The Kerblam! philosophy

Hello! This is the maintainer. This article covers the design principles behind how Kerblam! functions. It is both targeted at myself - to remind me why I did what I did - and to anyone who is interested in the topic of managing data analysis projects.

Reading this is not at all necessary to start using Kerblam!. Perhaps you want to read the tutorial instead.

I am an advocate of open science, open software and of sharing your work as soon and as openly as possible. I also believe that documenting your code is even more important than the code itself. Keep this in mind when reading this article, as it is strongly opinionated.

The first time I use an acronym I'll try to make it bold italics so you can have an easier time finding it if you forget what it means. However, I try to keep acronyms to a minimum.

Introduction

After three years doing bioinformatics work as my actual job, I think I have come across many of the different types of projects that one encounters as a bioinformatician:

1. You need to analyse some data either directly from someone or from some online repository. This requires the usage of both pre-established tools and new code and/or some configuration.
   • For example, someone in your research group performed RNA-Seq, and you are tasked with the data analysis.
2. You wish to create a new tool/pipeline/method of analysis and apply it to some data to both test its performance and/or functionality, before releasing the software package to the public.

The first point is data analysis. The second point is software development. Both require writing software, but they are not exactly the same.

You'd generally work on point 2 like a generalist programmer would. In terms of how you work, there are many different workflow mental schemas that you can choose from, each with its following, pros, and cons. Simply search for coding workflow to find a plethora of different styles, methods and types of way you can use to manage what to do and when while you code.

In any case, while working with a specific programming language, you usually have only one possible way to layout your files. A python project uses a quite specific structure: you have a pyproject.toml/setup.py, a module directory¹... Similarly, when you work on a Rust project, you use cargo, and therefore have a cargo.toml file, a /src directory...

At its core, software is a collection of text files written in a way that the computer can understand. The process of laying out these files in a logical way in the filesystem is what I mean when I say project layout (PL). A project layout system (PLS) is a pre-established way to layout these files. Kerblam! is a tool that can help you with general tasks if you follow the Kerblam! project layout system.

Since we are talking about code, there are a few characteristics in common between all code-centric projects:

• The changes between different versions of the text files are important. We need to be able to go back to a previous version if we need to. This can be due by a number of things: if we realize that we changed something that we shouldn't have, if we just want to see a previous version of the code or if we need to run a previous version of the program for reproducibility purposes.
• Code must be documented to be useful. While it is often sufficient to read a piece of code to understand what it does, the why is often unclear. This is even more important when creating new tools: a tool without clear documentation is unusable, and an unusable tool might as well not exist.
• Often, code has to be edited by multiple people simultaneously. It's important to have a way to coordinate between people as you add your edits in.
• Code layout is often driven by convention or by requirements of build systems/ interpreters/external tools that need to read your code. Each language is unique under this point.

From these initial observations we can start to think about a generic PLS. Version control takes care of - well - version control and is essential for collaboration. Version control generally does not affect the PL meaningfully. However, version control often does not work well with large files, especially binary files.

Design principle A: We must use a version control system.

Design principle B: Big binary blobs bad²!

Design principle A makes it so that the basic unit of our PLS is the repository. Our project therefore is a repository of code.

As we said, documentation is important. It should be versioned together with the code, as that is what it is describing and it should change at the same pace.

Design principle C: Documentation is good. We should do more of that.

Code is read more times than it is written, therefore, it's important for a PLS to be logical and obvious. To be logical, one should categorize files based on their content, and logically arrange them in a way that makes sense when you or a stranger looks through them. To be obvious, the categorization and the choice of folder and file names should make sense at a glance (e.g. the 'scripts' directory is for scripts, not for data).

Design principle D: Be logical, obvious and predictable

Scientific computing needs to be reproduced by others. The best kind of reproducibility is computational reproducibility, by which the same output is generated given the same input. There are a lot of things that you can do while writing code to achieve computational reproducibility, but one of the main contributors to reproducibility is still containerization.

Additionally, being easily reproducible is - in my mind - as important to being reproducible to begin with. The easier it is to reproduce your work, the more "morally upright" you will be in the eyes of the reader. This has a lot of benefits, of course, with the main one being that you are more resilient to backlash in the inevitable case that you commit an error.

Design principle E: Be (easily) reproducible.

Structuring data analysis

While structuring single programs is relatively straightforward, doing the same for a data analysis project is less set in stone. However, given the design principles that we have established in the previous section, we can try to find a way to fulfill all of them for the broadest scope of application possible.

To design such a system, it's important to find what are the points in common between all types of data analysis projects. In essence, a data analysis project encompasses:

• Input data that must be analysed in order to answer some question.
• Output data that is created as a result of analysing the input data.
• Code that analyses that data.
• It is often the case that data analysis requires many different external tools, each with its own set of requirements. These sum with the requirements of your own code and scripts.

"Data analysis" code is not "tool" code: it usually uses more than one programming language, it is not monolithic (i.e builds up to just one "thing") and can differ wildly in structure (from just one script, to external tool, to complex pieces of code that run many steps of the analysis).

This complexity results in a plethora of different ways to structure the code and the data during the project.

