Transcriptomics Flashcards
What is transcriptomics?
Techniques used to quantify the transcriptome + to analyse the results
What is a transcriptome?
Complete set of RNAs produced in a cell or sample
No. of published papers since 1990 referring to:
- RNA sequencing
- RNA microarray
- ESTs
Increased rapidly since 2009
- 2016 = 3000
Decreased rapidly since 2014
-max 2250
Decreased since 2012
- max 1000
SAGE technologies
- method
Reverse transcripton of mRNA -> cDNA
cDNAs digested w/ specific restriction enzymes
-> into ‘Tag’ fragments
- > fragments concatenated
- > fragments sequenced via Sanger
= have the sequence of every protein coding gene
Microarray
- relies on
- basic method
Relies on fluorescent probes complementary to cDNA being tested
Set no. of genes being tested for expression levels
- DNA probes printed onto glass slide
- Wash over fluorescent cDNA
Competition microarray
Test strength of colours to give relative abundance of 1 sample compared to another
RNA-sequencing
- method
- mRNA fragmented into fragments
- Reverse transcription
= ds-cDNA fragments - High throughput sequencing
- Sequences aligned to a reference genome
- > reconstruct which genome regions were transcribed
RNA-sequencing
- uses
Annotate where expressed genes are
Relative expression levels
Any alternative splice variants
Microarray vs RNA-seq
M - low RNA input
R- high
M - high labour intensity
R - low
M - no prior knowledge
R - reference transcripts for probes needed
M - lower sensitivity
R - higher
Why did micrsarrarys lose out?
Probe affinity not 100% specific
(is variable)
Only gene regions for which a probe is included can be assessed
- makes it difficult to merge data from different experiments
Cannot identify new genes or alternative splicing variants
Why did RNA-seq win?
With Illumnia’s invention:
millions of short reads can be obtained from a single sample
Can identify new genes + novel splicing events
Can help study ncRNAs
RNA-seq transcriptome analyses
- method
- Isolate RNA in sample
- Enrich RNAs of interest
- Convert to cDNA
- Construct library
- Sequence
- Curate by quality control
(too much uncertainty = remove sequence or genome from analysis) - Align
- Calculate diversity + abundance
RNA-seq
- uses
Can map the short reads back to genome
-> calculate measure of expression
Can reveal polymorphisms
- take samples from many individuals
- > uncover where there’s a different nt
(only coding polymorphisms shown because using RNA)
What can we learn from transcriptomes?
Better understand a gene’s function by examining other genes up + down regulated when the gene is knocked out
Identify genes related to a particular condition by looking for genes up or down regulated
Can reconstruct splicing variants + their expression patterns
Can reconstruct functional interactions between genes by building co-expression networks
PAPER
Transcriptomes of parents identify parenting strategies + sexual conflict on a subsocial beetle
- by?
- studied?
D. Parker et al
Time spent by parent on the carcass with offspring before dispersing
PAPER
Transcriptomes of parents identify parenting strategies + sexual conflict on a subsocial beetle
- behavioural results?
Biparental females spent more time than biparental males
Uniparental females + males spent similar amount of time to biparental female
PAPER
Transcriptomes of parents identify parenting strategies + sexual conflict on a subsocial beetle
- transcriptome results?
Can see which genes relate to parental care + bonding behaviour
Caring genes:
Biparental male has much lower expression levels than the others
Post-caring:
Both parenting types + sexes had similar levels of expression
(Biparental female highest)
Gene interaction networks
(co-expression networks)
- how are they built?
Compare expression between every possible gene pair in many different conditions or time points
Quantify similarity in expression patterns for each pair using correlation coefficient
Set threshold to consider any 2 genes co-expressed or -vely co-expressed
Changes in gene interactions
- e.g. between humans and chimps
PAPER
- by?
Same genes but connexions absent in chimps
Oldham et. al (2006)
Challenges faced
Computational processing capacity
Bioinformatics tools for sequence alignment
Had to adapt statistical and mathematical methods
Sequencing capacity
BUT costs are decreasing
