Download Free Medical Measuring Methylation-from arrays to sequencing PowerPoint Presentation

Measuring Methylation-from arrays to sequencing PowerPoint Presentation

DOWNLOAD MEDICAL POWERPOINT PRESENTATION

About This Presentation

Description : Check out this medical titled "Measuring methylation: from arrays to sequencing” by Jovana Maksimovic, PhD.

This medical is about methylation, an epigenetic modification that involves the addition of a methyl group to the cytosine base in DNA. Methylation can regulate gene expression and plays a crucial role in development and disease. There are different methods available for measuring DNA methylation, ranging from array-based approaches to sequencing-based approaches.

Array-based approaches, such as the Infinium Methylation Array, are widely used to measure DNA methylation. These arrays use bisulfite treatment to convert unmethylated cytosines to uracil while leaving methylated cytosines unchanged. The DNA is then hybridized to an array of probes that detect the methylation status of specific cytosine sites. The resulting data provides information about the methylation status of hundreds of thousands of CpG sites in the genome.

Sequencing-based approaches, such as whole-genome bisulfite sequencing (WGBS) and reduced representation bisulfite sequencing (RRBS), provide higher resolution data than array-based approaches. WGBS involves sequencing the entire genome after bisulfite treatment, while RRBS involves sequencing only a subset of genomic regions that contain CpG sites. Both methods generate single-base resolution data on the methylation status of CpG sites.

Other sequencing-based approaches include targeted bisulfite sequencing, which focuses on specific genomic regions of interest, and nanopore sequencing, which can detect modifications in real-time as the DNA molecule is sequenced.

Overall, the choice of method for measuring DNA methylation depends on the research question, the available resources, and the desired level of resolution. Array-based approaches are suitable for large-scale studies, while sequencing-based approaches provide higher resolution data but are more resource-intensive.

Tags : methylation, epigenetic modification, methyl group, cytosine base in DNA, DNA methylation, Infinium Methylation Array, Medical Genetics, Medical Genetics

Published on : Jun 01, 2023 | Views : 1253 | Downloads : 1

Shareable Url :

Embed Code :

Comment

Transcript

Slide 1-

Measuring methylation: from arrays to sequencing Jovana Maksimovic, PhD jovana.maksimovic@mcri.edu.au @JovMaksimovic github.com/JovMaksimovic
Slide 2-

Talk outline Epigenetics DNA methylation Measuring DNA methylation Methylation arrays How do they work? What do they measure? Example analysis Methylation sequencing What are the challenges? How does it work? Suggested analysis pipeline Summary
Slide 3-

I work at MCRI Me! I mostly work on human development & disease… I write software for analysing methylation array data missMethyl …and a lot of gene expression data RNAseq Microarrays I analyse a lot of epigenetic data… ChIPseq ATACseq BSseq Microarrays …sometimes using mice or other models
Slide 4-

What is epigenetics? Epigenetics refers to stable heritable traits not explained by changes in DNA sequence Greek prefix “epi” means “on top of” genetics Chromosome modifications that affect gene expression Histones, DNA methylation “Anything” that isn’t DNA! Essential for normal development Can be modified by environment Can be disrupted in disease
Slide 5-

Epigenetics brings DNA to life! embryogenesis blastocyst zygote sperm egg embryonic stem cells B cell T cell red bloodcell haematopoietic stem cell fatcell spermcell skin cell musclecell glandcell hormone-secretingcell germcell neuron astrocyte neuronal progenitor cell lungcell kidneycell identical DNA in every cell different epigenetic patterns Important in all species Modified from https://biology.mit.edu/research/stemcell_epigenetics intestine cell
Slide 6-

Epigenetics is CRAZY complicated! New sequencing & microarray technologies are enabling us to learn A LOT more about epigenetics Different data types need different analysis Today I’m only focussing on DNA methylation Roy et al. (2010), Science Me Me
Slide 7-

What is DNA methylation? DNA methylation primarily occurs at CpG dinucleotides C G A T C C
Slide 8-

