When I do the visualization of my sequence alignment data, I noticed that I could get the consensus. Then, what's the difference between sequence alignment and reference genome assembling?
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Building your sequence de novo generally requires higher coverage, and you'll end up with lots of contigs that can't be bridged due to repetitive or non-complex sequence. Aligning to a very closely related genome will allow you to easily find discrepancies, even if coverage is not so great. If your coverage is, say, an even 5x, you could call a lot of SNPs. I've found plenty of real, sanger-verified SNPs with 3x coverage. But no de novo assembler will do anything with that.
Also, for a lot of applications, what you really want are the discrepancies between your sample and a reference, so aligning to that reference is a much easier way to find them, rather than building a de novo sample reference, and comparing that to something. Also, de novo won't be totally accurate if your sample is heterozygous, or a mix of samples; at least not Velvet, which is a popular one, and the one I'm most familiar with. Whereas with an aligner, it isn't hard to see that so many reads show one allele at a locus, and so many reads show another.
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The immune system’s power comes from its genetic diversity, allowing myriad threats to be neutralized through first recognizing foreign antigens. That diversity is also what makes the immune system so difficult to study. Recent advances in sequencing technology and computational biology, however, are giving researchers new tools to understand immune responses and immune-related diseases in greater detail.
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