Bioinformatics or B101nformatics

My experience with Bioconductor I been involved with the Bioconductor project for several years and last year I attended the BioC2019 conference in New York. It was my first time in an international conference abroad. I'm posting here my experience with the project. Pre-conference The first time I heard about Bioconductor was in 2013 when in an internship, my main goal was to analyze data from Pichia pastoris and look at the functions of the differentially expressed genes through gene ontology. I did it with topGO and other packages included in Bioconductor. Later during the master in Bioinformatics for Health Science I studied, we used several packages from Bioconductor to analyze other data (the main project is still online ). Also as part of the master thesis I also used some other packages (the thesis involved using WGCNA ) but the main question roadblock was knowing the function and what are the genes doing. So I developed a package to compare the annotation of genes accord...

Yes, the title is true even if I do data science in bioinformatics, I don't do machine learning. As seen recently if used correctly, regressions tend to work as well as machine learning. Classic tools (?) still work, I can't say I have tried all of them, but they are quite useful. Also in bioinformatics it is hard to get a big number of samples to make both a good and reliable generalization and to train reliable a model with enough confidence. Last, most machine learning methods are to me black boxes, I don't understand them (yet). I like to understand what I use. (Although I can't say I have deeply understood the differences between some regression methods I use). Then, why I am writing this? Because it seems like an hype to say things like "powerful network medicine tools", "machine learning model", without explaining them in detail. So it becomes a black box, and science is not about black boxes. In science we want to increase the know...

I must confess this month I thought I had any new to share but right after I published last post I found one which I saved for this month: The first epigenomic's map:( Spanish,   English) The logo of the consortium ( Roadmap Epigenomics Mapping Consortium )  involved on the Epigenomic's roadmap Sometimes people complain that bioinformaticians think that a gene is defined by a position in a chromosome or a genetic element, plasmid, mitochondrial chromosome, genome... I don't think so, many bioinformaticians are biologist who know how replication, transcription, and translation works molecularly. However the easiest way to work with genomes implies omitting it, and work with known sequences and suppose that they are transcribed and translated literally. With this step and the previously know Encode project we learn more about splicing and epigenomics and in the future we will see how this will change bioinformatics work.

I will start with a monthly post about bioinformatics. It might contain news from Spain, or whatever I heard about. Changed the machine room of Embl Moving 20 Petabytes - blog post thanking the technical team @emblebi in the machine room move end of 2014. http://t.co/62nz6k8vwU — Ewan Birney (@ewanbirney) January 13, 2015 Read Ewan's blog post to learn about this silent move. I would also share this discussion from biostar : Why the bioinformatics field don't move to the " leading edge technology for biology big data analysis "? I personally agree with this long answer , which states that the problem is not the how we process information but, what we do with information. In genomics, metabolomics, interactomics, we don't need to process live streams of data, so technolgoy developed by Facebook, and Google towards solving this issues cannot be applied in -omics field. But I must disagree with the author, I don't think GO or other effort...