Making cakes in the year 2014

I've been trying to make a cake. There are lots of published recipes out there for how to make this cake, but the one that I used came with only a very blurry image of what the finished cake should look like. So I really had to hope that the recipe was a good one, because I wasn't entirely sure if I would be able to tell whether it worked or not.

To get started, I used one of those online shopping services that can deliver all the ingredients to your door. Even though they claimed that they stocked everything on my shopping list, they then informed me that there were a small number of ingredients that they were not able to physically access at the moment. Frustratingly, they weren't able to tell me which ingredients would be missing when they delivered them. How odd. 

Something else that seemed unusual was that my cake recipe specified that I needed almost 100 times the amount of ingredients compared to what will end up in the finished cake. Seems a bit wasteful, but who am I to argue with the recipe?

Before I could actually start the baking process, I found that there were a few issues that I had to overcome. Lots of the ingredients had become stuck to the packaging and I had to use a tool which could separate the two. Only, some of the time it didn't get rid of all the packaging, and some of the time it ended up getting rid of not just the packaging but some of the ingredient as well. There's actually several tools on the market for doing this, but they all seem to perform slightly differently.

After I got rid of the packaging I noticed that lots of the ingredients had started to spoil and had to be thrown away, but some of them could be salvaged by cutting off the bad parts. There also seemed to be a lot of implements that you can buy to help with the cutting. Wasn't obvious which one was the best, so I used the first one that Google suggested.

At this point it was kind of frustrating to notice that a small proportion of my ingredients weren't cake ingredients at all. I had to throw them all away, but I think that some of them may have ended up in the final cake.

When it came to the actual baking, I was a bit overwhelmed by the fact that there were dozens of different manufacturers who all claimed that I could make a better cake if only I used their brand of oven. Nearly all of these ovens just let you put your raw ingredients in one slot — after you have removed packaging, the spoilt ingredients, and the non-cake ingredients — and voila, out comes your cake!

I chose one of the more popular ovens on the market and waited patiently for many hours as my cake baked happily in the oven. When the timer buzzed and I took the cake out, I was surprised to that many of the raw ingredients were left behind in the oven's 'waste overflow unit'. The real surprise however, was that the finished cake didn't really look anything like the — admittedly blurry — photo that came with the recipe. 

The cake had many different layers, but they weren't quite all the same size and some of them seemed to have been assembled in the wrong order. The pattern on the cake decoration — yes this oven also decorates the cake — was inconsistent at best. It would mostly use one color of icing, but every now and then, it would insert a different color. The same thing happened with the fillings, it would randomly switch from one flavor to another, and then back again. It was almost like there were two different cakes which had  been squished together to make a new one.

When I finally showed the cake to one my baking friends, I was hoping that he would enjoy it. However, all he kept asking me was "How big are the layers?". When I told him, he replied "My cake has bigger layers so yours can't be very good", and then he left. How rude. I took it to another friend and she just said "Your cake is smaller than mine so mine must be better". She also left without trying it. Finally, I took it to another baking colleague. Before I could show him the cake he just said "My cake has most of the common ingredients expected in all cakes, how many does yours have?". I didn't know so he left.

Making cakes is a very strange business.

How would you pronounce the name of this bioinformatics tool?

From the latest issue of Bioinformatics we have a new tool that is an R package for the analysis of GWAS studies. Rather than name the tool, I want you all to first see it exactly as it appears in the journal:

View fullsize

The first character in the name of this software is a character which can often be hard to identify, particularly when certain fonts makes it look like it could be the letters L or I, or even the number 1.

This is not a name that is worthy of a JABBA-award, but it does fall in to my category of posts which I call almost JABBA, for software names that have various other issues. The particular issue in this case is that the name is hard to read and therefore hard to pronounce. I feel that the use of lower-case characters makes it more likely that the reader will attempt to pronounce this as a word, rather than read it as an initialism. E.g. maybe you saw this name and read it as 'Lurgpurr', or 'Ergpurr'.

The reason behind the name is not explained in the article, but when you go to the linked software page, all is revealed:

View fullsize

It's a bit odd that one of the five words that appear in this name ('Gaussian') doesn't get mentioned anywhere in the paper. But more importantly, why did they feel the need for using lower-case characters? 'LRGPR' would have been much easier to read and comprehend than the font-dependent 'lrgpr'.

Why the UCSC Genome Browser FTP site is one of my least favorite places to visit

If you visit the Golden Path directory of the UCSC Genome Browser FTP site (ftp://hgdownload.cse.ucsc.edu//apache/htdocs/goldenPath), you will come across the following quirks:

1. Multiple genomes for the same species are not grouped together under a parent directory for each species, so the number of items in this directory (~250) gives no indication of the number of species represented (~125).
2. Species identifiers are ambiguous. You have to know that 'mm9' refers to Mus musculus and not Macaca mulatta
3. Species identifiers are also inconsistent. Some species get just two lower-case characters (e.g. 'mm' = Mus musculus, 'dm' = Drosophila melanogaster) whereas most get six characters (e.g. 'felCat' = Felis catus, 'sacCer' = Saccharomyces cerevisiae).
4. Humans, hallowed species that we are, simply get 'hg' (presumably for 'human genome').
5. The six-character format reverses centuries (!) of naming convention by making the genus part of the name start with a lower-case character and the specific part of the name start with an upper-case character.
6. Some species also have date-versioned directories in addition to numerical-suffixed directories. So do you want to download the 'hg7' version of the human genome or instead get the 'hg7oct2000_oo21' (don't ask me what the 'oo_21' part means)?

