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Open source, computers, Africa and other more (or less) interesting stuff.

Tag: GenEpi

DatABEL v0.9-6 has been published on CRAN

This morning version 0.9-6 of the DatABEL R package was published on CRAN. This is only a minor update that consists of a few small changes and one bug fix. See the official announcement for more information.

DatABEL is an R package that allows users to access files with large matrices (of several gigabytes or more in size) in a fast and efficient manner. The package is mainly used for genome-wide association analyses using e.g. ProbABEL or OmicABEL.

ProbABEL v0.4.4 released

It was quite a long time in the making and then a bunch of other stuff came in between, but I finally managed to release v0.4.4 of ProbABEL!

ProbABEL is a toolset for doing fast, memory (RAM) efficient genome-wide regression tests.

This is a bugfix release, but a major one for those who use the Cox proportional hazards regression module. Thanks to some of our users on the GenABEL forum, a serious bug leading to way to many NaN’s in the output was discovered, fixed and tested. This is one of the best examples of community collaboration I have seen in the GenABEL project.

Another bug fixed in this release is one that caused a failed install on MacOS X and FreeBSD. Again a bug reported on the forum by one of our users. Great work!

Uploads to Debian and the Ubuntu PPA are coming ASAP.

Now, let’s get ready for a new feature release, which will include p-value calculation (a long-standing feature request) and major speed-ups (implemented by former colleague Maarten Kooyman). Time to get to work ;-)!

ProbABEL v0.4.2 released

During the Christmas holidays I released a new version of ProbABEL (v0.4.2). The official release announcement can be found here. ProbABEL is a toolset that allows running GWAS (Genome-Wide Association Studies) in a fast and efficient manner. It implements regression using the linear, logistic or Cox proportional hazards models.

This version is mostly a bug fix release. The most important user-visible change is the fact that the ‘official’ name for the wrapper script that runs a GWAS over a range of chromosomes is now called probabel instead of probabel.pl. This change was induced by my attempts to get ProbABEL packaged in the Debian Linux repositories. One of the warnings that occurred during the package creation process was a Lintian warning that said that scripts with ‘language extensions’ are not allowed. There are several reasons for that, but the one I found most compelling was the fact that the user shouldn’t be concerned with the programming/scripting language we used to write it in. Moreover, being ‘agnostic’ in this matter also allows us to write such a script in a different language.
Of course, we have left the original name in place (via a symlink) in order not to disrupt any current pipelines. If the user runs the script with the old name a warning appears, urging him/her to start using the new name and that the old name will be deprecated in the future.

In the mean time, ProbABEL v0.4.1 has been accepted in Debian (unstable) and as of today it is also available in Debian ‘testing’. Lots of thanks to the Debian Med team that helped me a lot in preparing the .deb package. Note that the package has been split up in probabel (architecture-dependent files) and probabel-examples (with architecture independent files: the examples). See the Debian Package Tracking System page for ProbABEL for more details of the package.

From Debian the package has trickled down to Ubuntu as well (Launchpad page here), so it will be available by default in the next Ubuntu release (14.04, a.k.a. Trusty Tahr).

Doing a quick fixed-effects meta-analysis using the Rmeta package

This is a quick example of how to do a fixed meta-analysis using the R package Rmeta, just so I dont have to look it up again next time:

## Create data frame containing betas and standard errors
df <- data.frame()
df <- rbind(df, c(2., 0.2))
df <- rbind(df, c(2.5, 0.4))
df <- rbind(df, c(2.2, 0.2))
 
## Add study names
df <- cbind(df, c("study 1", "study 2", "study 3"))
 
colnames(df) <- c("beta", "se_beta", "name") 
 
## Do the meta-analysis 
ms <- meta.summaries(df$beta, df$se_beta, names=df$name)
 
## Add some colors
mc <- meta.colors(summary="darkgreen", zero="red")
 
## Make a forest plot
plot(ms, xlab=expression(beta ~ " (mmol/l)"), 
     ylab="Study", colors=mc, zero=2.6)

The resulting plot looks like this:
Forest plot of fake data

ProbABEL v0.4.1 released

Last week I released v0.4.1 of ProbABEL, just a few days after releasing v0.4.0, which contained a small, but irritating bug.

