07 December 2015

Getting Started

Firstly, a few basic concepts. Conda is a portable package manager primarily for Python and precompiled binaries. Miniconda is the base system of conda. It includes a standard python and a few required dependencies such as readline and sqlite. In conda, a channel contains a set of software typically managed by the same group. Bioconda is a channel of conda focusing on bioinformatics software. The following shows how to install and use conda.

# Download the miniconda installation script for Python2
wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh
# Install conda; it will ask a few questions, including the installation path
sh Miniconda-latest-Linux-x86_64.sh
. ~/.bashrc   # or relogin to get PATH updated
# A few examples
conda info
conda search -c bioconda bwa
conda install -c bioconda bwa
conda list

In this example, option -c bioconda specifies that the package comes from the bioconda channel; otherwise the default channel is used.

Why conda?

Firstly, conda ships precompiled binaries, not source code. In my experience, shipping source code has not worked well on “managed” Linux clusters. Compiling source code is error prone, space wasting and time consuming.

Secondly, conda assumes the users do not have the root permission. Of course not all software can be installed without the root permission, but most enduser applications should not require the privilege.

Thirdly, conda is self contained. It puts all files in one root directory. It does not taint other system paths. Conda installs its own dependencies. Unless you want to build new packages, you do not need compilers or an existing python installation.

Fourthly, conda ships portable binaries. It uses rpath to make sure non-default dynamic libraries are loaded from a fixed relative path, not from the system paths. Rpath solves one of the portability difficulties. The other is libc, which can be solved by compiling on an old Linux system.

What does conda lack?

I could be wrong – it seems to me that conda does not provide a fully automated system to build on old systems. The package maintainers need to find a machine/VM/docker image with an old Linux system and have reasonable skills to create portable packages. I think a missing step is to allow package maintainers to (either manually or automatically) build tools on a CentOS5 AWS instance.

Another missing link is to allow maintainers to bypass the conda build process. For example, if I can build a portable package in my own way, it would be good to let me add a package without having to go through conda build. Conda could provide a script to check the structure of a package and test it on a CentOS5 machine.

A third problem is documentation. Conda is in fact simple, but its documentation is complex. It is too verbose for a beginner like me. I need to read through a lot of pages to understand the basis of conda. As a minor complaint, I do not like the documentation generator conda is using. I prefer the entire documentation or a sufficiently long section to be contained in a single web page such that I can go back and forth easily. The conda documentation has many separate pages, making navigation quite difficult. Ok, maybe it is just me.

Due to these problems, I suspect it might not be easy for every tool developer to contribute to conda. Bioconda is currently maintained by several experienced developers, but we need more to push it further. Automation is the key, in my opinion.

(PS: another concern is that conda is a commercial product of Continuum Analytics. What if the company fails to make profit or decides to discontinue conda? I know conda is an open-source project, but not every open-source project can healthly grow on its own.)

My thoughts

I have tried a few package management systems such as Linuxbrew and Gentoo Prefix, and checked Guix. Conda is the closest to the system in my mind and in serveral ways better. It is promising. I really hope it can be a success.

Appendix: my summary of Loman’s survey

Nick Loman conducted a survey, where part of the questions are about the difficulties in data analysis and running software. The answers are in free text. I have read through all of them and classified them into several categories. In the end, I collected 233 non-duplicate replies that have answers to the related questions. The leading difficulty is installation problems (132/233; category 3), followed by insufficient computing resources (88/233; 7 and 8), lack of interoperability (72/233; category 5) and bad documentations (69/233; category 6). Not surprisingly, software installation is less an issue to skilled researchers (27/62 for skill level 8 or above), but 44% is still a large percentage and the fraction of junior bioinformaticians is probably larger in the community that has not participated the survey.

The software installation problem is real.

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