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On the ruffus mailing list[1] I am participating in a discussion on whether a pipeline should contain ‘if‘ conditions and loops.

I don’t like to see conditions in a pipeline. In the real pipelines, the ones with pipes and water in them, there is no equivalent of ‘if‘ conditions. The tubes can be either be closed or not, but that is defined in the pipeline structure. It is not that the pipeline can change its structure and open/modify its path depending on whether there is water or oil running in it, or that the water can choose whether to enter in a tube or note.

With Makefiles, you usually avoid having if and while conditions, to keep the code easier to understand. Moreover with make, when you have to execute the same task for multiple elements (e.g. call the same program with different inputs) you rather launch a series of parallel jobs instead of having a loop: that is similar to splitting a pipeline into different tubes.

So, for me a pipeline is just a script that you call it and it executes a series of steps. If you start putting if and loops in it, then you won’t be able to tell which steps are called every time you launch it, and it will be more difficult to understand the code.

What do you think? Am I being too silly? W the pipelines!!

[1] ruffus is a tool to define bioinformatics pipelines with a python-like syntax, alternative to Makefiles

Last week we hosted the visit of G.Scardoni, the author of Centiscape, a Cytoscape plugin to calculate different measures for Node Centralities in a network.

I recommend you to read the supplementary material 1 of his paper (it’s a pdf), because it has a good description of many measures of node centralities and their possible explication in a biological context.

A node centrality is a parameter that, given a node’s position and interactions in a network, determine its importance. To understand it better, think that one of the main purposes of centralities for biology is to identify which genes are more important in a biological process: which are in a bottleneck position, which are required for having a proper function and which ones are only redundant.

The simplest measure of node centrality is the Degree, which is the number of connections of a node. It seems logic to think that genes with an higher degree (higher number of interactions) should be more important than the others, because a loss of function there will affect more interactions. However, after reading at the Centiscape plugin I realized that there are a lot of measures for node centralities, including closeness, betweenness, stress, centroid, etc. The degree is not the best parameter to identify genes in bottleneck positions, for which we should use betweenness or stress instead.

wikipedia image showing the Betweenness in a network.

Just to make this post round, I have opened a discussion on biostar about which measures of Node Centrality can be applied to biological networks. Let’s see what comes out from that discussion, and if there are other centralities I do not know yet .

These are links that I have collected in the past two months. I am copying them in a pseudo-random order.

PhD students life / becoming a better PhD student

• English Communication for Scientists – Nature has published a very nice manual on how to improve English Communication skills for young researchers. Check it out!
• The care and maintenance of your adviser (from Nature/Jobs). A nice article to explain what a PhD student should really expect from her/his adviser.

1000 genomes & co

• The rise of the genome bloggers – very nice article to resume how 23andMe and other personal genomics services are being discussed on scientific blogs.
• 1000 genomes project tutorial videos – check this series of tutorials from 1000g

Apps and Games

• FreePub is a mind-mapping software to organize scientific materials. Check also this presentation
• After the games about protein folding and multiple alignments, a new geeky bioinformatics game has been published on Internet. Check out EteRNA!

If you are a N-Glycosylation geek, check out this interview with Ajit Varki, a guru of the field:

Interview with A. Varki on the importance of studying post-translational modifications

A way to explain what Blast is to young students or non-scientists is to say that ‘Blast is the equivalent of Google for searching sequences‘.

This analogy is controversial and not all the bioinformaticians would agree on it: but it is one of my favorite ways to explain what is Blast to people outside science, and it is the explication I use during Open Days or Science Meets Society events.

In this post I will discuss the Pros and the Cons of explaining Blast as the correspondent of Google for scientists. It is up to you to judge and start using the analogy for your own.

Pros

- Both are for searching.

The most common usage for Blast is to search for a sequence, to see whether it exists or if a similar sequence is already known. In this sense, a Blast query is equivalent to a Google query, where you type a word or a phrase and you want to know what is already known about it.
Let’s say you have the sequence ACGAGGGCATCGATCGACCTATCTCTTTCTAGGCAATC: what would be the first thing to do to know it’s function and role? Just blast it, and see which results come out. Similarly, let’s say that you encounter a phrase you don’t of which know the meaning, like ‘Asparagine N-Glycosylation’: how can you know what does it mean? The easiest solution is just to google the phrase and see what comes out. I think that it is important, for a student, to understand this analogy: Blast can be used to understand what it is known about a  sequence of nucleotides or aminoacid.

- Both are used because they are popular.

What makes of Blast the most used alignment engine, while there are a lot of alternatives available?
The main reason is that Blast is the most popular and well known tool of the genre, so many researchers just use it as the default alternative. Moreover, it is robust and people trust the results it returns, like for Google.
This does not mean that Blast is the best alignment tool for everything: for certain tasks it may be better to try an alternative, as there are alternatives to Google.

Continue reading →

After the experience with the Post-GWAS article on WikiGenes I started looking at the resources on Nature Precedings, which is where the original idea of that collaborative article came from.

Nature Precedings is a Nature Network website where researchers can post drafts, ideas, presentations about work that can be published. This is exactly what I suspect has happened for the WikiGene article: the authors from the Post-GWAS consortium published a draft of a letter there, and the letter has been noted by some Nature editor, who suggested it to transform into an article and to open it to a collaborative editing.

I am looking at the presentations on Nature Precedings and thinking that maybe, some of the presentations I made or attended may be posted there. It is not very clear how the requisite for publication there are interpreted: most of the documents present pre-print papers and drafts, but some of the presentations illustrate software tools that have been published.

In any case, I think I will start putting something there… I have some ideas that I do not have the time to develop by myself, maybe if I upload them there, I can find somebody wishing to collaborate with me.