This release took quite some time to create, but features quite a few bug fixes, including a major one: for the first time since the filevector format was introduced somewhere in 2009/2010, the Cox proportional hazards regression module works with filevector/DatABEL files. This is a major step forward, because up till now we had to maintain two branches of code: trunk, with all the regular updates and improvements, and the old branch that contained the Cox PH module that was only capable of reading text files.

Another notable change is the incorporation of [latex]\chi^2[/latex] values in the output files. At the moment these are based on the LRT (likelihood ratio test), except where that doesn’t make sense (e.g. when using the --mmscore option. The implementation was relatively easy, because part of the code was still there from previous versions; it was disabled however, because it didn’t deal with missing genotype data. Now it does. Using the LRT is also easier in the case of the 2df (or genotypic) genetic model, where using the Wald test is not straightforward.

The third user-visible change was a change in the [code]probabel.pl[/code] script that hides some of the details (e.g. the location of the files with genotype data) of running a regression for the user. Previously, using the -o option meant that the output file name was constructed from the name of the phenotype file, the argument of the -o option and a fixed extension that depends on the model(s) being run. Starting with v.0.4.0 this behaviour has changed. If the -o option is specified its argument is used as the start of the output file name, with only the fixed extension appended to it. This allows users to specify output in a different directory than the one where the phenotype file was created.

Packages for Ubuntu Linux (or one of its derivatives and probably also Debian) can be found in the GenABEL PPA (personal package archive). Previously we also released pre-compiled Windows binaries, but I’ve stopped doing that. They were never tested anyway, and I think there isn’t much demand for them anyway. Most people who do genome-wide association studies use Linux servers anyway.

Development of ProbABEL (and other members of the GenABEL suite) takes place on this R-forge page. If you are in search of an open source project to contribute to, feel free to contact us!

User support for the GenABEL suite can be found at our forum.

Viewing a .bam file in the console

One thing we do regularly at work is taking a look at aligned sequences of human DNA as generated by what is called “next-generation sequencing”. This data is stored in so-called .bam files, which can get pretty large. For example, the .bam file for an individual whose whole genome is sequenced at 12x coverage is approximately 60GB.
To view these files, to check the alignment, look at the coverage of a specific region, etc, people typically use graphical browsers like the IGV or Savant. However, these require you to either run the tool on the server (which means relatively slow X-forwarding over SSH) or copying the BAM file to your local machine, which also takes a lot of time, especially if you want to take a look at a single region for a bunch of people.

For jobs like that I’ve found the text-based viewer integrated in SamTools to be very convenient. It’s a matter of running

samtools tview sample.bam /path/to/reference.genome.fasta

after which you get a view like this:

1000821   1000831   1000841   1000851   1000861   1000871   1000881   1000891   1000901
GGCCAGGCAGGGCTTCTGGGTGGAGTTCAAGGTGCATCCTGACCGCTGTCACCTTCAGACTCTGTCCCCTGGGGCTGGGGCAAGTGCCCGATGGGAGCGCA
.....................................................................................................
..........          ......................A.......................T...............G........A........C
...........                                     .....................................................
............                                           ..............................................
..........................................................C...........      .......................A.
...................................................................................        ..........
                                                                                           ..........

Using g followed by 1:23000000 you will jump to the given position on the given chromosome.
If the 1:23000000 doesn’t work, check the header of the BAM file to see how the chromosome is specified (sometimes it is chr1:23000000, for example):

samtools view -H sample.bam

In the above example the dots indicate nucleotides that are identical to the reference (shown in the second line), the positions with letters indicate reads where a different base was read. In this example all of them are probably sequencing or alignment errors because only one discordant read is observed at any position. If you find a column with letters that means this position is indeed different from the reference. Also notice how the various reads are aligned and that in this case the coverage doesn’t seem to be very high.

