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...

Since some time I am working with Crohn's Disease. One of the problems with the disease is that it is not known what happens. People has found associations with microorganisms, but the relationship between those microorganisms and the patient is still unknown. Also the risk factors for complications is largely unknown. This post follows up the use of a patient cohort data enrolled for identifying the risk factors of complications and health-care costs in pediatric and adult onset Crohn’s disease. Where we can see some usage of the data and the problems of unclear descriptions when using the same data. Articles describing the RISK cohort The first mention to the RISK cohort I could found is in this article [1] where they describe a cohort as: an observational research program that enrolled patients younger than age 17 diagnosed with in flammatory (nonpenetrating, nonstricturing) CD from 2008 through 2012 at 28 pediatric gastroenterology centers in North America. In that ar...

Gene Set Enrichment Analysis is a test thought to find if the position of a group along a list implies some difference. The most know method is the one maintained by the Broad Institute . As it was the first widely used in biology and holds several collection of gene sets. A gene set is a collection of genes related, by either a function or an experiment, it is as fuzzy described as a pathway. In Bioconductor there is the under used tool of BiocViews , a topic for package classificacion. We can find a category for GSEAs under Software>BiologicalQuestion>GeneSetEnrichment . This category list 74 packages at the time of writing, which provide function for Gene Set Enrichment Analysis. It will be too long (and too hard for me) to describe all the packages in that category. However, it doesn't include all the packages that perform gene set enrichment. The first package for GSEA in Bioconductor one should look is GSEABase which provides with tools for reading files from...

I am renaming the blog, as I talk very little about biotech and a lot more of bioinformatics :D You can check it yourself!

One of my projects for the master was to investigate using data of the TCGA project. We (my teammates and me) decided to use data of the thyroid. We aimed to discover the reasons why women suffer more thyroid carcinoma than men. Distribution of samples of the TCGA dataset from the thyroid, separated by sex I don't want to repeat all the information on the report we did, you can find the work we did, you can find everything here .  The summary of the study is: Papillary Thyroid Carcinoma (PTC) is the most common type of thyroid cancer (Agrawal et al. 2014). It is more prevalent in women than men and its common diagnosis occurs between 25 and 65 years old (The Cancer Genome Atlas Accessed: 2016-06-1). The aim of our project is to study differentially expressed genes between tumor and normal samples taking into account whether there could be a gender effect on the tumorgenesis of PTC. Using data from the The Cancer Genome Atlas (Accessed: 2016-06-1) we have performed a diffe...

Do you ever heard about hexamer likelihood used to determine if a sequence is coding or not? In one of my assigments on my master of Bioinformatics there was a project to use hexamers instead. The hexamers are defined with 6 nucleotides on a sequence. The idea behind using hexamers is considering that in coding regions there is an influence of the previous codon to the next codon, whereas on the intronic or non-coding regions there isn't. The script works need three fasta files, the first one with intronic sequences, the second one with coding sequences, and the last one with sequences to analyze. All files must use the IUPAC code for sequences, and on the right frame. This is due to the way of signaling in which frame a sequence should be, which varies between programs and descriptions. It is also more difficult to calculate the best score for the unknown sequence, without presenting two scores or biasing for a training set. Assuming everything is on frame is safer and easi...