Miswi/RiboCrypt
Interactive visualization in genomics

Package index
Search the Miswi/RiboCrypt package
Vignettes
Functions
233
Source code
53
Man pages
26
Home
/
GitHub
/
Miswi/RiboCrypt
/

In Miswi/RiboCrypt: Interactive visualization in genomics 

knitr::opts_chunk$set(echo = TRUE, fig.align = "left")

Introduction

Welcome to RiboCrypt

RiboCrypt is an R package and web server for interactive visualization and analysis in genomics. RiboCrypt works with most NGS-based methods, but much emphasis is put on Ribo-seq data visualization. RiboCrypt uses ORFik experiment objects, so to ensure compatibility use ORFik and massiveNGSpipe for processing own (unpublished) data. If you encounter any issues, please, contact us using the info in the footnote.

The following is web server walk through listing all utilities and options therein.

Ribo-seq overview

For more general material about ribosome profiling, we suggest reading this review. For more technical explanation of data processing steps and specific analyses, it's best to see ORFik Overview vignette, especially chapter 6 "RiboSeq footprints automatic shift detection and shifting".

Browser {#browser}

The browser is the basic coverage display page. It contains a selection panel on the left side and a display window (browser) on the right. It displays coverage of sequencing data in either transcript coordinates (default, collapsed introns), or genomic coordinates. The following (Fig. 1), SRD5A1 gene, has recently been shown to be decoded in three frames.

knitr::include_graphics("../images/tutorial_fig1.png")

Figure 1. SRD5A1 gene displayed using default options. Using API, this browser view can be re-generated using this link.

Browser window

When you press the "plot" button on the selection panel, the selected data will be displayed according to specified options, explained later in the tutorial.

The browser window consists of the specific parts:

  1. Single or multiple density tracks are displayed on top. By default Ribo-seq is rendered in 3 colors, where

  2. red is 0 frame, the start frame of reference transcript.

  3. green is +1 frame

  4. blue is +2 frame

  5. Sequence track (top middle), displayes DNA sequence when zoomed in (\< 100nt)

  6. Annotation track (middle), the annotation track displays the transcript annotation, together with black bars that is displayed on top of the data track. CDS and other annotations are color-coded according to relative reading frame

  7. Frame track (bottom), the 3 frames displayed with given color bars:

  8. white (start codon, ATG)

  9. black (stop codons, TAA \| TAG \| TGA)

  10. brown (custom motifs, e.g. NTG all start codon alternatives)

    When zoomed in, the amino acid sequence is displayed within each frame.

  11. Additional displays, for example protein structure viewer, which appears after clicking on isoform ID on annotation track.

In the example above (Fig. 1), notice green coverage preceding CDS (in Blue). It corresponds to overlapping open reading frame, so it is clear that there are two regions that undergo translation simultaneously in this locus.

Selection panel (browser)

The display panel shows the primary settings, (study, gene, sample, etc):

Experiment {#exp}

Gene {#gene}

Library {#lib}

Usually each experiment have multiple libraries. In the case of merged experiments, different modalities (RFP, RNA, disome, TI, see Fig. 2) can be selected in this field. Select which one to display, if you select multiple libraries they will be stacked in the browser as multiple tracks.

Library are by default named:

The resuting name could for example be:

It's normal to see that if condition is KO (knockout), the fraction column contains a gene name (the name of the gene that was knocked out). Currently the best way to find SRR run number for respective sample is to go to metadata tab and search for the study.

Display type {#linetype}

Notice, how in figure 2 reducing K-mer length to 1, changing display type to columns and adding TI-seq (Translation Initiation profiling, samples treated with translation initiation inhibitors - either lactimidomycin or harringtonin) enables detection of translation in all three reading frames. In fact, experienced user can notice drop of signal in red frame after stop codon of the corresponding ORF (hidden behind most abundant, green), further corroborating overlapping translation. If this process seems somewhat imprecise and not fully defined - it is by design - annotating such elusive phenomena is notoriously difficult to tackle algorithmically, and doing so successfully requires deep intuition in how the signal and noise behave, so manual investigation into many instances like this. Thus, RiboCrypt can be treated as a hypothesis building tool, rather a database of fixed answers. Try modifying yourself, for example switching on RNA-seq track, or altering smoothing window width.

knitr::include_graphics("../images/tutorial_fig2.png")

Figure 2. Comparison of display types and smoothing window widths.