Patterson et al. 2011, J Vis Exp DNA methylation in the genome The human genome contains ~30,000,000 CpGs (~1%) VERY different between different species CpGs are not evenly spaced across the genome Tend to be present in clusters called CpG islands CpG methylation is spatially correlated Eckhardt et al. 2007, Nature Genetics ~500bp Methylation correlation with distance
Slide 9-

Methylation can regulate gene expression Plot from Peter Hickey http://meeting.dxy.cn/oemethylation2012/article/i18782.html Methylation at a single CpG vs. gene expression Each point is one sample
Slide 10-

Methylation changes coat colour of Agouti mice Dolinoy 2008, Nutr Rev. This gene controls coat colour in Agouti mice These CpG sites in the promoter change PS1A expression depending on methylation These mice are genetically identical Hypomethylated Hypermethylated Coat colour different due to different maternal diet i.e. environment!
Slide 11-

Cridge et al. 2015, Nutrients Methylation makes worker bees! These larvae are genetically identical Hypomethylated Hypermethylated
Slide 12-

Methylation is cool What do we usually want to know about it?
Slide 13-

Finding methylation differences can tell us a lot Methylation is critical in determining cell type Regulatory T-cell vs. Naïve T-cell Methylation can be disrupted in disease Cancer vs. Normal Methylation is affected by the environment Smokers vs. Non-smokers Collect appropriate samples Extract DNA and measure methylation Statistical analysis Normal Cancer
Slide 14-

Epigenome-wide association studies (EWAS) Similar to GWAS Compare lots of cases to lots of controls Often looking for small effects e.g. complex disease or environmental effects Need lots of samples 100s or 1000s of cases & controls https://en.wikipedia.org/wiki/Epigenome-wide_association_study_(EWAS)
Slide 15-

How do we measure methylation? Bisulphite conversion Create “SNPs” Single nucleotide resolution Array Sequencing Enrichment of methylated DNA Restriction enzymes Affinity Regional resolution Array Sequencing
Slide 16-

What is bisulphite conversion? Chemical process Unmethylated Cs get converted to Ts Methylated Cs are protected Creates “SNP” Used to call methylation PCR
Slide 17-

Methylation arrays What are they and how do they work?
Slide 18-

Illumina Infinium HumanMethylation BeadChips Human only Gene biased; selected to be relevant to human development & disease eg. TSS, promoters, CpG islands, enhancers, ... 1 chip = 12 samples >27,000 unique CpG sites measured in each sample 1 chip = 8 samples 1 chip = 12 samples 27k array (2009) 450k array (2011) 850k array (2015) >450,000 unique CpG sites measured in each sample >850,000 unique CpG sites measured in each sample Modified slide from Belinda Phipson
Slide 19-

Methylation arrays are based on SNP array technology Methylation array “SNPs” (C/T) are created by bisulphite conversion Comparing the intensity of C/T gives the proportion of methylation at single CpG What is this base? Measure fluorescence intensity
Slide 20-

What methylation values can we get? On an array, we measure methylation in a population of cells Individual cell can be either 0, 0.5 or 1 at one CpG Across a population we get a continuous measurement between [0-1] CH3 CH3 CH3 0 0.5 1 A sample Many cells in single sample
Slide 21-

Measures of methylation Arrays measure both methylated (C) and unmethylated (T) signal to get proportion of methylation at a CpG Intuitive, easy to interpret, great for visualisation M value Beta value Du et al. 2011, BMC Bioinformatics Can convert between them via a logit transformation Better statistical properties, recommended for statistical testing
Slide 22-

What does the data look like? … ... ... CpG sites Table of M-values
Slide 23-

Array analysis pipeline QC: b density plots, control probes, MDS/clustering plots, … Normalization: within and between arrays Statistical testing for differential methylation, CpGs & regions Annotation to genes, gene set testing, visualization, … Combine with other data types Transform data to remove unwanted variation minfi, missMethyl, wateRmelon Estimate means and variances and borrow information across probes limma, bumphunter, DMRcate Think about biological interpretation missMethyl, Gviz e.g. gene expression GenomicRanges Remove bad samples and poor performing probes (CpGs) minfi, methylumi, limma Software
Slide 24-

