genomics - RNA biology I

transcription 3 steps

initiation

elongation

termination

transcription initiation

-TFIID binds to TATAA box

-binding of TFIIB

-binding of RNA pol + TFIIF

-binding of TFIIE + TFIIH

RNA Polymerase II

responsible for the synthesis of mRNA from protein-coding genes

general transcription factors

-basic transcription machinery

transcription from all pol II promoters

regulatory transcription factors

regulatory proteins whose function is to activate (or more rarely, to inhibit) transcription of DNA by binding to specific DNA sequences

ncRNAs

non-coding RNAs

-heterogenous group of transcripts that are not translated into proteins

rRNA

ribosomal RNA

ncrna

tRNA

transfer RNA

ncrna

snRNA

small nuclear RNAs

ncrna

critical components of the spliceosome that catalyze the splicing of pre mrna

snoRNAs

small nucleolar RNAs

ncrnas

widely present in the nucleoli of eukaryotic cells

important for RNA biogenesis and chemical modications of rRNA, tRNA, and mRNA

siRNAs

small interfering RNAs

~20 bp

ncrna

induces gene silencing by targeting complementary mRNA for degradation

miRNAs

~22 nt

ncrna

leads to mRNA degradation or inhibition of mRNA translation

miRNA origin

miRNA originate from endogenous transcripts(have their own genes)

siRNA origin

exogenous dsRNA

RISC

RNA-induced silencing complex

miRNA RISC

formed with Ago1-4

siRNA RISC

formed with Ago2

miRNA target mrna binding

imperfect complementary binding

translational repression and mrna degradation

siRNA target mRNA binding

specific target sequence binding

mrna cleavage

piRNAs

small PIWI-interacting RNAs

21/24/26-31 nucleotides

piRISC: piRNA-induced silencing complex

-protects genome integrity

binds to PIWI protein

3’ end modification: 2’-o methyl

transposon silencing

lncRNAs

long non-coding RNAs

noncoding transcripts of more than 200 nucleotides

linear lncrnas and circular rnas

RNA polymerase III

synthesizes tRNA and 5S rRNA, some small RNA

RNA polymerase I

synthesizes 5.8S, 18S, and 28S rRNA

gene expression importance

-provide a snapshot of cellular/tissue state at the molecular scale

-provide snapshot of cumulative interactions of many regulatory relationships

-proxy measure for transcription/translation functional events

gene expression assumptions

-assume that gene expression levels correspond to functional protein levels

-assume that a normal cell has a standard expression profile/signature

-assume that changes in expression profile indicate that some property or functional changes

how much of the genome is transcribed

eukaryotic genomes transcribe up to 90% of the genomic DNA

how much of mRNA gets encoded into protein

only 1-2% of transcripts encode for proteins, the vast majority are transcribed as non-coding RNAs

mRNA levels vs protein levels

significant discrepancy between mRNA and protein levels

-differentially expressed mRNAs correlate significantly better with their protein product than non-differentially expressed mRNAs

regulation of protein abundance

-chromatin regulation

-mrna stability

-translational efficiency

-decay rates

-copy number variation

-promoters, enhancers, silencers, insulators

-histone and dna modifcations

northern blot

targeted RNA quantification

to determine the size and quantity of specific RNA molecules among a mixture of RNA

best for determining the size of a specific rna transcript

-load RNA samples into gel, gel electrophoresis

-blot onto a filter

-expose filter to a labeled hybridization probe: complementary to target RNA sequence, single stranded, labeled with radioactive isotope or fluorescent dye

-wash away unhybridized prob

northern blot pros

-simplicity of the procedure and low cost

-very sensitive due to use of radioactive probes

-nearly infinite dynamic range

northern blot cons

-time-consuming

-only a small number of samples can be analyzed at one time

-requires a large amount of starting material

-quality control (non-specific hybridization)

in situ hybridization

-to localize and detect RNA or RNA sequences in morphologically preserved cells, tissue sections, and even whole tissue

visualize the location of a specific RNA within a tissue or cell, spatial information

in situ hybridization pros

-provide spatial information of cellular content

-single-cell sensitivity

-spatial and temporal analysis

-can be used on archival tissues

in situ hybridization cons

-expensive

-time consuming

-require experienced personnel

-probe and sample-specific, have to be optimized for each set of conditions empirically

RT-qPCR

Reverse transcription quantitative PCR

-accurate, sensitive and fast method of nucleic acid detection and quantification

-relies on fluorescence to detect and quantify nucleic acid amplification products

The most sensitive method for detecting and quantification of gene expression

Ideal for analyzing a few genes with high accuracy

1) convert total RNA to cDNA

2) add cDNA to RT qPCR master mix and aliquot mixture across PCR array

3) run in RT-qPCR instrument

phases of PCR amplification curve

-linear ground phase

-early exponential phase

-log-linear phase

-plateau phase

good RT-qpcr designs will have these distinct phases

RT-qPCR pros

-highly sensitive, quantitative and reproducible

-’gold standard’

