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> How do you prevent mutations from ruining the data? In much the same way as is done when using existing data storage systems, as well as in telecommunications
by jaskcheng 8y ago
> How do you prevent mutations from ruining the data?
In much the same way as is done when using existing data storage systems, as well as in telecommunications: error detection and correction.
A recent approach (https://www.biorxiv.org/content/early/2018/06/16/348987 https://www.biorxiv.org/content/early/2018/06/16/348987) includes synchronization nucleotides. This constrains the sequence space, aiding recovery of data from multiple strands, even if those strands contain errors (provided that they do not all include the same error).
Additionally, different types of errors are more likely in certain contexts, depending on the synthesis and storage approach. This particular paper utilizes enzymatic synthesis (which is the new hotness in DNA technology) and the authors model these error probabilities.
Combined, with a couple of other nifty tricks, they demonstrate ~30% error tolerance across 10 strand variants (i.e. copies of the same data containing errors), albeit for a very short sequence.
> If the DNA isn't being biologically replicated, why use DNA over any other 4 distinct chemical structures?
In many cases, the DNA is being biologically replicated -- not within organisms, but biochemically via enzymatic reactions. One attractive feature of DNA storage aside from its density is that it's cheap to make a ton of copies. After all, Nature does this all the time as cells divide. And given the cost of mutations (most are detrimental), DNA polymerases have evolved (ironically?) to be high-fidelity. Something like 1 error in 10^8 base pairs.
And that touches upon a broader point: DNA is an attractive substrate for alternative storage relative to other polymers because its so widely utilized in nature and, as a direct result, we've developed a lot of technology and infrastructure around it. However, much of this development has been around sequencing (reading), alignment and assembly algorithms, and, though still sorely lacking, biological interpretation. It's exciting to see growing interest over the past few years in DNA encoding and synthesis. Remains to be determined which approach(es) become workable standards for the field.