M28 M29 M30 naive activated naive activated rTreg rTreg
Slide 25-

After QC, data exploration is your friend! Dimension 1 Dimension 3 Dimension 2 Dimension 4 Clustering by individual and cell type MDS plots showing largest sources of variation in the data
Slide 26-

Statistical testing:Look for differences at single CpGs Differential methylation Phipson & Oshlack 2015, Genome Biology Linear model : Smyth, 2004 Adjust the p-values using Benjamini and Hochberg’s FDR Can take into account any other covariates One test per CpG! Modified slide from Belinda Phipson & Alicia Oshlack Lots of differences between immune cell types!
Slide 27-

Statistical testing:Differences across CpG dense region Recall: CpG methylation is spatially correlated Can we find consistent group-average level differences between CpGs that are close together? More functionally relevant than differences at individual CpGs? Aryee et al. 2014, Bioinformatics Lots of DMRs between immune cell types!
Slide 28-

You can do other cool stuff! Unmethylated regions in rTreg compared to naïve cells enriched for FOXP3 binding motifs! Forkhead- binding motif Consensus motif from DMR seqs. DMR consensus motif matches Forkhead-binding motif Differences in cell types controlled by FOXP3! Modified slide from Alicia Oshlack
Slide 29-

Methylation array analysis is very mature: lots of methods! https://www.bioconductor.org/ https://f1000research.com/articles/5-1281/v3
Slide 30-

Methylation sequencing AKA bisulphite sequencing: the good, the bad and the ugly
Slide 31-

Two main types of bisulphite sequencing Whole-genome bisulphite sequencing (BS-seq) Gold standard Genome-wide (~30,000,000 CpGs in human) Expensive but covers almost everything Need high (10-30x) coverage to reliably call methylation Targeted BS-seq Only sequence regions of interest Reduced representation BS-seq (restriction enzyme) Capture BS-seq (similar principal to exome) Cheaper but can miss a lot of stuff Can usually do higher (20-60x) coverage
Slide 32-

What was bisulphite conversion again? DNA fragment All four of these can be sequenced!
Slide 33-

What are the challenges? Like calling SNPs, methylation in BS-seq inferred by comparison to unconverted reference sequence Correct alignment is critical More challenging than usual! Aligned sequences do not exactly match reference Complexity of libraries is reduced Many Cs become Ts, so less info for mapping! Methylation is not symmetrical Two strands of DNA in the reference genome must be considered separately
Slide 34-

Mapping (Bismark) DNA fragment BS conversion & PCR
Slide 35-

Mapping (Bismark) TCGGTATGTTTAAACGTT DNA fragment BS conversion & PCR
Slide 36-

Mapping (Bismark) TCGGTATGTTTAAACGTT TTGGTATGTTTAAATGTT TCAATATATTTAAACATT In silico read conversion C-to-T G-to-A DNA fragment BS conversion & PCR
Slide 37-

Mapping (Bismark) TCGGTATGTTTAAACGTT TTGGTATGTTTAAATGTT TCAATATATTTAAACATT In silico read conversion C-to-T G-to-A …TTGGTATGTTTAAATGTT… …AACCATACAAATTTACAA… …CCAACATATTTAAACACT… …GGTTGTATAAATTTGTGA… Align to in silico bisulphite converted genome Fwd strand C-to-T converted genome Fwd strand G-to-A converted genome Reverse complement Reverse complement DNA fragment BS conversion & PCR
Slide 38-

TCAATATATTTAAACATT TCAATATATTTAAACATT TCAATATATTTAAACATT TCAATATATTTAAACATT Mapping (Bismark) TCGGTATGTTTAAACGTT TTGGTATGTTTAAATGTT TCAATATATTTAAACATT In silico read conversion C-to-T G-to-A …TTGGTATGTTTAAATGTT… …AACCATACAAATTTACAA… …CCAACATATTTAAACACT… …GGTTGTATAAATTTGTGA… Align to in silico bisulphite converted genome Fwd strand C-to-T converted genome Fwd strand G-to-A converted genome Reverse complement Reverse complement …TTGGTATGTTTAAATGTT… …AACCATACAAATTTACAA… …CCAACATATTTAAACACT… …GGTTGTATAAATTTGTGA… x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x Read all alignment outputs simultaneously to determine if sequence can be mapped uniquely DNA fragment BS conversion & PCR
Slide 39-

