I’m back… aka restarting blogging in 2017

So… I’ve been busy. Lots of stuff including some published work (see http://www.markbieda.com).

But I want to get going again – will be blogging here a lot coming up. So much fun stuff to discuss: docker, R, NGS…


Docker for Bioinformatics: An enormous set of images (3007! at last count)

keywords: docker, bioinformatics, software

I’m a huge fan of docker (like most everyone, it seems). My lab has been working on some docker images and pipelines including our custom code (not released yet). I’ve been using a lot of docker images to do quick analysis – I’ll write more on this in another post.

I just ran across an enormous set of “dockerized” bioinformatics software – look at


As of today, there are 3007 images and they seem to encompass a lot of popular packages – like samtools.

I really like the documentation of the images and dockerfiles on this site – very easy to see what is actually in the image.

One issue: some packages are frequently updated – and the updates are important but the images are a bit behind. So be careful with version issues. The bcftools image is at least one version behind, for example.

Always, comments welcome.

2009 post: Key Bioinformatics Computer Skills

Note: this was written in 2009 so… out of date somewhat!

I’ve been asked several times about which computer skills are critical for bioinformatics. Important – note that I am just addressing the “computer skills” side of things here. This is my list for being a functional, comfortable bioinformatician.

  1. SQL and knowledge of databases. I always recommend that people start with MySQL, because it is crossplatform, very popular, and extremely well developed.
  2. Perl or Python. Python wins now! (2017 update!)  Preferably perl. It kills me to write this, because I like python so much more than perl, but from a “getting the most useful skills” perspective, I think you have to choose perl.
  3. basic Linux. Actually, being at a semi-sys admin level is even better. I always tell people to go “cold turkey” and just install Linux on their computer and commit to using it exclusively for a while. (Due to OpenOffice etc, this should be mostly doable these days). This will force a person to get comfortable. Learning to use a Mac from the command line is an ok second option, as is Solaris etc. Still, I’d have to say Linux would be preferred.
  4. basic bash shell scripting. There are still too many cases where this ends up being “just the thing to do”. And of course, this all applies to Mac.
  5. Some experience with Java or other “traditional languages” or a real understanding of  modern programming paradigms. This may seem lame or vague. But it is important to understand how traditional programming languages approach problems. At minimum, this ensures some exposure to concepts like object-oriented programming, functional programming, libraries, etc. I know that one can get all of this with python and, yes, even perl – but I fear that some many bioinformatics people get away without knowing these things to their detriment.
  6. R + Bioconductor. So many great packages in Bioconductor. Comfort with R can solve a lot of problems quickly. R is only growing; if I could buy stock in R, I would!

This may seem like a lot, but many of these items fit together very well. For example, one could go “cold turkey” and just use Linux and commit to doing bioinformatics by using a combination of R, perl and shell scripting, and an SQL-based database (MySQL). It is very common in bioinformatics to link these pieces, so… not so bad, in the end, I think.

As always, comments welcome…

2009 post: Free, easy, quick, great PDF creation: Try OpenOffice

keywords: free software, opensource, OpenOffice, grantwriting

I try to give credit where credit is due.

I have written before about using OpenOffice (version 2.4) for “real professional work.” In an earlier post, I wrote about successfully writing an entire grant application using OpenOffice for wordprocessing and figure creation in conjuntion with Zotero for references (and the grant was funded, so…).

PDF creation from OpenOffice (use “Export to PDF” in the File menu) simply works great. It is very fast and the pdf quality is excellent. One note – it does not open the pdf automatically – it just stores the file – so pay attention to this. This works much better than printing to a pdf using the Adobe PDF printer or using the Microsoft Office 2007 export to pdf functions (which, besides being slow, caused Microsoft Office to crash occasionally on my machine).

Also, before I forget, I really like OpenOffice Draw for scientific figure creation – I use it a lot in my work and I have been quite happy with it. I’m using Microsoft Office a fair amount now, but I still use draw to make figures. I’ve used Zotero and Draw for well over a year now, with fairly intense use.

Note: This is almost entirely based on using OpenOffice 2.4. The current version is 3.0, which I just downloaded.

2008 post: Bioinformatics: Sequence Alignment Is Central…?

Keywords: Illumina, Sequence Alignment, algorithms, teaching, next-generation sequencing

I haven’t posted in a while; I have been busy teaching bioinformatics. I do receive an occasional email or question about learning bioinformatics, so why don’t I just write what I taught here?

Here, at least, was my thinking on the subject. Remember that I was teaching second year students with a variety of backgrounds.

The first point is that sequence analysis/alignment is the heart of bioinformatics. Ok, you can argue with me on this. But I think that sequence alignment is, without question, a major – if not THE major – success in bioinformatics. Why do I say this?

1. Sequence alignment is non-trivial.

2. Sequence alignment approaches derive from a solid mathematical basis.

3. There are well worked out statistics for sequence alignment.

4. Sequence alignment is extremely prevalent and popular as an application of bioinformatics – not least of which is evolutionary studies of gene change and, of course, analysis of the rapidly growing number of fully sequenced genomes (or even partially sequenced ones, for that matter).

