Understanding the fasta File Format I'm a computer scientist teaching algorithms development in the Fall. One of the algorithms we teach is called Edit Distance, and our folklore is that it is used to compare RNA sequences (is this actually true in practice?).
I would like to have students implement the edit distance algorithm and run it on actual SARS-COV-2 sequences, so I'm trying to understand exactly what I get from the GenBank database. I downloaded this one: https://www.ncbi.nlm.nih.gov/nuccore/1798174254
I am looking at the genomic.fna file. So this is apparently the FASTA file format, and the lines that begin with &gt;MN988669.1 ... are comments. I see comments like:
&gt;MN988669.1 Severe acute respiratory syndrome coronavirus 2 isolate 2019-nCoV WHU02, complete genome

Followed by an RNA string. Is this the start of a new sequence for a different coronavirus specimen? So I could have students extract each of these and run edit distance and then produce a dendrogram or something? How do I find more information on where the samples came from? Is this the right file to be using, or should I use the gbff file? And are the PDB files interesting to me at all (I actually do know what PDB files are)?
Also, are there any recommended data sets where we could do something like track mutations in the virus (and see, e.g., that the NYC outbreak originated from Europe and not China)? Are there other useful algorithms / data that might be interesting for students to study in this vein? Specifically interesting to me would be graph-search algorithms, minimum spanning trees, and network flow. Also any NP-complete algorithms that we could run backtracking on. Obviously taking the theoretical study of algorithms to something as currently topical as the coronavirus has pedagogical value.
Thanks
EDIT:
Based on comments below here is what is taking shape.

Have students implement vanilla EditDistance (which there seems to be some disagreement about which algorithms are named what, so let's say insertions and deletions only, which I would call Longest Common Subsequence LCS). Then a variant that also does alignment (i.e. full Levenshtein distance computation, which I would call EditDistance, but Wikipedia calls the Needleman-Wunsch algorithm with a gap penalty of 1), then maybe Needleman-Wunsch with different gap penalties (if someone tells me what would make sense biologically).
Have students implement basic heirarchical clustering / phylogenetic tree generation algorithms a la https://www.ncss.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Hierarchical_Clustering-Dendrograms.pdf.
Have students run their sequence alignment variants and different clustering algorithms on SARS-COV-2 sequences and report on how the choices of parameters in 1 and 2 change the result and therefore potentially the analysis.
Ask some open ended written response questions on what this might mean for society, whether this introduces ethical considerations for an algorithms designer or are they doing just the math, etc.

My learning objectives (as they are now shaping up) are:

Students will understand that just because their algorithm comes with a proof of correctness, it doesn't mean it's the correct algorithm for the job.
Students will understand that different models / parameters to a model result in different outcomes, and thus even computational problems are not purely computational.
Doing theoretical computer science / mathematics is not devoid of ethical considerations.

I would very much appreciate thoughts on the above.
Your understanding of FASTA format is about right. The type of basic problem you're eluding to we term &quot;sequence alignment&quot;- edit distance might be okay for teaching but in practise we use other algorithms, e.g. you might be interested in the Needleman–Wunsch or Smith–Waterman algorithms. Richard Durbin et al. wrote a great book that covers these any much more https://www.amazon.com/Biological-Sequence-Analysis-Probabilistic-Proteins/dp/0521629713
Tracking mutations etc. requires more than just alignment though, see &quot;phylogenetics&quot; (i.e. building genetic trees) and &quot;variant calling&quot;. Also checkout what the nextstrain team is doing https://nextstrain.org/ncov/global
In general, looking for practical applications for your algorithms is great, but be very careful before drawing any real world conclusions about the coronavirus outbreak from such analyses