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Samenvatting Regulome HC2 $6.16   Add to cart

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Samenvatting Regulome HC2

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College-uitwerkingen van het onderdeel regulome, hoorcollege 2. Inclusief afbeeldingen

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  • August 18, 2020
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  • 2018/2019
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REGULOME – Lecture 2
COO1
p-value en log fold change bepalen drempel vanaf wanneer het differential gene expression is
 Always statistical significant
p-value (adjusted for multiple testing) < 0.05
P-value (adjusted, FDR) < 0.1
 Fold Change (log2 scale): how much change do I consider biologically relevant?
>1 or <-1
Too little genes --> accept to 0.6 (50% op or 25% down)
Ignore it completely --> 0
 Amount of genes
Between 200 and 2000
Good for analysis with Gene Ontology or pathway analysis
 Pathway analysis seperately for up- and downregulated genes!



RNA sequencing, transcriptome and
expression quantification
Part 2: RNA-seq (RNA-seq data analysis)
Analyse whole transcriptomes rather efficiently




Start with RNA, not every RNA species that is in the cell (95% is ribosomal RNA)
How to get rid of ribosomal RNA:
o Fish out with probes
o Select only poly-A mRNAs
Fragmentation to short pieces (enzymes, heat) to 100s of bp
Reverse Transcription --> cDNA = sequence library
(Eerst RT dan frag of omgekeerd)

Sequence library has short, random fragments of RNA

, 1: preselect mRNA species,
2: short sequence reads from random parts of mRNAs

After analysis try to reconstruct original transcripts

Benefits (vs. Microarray)
 Independence on prior knowledge (just sequence) --> discover new things
 High resolution (every single nucleotide is sequenced), good sensitivity (efficient library
preparation, aslo lowly expressed genes), large dynamic range (no problems with highly
expressed genes, ratios reflect real abundance well)
 Unravel previously inaccesible complexities (in transcripts)
Challenge
 Interpretation is not straightforward
 Procedures continue to evolve, no good standards (how many reads?)

Applications (what can you detect?)
 Differential gene expression
 Gene fusions
 Alternative splicing
 Novel transcribed genes (no prior knowledge)
 Allele-specific expression (one variant more abundantly expressed)
 RNA editing (RNA not 100% stable, some bases can be edited later)
 Transcriptome for non-model organisms (sequence any organisms you want without
knowing its genome)

From reads to differential expression




Raw sequence data (FASTQ files) --> quality check
Reads mapping. QC for mapped reads

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