Read a couple of articles this week Inching closer to a DNA-based file system in ArsTechnica and DNA storage gets random access in IEEE Spectrum. Both of these seem to be citing an article in Nature, Random access in large-scale DNA storage (paywall).
We’ve known for some time now that we can encode data into DNA strings (see my DNA as storage … and Genomic informatics takes off posts).
However, accessing DNA data has been sequential and reading and writing DNA data has been glacial. Researchers have started to attack the sequentiality of DNA data access. The prize, DNA can store 215PB of data in one gram and DNA data can conceivably last millions of years.
Researchers at Microsoft and the University of Washington have come up with a solution to the sequential access limitation. They have used polymerase chain reaction (PCR) primers as a unique identifier for files. They can construct a complementary PCR primer that can be used to extract just DNA segments that match this primer and amplify (replicate) all DNA sequences matching this primer tag that exist in the cell.
DNA data format
The researchers used a Reed-Solomon (R-S) erasure coding mechanism for data protection and encode the DNA data into many DNA strings, each with multiple (metadata) tags on them. One of tags is the PCR primer tag header, another tag indicates the position of the DNA data segment in the file and an end of data tag that is the same PCR primer tag.
The PCR primer tag was used as sort of a file address. They could configure a complementary PCR tag to match the primer tag of the file they wanted to access and then use the PCR process to replicate (amplify) only those DNA segments that matched the searched for primer tag.
Apparently the researchers chunk file data into a block of 150 base pairs. As there are 2 complementary base pairs, I assume one bit to one base pair mapping. As such, 150 base pairs or bits of data per segment means ~18 bytes of data per segment. Presumably this is to allow for more efficient/effective encoding of data into DNA strings.
DNA strings don’t work well with replicated sequences of base pairs, such as all zeros. So the researchers created a random sequence of 150 base pairs and XOR the file DNA data with this random sequence to determine the actual DNA sequence to use to encode the data. Reading the DNA data back they need to XOR the data segment with the random string again to reconstruct the actual file data segment.
Not clear how PCR replicated DNA segments are isolated and where they are originally decoded (with a read head). But presumably once you have thousands to millions of copies of a DNA segment, it’s pretty straightforward to decode them.
Once decoded and XORed, they use the R-S erasure coding scheme to ensure that the all the DNA data segments represent the actual data that was encoded in them. They can then use the position of the DNA data segment tag to indicate how to put the file data back together again.
What’s missing?
I am assuming the cellular data storage system has multiple distinct cells of data, which are clustered together into some sort of organism.
Each cell in the cellular data storage system would hold unique file data and could be extracted and a file read out individually from the cell and then the cell could be placed back in the organism. Cells of data could be replicated within an organism or to other organisms.
To be a true storage system, I would think we need to add:
- DNA data parity – inside each DNA data segment, every eighth base pair would be a parity for the eight preceding base pairs, used to indicate when a particular base pair in eight has mutated.
- DNA data segment (block) and file checksums – standard data checksums, used to verify and correct for double and triple base pair (bit) corruption in DNA data segments and in the whole file.
- Cell directory – used to indicate the unique Cell ID of the cell, a file [name] to PCR primer tag mapping table, a version of DNA file metadata tags, a version of the DNA file XOR string, a DNA file data R-S version/level, the DNA file length or number of DNA data segments, the DNA data creation data time stamp, the DNA last access date-time stamp,and DNA data modification data-time stamp (these last two could be omited)
- Organism directory – used to indicate unique organism ID, organism metadata version number, organism unique cell count, unique cell ID to file list mapping, cell ID creation data-time stamp and cell ID replication count.
The problem with an organism cell-ID file list is that this could be quite long. It might be better to somehow indicate a range or list of ranges of PCR primer tags that are in the cell-ID. I can see other alternatives using a segmented organism directory or indirect organism cell to file lists b-tree, which could hold file name lists to cell-ID mapping.
It’s unclear whether DNA data storage should support a multi-level hierarchy, like file system directories structures or a flat hierarchy like object storage data, which just has buckets of objects data. Considering the cellular structure of DNA data it appears to me more like buckets and the glacial access seems to be more useful to archive systems. So I would lean to a flat hierarchy and an object storage structure.
Is DNA data is WORM or modifiable? Given the effort required to encode and create DNA data segment storage, it would seem it’s more WORM like than modifiable storage.
How will the DNA data storage system persist or be kept alive, if that’s the right word for it. There must be some standard internal cell mechanisms to maintain its existence. Perhaps, the researchers have just inserted file data DNA into a standard cell as sort of junk DNA.
If this were the case, you’d almost want to create a separate, data nucleus inside a cell, that would just hold file data and wouldn’t interfere with normal cellular operations.
But doesn’t the PCR primer tag approach lend itself better to a key-value store data base?
Photo Credit(s): Cell structure National Cancer Institute
Guide to Open VMS file applications
Unix Inodes CSE410 Washington.edu
Key Value Databases, Wikipedia By Clescop – Own work, CC BY-SA 4.0, Link
Read an article the other day from MIT News (
The Data Civilizer takes all this tabular data and analyzes it all, column by column, and calculates descriptive characterization statistics for each column.
