Transcriptomics

Question 1

RNA loss/gain of function Causes

Answer 1

Splice site alteration, exon inclusion/exclusion, regulatory site alteration, altered translation

Question 2

RNA loss/gain of function causes

Answer 2

point mutation, insertion/deletion, alternative splicing/translation

Question 3

RNA loss/gain of function Result

Answer 3

Disturbed Protein Homeostasis, Canonical Protein Loss and Toxic Isoform Expression

Question 4

RNA and RNA-protein Interaction Mechanisms

Answer 4

RNA phase transitions, abnormal interactions, foci formation

Question 5

RNA and RNA-protein Interaction Causes

Answer 5

RNA expression dysregulation and repeat microsatellite

Question 6

RNA and RNA-protein Interaction Results

Answer 6

Functional loss: cellular body disruption, protein function disruption

Question 7

novel RNA species mechanism

Answer 7

RNAi dependent small RNA

Question 8

novel RNA species Causes

Answer 8

microsatellite repeat exapnsion

Question 9

novel RNA species Result

Answer 9

gene expression dysregulation

Question 10

RNA Structure Mechanisms

Answer 10

G-quadruplexes formed, regulatory factors sequestering and

Question 11

RNA Structure Causes

Answer 11

Microsatellite Repeat Expansions

Question 12

RNA Structure Results

Answer 12

Less efficient transition

Question 13

‘ome’ Meaning

Answer 13

Large scale systems. Eg; genome, metabolome

Question 14

‘omics’ Meaning

Answer 14

Study of systems through use of high-throughput technologies

Question 15

Transcriptomics Outline

Answer 15

Study of all expressed genes in genomes simulateneously. All RNA molecules; tRNA, mRNA, rRNA, miRNAs and small non-coding RNAs. Assessed by differential gene expression

Question 16

Differential Gene Analysis Methods

Answer 16

Next Gen Sequencing, Microarrays and Taqman cards

Question 17

Array Based Technologies Outline

Answer 17

Allow assessment of thousands genes simultaneously. Fluroescent probes bind sample vs control. Greater fluroescence = greater expression

Question 18

Taq-man Low Density Arrays

Answer 18

Used for micro-RNAs

Question 19

RNAseq Outline

Answer 19

RNA sequencing from experiment/library from thousands of gene at once.

Question 20

Why is RNAseq better then Array Based

Answer 20

Not limited to known genomes, lacks probe bias, detects novel transcripts, alternative splice variants

Question 21

RNAseq Aims

Answer 21

Differential expression, profiling, full transcriptome annotation, fusion gene discovery, miRNA annotations and non-coding. RNA cohort

Question 22

RNAseq Disadvantages

Answer 22

More data causes longer processing times

Question 23

Single Cell RNA Sequencing

Answer 23

Tissue dissection, tissue cellular composition, FAC sorting, single cell sequencing, cell expression profiles (heatmap profile), clustering and cell type identification

Question 24

RNASeq Experimental Design Considerations

Answer 24

single vs paired end sequencing, longer vs shorter read sequencing and higher sample number vs high read depth

Question 25

Single End Sequencing Outline

Answer 25

Sequence read from 1 end. More economic. Best for miRNAs

Question 26

Paired End Sequencing

Answer 26

Sequence read from both ends. More accurate can detect insertions or deletions

Question 27

Short Reads Outline

Answer 27

~50-100 bps. Small RNA sequencing. Used in gene expression profiling

Question 28

Longer Reads Outline

Answer 28

150+ bps. De novo sequencing. Genome profiling

Question 29

Required Sequencing Depth Considerations

Answer 29

Cost and replicates. Higher depth = more expensive = fewer replicates required (more accurate)

Question 30

RNA Types

Answer 30

Ribosomal (80%), Transfer (10-15%), mRNA (3-7%) and miRNAs (<0.5%)

Question 31

Number of Replicates Required Considerations

Answer 31

Cost, Time, Sample Availability, Sample Variability, Statistical Process

Question 32

Example of Samples

Answer 32

cell lines (4 replicates average), model organisms (4 replicates average) and human tissue (as many replicates as allowed)

Question 33

Batch Effects Outline

Answer 33

When samples are grouped together not based on biological characteristics but experimental design

Question 34

Examples of Groupings for Batch Effect

Answer 34

Lab test was performed in, hospital sample was collected in, lane sequence was run, time experiemnt was run, technician doing experiment

Question 35

RNA-seq Analysis Pipeline

Answer 35

Sequence Data, Sequence Quality Checks, Experiment metadata collection, Mapping reads, feature counting, Data structure & normalisation fitness checks, Differential comparison (2 group OR GLM based), results inspection and sanity checks

Question 36

DNA Library Construction

Answer 36

Fragmentation, PCR (amplification), adapter ligation, PCR

Question 37

RNA Library Construction

Answer 37

RT-PCR (cDNA), adaptor ligation, PCR

Question 38

Pre-Alignment Quality Control Outline (RNA library specific)

Answer 38

Identifies problems in library prep or sequencing. By assesing quality (Fast QC raw), trim sequence (BBMAp) and reassesing quality (Fast QC trimmed sequences)

Question 39

Alignment Pipeline

Answer 39

Index generation, Sequence aligning, Post-alignment QC, Alignment to counts and Dataset Parsing/Alignment

Question 40

Post Alignment QC

Answer 40

plot mapped reads and plot library sizes

Question 41

Library Mapped Reads Outline

Answer 41

Reads are plotted out and compared between library. Want consistent ration of mapped to unmapped as variance suggest sample issue

Question 42

Downstream Analysis Steps

Answer 42

Data normalisation, data exploration, differential expression analysis and pathway enrichment analysis

Question 43

DNA Normalisation

Answer 43

Data processing to make replicates in different experimental conditions comparable (different due to natural variation or technical error)

Question 44

Library Based Normalisation

Answer 44

Data adjusted for library size (removes risk of sample composition (gene number) bias)

Question 45

Fast QC Outline

Answer 45

Evaluates RNA quality (is there biases in data)

Question 46

RSUbeads Outline

Answer 46

Software that aligns sample with comaprison

Question 47

Limma/DEseq2/Ege R Outline

Answer 47

Gene expression differnces

Question 48

Data Ordination Outline

Answer 48

Plotting samples relative to distance from eachother eg PCA

Question 49

Scaling Factor (DSeq and edgeR) Normalisation

Answer 49

Identifies genes that don't change expression between samples and normalises against them

Question 50

Bias Correction Normalistion (RPKM and FKBPM)

Answer 50

Ensures sequence length isn't a factor. evaluates per kilobase per million reads (RPKM) and fragments per kilobase per million fragments mapped (FKBPM)

Question 51

Methods of RNASeq Visulisation

Answer 51

Boxplots and Volcano Plots

Question 52

Pathway Enrichment Analysis

Answer 52

Assigning biological meaning to gene lists (that are present more then can be explained by chance). Compare to libraries with genes annoted to assocuated biological pathways

Question 53

Pathway Enrichment Libraries

Answer 53

KEGG, GO and WP