Posts

My personal experience giving up on academia to bet on industry

I found myself to force to use cellranger. Meanwhile it helps a lot to run from bcl files to single cell counts matrixes, I discovered that is quite difficult to control many options related to optimization.

We use bcbio-nextgen for the analysis of sequencing data, mainly, (sc)RNAseq, smallRNAseq, DNASeq and ChIPSeq. It is not rare that we get collaborators who wants to re-analyze public data-set. Inside bcbio, we have bcbio_prepare_samples.

This post will show how to configure quickly the colors for the annotation of rows/columns that go on top or on the side of a heatmap. I normally use pheatmap a lot.

This post should show you an easy way to get the following data type integrated into a figure: functional enrichment analysis gene expression data from any technology miRNA expression data from any technology I am using the function isoNetwork from the package isomiRs, that of course is developed by me :) My ego is not that big, it is just I wanted a figure showing that information, and I couldn’t find any at a time, but if you know any, tweet me about it to @lopantano.

This is a funny story, and I will try to tell you how I realized I don’t know anything about R in 400 words. I work at the Bioinformatic Core at Harvard TH School.

Differentially gene expression analysis with RNA-seq data is quite common nowadays, and there are pretty good Bioconductor packages for that: limma::voom, DESeq2 … The code for that part is quite simple, being super quick to get a list of de-regulated genes.

In summary: I will show which is the best miRNA mapping tool. I used several options for this benchmarking: miraligner from SeqBuster suit (I am one of the authors) bowtie2 and bowtie novoalign from novocraft suit GEM srnabench microrazer STAR miRExpress I think that these are the most used, and other not used but good to try them.

My PhD was focused on small RNA sequencing data. I had a problem when I wanted to visualized the amount of small RNAs from the beginning. Here the problem, assume that you have a certain distribution of small RNA sequences abundance:

What is the lying factor in figures? It is the ratio between the difference of two numbers and the difference of the visualization of those two numbers. For instance: You have two groups, each group is represented by 4 and 2.