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Genomics > Transcriptomics > Flashcards

Transcriptomics Flashcards

1
Q

What is transcriptomics?

A

Techniques used to quantify the transcriptome + to analyse the results

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2
Q

What is a transcriptome?

A

Complete set of RNAs produced in a cell or sample

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3
Q

No. of published papers since 1990 referring to:

  • RNA sequencing
  • RNA microarray
  • ESTs
A

Increased rapidly since 2009
- 2016 = 3000

Decreased rapidly since 2014
-max 2250

Decreased since 2012
- max 1000

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4
Q

SAGE technologies

- method

A

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

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5
Q

Microarray

  • relies on
  • basic method
A

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
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6
Q

Competition microarray

A

Test strength of colours to give relative abundance of 1 sample compared to another

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7
Q

RNA-sequencing

- method

A
  1. mRNA fragmented into fragments
  2. Reverse transcription
    = ds-cDNA fragments
  3. High throughput sequencing
  4. Sequences aligned to a reference genome
    - > reconstruct which genome regions were transcribed
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8
Q

RNA-sequencing

- uses

A

Annotate where expressed genes are

Relative expression levels

Any alternative splice variants

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9
Q

Microarray vs RNA-seq

A

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

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10
Q

Why did micrsarrarys lose out?

A

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

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11
Q

Why did RNA-seq win?

A

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

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12
Q

RNA-seq transcriptome analyses

- method

A
  1. Isolate RNA in sample
  2. Enrich RNAs of interest
  3. Convert to cDNA
  4. Construct library
  5. Sequence
  6. Curate by quality control
    (too much uncertainty = remove sequence or genome from analysis)
  7. Align
  8. Calculate diversity + abundance
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13
Q

RNA-seq

- uses

A

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)

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14
Q

What can we learn from transcriptomes?

A

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

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15
Q

PAPER
Transcriptomes of parents identify parenting strategies + sexual conflict on a subsocial beetle
- by?
- studied?

A

D. Parker et al

Time spent by parent on the carcass with offspring before dispersing

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16
Q

PAPER
Transcriptomes of parents identify parenting strategies + sexual conflict on a subsocial beetle

  • behavioural results?
A

Biparental females spent more time than biparental males

Uniparental females + males spent similar amount of time to biparental female

17
Q

PAPER
Transcriptomes of parents identify parenting strategies + sexual conflict on a subsocial beetle

  • transcriptome results?
A

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)

18
Q

Gene interaction networks
(co-expression networks)
- how are they built?

A

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

19
Q

Changes in gene interactions
- e.g. between humans and chimps

PAPER
- by?

A

Same genes but connexions absent in chimps

Oldham et. al (2006)

20
Q

Challenges faced

A

Computational processing capacity

Bioinformatics tools for sequence alignment

Had to adapt statistical and mathematical methods

Sequencing capacity
BUT costs are decreasing

Genomics (6 decks)

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