Nucleotid.es benchmarking data has been available for seven months beginning with a single table of results for one organism to the current ~1,900 replicated benchmarks across multiple organisms. There have been discussions on the applicability of using containers for this kind of approach. In particular one question is how does this lend to reproducibility in science? For example Titus Brown wrote a blog post describing a post-apocalyptic world of binary containers and a discussion started on twitter by Shaun Jackman led to many replies.

Reproducibility

If we specifically discuss the Docker implementation when we talk about containers, and we almost certainly are, then I disagree with the description of these as 'binary blobs' that cannot be understood. You can run the docker export command to get a .tar of the container's file system. A Docker container is not compiled in the way a C or Java program is, instead it is a series of transparent file system layers. The act of containerising scientific software does not obscure how it works or make it inaccessible.

I think that containers make for more reproducible science. A Dockerfile allows for the opportunity to explicitly show the steps required to compile and organise the code. This is better than simply providing the source code alone. I can illustrate this with two example Dockerfiles for genome assembly containers:

I hope that we can agree that neither of these are trivial installs. The advantage of using a container is that it saves everyone else from having to do this. More importantly it saves them from having to learn how to do this. There is a case for encouraging non-computational biologists to learn to code but not for forcing them to debug g++ errors.

My favourite way to describe this is as "deduplicaton of agony". We can take the pain of compiling and installing, often buggy and undocumented, bioinformatics code which we force on our users and move that into a container. Instead of making everyone else do this work we can just ask the person who knows best to do it: the developer.

Standardisation

A second argument is that containers are 'black boxes' and cannot be used with other tools. For instance if I can give you a container with a working version of Spades or ABySS. This has solved the problem of getting the software to run but now you have to use it to produce results. This is what nucleotid.es aims to solve.

I have taken some of the most popular genome assemblers and containerised them. Importantly these have all been standardised behind the same interface so they can all be used in exactly the same way. This means that if you are using assembler X and then new data suggests that assembler Y is better, you can immediately switch between the two containers because they are run on the command line identically. You can use all these containers interchangeably in your own custom pipelines with minimal development effort.

Nucleotid.es provides the data to allow you to make the decisions about which assembler to use. I have taken Illumina reads from bacterial organisms of different sizes and %GC content and assembled them using the assembler containers. This provides concrete information how you might expect each assembler to perform on variety of data. Furthermore because the assembler was benchmarked as a container, the results are guaranteed to be the same for you as they were for me when I ran the analysis. This would not be the case if there was not a standardised interface because I couldn't share the container with you and expect you to reproduce my results. This is why standardisation is equally as important as containerisation.

Summary

At the JGI we produce thousands of assemblies and terabases of sequence data each year. The days where we manually improved genomes drafts have long passed. This may not be the case for smaller research labs, however as sequencing becomes cheaper and generated in ever larger volumes it soon will be. Nucleotid.es aims to allow us to make data-driven decisions about what kind of software to use that we can do assembly in-the-large.

Using containers allows us to reliably understand what kind of results we might expect from an assembler and when someone inevitably produces a better assembler we can identify it immediately and insert it into our pipelines, and allows you to do the same.