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The third line has the name attached after the "+", which is an artifact of a storage method that we won't go into here. However, everything else is basically the same - read name, followed by sequence, strand, and quality scores. However, note the string of A's towards the end. This is because, as for many very short RNAs, our read extends past the actual RNA fragment. In this case, the 'adapter' sequence is obvious - it is just a poly-A string. However, what if it wasn't? Indeed, working with publicly available small RNA data, you will often not know what the adapter is (it may not be obvious), or you might not even know if for data coming from your lab (if we're being honest).
So, we would like to use an alignment strategy that can intelligently ignore the parts that won't align to a reference (the 'adapter') and align correctly the parts that align well. This is called a 'local' alignment, in contrast to a 'global' alignment, which would count the 'mismatches' in the adapter against the alignment score. Fortunately, you have already used a local-alignment-capable aligner in this class. Tophat2 runs on the Bowtie2 alignment engine, which (if used directly, i.e. not with Tophat2), can perform local alignment. So, that won't be a problem.
But wait!
Exercise #2: Ribonucleoprotein Immunoprecipitation and Sequencing (RIP-seq)
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