Genome Spot

Since stumbling upon GTFtools recently, I found that it has other another interesting use - to generate coordinate sets around transcriptional start sites (TSSs). This is really important for ChIP-seq analysis when we want to compare for example the strength of enrichment of histone modificaions at TSSs and compare it to RNA expression. Using GTFtools, it is a one line command to extract these positions: gtftools.py -t Homo_sapiens.GRCh38.94.gtf.tss.bed -w 1000  Homo_sapiens.GRCh38.94.gtf Where "-t" is the output file flag, "-w" is the desired TSS distance to cover, in this case +/- 1000 bp, and the last argument is the input gtf file which needs to be Ensembl or Gencode (other ones don't work due to differences in formatting)  If I had to do this without GTFtools, it would end up being more complicated, as TSS positions (exon 1 starts) would need to be extracted from the GTF file separately for the top and bottom strands and then merged. 

While Linux remains the OS for developers, data scientists and bioinformaticians, uni classrooms remain stubbornly dependent on windows based applications. Yes, on individual PCs you can install Ubuntu command line apps but ask any IT dept about doing this for an entire classroom and you will undoubtedly receive an emphatic "NO". So how does one do bioinformatics training when students cannot even access the simplest foundation, the OS? Good question, and none of the potential answers are optimal to be honest. But it's what we need to deal with until Unis realize that open source software is actually good enough to run entire enterprises. My first thought was to get students to use Putty to log in to a bioinformatics server with SSH. This would be OK, but would be a bit of a headache to manage all the accounts on the server. Also my feeling is that much of the skills learned by the students would be forgotten again as soon as access to the server is revoked. There...

The dee2.io project has been a labor of love since 2013/2014, has undergone a major overhaul and has finally been published online in GigaScience . The great thing about this journal is not only are the articles open access, but also the reviewer's comments. We had great suggestions and they improved the resource tremendously. It's great that it has been published finally, but publication is not the end goal of the project. The goal is to democratize omics data to a point where it can be done by biologists without any coding experience, undergrad students, high school students, practically anyone with a smart phone and an internet connection. So instead of being the end of the project, this is really the end of the beginning. Not only will we be keeping up with new SRA submissions over the next year of so, we will be incorporating new features, new species and perhaps some new data types. If you have suggestions, feedback of comments I would be very grateful!