Genome Spot

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!

Dee2.io is a portal for accessing gene expression data derived from public RNA-seq datasets. So far there are over 400k available datasets and its growing every day. While there are existing databases of such as Expression Atlas, Recount2 and ARCHS4, dee2.io offers a number of unique benefits. For instance, dee2 includes gene-wise counts fron STAR as well as transcript-wise quantifications from Kallisto. There are a few ways you can access these data. Firstly, there is a nice web interface that is mobile friendly. Secondly, there are data dumps available if you are running a large scale analysis.  But the purpose of this post is to demonstrate the improved R interface in action together with SRAdbv2 and statistics with edgeR and DESeq. The official documentation is available on GitHub.
Getting started This tutorial provides a walkthrough for how to work with dee2 expression data, starting with dataset searches, obtaining the data from dee2.io and then performing a differential analysi…

Today I'd like to share some updates on the DEE2 project, which I wrote about  in an earlier post. The  project code can be viewed on github here. Pipeline and images As the pipeline was recently finalised, I was able to roll out the working docker image. To facilitate users without root access, this image was ported to singularity. This took a lot of effort and some expertise from our local HPC team to get things working (many thanks to the Massive/M3 team). The singularity image is available from the webserver (link) and instructions for running it are available on github here. I have started testing a heavyweight singularity image, which includes the genome indexes, which will be more efficient for running jobs with large genomes and will make it available once testing is complete. Queue management It may sound simple to write a script to determine which datasets have been completed and add new datasets to the queue but when taking about tens of thousands of datasets it's …

Back in 2015, our group described DEE, a user friendly repository of uniformly processed RNA-seq data, which I covered in detail in a previous post. Ours was the first such repository that wasn't limited to human or mouse and included sequencing data from a variety of instruments and library types. The purpose of this post is to reflect on the mixed success of DEE and outline where this project is going in future.

Overall I've received a lot of positive feedback from users and a number of citations to our poster. Thanks to everyone who used, gave suggestions, comments, bug reports, etc! However our attempt to have the repository published wasn't so successful due to reviewer niggles over what I consider minor points but hard to implement quickly. The main points raised by reviewers were:

Is it reasonable to treat all data sets as if they were single end? For this one, the reviewers were split, one said it was OK and the other was adamant that it was unacceptable despite my …

RNA-seq has been a blessing for molecular biologists, not only does RNA-seq provide unbiased transcriptome wide expression analysis, it can be mined for a variety of other information like splicing, SNV identification, RNA editing, TSS usage, etc. As the cost of RNA-seq declines, more and more labs are using it hence more and more data is being deposited at databases such as SRA and GEO.

But there is a growing problem.

The problem is a lack of uniformity of processed data on GEO. Processed data has assorted reference genomes, gene annotation sets, accession numbers, software pipelines, statistical analyses and output formats, that in most cases makes comparison of two experiments hard if not impossible, let alone three or more experiments. This is a burden on researchers who want to quickly extract expression information from public RNA-seq data. Many researchers then resort to downloading the raw data in SRA format then processing it with QC/alignment/quantification/statistical pipel…