Contribution of Epigenetic Variation to Expression Changes Among Tissues and Genotypes Steve Eichten – Springer Lab PAG iPlant Workshop 1/17/12

Outline Questions we are interested in Using iPlant to assist in answering these questions Current and future analysis

Epigenetics & Gene Expression Heritable variation not due to sequence variation – DNA methylation, histone modification, etc – Classic examples: imprinting, paramutation Areas of research: Epigenomic variation across genotypes & development Relationship between genetic & epigenetic variation Role of epigenetic variation in phenotype (Chandler & Stam, 2004)

Epigenetic variation Across genotypes: – Maize Nested Association Mapping population (26 inbreds) Across development: – 5 tissues Embryo Endosperm Leaf Immature ear tassel

To answer these questions… Genome-wide methylation assessment – meDIP-chip (2.1M probe platform) – BS-seq (for B73 and Mo17 inbreds) Genome-wide expression assessment – RNA-seq (~20M reads x 25 inbreds) TechnologyLeafEmbryoEndospermEarTasselTOTAL meDIP-chip arrays BS-seq 179,109, ,109,340 reads RNA-seq 558,210,73242,581,92443,419,68085,671,33073,105, ,989,001 reads ~98,200,000,000 bases 209,949,399 array data points Bioinformatic knowhow Computational power and storage – iPlant!

Old way 1. Buy larger hard drives 2. Upgrade computers 3. Buy more hard drives 4. Gain basic understanding of UNIX / Perl / R / etc… 5. Lots of command line work / installation / troubleshooting - Array normalization / fastqc / tophat / cufflinks / DEGseq / …… 6. Realize you want it run analyses a different way. {GOTO 4} 7. Realize you really need more computing power to do what you want. {GOTO 2 or make some friends at a supercomputing institute} 8. Train others in your lab group how to do it! {Document everything and GOTO 1}

New way Move sequence data to iPlant DE Select apps for desired analysis Run software faster than you can locally Quickly adapt methods to find optimum Develop analysis pipeline for others to use Store large files for others in your lab group to access Larger storage + Faster Computing + Faster Training + Faster Adaptation + Faster Implementation = more user-friendly, more powerful computing

Applying iPlant to maize epigenomics fastq files Tophat aligner to pre-indexed maize reference genome Cufflinks transcript assembly against maize gene models iRODS file transfer to iPlant iPlant integrated Apps Download files from iPlant, display in IGV Assess read quality (FastQC) iPlant integrated Apps

Methylation variation in B73 and Mo17 Chromosome Most locations show similar methylation profiles Identified ~700 differentially methylated regions (DMRs) in B73 and Mo17 inbreds Eichten et al., PloS Genetics (B73 methylated, Mo17 not) (Mo17 methylated, B73 not)

Examples of expression changes correlated with epigenetic state Chromosome 6 Methylation inversely correlated with expression state Inbred-specific examples of methylation & expression variation Hundreds of genes correlated with epigenetic state

Methylation & Expression variation across NAM Chromosome 4 Putative targets of heritable epigenetic variation

Analysis of DNA methylation patterns across additional tissues and genotypes B73 embryo B73 endosperm B73 leaf Mo17 embryo Mo17 endosperm Mo17 leaf Ki11 leaf Mo18w leaf NC358 leaf Oh7b leaf DNA methylation patterns are generally quite similar among genotypes and tissues. However, there are ~1000 DMRs between any two genotypes. Variation frequently acts equally upon all tissues. Few Tissue specific DMRs and rarely conserved between genotypes

Wrap Up Epigenetics – Epigenetic variation exists in maize – Examples of gene expression states correlated with epigenetic state can be identified – Few tissue-specific methylation variants Utility of iPlant – Fast & remote location for storing large amounts of data – Fluid analysis of sequence data to develop transcript alignment and quantification

Springer Lab – Amanda Waters – Ruth Swanson-Wagner – Peter Hermanson – Nathan Springer iPlant & TACC – Matthew Vaughn NSF Thanks!