BLT Stages

 

The GWIPS-viz browser (https://gwips.ucc.ie/) is an on-line genome browser specifically tailored for exploring ribosome profiling (Ribo-seq) data and their RNA-seq controls. As GWIPS-viz is a partial mirror browser of the UCSC Genome Browser, its functionality is very similar, with some additional functionality for exploring Ribo-seq data. For the genomes that are common between GWIPS-viz and the UCSC Genome Browser (hg38, mm10, sacCer3, rn6) the gene annotations (UCSC RefSeq) were previously automatically updated in GWIPSviz by in-house scripts. However, changes in the configuration of these gene annotations on the UCSC Genome Browser resulted in the GWIPS-viz automated update pipeline to be broken. The aim of my project was to troubleshoot the causes and then try to resolve the issues by modifying and updating the existing scripts so that the gene annotations will automatically update in future. After exploring the previous automation scripts and the current UCSC RefSeq gene annotations configuration on the UCSC Genome Browser, I found that SQL database tables had substantially changed from the previous configuration. Hence, rather than trying to update the previous configuration, I familiarised myself with the recent NCBI RefSeq and Gencode annotation tables configuration and downloaded the latest versions of the NCBI RefSeq and Gencode annotation tables. Next, I wrote new automation scripts for the NCBI RefSeq and the Gencode annotations so both sets of gene annotations get updated automatically.

 

The ribosome profiling (RiboSeq) technique was developed in 2009 to assess gene expression at the translation level at the scale of the entire cell transcriptome [1]. The ability of the technique to produce unprecedentedly detailed quantitative characterization of gene expression on a global scale has made it popular. The applications of the technique prompted researchers to reconsider the current views on how protein coding information is organized in the genomes and on how protein synthesis is carried out and regulated in the cells [2-4]. However, ribosome profiling data are highly heterogeneous and difficult to analyze due to the presence of sporadic technical noise. A normalization technique (RUST) was recently developed at LAPTI lab, University College of Cork. RUST allows for circumvention of these problems and can be applied to RiboSeq data obtained in eukaryotic organisms [5]. RUST can be used for assessing the quality of datasets and estimating how properties of mRNA affect the speed of elongating ribosomes. RUST profiles for many of the eukaryoytic RiboSeq datasets are available on GWIPS-Viz (https://gwips.ucc.ie)[6]. This is a UCSC genome browser based tool for the analysis ad visualization of RiboSeq data obtained with ribosome profiling technique.

Because of intrinsic differences between bacterial and eukaryotic translation, in particular because of widespread mRNA-rRNA interactions [7], direct application of RUST to bacterial RiboSeq data is not practical. Therefore, the first goal of the project was to adapt RUST for bacterial data. RUST profiles using a static 3’ offset of 12 nucleotides for all read lengths were generated for 25 bacterial datasets (E.coli, etc) and compared to RUST profiles where a variable three prime offset for each individual read length were generated for the same datasets. No large improvements by the use of a variable offset was observed.

A broader distribution of reads, particularly a higher number of short reads (<25nt) was observed for bacterial RiboSeq datasets compared to eukaryotic RiboSeq datasets. Therefore RUST was tested on a dataset where the reads had a minimal read length of 15nt and compared to the in-house default setting of minimal read length of 25nt when mapped to the reference genome. The number of mapped reads were larger but the RUST profiles did not have any distinct changes. The decision was made to use a static 3’ offset of 12nt and keep the default minimal read length of 25nt. Hence remapping all the bacterial RiboSeq datasets for 25studies was considered unnecessary.

After completion of the exploratory analysis, RUST was applied to all the bacterial datasets (25) in GWIPS-viz . The users can explore the profiles in a similar way to what was already available for eukaryotic datasets.

Figure 1 shows an example of a RUST profile. It consists of 3 different plots, the metagenefootprint profile with a Kullback-Leibler divergence (top), a plot with relative coding rates (bottom left) and a panel that shows the triplet periodicity (bottom right).

In the top panel, each gray line represents a codon and it's RUST ratio, this is a log scale of the normalized observed to expected RUST ratio. The 0 coordinate is the A site of the ribosome and the gray area shows the footprint of the ribosome. The Kullback-Leibler divergence at a particular position indicates the variation in Ribo-seq footprint occupancy across all sense codons. The higher the Kullback-Leibler divergence, the less uniform the distribution of RUST values is in the corresponding position. Two codons can influence each other, therefore the adjacent Kullback-Leibler divergence is shown as well. Ideally, the highest divergence should be at the A-site representing that it is the de-coding center of the ribosome that contributes to the highest divergence in footprint occupancy.

In the bottom left panel, the relative RUST ratio for each amino acid is presented. This plot was optimized during the project. It is now possible to distinguish the different synonymous codons for each amino acid.

The panel in the bottom right was also modified to include reads from 36 to 40 nucleotides.