTR gene

45 kbp

Rob MitraVincent ButtyJay ShendureBen Williams

Quantitative removal of Fluorophores

Rob Mitra

Template ST30:3' TCACGAGT

Base added: (C) A G T (C)

(A) G (T) C (A)

(G) T C A

3' TCACGAGT AGTGCTCA

Sequencing multiple polonies

Rob Mitra

Mutiple Image Alignment

Metric based on optimal coincidence of high intensity noise pixels over a matrix of local offsets (0.4 pixel precision)

Polony exclusion principle &Single pixel sequences

Mitra & Shendure

DNA RNA Proteins

Metabolites

Replication rate

Environment

Biosystems Integrating Measures & Models

Microbes Cancer & stem cells Darwinian optimaIn vitro replicationSmall multicellular organisms

RNAiInsertionsSNPs

interactions

Alternatively Spliced Cell Adhesion Molecule

Specific variable exons are up-or-down-regulated in various cancers

Controversial prospective diagnostic / prognostic marker (>1000 papers)

Can full isoforms resolve controversy and/or act as superior markers?

Eph4 = murine mammary epthithelial cell line

Eph4bDD = stable transfection of Eph4 with MEK-1 (tumorigenic)

F R

v1 v2 v3 v4 v5 v6 v7 v8 v9 v10

TMA

CD44

CD44 Exon Combinatorics (Zhu & Shendure)

1. Search Signature Image for qualified ‘objects’

a. > 50 connected pixels with same signature valueb. ‘solidity’ of > 0.50c. long axis / short axis ratio < 3

OR

a. > 25 connected pixels with same signature valueb. ‘solidity’ of > 0.80c. long axis / short axis ratio < 1.5

2. Search for internal regional maxima within each object (lest two adjacent polonies with same signature get counted as one)

3. Assign centroid locations as qualified individual ‘polonies’

Trial & Error Derived Algorithm for Polony Finding

V1

V2

V3

V4

V5

V6

V7

V8

V9

V1

0

Examples of Counts (isoforms) of 8000 analyzed

Jun Zhu

------------7-8-9-10 609 764 1373 1.17 1E-4--------------8-9-10 320 390 710 1.13 3E-2----------6-7-8-9-10 431 251 682 -1.85 4E-18------4-5-6-7-8-9-10 218 216 434 -1.08 2E-1----------------9-10 68 143 211 1.96 7E-7--------5-6-7-8-9-10 86 39 125 -2.37 2E-6----3-4-5-6-7-8-9-10 40 56 96 1.30 9E-2------4-5---7-8-9-10 16 74 90 4.30 2E-9--2-3-4-5-6-7-8-9-10 44 28 72 -1.69 1E-21-2-3-4-5-6-7-8-9-10 22 5 27 -4.73 3E-4--------5---7-8-9-10 5 19 24 3.53 3E-3----3-4-5---7-8-9-10 1 15 16 13.95 4E-4--2-3-4-5---7-8-9-10 1 10 11 9.30 5E-3

Summary of Counts (isoforms)

Jun Zhu

EXON PATTERN Eph4 Eph4bDD TOTALEph4 FRATIO LSTP-PV------------7-8-9-10 609 764 1373 1.17 1E-4--------------8-9-10 320 390 710 1.13 3E-2----------6-7-8-9-10 431 251 682 -1.85 4E-18------4-5-6-7-8-9-10 218 216 434 -1.08 2E-1----------------9-10 68 143 211 1.96 7E-7--------5-6-7-8-9-10 86 39 125 -2.37 2E-6----3-4-5-6-7-8-9-10 40 56 96 1.30 9E-2------4-5---7-8-9-10 16 74 90 4.30 2E-9--2-3-4-5-6-7-8-9-10 44 28 72 -1.69 1E-21-2-3-4-5-6-7-8-9-10 22 5 27 -4.73 3E-4--------5---7-8-9-10 5 19 24 3.53 3E-3----3-4-5---7-8-9-10 1 15 16 13.95 4E-4--2-3-4-5---7-8-9-10 1 10 11 9.30 5E-3

Eph4 = murine mammary epthithelial cell line

Eph4bDD = stable transfection of Eph4 with MEK-1 (tumorigenic)

Polony Flavors

1. Replica Plating of DNA images [Mitra et al. NAR 1999]

2. Long Range Haplotyping [Mitra et al. PNAS 2003]

3. Allelic mRNA Quantitation (HEP) [Mitra et al. 2003]

4. Alternative Splicing Combinatorics [Zhu et al. 2003]

5. Precise SNP-mutant & mRNA ratios [Merrill et al. 2003]

6. Fluor in situ Sequencing (FISSEQ 1) [Mitra et al. 2003]

7. Multiplex Genotyping (ApoE, Hyman, Shendure & Williams)

8. In situ / single-cell extensions of the above (Zhu & Williams)

DNA RNA Proteins

Metabolites

Replication rate

Environment

Biosystems Integrating Measures & Models

Microbes Cancer & stem cells Darwinian optimaIn vitro replicationSmall multicellular organisms

RNAiInsertionsSNPs

interactions

Link et al. 1997 Electrophoresis 18:1259-313 (Pub)

Comparison of predicted with

observed protein properties

(abundance, localization, postsynthetic modifications)

E.coli

Circadian Cycle Proteogenomic Map 1/4

Circadian Cycle Proteogenomic Map 2/4

Circadian Cycle Proteogenomic Map 3/4

Circadian Cycle Proteogenomic Map 4/4

Numbers on top in basepairs. 1700 ORFs are predicted . Proteomic Model is based on Mass-spectrometry of peptides at 24h time points. DifferenceMap indicates new peptide regions. The 6 colors represent ORFs in the 6 reading frames.(Harvard-MIT GtL: Jaffe, Church, Lindell, Chisholm, et al. )

Circadian &Cell Cycle Proteogenomic Map (zoom)

Circadian time-series (Prochlorococcus) RNA & protein quantitation:

R2=.992 R2=.635 Linear Regression R2=.1

(Harvard-MIT GtL: Jaffe, Church, Lindell, Chisholm, et al. )

RNA (3 AM)RNA (3 AM)

DNA RNA Proteins

Metabolites

Replication rate

Environment

Biosystems Integrating Measures & Models

Microbes Cancer & stem cells Darwinian optimaIn vitro replicationSmall multicellular organisms

RNAiInsertionsSNPs

interactions