PowerPoint Presentation

Epigenetics QC (EpiQC) for Earth and Space(FDAs SEQC2 / ABRF / NIST GIAB)

Christopher E. MasonAssociate ProfessorDepartment of Physiology and Biophysics &The Institute for Computational Biomedicine at theWeill Cornell Medicine and theTri-Institutional Program on Computational Biology and MedicineSeptember 15th, 2016_6

@mason_lab

Epigenome

Epitranscriptome

Epiproteome (PTM)

DNARNAProteintranscriptiontranslation

RT

ACGT

viRNA

I N H E R I T A N C E or T R A N S M I S S I O NprionsiDNAmC,hmC, 8oxoG, m6A

(sn/sno/g)RNA(t/r/tm)RNA

(lnc/nc/mi/piwi/si/vi)RNAscRNARbPRibozymes

PrionsiRNAiProteinmC,hmC, 8oxoG, m6A, m6A, 5G,polyA, Acet, Phos, Citr, SUMO, from Saletore et al., Genome Biology, 2012

2Guide RNAs meditate RNA editing in mitochondria and serve as template correction, transfer-messenger RNAs in bacteria, small cytoplasmic RNA

Epigenetics data sets for EpiQC

Co-Chairs: Youping Deng, Sheng Li, and Christopher MasonCompleted for HG-002/3/4WBGS 30X WGBS data (Mason & NYGC)Illumina 450K methylation array data (Deng)Agilent 244K (G4492A ) methylation array (Deng)Roche CpGiant capture data vs. Agilent SureSelect vs. ERRBS (Mason, HG001)PacBio RSII (60X HG002, 30X for HG003/4 from NIST)

Pending for HG-002/3/4/otherIllumina EPIC 850K chip (Deng)Oxidative-bisulfite sequencing (Ox-BS) for the GIAB samples (Li)Single-cell RRBS on the Fluidigm C1 (Mason)AbbVie ANDI study on EPIC 850K methylation QC (1,900 samples)

Assays/Data for the EpiQC in the SEQC2/GIABSamplem6Am4Cm5Chm5C RSIIMinION450/850K Methyl ArrayoxBSWGBSRRBSEarth /SpaceHG001+-+++++/+++++/-HG002+-+++++/+++++/-HG003+-+++++/+++++/-HG004+-+++++/+++++/-IMR90+-++--+/+-+++/-DNMT3A/1 KO+-++--+/+-+++/-Mouse (BALB/)C+-++++NA++++/+E. coli (K-12)+++-++NA++-+/+Lambda - l++--++NA-+-+/+

Base modificationPlatform / Assay / Location

Why?

DNA methylation defines cellular phenotypes and lineage specification, and much of the action is beyond CpG islandsFernandez et al, Genome Research, 2012

Unsupervised hierarchical clustering and heatmap including CpG dinucleotideswith differential DNA methylation encountered between different normal primary samples. Tissue type and development layers are displayed in thedifferent colors indicated in the figure legends. Average methylation values are displayed from 0 (green) to 1 (red). (B) Deviation plot for the 1322 CpGsites studied in leukocyte samples showing that little CpG methylation heterogeneity (yellow area) occurs overall at CpG sites within CpG islands (red linesin the track below), while more differences in CpG methylation are observed outside CpG islands (blue lines in the track below). (C ) Unsupervisedhierarchical clustering and heatmap including sets of genes with high correlation values between hypomethylation (up) and hypermethylation (down)with aging. (D) Unsupervised hierarchical clustering and heatmap showing the DNA methylation patterns of embryonic and adult stem cells, comparingthem with corresponding normal and differentiated tissues (muscle, bone, and neuron; and muscle and brain, respectively).6

Steve Horvath, DNA methylation age of human tissues and cell types, Genome Biology, 2013.Age of methylation increases in all tissues as time passes; is accelerated in cancer

Horvath S. DNA methylation age of human tissues and cell types. Genome Biology. 2013;14(10):R115.Horvath S. Genome Biology. 2013.

Weidner CI et al., Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol. 2014 Feb 3;15(2):R24.DNA Marks can predict your age with few CpGs

http://www.yoloids.com/

Covert high school and college drinking WILL get harder

Li S, Garrett-Bakelman F, et al., 2016, Nature Medicine.

Can we sequence DNA in space?http://www.nasa.gov/mission_pages/station/research/experiments/2181.html

Hardest Pipetting Job everDr. Andrew Feinberg

Open-source Shazam (UNFOG)song of genomes algorithm onlinehttp://biorxiv.org/content/early/2015/12/10/032342.abstract, in press at Nature Microgravity

SpaceX CRS-9: perfect launch and booster returnJuly 18, 2016

Flight data shows very good accuracy (89-92%) for 2D reads

Plus, good read accuracy (76-79%) for 1D readsfor the template/complement measures.

Flight Data Read Accuracy

(% of reads)

https://spacegenetics.hms.harvard.edu/

https://www.nasa.gov/content/nasas-journey-to-mars12ETA: 2035

Next GIAB Project: Genome on a Planet (GIAP) to test Interplanetary Reproducibility

Thanks Alexa McIntyre (WCM)Sheng Li (Jackson)Noah Alexander (WCM)Yuta Suzuki and Shinichi Morishita (University of Tokyo)J. Wade Davis (AbbVie)Youping Deng (Rush)Charles Wang (LLU)Weida Tong (FDA)Leming Shi (Fudan)Wenming Xiao (FDA)Joshua Xu (FDA)Baitang Ning (FDA)

Thanks to Funding from:

@mason_lab

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Deep Gratitude to Many People:IlluminaGary SchrothMarc Van OeneUniv. ChicagoYoav GiladFDA/SEQC/Fudan Univ.Leming ShiNIH/NCBIJean & Danielle Thierry-MiegBaylorJeff RogersMSKCCDanwei HuangfuChristina LeslieRoss LevineAlex Kentsis

HudsonAlphaBraden BooneShawn LevyMason LabEbrahim AfshinnekooSofia AhsanuddinNoah AlexanderPradeep AmbroseDaniela BezdanMarjan BozinoskiDhruva ChandramohanChou ChouTim DonahoeFrancine Garrett-BakelmanJonathan FooxElizabeth Hnaff Alexa McIntyreCem MeydenNiamh OHaraReika OshimaRachid OunitLenore PipesJake ReedDarryl ReevesHeba ShabaanPriyanka VijayDavid WestfallCornell/WCMScott BlanchardSelina Chen-KiangOlivier ElementoSamie JaffreyAri MelnickMargaret RossEpigenomics Core

Horner LabStacy HornerIcahn/MSSMEric Schadt, Andrew Kasarskis,Joel Dudley, Ali Bashir, Bobby SebraABRFGeorge GrillsScott TigheDon BaldwinMiamiMaria E FigueroaAMNHGeorge AmatoMark Sidall@mason_lab

NYUMartin BlaserJane CarltonJulia Maritz

MIT Media LabKevin SlavinDevora NajjarRegina FloresRockefellerJeanne GarbarinoCharles RiceNASAAaron BurtonSarah Castro-WallaceKate RubinsGraham ScottCraig KundrotJackson LabsSheng Li

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