5. New situations that are variants/subsets/offshoots of sequence alignment are emerging that have already produced new algorithmic/computational frameworks. So, although this is arguably a fairly mature area of study (I think so), there is new work being done. Specifically, I am thinking of new sequence alignment approaches for next-generation sequence data (esp. short reads like Illumina, ABI) and (probably) also for metagenomics data. In the case of next-generation sequencing, mostly we want to align near-perfect reads – optimizing this for tens of millions of reads is non-trivial. Some recent work that looks good is ZOOM! in Bioinformatics 2008 24:2431 and SeqMap in Bioinformatics 2008 24:2395. (But note that I have not used either at all yet).

As a route to teaching bioinformatics, I also like sequence alignment because it touches on major topics in bioinformatics/biology: alignment itself, evolution of sequence (including phylogenetic tree construction), hidden markov models (profile HMMs, pair HMMs, PAM for alignment), etc. So just by examining sequence alignment, I end up introducing major “techniques” in bioinformatics (note that this point is certainly not original; you see it in the famous Durbin et al. book Biological Sequence Analysis and in other books like Mount’s text Bioinformatics).

2008 post: TAMALg: is the package available?

I’ve received a lot of questions recently about TAMALg availability. Unfortunately, there is only a difficult-to-install package available right now; I sent it to someone recently and they had a terrible time getting it going.

I do describe the algorithm in the supplementary materials to the ENCODE spike-in competition paper (Johnson et al, Genome Research 2008).

I would love to have a simple package to distribute, but this is little supported in today’s granting environment; in fact, I don’t think that making algorithms widely available has ever been well-supported by any US funding agency. And I doubt the situation is different here in Canada.

I may be getting another undergrad soon and would task that person with working on the package. As a new faculty member, I am simply overwhelmed with basics like getting my lab going right now.

I do hope that this situation changes and thanks to all for patience.

As I have noted previously, the L2L3combo predictions produced by the TAMALPAIS server (see previous posts on this or just search for “TAMALPAIS Bieda” – no quotes, though) are the same predictions as made by TAMALg. TAMALg also adds the step of estimating enrichment via using maxfour type methodology.

So you can get good TAMALg predictions of sites just by using the webserver. I suggest going this route.

And to repeat – TAMALg is almost certainly NOT what you want for promoter arrays. Except if you have a factor in only a tiny fraction of promoters or one of the newer designs with very long promoter regions (e.g. for 10 kb promoters, might be ok).

2008 post: Jobs: Postdoctoral Positions in my lab

Update: I just hired an experimental postdoc – thanks to all that applied – and I am temporarily suspending the search for a computational postdoc.

I’m looking for two postdocs: one computational (bioinformatics) postdoc and one molecular biology postdoc.

I just posted this ad to naturejobs, so here is the info:


2 Postdoctoral Positions Total

1 Computational (Bioinformatics) Postdoctoral Fellow

1 Experimental (Molecular Biology) Postdoctoral Fellow


These positions are in the laboratory of Mark Bieda. The lab focuses on (1) development of novel statistical and computational approaches to ChIP-seq and ChIP-chip data and (2) investigating the changes in chromatin marks in cancer using chromatin immunoprecipitation and related molecular biology approaches. These positions offer an excellent opportunity for cross-training (e.g. bioinformatics training for an experimentalist, experimental training for a computational postdoc).

Bioinformatics Position: The computational position will focus on novel statistical and algorithmic methods for analysis of microarray (ChIP-chip) and high-throughput sequencing (ChIP-seq) experiments. This project will afford the opportunity for large-scale experimental validation of predictions within the lab. The successful computational candidate will be comfortable thinking statistically and have good programming skills with a keen interest in large-scale data analysis. Experimental Position: The experimental position will focus on examining chromatin organization in brain tumor models (primarily gliomas). There is also opportunity for work on other projects in neurogenomics. Previous experience/familiarity with neuroscience is a plus, but not required. The successful candidate will have experience with a wide range of molecular biology techniques.

Both positions offer opportunities for both formal collaborative and informal interactions with other strong research groups, including a very active Brain Tumor Group at the university. The PI is committed to developing the careers of members of the laboratory.

The University of Calgary offers an excellent environment with a rapidly growing pool of biomedical research labs and significant shared facilities. We encourage all qualified persons to apply. The University of Calgary hires on the basis of merit and is committed to employment equity. However, Canadians and permanent residents of Canada will be given priority.

Calgary is a city of ~1 million people and is located only about 1.5 hours from world-renowned recreational areas (Banff and Jasper).

To apply, please send (1) cover letter, (2) CV and (3) names and contact information for three references to Aarif Edoo (aedo@ucalgary.ca). PDF format for application materials is preferred. Letters should be addressed to Mark Bieda, Ph.D.