Functional Neurogenomics - 2 Flashcards
ChromHMM
Segments the genome & assigns chromatin states to whole-genome regions –> Hidden Markov Model
Methods to asses Histone modifications/DNA accessibility
ChIP-seq: Method to map histone modifications
Bisulfate sequencig: Method to map DNA modifications
ATAC-seq: Method to map DNA accessibility
ATAC-seq
Assay for Transponase-Accessible Chromatin
Probing open chromatin regions with hyperactive mutant TN5 Transponase that inserts sequencing adaptors into open regions of the genome –> Tn5 cleaves & tags dsDNA with sequencing adatpros –> Tagged DNA fragments are purified, PCR-amplieifed & sequenced by NGS
Methods to study chromatin organization
Ligation-based HiC
Non-ligation based GAM –> Genome wide identification of chromatin
Ultrathin sectioning of nuclei –> DNA-sequencing –> Measures contacts, compaction & radial positioning
Linkage mapping
For monogenic disorders - in affected families
Exploits human polymorphic markers to search for co-inheritance
Test for co-segregation in many independent families
Polymorphic markers: Heterozygous in population –> Mark specific points in the genome (e.g. Can microsatellites)
Positional cloning necessary to discover disease-causing mutations
SNP array
Small fragments of DNA where multiple alleles exist
Arrays contain immobilized allele-specific oligonucleotide (ASO) probes –> Patient DNA hybridizes onto it -> Detection
Copy numbers determined by the relative intensity of bound DNA at each allele
GWAS (Genome-Wide Association studies)
Compare frequency of genetic markers (SNPs) between patients & controls –> Find genetic variants that are siginifcantly more frequent in cases than controls
Significant: If 5x10^8 (P = 0.05)
Results shown in LD or Manhattan plot
Terminology of GWAS
Tag SNP: SNP on microarray in LD with neighbouring SNPs, occur one every 1000 nucleotides, found in databases; surrogates for large regions
Risk-associated SNP: Statistically significantly over-represented in disease populations –> Might be an artifact of LD!
Index/Lead SNP: SNP with smallest p-values in distinct regions –> Not necessarily a causal variant
eQTL (GWAS integration)
= Region of DNA associated with a trait with continous variance (e.g. height) –> Altered TF binding, altered histone modification, altered splicing, altered mRNA silencing
Cis-eQTLs: Variant resides in close proximity to target gene location
Trans-eQTLs: Variant resides very distant to the target –> Mechanisms less clear
Hypothesis: If a risk variant also causes gene expression changes – Might identified a potentially causal biological mechanism –> Co-Localization of GWAS results & eQTL
Epigenimic GWAS integration
- Expand each GWAS locus using SNP linkage disequilibrium
- Overlap SNPs with tissue-specific enhancers to find relevant cell types
- Search for enriched TF motifs in multiple GWAS loci to find driver TF
- Recognize target genes
GWAS summary
- Sequence tagSNPs –> Identify risk-associated SNP
- molQTL mapping & Chromatin interaction mapping –> Fine mapping
- Positional mapping –> Map SNPs to genes based on physical location (For variants in coding region)
- Gene-set analysis –> Test if trait associated variants converge on specific gene sets
Genome sequencing
Whole-genome sequencing: Captures all types of genetic variation –> Coding & non-coding SNVs, indels & CNVs (Complicated analysis)
Whole-exome sequencing: Focused on protein coding parts (SNVs & Indels) –> Less expensive
Droplet-based single-RNA sequencing
RNA expression profile of a single cell
1. Single-cell (nuclei) suspension from a tissue
2. Microfluidic device –> Encapsulate cell in nanoliter droplets with barcoded beads –> Cell lysis –> Captur of poly-A- RNA, RT with molecular identifiers + DNA amplification
3. NGS of cDNA library –> Solid-phase ampliification
Analysis: Clustering of cells
Clusters assigned to specific cell types on basis of marker genes
Pseudo-time plot: Order cells along one/more trajectories –> Represent developmental ones
MPRA (Massively parallel reporter Assays)
Gene regulatory effects of thousand of sequences assayed in parallel
Regulatory element drives RNA expression of unique barcodes –> Transcriptional activity is quantified as barcode transcription (RNA-seq of barcodes) normalized to initial input of barcodes
Limitations of MPRA
Limitations: Cannot identifiy target genes (Identifiy variant &element with regulatory activity)
Use of exogenous DNA (Do not account for effect in genomic context)
CITE-seq
Cellular indexing of Transcriptomes & Epitopes by Sequencing
DNA-barcoded ABs to convert detection of protein into a quantitative sequencable readout
CRISPR editing
DNA + sg RNA + Cas9 + Repair template = HR = Edited DNA
DNA + sgRNA + Cas9 = NHEJ = Repair errors = Frameshift & LoF –> If at two sites in parallel –> Loss of big pieces
CRISPR interference & activation
dCas): Cas9 with removed endonuclease activity –> Binding only
Coupled to Base-modifying enzyme = Base editing = Single variant editing
Coupled to Repressor = CRISPR interference
Coupled to Activator = CRISPR activation
CRISPR screen
Helps validate subsets of interactions identified by MPRA in genome-specific context
sgRNA pool introduced in bulk of cells (induce pertubations)
Integration os sgRNA in DNA upon pertubation –> Cell proliferates under biological challange –> Pertudbation induced effects analyzed by sequencing-based counting og the guide RNAs that specify each pertubation
Result: Ranked list of genes that confer sensitivity or resistance to challange
Types of CRIPSR screens
Arrayed: Each well with specific & known sgRNA targeting specific gene
Pooled: Cells transduced with pooled sgRNA library –> Single RNA-seq –> High dimensional transcriptomic readout
