About RiboA

Identifying the A-site and P-site locations on ribosome-protected mRNA fragments from Ribo-Seq experiments is a fundamental step in the quantitative analysis of transcriptome-wide translation properties at the codon level. Ahmed, N. et al developed an integer programming method to identify the A-site location by maximizing an objective function that reflects the fact that the ribosome’s A-site on ribosome-protected fragments must reside between the second and stop codons of an mRNA. This identifies the A-site location as a function of the fragment’s size and the reading frame in Ribo-Seq data generated from S. cerevisiae, E. coli and mouse embryonic stem cells. The correctness of the identified A-site locations is demonstrated by showing that this method, as compared to others, yields the largest ribosome density at established stalling sites. By providing greater accuracy and utilization of a wider range of fragment sizes, this method increases the signal-to-noise ratio of underlying biological signals associated with translation elongation at the codon length scale [1].

RiboA outputs three sets of A-site density profiles: (1) the A-site reads per nucleotide, (2) the A-site reads per nucleotide mapped to Frame 0 by applying the transformation that for reads in frame 1 and 2 the offset is reduced by 1 and 2, respectively, (3) the A-site reads per codon.

RiboA assumes the ribosomes are undergoing steady-state translation, and it can only be applied to steady-state ribosome profiling data. RiboA is not appropriate for datasets from non-steady-state experiments, such as the ribosome run-off experiments where initiation is blocked by antibiotics, such as harringtonine treatment.

How to use this web service

  1. Login. You can login with your existing Google account or sign up with the website. While login is not required to submit jobs, users who have logged in are able to upload their own files and review their job history.
  2. Upload Data. This web service provides three default datasets. You can also upload your own files. Each file is limited to 20.0 GB and will be stored for up to 7 days.
  3. Get A-site Offsets. Here, you can submit a job to calculate A-site offsets. You can also choose whether to generate A-site read density profiles from the calculated offsets. An email address is required so that a notification can be sent to you once the job finishes.
  4. Get A-site Profiles. You can also upload your own A-site offset table and generate A-site read density profiles from that. An email address is required so that a notification can be sent to you once the job finishes.
  5. My Jobs. This page lists your jobs grouped by job type. Following the link to each job, you can view and download the results. You can also cancel a job on this page if the job has not finished yet.

How to set up your own web service

The codes for this containerized web application are available at Github. You may follow the link https://github.com/obrien-lab/aip_web_docker to clone the codes and set up your own web service.

Advanced usage

The key script to identify A-site offsets and to generate A-site density profiles is here. Specifically, the function "run_offset" is the entry point to identify A-site offsets, and the function "run_profile" is the entry point to generate A-site density profiles. Computationally literate users can run this script in command line or adapt the script to their own need.

Contact

E-mail: epo2@psu.edu

Reference

[1] Ahmed, N., Sormanni, P., Ciryam, P. et al. Identifying A- and P-site locations on ribosome-protected mRNA fragments using Integer Programming. Sci Rep 9, 6256 (2019). https://doi.org/10.1038/s41598-019-42348-x