Parallel C++ on AWS Lambda for CRISPR
benchling.engineering
benchling.engineering
The only other perk I've found is that the serverless billing model makes it easier to estimate costs.
You might jokingly say we are reintroducing the servers into the serverless concept :D
But I think we are just giving freedom back to the users.
[0]: https://iplayer.engineering/evaluating-tensorflow-models-in-...
> Because we run 200 invocations or so in parallel we’ll only need to download the model once and save it there
From my own reading of the Lambda docs, it seems that a simultaneous request for the same Lambda may or may not spin up a new container, ie while serial requests within the 15 minute timeout will likely reuse the same instance with the same frozen/cached tmp data, parallel requests do not have that assumption.
Was this your finding in practice? If so, were your CloudWatch “keep warm” events a series of just 1 Lambda invocation ~10/15min apart of 200 simultaneous requests serially spaced to keep the instances spun up?
This is true. I learned this a hard way some time ago while trying to understand why our lambda function crashes from time to time. It turned out we didn't clean the /tmp folder and we used the space quite extensively. There's a good article on this:
https://aws.amazon.com/blogs/compute/container-reuse-in-lamb...
I do not remember having had issues with it, but honestly, I don't think I actually have stats on that anymore.
I've just checked in S3, but it doesn't look like we have request or data transfer metrics enabled on the model bucket. I may enable those next week to monitor the effectiveness of our strategy better.
Good luck!
You're right - conceptually the CRISPR search problem and DNA sequence alignment are related. In both, you're looking for place where two (or more) sequences are very similar. I would say there are two major differences.
The first is in the goal of the search. Typically, alignment tools try to find the best positional alignment for two (or more) sequences. The CRISPR search problem is to find every possible match above some similarity threshold.
There are also a few constraints on the CRISPR search problem that allow us to make this much faster than a general DNA sequence alignment tool:
1) We know that that guide sizes tend to be very small (~20bp) 2) Part of the guide must match exactly (the PAM site), allowing us to restrict our search even further. 3) We don't need to worry about insertions or deletions in our search.
Using those three constraints, we can do this search a lot faster than a more general DNA alignment tool!
How do you deal with natural variation in the genome? Can you be sure your gRNA doesn't target an essential locus in some percentage people who carry a particular allele? The data to solve this is out there (1000 Genomes for instance).
Edit: excuse me, I appreciate that you are using a collection of whole genomes as your target. Will this reliably scale to thousands or millions of genomes and likely recombinations between them?
Adding to Vineet's response, Benchling has both CRISPR search and sequence alignment, and they have different use cases. CRISPR search uses the custom algorithm described in the article (and the "last post" linked at the top), while alignments use the two popular alignment tools Clustal Omega[1] and MAFFT[2]. It's certainly true that the general alignment problem is complex had has undergone plenty of research around performance and producing good results, which is why we used an off-the-shelf tool for computing the alignment itself (but a custom UI for viewing the result).