Lecture 29 Flashcards Preview

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Flashcards in Lecture 29 Deck (16):

Measuring gene expression:

- Can be measured at various levels (and spatially and temporally)
- Changes in expression can also be detected


The phenotypic approach of understanding gene function:

- Forward genetics
- Identified genes by isolating mutants
- Clone the genes and characterise them


The candidate approach of understanding gene function:

- Reverse genetics
- Identify genes based on their predicted function
- Clone the genes and characterise them
- Limiting as it requires prediction of what it is involed


Identifying interesting mutants (phenotypic approach):

- Clone genes using
- Positional cloning: determine position of the gene, generate a physical map and use chromosome walking
- Complementation: DNA mediated transformation if you have the right organism
- Insertional mutagenesis: Tag genes by insertion of DNA then screen for phenotypes


Identifying interesting mutants (candidate approach):

- Clone the genes
- Candidate genes selected on the basis of the encoded gene product
- Information already obtained can help, and massive technical sequencing capacity now exists


Cloning by homology (good for short genes):

- If your organism has been sequenced, clone the gene by PCR
- Identify probable homologue by search databases for similarity and use databases as subjects
- If your organism doesn't have a sequence, sequence its genome then clone by PCR, identify genes of interest in related organisms, using conservation


Identifying conserved motifs and using homology:

- Clone the gene of interest, and identify genes in other organisms that have homology with our gene of interest
- Back-translate protein to DNA
- Design degenerate PCR primers
- Restriction nuclease digestion and DNA cloning, probe the library using your gene of interest and identify where your gene of interest is found


Annotating genes:

- Compare genomic with cDNA to identify introns and exons 5' UTRS and polyA sites


Gene organisation:

- One gene can encode multiple transcripts (mRNA) which can encode multiple proteins (multiple start sites, polyA sites and splicing)
- The number of genes doesn't necessarily correlate with complexity
- Different gene expression is also a factor to consider


Determining gene structure:

- Transcript mapping via PCR and RT-PCR, Rapid amplification of cDNA ends (RACE), comparison of genomic and cDNA
- Identify open reading frames from consensus sequences or conservation



- ds cDNA is a copy of the original mRNA
- hybridise mRNA with polyT primers
- Make complementary DNA copy with RTs
- Degrade RNA with RNase
- Synthesise a second cDNA strand using DNA polymerase
- The RNA fragment acts as a primer
- can work out the intron sites, 3' end and polA tail



- Based on comparing the product of PCR reactions
- PCR mRNA and the genomic sequences
- the Gene produce includes intron sequences
- The mRNA produce lacks intron sequences
- The size different indicates the size of the intron, and lining them up can determine the location of the intron!



- Working out the 5' and 3' ends
- Reverse transcription reaction using a gene specific primer
- Use another nested primer to amplify the fragment of interest
- Use PCR
- This can be done one at a time, or all at once
- Random primers can be used to create short fragments of every transcript


EST libraries:

- Matures transcripts that cover the entire genome
- Random primers are used so short fragments of every part of the genome will be able to be generated into a library


Predicting ORFs:

- Know the direction, so only need 3 frames
- Look for ATG's that could be the start
- Look for termination codons
- Find blocks that create proteins, and see what kind of protein this will create
- Not ideal for an entire genome, but ok for a single gene


Predicting ORFs for a single gene:

- Is this segment of DNA coding?
- What is it's reading frame?
- Search for consensus splice signals, stop codons, conserved domains/motifs,
- Either search databases of the sequence, or databases of similar organisms
- When aiming to identify function we can use conserved domains or motifs between organisms that show sequence homology
- Mis-annotations can be identified