TCAATATATTTAAACATT TCAATATATTTAAACATT TCAATATATTTAAACATT TCAATATATTTAAACATT Mapping (Bismark) TCGGTATGTTTAAACGTT TTGGTATGTTTAAATGTT TCAATATATTTAAACATT In silico read conversion C-to-T G-to-A …TTGGTATGTTTAAATGTT… …AACCATACAAATTTACAA… …CCAACATATTTAAACACT… …GGTTGTATAAATTTGTGA… Align to in silico bisulphite converted genome Fwd strand C-to-T converted genome Fwd strand G-to-A converted genome Reverse complement Reverse complement …TTGGTATGTTTAAATGTT… …AACCATACAAATTTACAA… …CCAACATATTTAAACACT… …GGTTGTATAAATTTGTGA… x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x TTGGTATGTTTAAATGTT TTGGTATGTTTAAATGTT TTGGTATGTTTAAATGTT TTGGTATGTTTAAATGTT x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x Read all alignment outputs simultaneously to determine if sequence can be mapped uniquely DNA fragment BS conversion & PCR
Slide 40-

Calling methylation TCGGTATGTTTAAATGTT TATGTTTAAATGTT …TCGGTATGTTTAAAT …TCGGTATGTT AAACGTT… …TCGGTATGTTTAAATGTT GTT… …TTG …CCGGCATGTTTAAACGCT… …TCGGTATGTTT …TCGGTATGT TTAAATGTT… ATGTT… …TCGGTATGTTTAAAT TT… …TTGGTATGTTTA ATGTT… …TCGGTATGTTTAAACGT Genome reference
Slide 41-

Calling methylation TCGGTATGTTTAAATGTT TATGTTTAAATGTT …TCGGTATGTTTAAAT GTT… …TTG …CCGGCATGTTTAAACGCT… Genome reference Good coverage is very important for reliablemethylation calls!
Slide 42-

Some real BS-seq mapping results https://software.broadinstitute.org/software/igv/interpreting_bisulfite_mode
Slide 43-

Methylation calling output chr1 753479 753479 50 1 1 chr1 753492 753492 66.67 2 1 chr1 753540 753540 100 1 0 chr1 753541 753541 50 1 1 chr1 753667 753667 25 1 3 chr1 753724 753724 66.67 2 1 chr1 753763 753763 0 0 2 chr1 753785 753785 0 0 1 chr1 759932 759932 100 1 0 chr1 760913 760913 0 0 1 chr1 761299 761299 100 2 0 chr1 761371 761371 80 8 2 chr1 761377 761377 100 10 0 chr1 761446 761446 92.86 13 1 chr1 761460 761460 53.85 7 6 chr1 762005 762005 100 1 0 chr1 762114 762114 0 0 5 chr1 762176 762176 0 0 7 chr1 762180 762180 0 0 8 No. unmethylated reads No. methylated reads % methylation Sum for total coverage Position of C in genome This is what we work with!
Slide 44-

Krueger et al. 2012, Nature Methods Analysis pipeline Thorough QC is VERY important for BS-seq Need to be brutal with trimming off poor quality bases… …and adapters As with SNP calling, removing PCR duplicates is a good idea for better methylation calling Other stuff to find cool biology!
Slide 45-

Summary Methylation arrays very popular Only for human Great for EWAS Analysis very mature Bioconductor is the place to go! BS-seq best option for genome-wide single nucleotide resolution Only option for species other than human Pre-processing, mapping, etc. pretty good Statistical analysis still developing Bioconductor is a valuable resource Downstream analysis dependent on biological question Methylation is interesting & we know how to measure it Best technology for the job depends on what you want to know!
Slide 46-

Acknowledgments Murdoch Childrens Research Institute Alicia Oshlack Belinda Phipson MCRI Bioinformatics group! Johns Hopkins University Peter Hickey missMethyl https://www.bioconductor.org/packages/release/bioc/html/missMethyl.html jovana.maksimovic@mcri.edu.au @JovMaksimovic github.com/JovMaksimovic https://f1000research.com/articles/5-1281/v3