Fixing the NFS check plugin in Nagios (in Ubuntu)

For some time (probably after an upgrade, I actually don’t remember anymore) we had problems with the NFS check in Nagios on our Ubuntu 12.04 servers. The check would return UNKNOWN: RPC program nfs udp is not running. When running the actual check from the command line:

/usr/lib/nagios/plugins/check_rpc -H '$HOSTADDRESS$' -C nfs -c2,3

the output would be: Can't fork for rpcinfo.
It turns out that the file /usr/lib/nagios/plugins/utils.pm has the wrong path to the rpcinfo binary. Instead of /usr/sbin/rpcinfo it lists /usr/bin/rpcinfo. So, like most of the times, the fix is easy, but pinpointing the exact problem isn’t.

Don’t forget to restart Nagios after changing the path as utils.pm needs to be reloaded.

As Ubuntu is based on Debian, I expect this fix to work there as well. According to this Launchpad bug report this issue was fixed in January in version 1.4.16-1ubuntu1 of the nagios-plugins package, which is not in Ubuntu 12.04.

ProbABEL v0.3.0 released

On New Year’s day I released version 0.3.0 of ProbABEL, almost two months after the previous release.

This update contains a few small bug fixes, but the most important feature of this new release is that thanks to the work of Maarten Kooyman we have a four to five-fold speed increase for the types of GWAS we run at work. In his e-mail to the GenABEL developers list he explains what he did to achieve this. The take-home-message of it is that you should always look for a suitable library for important tasks of any program you write. The old ProbABEL was based on a self-written matrix class that handled things like matrix multiplication and matrix subsetting. In the new release we make use of the Eigen C++ template library, maintained and developed by people who know much more about fast implementations of linear algebra than we do.

For those of you running Ubuntu Linux (or one of its derivatives and probably also Debian) I have set up the GenABEL PPA (personal package archive) where you can download and install the ProbABEL .deb package and stay up to date with future updates.
ProbABEL is also available for MS Windows, although we don’t have much experience running it on that platform.

Development of ProbABEL (and other members of the GenABEL suite) takes place on this R-forge page. If you are in search of an open source project to contribute to, feel free to contact us!

User support for the GenABEL suite can be found at our forum.

ProbABEL 0.2.2 released

On November 7th I released version 0.2.2 of ProbABEL, a set of programs that allow scientists (usually geneticists and epidemiologists) to run Genome-wide association studies (GWAS) in a fast and efficient way, even on machines with low amounts of RAM.

ProbABEL is part of the GenABEL suite, wich is a set of open source package for statistical genomics. Its main developer is Yurii Aulchenko, my former supervisor at the Erasmus Medical Centre.

This update contains a few small bug fixes and an update of the probabel.pl wrapper script that enables the use of chunked imputation output files as input. For more detailed changes, check the announcement.
For those of you running Ubuntu Linux (or one of its derivatives and probably also Debian) I have set up the GenABEL PPA (personal package archive) where you can download and install the ProbABEL .deb package and stay up to date with future updates.
ProbABEL is also available for MS Windows, although we don’t have much experience running it on that platform.

Development of ProbABEL (and other members of the GenABEL suite) takes place on this R-forge page. If you are in search of an open source project to contribute to, feel free to contact us!

User support for the GenABEL suite can be found at our forum.

A new job in genetic epidemiology

Last week I started my new job as a researcher in the field of genetic epidemiology at a university hospital in the Netherlands. Working for a UNIX consultancy firm for some time was a lot of fun, but being back in science is even more fun!

My job will keep me occupied with system administration of the servers used for genetic computations, improving several genetics packages for the programming language R, including getting them adapted to multi-CPU environments, and, maybe later, even try to get those calculations done on graphics cards (GPGPU computing/CUDA). And, of course, I need to get up to speed with genetics and epidemiology, and the math involved, as quickly as possible :-). So, no physics involved here, but who knows where this will bring me. And so far it’s been a lot of fun!

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