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As discovered in previous sections, tophat maps your data to your reference in a transcriptome-aware manner, that will also identify junctions. We've already looked at how you can tell it to identify novel junctions.
tophat recap: options for novelty
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What does cufflinks do? and how does it do it?
For each separate dataset representing a specific replicate and condition, cufflinks assembles a map of genomic areas enriched in aligned reads. cuffmerge then takes the set of individual assemblies and merges them into a consensus assembly for all the provided datasets. The consensus may include known splice variant annotations if you have provided those to the program
assembly figure
rabt figure
OUTput files
CUFFMERGE CUFFCOMPARE
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Next, cuffdiff uses the consensus splice variant annotations (and/or the known splice variants) to quantify expression levels of genes and isoforms, using FPKM (fragments per kilobase per million reads) metrics.
Step 2: Run cufflinks
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