Selection panel (Settings)

Settings tab contains additional parameters:

Uniquely, RiboCrypt allows for toggling between transcriptomic and genomic views, while displaying coding exons in colors according to the correct reading frame. Moreover, extensions allow for exploration of large chunks of the genome, even tens of thousands of bases. Try finding all genes in this browser window, and then turn full annotation on to see what you missed! (loading may be a bit slow, but displaying over region 30 thousands bases long here isn't practical)

knitr::include_graphics("../images/tutorial_fig3.png")

Figure 3. Comparison between transcriptomic and genomic view on yeast EFM5 and ABP140 genes.

Mega Browser {#mbrowser}

Display all samples for a specific organism over selected gene (Fig. 5). This tab does not use bigwig files to load (as that would be very slow). It uses precomputed fst files of coverage over all libraries. Note: Not all isoforms are computed, by default the longest isoform is computed.

knitr::include_graphics("../images/tutorial_fig5.png")

Figure 5. Heatmap of thousands of libraries coverage over ATF4 transcript. Clustered using k-means = 2, with summary track displayed on top.

Selection panel (Browser)

Organism, experiment and gene explained above

Selection panel (Settings)

Statistics tab

This tab gives the statistics of over representation analysis per cluster of the metabrowser plot. Using chi squared test, it gives the residuals per term from metadata (like tissue, cell-line etc). If a value is bigger than +/- 3, it means it is quite certain this is over represented. This is shown as a red line.

If no clustering was applied, this tab gives the number of items per metadata term (40 brain samples, 30 kidney samples etc).

Requirements

This mode is very intensive on CPU, so it requires certain pre-computed results for the back end. That is namely: - Premade collection experiments (an ORFik experiment of all experiments per organism) - Premade collection count table and library sizes (for normalizations purpose) - Premade fst serialized coverage calculation per gene (for instant loading of coverage over thousands of libraries)

Note that on the live app, the human collection (4000 Ribo-seq samples) takes around 30 seconds to plot for a \~ 2K nucleotides gene, \~99% of the time is spent on rendering the plot, not actual computation. Future investigation into optimization will be done.

Analysis

Here we collect the analysis possibilities, which are usually operating on meta-gene or multi locus scale.

Heatmap {#heatmap}

This tab displays a heatmap of coverage per readlength at a specific region (like start site of coding sequences) over all genes selected.

knitr::include_graphics("../images/tutorial_fig4.png")

Figure 4. Metagene per-readlength heatmap of before (upper panel) and after (bottom panel) P-shifting. Notice emerging periodicity.

Selection panel (heatmap)

Study and gene select works same as for browser specified above. In addition to have the option to specify all genes (default).

Selection panel (settings)

Here additional options are shown:

Metagene analysis with heatmap module can be used, for example, to investigate how well P-site positioning was performed (Fig. 4)

Codon analysis

This tab displays a heatmap of codons dwell times over all genes selected, for both A and P sites. When pressing "Differential" you swap to a between library differential codon dwell time comparison (minimum 2 libraries selected is required for this method!)

Display panel (codon)

Study and gene select works same as for browser specified above. In addition to have the option to specify all genes (default).

Filters {#codonfilt}

Codon plot

Display is the score per codon (amino acid), in addition there are 2 custom "amino acids", * as in * : TGA, means TGA is a stop codon (last codon in CDS). Similar is #, as in # : ATG which means ATG as start codon (first codon in CDS). For P-sites start codons should be enriched, while for A-sites there should be a richer variability, often with a small enrichment for stop-codons. We will implement a richer model eventually using the more correct negative binomial relationships between E, P and A sites, i.g. the motif PPP (triplet-proline in E,P,A site) is much stronger than a single P in the A site etc. alone etc.

Differential gene expression {#diffexp}

Given an experiment with a least 1 design column with two values, like wild-type (WT) vs knock out (of a specific gene), you can run differential expression of genes. The output is an interactive plot, where you can also search for you target genes, making it more useable than normal expression plots, which often are very hard to read.

Selection panel (Differential expression)

Organism and experiment explained above - Differential method: FPKM ratio is a pure FPKM ratio calculation without factor normalization (like batch effects), fast and crude check. DESeq2 argument gives a robust version, but only works for experiments with valid experimental design (i.e. design matrix must be full ranked, see deseq2 tutorial for details!) - Select two conditions (which 2 factors to group by)

Selection panel (settings)

Read length (QC)

This tab displays a QC of pshifted coverage per readlength (like start site of coding sequences) over all genes selected.