-excellent dynamic range

-fast results

RT-qPCR cons

-expensive

-not high-throughput

-non-specific amplification can lead to false positives

-always have positive and negative controls

reporter gene assay

tag gene with a fluorescent reporter or something that can be quantified

Used to study regulatory elements of a gene by monitoring the activity of a reporter gene under different conditions

applications:

-determine promoter or enhancer strength

-interactions between transcription factors and promoters

-protein-protein interactions

-signal transduction

-drug screening both in vitro and in vivo

reporter assay pros

-in vivo applications

-highly sensitive

-new technology enables longitudinal studies

reporter assay cons

-stability issues

-not high-throughput

-quantification in vivo is affected by many variables

micro array

hybridization between dna strands

quantifies RNA through template hybridization and dye intensity

Allows for simultaneous analysis of thousands of genes, useful for exploring global gene expression patterns of model organisms

-control and experimental group

-make cDNA reverse transcripts

-label cDNAs w fluorescent dyes

-hybridization to microarray

-laser excitation

-computer calculates ratio of emission intensity

microarray pros

-high throughput

-reliable and more cost effective than rna-seq for gene expression profiling in model organisms

-kit systems: easy to use

microarray cons

-need target transcripts information

-quality and quality control highly variable

-cross hybridization

-multiple tissue samples cannot be tested in one assay, a control and test tissue sample need to be prepared separately

RNA-seq

uses next gen sequencing to analyze the quantity and presence of RNA molecules in a biological sample

Provides a comprehensive view of the transcriptome, including novel transcripts and isoforms, offering the most detailed information about gene expression

-RNA extraction and target enrichment

-fragment, reverse transcribe, ligate adapters, amplify

-sequence

-transcriptome/genome mapping

-data analysis

RNA-seq pros

-high throughput

-transcript identification and quantification in a single assay

-very direct and quantitative

-no prior knowledge of genome required

-a greater dynamic range to quantify transcripts allows more differentially expressed gene detection

-single-nucleotide resolution allows the detection of genetic variants, transcript isoforms and splice variants

RNA seq cons

-amplification steps can offset balance between high/low abundance transcripts

-higher cost than microarray

-analysis is non-trivial

H0, the null hypothesis

asserts that there is no effect or differences between treatment and control groups

Ha, the alternative hypothesis

there is an effect or differences

hypothesis testing

-state hypothesis

-set signficance level (a) BEFORE the experiment

-collect and prepare data: representative of the population, appropriate sampling method, determining sample size

-choose appropriate statistical test: continuous or categorical? normal distribution or not? sample size, number of groups being compared

-calculate p-value

-make a decision

p-value

statistical test quantifies how much the sample data deviates from the null hypothesis

p-value = probability of observing results as extreme as the sample data, assuming the null hypothesis is true

p-value <= a

reject null hypothesis

p-value >= a

fail to reject null hypothesis

Type-I error (a)

Concluded that null hypothesis could be rejected, but null hypothesis is actually true

Type II error (B)

Concluded that null hypothesis couldn’t be rejected, even though the null hypothesis is false

a

alpha

the maximum probability of making a type I error

usually 0.05

B

beta

when the alternative hypothesis is true, the probability of rejecting it

the probability of making a type II error

Power

= 1 - B

the ability of a test to detect a true effect when it’s there

Confidence interval

=1-a

commonly 0.95

power analysis

to determine the necessary number of subjects needed to detect an effect of a given size

to determine power, given an effect size and number of subjects available

no point in conducting a study that is seriously underpowered

power analysis softwares

G*power 3

Power analysis & sample size

R: pwr package

power and sample size

increased sample size leads to increase of power

plateaus when theres a lot of subjects

cohen’s d

difference between 2 means divided by the pooled standard deviation

d= 0.01 → very small effect size

d = 0.8 → large effect size

bonferroni correction

p-value for each test must be equal to its alpha divided by the number of tests performed

a/g

g = number of null hypotheses being tested

false discovery rate

n_DE * a

types of replicates

biological replicates: have as many as possible

technical replicates

RNA extraction

-RNA extracted from tissue is very heterogenous, many cells and diff cell types

-total RNA contains diff types of RNA

-extremely susceptible to degradation

RNA quality control

-measure RNA concentration: spectrophotometer, A260nm

-measure RNA purity: A260/A280 = between 1.8 and 2

-other contaminants: A230, salts or phenol

measuring RNA integrity

can be visualized on a gel and using bioanalyzers

RNA integrity number (RIN): 2.5 means really degraded

data normalization

need to account for different loading quantities, different input number of cells, different transfection efficiency

two-tailed statistical test

no particular direction of expected difference is assumed

one tailed statistical test

should only be performed when there is clear evidence that the intervention should only act in one direction