I will not say that the Kerblam! way is the one-and-only, cover-all way to structure your project, but I will say that it is a sensible default.

Kerblam!

The kerblam! way to structure a project is based on the design principles that we have seen, the characteristics of all data analysis project and some additional fundamental observations, which I list below:

1. All projects deal with input and output data.
2. Some projects have intermediate data that can be stored to speed up the execution, but can be regenerated if lost (or the pipeline changes).
3. Some projects generate temporary data that is needed during the pipeline but then becomes obsolete when the execution ends.
4. Projects may deal with very large data files.
5. Projects may use different programming languages.
6. Projects, especially exploratory data analysis, require a record of all the trials that were made during the exploratory phase. Often, one last execution is the final one, with the resulting output the presented one. Having these in mind, we can start to outline how Kerblam! deals with each of them.

Data

Points 1, 2, 3 and 4 deal with data. A kerblam! project has a dedicated data directory, as you'd expect. However, kerblam! actually differentiates between the different data types. Other than input, output, temporary and intermediate data, kerblam! also considers:

• Remote data is data that can be downloaded at runtime from a (static) remote source.
• Input data that is not remote is called precious, since it cannot be substituted if it is lost.
• All data that is not precious is fragile, since it can be deleted with little repercussion (i.e. you just re-download it or re-run the pipeline to obtain it again.

To make the distinction between these different data types we could either keep a separate configuration that points at each file (a git-like system), or we specify directories where each type of file will be stored.

Kerblam! takes both of these approaches. The distinction between input/output/temp/intermediate data is given by directories. It's up to the user to save each file in the appropriate directory. The distinction between remote and local files is however given by a config file, kerblam.toml, so that Kerblam! can fetch the remote files for you on demand³. Fragile and precious data can just be computed from knowing the other two variables.

The only data that needs to be manually shared with others is precious data. Everything else can be downloaded or regenerated by the code. This means that the only data that needs to be committed to version control is the precious one. If you strive to keep precious data to a minimum - as should already be the case - analysis code can be kept tiny, size-wise. This makes Kerblam! compliant with principle B⁴ and makes it easier (or in some cases possible) to be compliant with principle A⁵.

Execution

Points 5 and 6 are generally covered by pipeline managers. A pipeline manager, like snakemake or nextflow, executes code in a controlled way in order to obtain output files. While both of these were made with data analysis in mind, they are both very powerful and very "complex"⁶ and unwieldy for most projects.

Kerblam! supports simple shell scripts (which in theory can be used to run anything, even pipeline managers like nextflow or snakemake) and makefiles natively. make is a quite old GNU utility that is mainly used to build packages and create compiled C/C++ projects. However, it supports and manages the creation of any file with any creation recipe. It is easy to learn and quick to write, and is at the perfect spot for most analyses between a simple shell script and a full-fledged pipeline manager.

Kerblam! considers these executable scripts and makefiles as "pipes", where each pipe can be executed to obtain some output. Each pipe should call external tools and internal code. If code is structured following the unix philosophy, each different piece of code ("program") can be reused in the different pipelines and interlocked with one another inside pipelines.

With these considerations, point 6 can be addressed by making different pipes with sensible names, saving them in version control. Point 5 is easy if each program is independent of each other, and developed in its own folder. Kerblam! appoints the ./src directory to contain the program code (e.g. scripts, directories with programs, etc...) and the /src/workflows directory to contain shell scripts and makefile pipelines.

These steps fulfill the design principle D⁷: Makefiles and shell scripts are easy to read, and having separate folders for pipelines and actual code that runs makes it easy to know what is what. Having the rest of the code be sensibly managed is up to the programmer.

Principle E⁸ can be messed up very easily, and the reproducibility crisis is a symptom of this. A very common way to make any analysis reproducible is to package the execution environment into containers, executable bundles that can be configured to do basically anything in an isolated, controlled environment.

Kerblam! projects leverage docker containers to make the analysis as easily reproducible as possible. Using docker for the most basic tasks is relatively straightforward:

• Start with an image;
• Add dependencies;
• Copy the current environment;
• Setup the proper entrypoint;
• Execute the container with a directory mounted to the local file system in order to extract the output files as needed.

Kerblam! automatically detects dockerfiles in the ./src/dockerfiles directory and builds and executes the containers following this simple schema. To give as much freedom to the user as possible, Kerblam! does not edit or check these dockerfiles, just executes them in the proper environment and the correct mounting points.

The output of a locally-run pipeline cannot be trusted as it is not reproducible. Having Kerblam! natively run all pipelines in containers allows development runs to be exactly the same as the output runs when development ends.

To be compliant with principle D⁷, knowing what dockerfile is needed for what pipeline can be challenging. Kerblam! requires that pipes and the respective dockerfiles must have the same name.

Documentation

Documentation is essential, as we said in principle C⁹. However, documentation is for humans, and is generally well established how to layout the documentation files in a repository:

• Add a README file.
• Add a [LICENSE], so it's clear how other may use your code.
• Create a /docs folder with other documentation, such as CONTRIBUTING guides, tutorials and generally human-readable text needed to understand your project.

There is little that an automated tool can do to help with documentation. There are plenty of guides online that deal with the task of documenting a project, so I will not cover it further.