Display panel (Read length QC)

The display panel shows what can be specified to display, the possible select boxes are same as for heatmap above:

Plot panel

From the options specified in the display panel, when you press "plot" the data will be displayed. It contains the specific parts:

Top plot: Read length relative usage

  1. Y-axis: Score
  2. Color: Per frame (red, green, blue)
  3. Facet box: the read length

Bottom plot: Fourier transform (3 nucleotide periodicity quality, clean peak means good periodicity)

Fastq (QC)

This tab displays the fastq QC output from fastp, as a html page.

Display panel (Read length QC)

The display panel shows what can be specified to display, you can select from organism, study and library.

Plot panel

Displays the html page.

Metadata

Metadata tab displays information about studies and custom predictions. RiboCrypt is integrated with Ribo-seq Data Portal, refer to this paper for details on metadata curation and standardization.

## SRA search

Search SRA for full information of supported study

Study accession number

Here you input a study accession number in the form of either:

Output

On top the abstract of the study is displayed, and on bottom a table of all metadata found from the study is displayed.

Studies

Full table of supported studies with information about sample counts

Predicted translons {#translon}

Full list of predicted translons on all_merged tracks per species. Each translons has a link that directs you to the browser tab and displays the transcript zoomed in on the translons with a light yellow highlighting of the region.

Settings:

Translon annotation scheme:

Additional information

All files are packed into ORFik experiments for easy access through the ORFik backend package:

File formats used internally in experiments are:

massiveNGSpipe

For our webpage the processing pipeline used is massiveNGSpipe which wraps multiple tools:

  1. Fastq files are download with ORFik download.sra
  2. Adapter is detected with either fastqc (sequence detection) and falls back to fastp auto detection.

  3. Reads are then trimmed with fastp (using the wrapper in ORFik)

  4. Adapter removal specified

  5. minimum read size (20nt)

  6. Read are collapsed (get the set of unique reads and put duplication count in read header)

  7. Reads are aligned with the STAR aligner (using the wrapper in ORFik), that supports contamination removal. Settings:

  8. genomic coordinates (to allow both genomic and transcriptomic coordinates)

  9. local alignment (to remove unknown flank effects)

  10. minimum read size (20nt)

  11. When all samples of study are aligned, an ORFik experiment is created that connects each sample to metadata (condition, inhibitor, fraction, replicate etc)

  12. Bam files are then converted to ORFik ofst format
  13. These ofst files are then pshifted
  14. Faster formats are then created (bigwig, fst and covRLE) for faster visualization

API for URL access and sharing

RiboCrypt uses the shiny router API system for creating runable links and backspacing etc. The API specificiation is the following:

Primary url:

https://ribocrypt.org/ (This leads to browser page)

Page selection API:

Page selection is done with "#" followed by the page short name, the list is the following:

Example: https://ribocrypt.org/#tutorial sends you to this tutorial page

Parameter API: {#API}

Settings can be specified by using the standard web parameter API:

Example: https://RiboCrypt.org/?dff=all_merged-Homo_sapiens&gene=ATF4-ENSG00000128272#browser will lead you to browser and insert gene ATF4 (all other settings being default).

A more complicated call would be: https://RiboCrypt.org/?dff=all_merged-Homo_sapiens&gene=ATF4-ENSG00000128272&tx=ENST00000404241&frames_type=area&kmer=9&go=TRUE&extendLeaders=100&extendTrailers=100&viewMode=TRUE&other_tx=TRUE#browser

browser:

About

This app is created as a collaboration with:

Main authors and contact:

```{=html} 

```{=html}
<script>
window.addEventListener("message", function(event) {
  console.log("Received message:", event);  // Debugging
  if (event.origin !== window.location.origin) {
    console.log("Invalid origin:", event.origin);
    return;
  }

  if (event.data.scrollTo) {
    var target = document.getElementById(event.data.scrollTo);
    if (target) {
      console.log("Scrolling to:", event.data.scrollTo);  // Debugging
      target.scrollIntoView({ behavior: "smooth" });
    } else {
      console.log("Anchor not found:", event.data.scrollTo);  // Debugging
      console.log("Available elements in iframe:", document.body.innerHTML); // Debugging
    }
  }
});
</script>


Miswi/RiboCrypt documentation built on Feb. 27, 2025, 6:09 p.m.

R Package Documentation

rdrr.io home R language documentation Run R code online

Browse R Packages

CRAN packages Bioconductor packages R-Forge packages GitHub packages

We want your feedback!

Note that we can't provide technical support on individual packages. You should contact the package authors for that.
GitHub issue tracker
ian@mutexlabs.com
Personal blog

 
Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.

Close