DEE2 projects on demand

We have noted that the time between new datasets appearing on SRA and being processed by DEE2 has been about 3 to 6 months. Our dream is to shrink this down to two weeks, but we simply do not have access to that much compute power at the moment. To address this we have devised an "on-demand" feature so that you can request certain datasets to be processed rapidly. We think this is a great feature because it serves the main mission of the DEE2 project which is to make all RNA-seq data freely available to everyone. 

Here's how to use it: 

1. Visit http://dee2.io/request.html and you will be greeted with a webform. Select the organism of interest.

2. Provide the SRA project accession number of the dataset. These numbers begin in SRP/ERP/DRP. If you have a different type of accession such as GEO Series (GSE) or Bioproject (PRJNA) then you will need to navigate NCBI to find the SRP number. 

3. Check that the SRP number is in the standard DEE2 queue. To do that, follow the link provided above the request web form, click on the queue that corresponds to the organism of interest and use ctrl+f to search the text file.

4. If it's there, return to the webform and paste or type the SRP accession into the first text box. Give your email address as well if you want to be notified that processing has completed.

5. You will receive an email when processing is complete. The email will indicate whether any errors were encountered. There will be a link to a folder of recently requested datasets here. 

6. Download and enjoy the dataset as you would any DEE2 data.


Video walkthrough

Check out this video guide to using the new feature!



Boring technical details

Here how it works:

* There is a html webform which executes a cgi script and writes to a file on the webserver that stores the request. Each request consists of a species, SRP number and optional email address.

* Every 10 minutes, a remote computer runs a script which downloads the request file and checks to see if there are any new requests. If there are, it processes all runs in the SRA project with the docker pipeline. 

* After processing, the data files are copied out of the docker container, tabulated into matrix format for downstream analysis, zipped and uploaded to the webserver.

* Every 5 minutes, the webserver runs a separate script which checks whether requested datasets have been completed and present in the folder. If it is, then an automated email is sent to the user to notify the data is ready.

This efficient approach ensures that users can rapidly access new and interesting datasets, and that these data are incorporated into the monthly updates. If this becomes popular then there is definitely scope to increase the available compute power.

I would love to hear your feedback and suggestions.

Popular posts from this blog

Mass download from google drive using R

Google drive is great for sharing documents and other small files but it's definitely not suited to moving  many large files around. For example I just received 170 fastq files that are about 200 MB in size. If you use the browser to download the whole folder, the web app will zip the contents for you which will take a LOOONG time. Alternatively you can download each and every one of those files one by one, which is annoying and prone to human error. You can insist to your collaborators to transfer in a different way, but there are not that many user-friendly and economic approaches. Your biologist collaborators probably won't be able to use rsync  to get the data to you safely. And fast convenient tools for moving around large files like Hightail cost a lot of money. A good solution to this problem is to use the R package googledrive  which enables command line automation of tasks that might take a long time manually. The package vignette has a good overview of the main comma
Read more

Data analysis step 8: Pathway analysis with GSEA

Image
In our RNA-seq series so far we've performed differential analysis and generated some pretty graphs, showing thousands of differentially expressed genes after azacitidine treatment. In order to understand the biology underlying the differential gene expression profile, we need to perform pathway analysis. We use Gene Set Enrichment Analysis ( GSEA ) because it can detect pathway changes more sensitively and robustly than some methods. A 2013 paper compared a bunch of gene set analyses software with microarrays and is worth a look. Generate a rank file The rank file is a list of detected genes and a rank metric score. At the top of the list are genes with the "strongest" up-regulation, at the bottom of the list are the genes with the "strongest" down-regulation and the genes not changing are in the middle. The metric score I like to use is the sign of the fold change multiplied by the inverse of the p-value, although there may be better methods out there
Read more

Generating a custom gmt file for gene set analysis

Image
Pathway and gene set analysis is a common procedure for interpretation of RNA-seq or other genome-wide expression assays. Most of the time, we use GSEA to tell us whether our gene sets of interest are up- or down-regulated. We can use gene sets from KEGG , Reactome , GO ,  MSigDB  and other sources, but you can also generate your own gene sets. The format used for GSEA is gmt . I'm going to take you through two examples of generating custom gene sets: Generate gene sets from published data sets using GEO2R Let's say you're interested in the transcription factor STAT1. I found a dataset in GEO called "Knockdown of STAT1 in SCC61 tumor xenografts leads to alterations in the expression of energy metabolic pathways", which has a paper in BMC Med.  Most uploaded array data sets can be reanalysed with GEO2R, which runs the array analysis tool Limma but this is embedded in the webpage and has a GUI which makes it very accessible for biologists. Click this link
Read more