If you want a challenge, try writing some bioinformatics software that goes from the Latin name for a species to the correct directory on their FTP site! I guess the UCSC team are going to hope that six characters is enough to uniquely identify any future species that end up here. So I hope they don't start sequencing too many more Drosophila species. E.g.

View fullsize

Compare this madness — and it is madness — to the calming orderliness of the Ensembl Genomes FTP site (e.g. ftp://ftp.ensemblgenomes.org//pub/release-23/metazoa/fasta):

A view from UCSC Genome Browser FTP site…

View fullsize

…compared to a view from the Ensembl Genomes FTP site

I think the key point from this story is that a lot of bioinformatics research can be hard enough without the added complexities of working with unstructured data. When you start building any new resource in bioinformatics, be it an FTP site, web site, GitHub repository, you should plan for the future! I.e. expect things to expand, grow, and greatly increase in complexity.

Even if you intend for a resource to only ever contain information for a single species, assume that it will end up containing hundreds of species. You should also assume that people may wish to automate the querying of your data. If you plan for these things from the moment you start building your resource, you might make some bioinformaticans happy — and you certainly don't want to make us angry…you wouldn't like us when we're angry.

How does the popularity of the UC Davis Genome Center vary with geographic location?

If I perform a Google search for the two words genome center, I see that the UC Davis Genome Center (henceforth UCDGC) is the top hit. But this is to be expected because Google has been personalizing search results for some time now, so this result is obviously tailored to me (if you didn't know, I work at the UCDGC).

If you are signed in to Google when you perform a search, the results will be heavily influenced by your search history and by what Google knows about you and your interests. Even if you sign out of Google, the search engine giant can track some information via cookies. Even if you disable cookies or use a private browsing mode, Google is still altering your search results because it knows your location (even if only approximately).

This explains why I will almost always see UCDGC as the top result when I search for 'genome center'. To get around this, I could use a search engine that doesn't track my activity, or I could use a private browsing mode in combination with a little-known feature of Google, that of changing your search location. It's possible to perform a search as if I was located in any major city or state in America.

So this allows me to see how often the UCDGC appears in the #1 position as I move around the country. I first performed a search for 'genome center' as if I was located in each state (e.g. set location to be 'Alabama', 'Alaska', 'Arkansas' etc.):

View fullsize

Ranking of UC Davis Genome Center among Google search results when searching for 'genome center' in each state

When you search for 'genome center', the UCDGC is the top search result in every state! One caveat to this approach is that it may not be all that meaningful to set your location to be an entire state. So I repeated the approach but this time I set my location to be the most populous city in each state:

View fullsize

Ranking of UC Davis Genome Center among Google search results when searching for 'genome center' in the most populous city of each state (as indicated by position of marker within each state). 

This shows that UCDGC is the #1 search result for cities in 36/50 states. The places where UCDGC is not #1 are all cities that have a notable genome center of their own (or are located close to one). A few notes relating to this:

1. The New York Genome Center dominates results not only in New York City (NY), but also in Newark (NJ), Bridgeport (CT), and Philadephia (PA)
2. The #1 result in Baltimore (MD) is for the Institute of Genome Sciences at the University of Maryland
3. St. Louis (MO) sees The Genome Institute at Washington University take the top spot
4. In the north west, a search from Seattle gives the Seattle Structural Genomics Center for Infectious Disease as the most popular result. But if you head to Spokane (Washington's 2nd city), then the UCDGC becomes the #1 result again
5. In Texas, the Department of Genomic Medicine at the Houston Methodist Research Institute, pushes UCDGC to 4th place. However, move to San Antonio or Dallas and the UCDGC regains first place
6. Chicago (IL) has the Institute for Genomics and Systems Biology at #1
7. In Minneapolis (MN) it is the University of Minnesota Genomics Center who is the top dog
8. The home of the King (Memphis, TN) is also home to the W. Harry Feinstone Center for Genomic Research which takes the #1 position. Once again, if you move to this state's second city (Nashville), the UCDGC regains the top spot in the search results.
9. Las Vegas, NV is home to the University of Nevada Las Vegas Genomics Core Facility. Moving to Nevada's second city (Henderson) puts UCDGC back on top.
10. In Salt Lake City (UT) you can find the Utah Genome Depot at the University of Utah dominating the rankings.
11. Finally, in Atlanta (GA), it is the Emory University Integrated Genomics Core which denies the UCDGC the #1 position

The UC Davis Genome Center is not only the top hit when you search for 'genome center' in various locations in the USA. If you use the Google location option to go truly global, you will see that we rank as the top search result for 'genome center' in London, Paris, Berlin, Moscow, Dehli, Seoul, Cairo, Buenos Aires, Bogota, Rio de Janeiro, Cape town, Kuala Lumpur, and Sydney!

While this could all be the result of UC Davis spending millions of dollars to adopt search engine optimization strategies to unduly influence our position in the search results, I prefer to believe that it reflects our reputation for world-class genomics research and training.