Welcome to RiboCrypt
RiboCrypt
is an R
package for interactive visualization in genomics.
RiboCrypt
works with any NGS-based method, but much
emphasis is put on Ribo-seq data visualization.
This tutorial will walk you through usage of the app.
RibCrypt
app currently supports creating interactive
browser views for NGS tracks, using ORFik, Ribocrypt and massiveNGSpipe
as backend.
The browser is the main coverage plot display page. It contains a click panel on the left side and display panels on the right. It displays coverage of NGS data in either transcript coordinates (default), or genomic coordinates (like IGV). Each part will now be explained:
The display panel shows the primary settings, (study, gene, sample, etc), the possible select boxes are:
Each experiment usually have multiple libraries. Select which one to display, by default if you select multiple libraries they will be shown under each other.
Library are by default named:
The resuting name above could be:
A normal thing to see is that if condition is KO (knockout), the fraction column usually contains a gene name (the name of the gene that was knocked out) Currently, best way to find SRR run number for respective sample is to go to metadata tab and search for the study.
Here additional options are shown:
From the options specified in the display panel, when you press “plot” the data will be displayed. It contains the specific parts:
Here we collect the analysis possibilities, which are usually on whole genome scale.
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!)
Study and gene select works same as for browser specified above. In addition to have the option to specify all genes (default). - Select libraries (multiple allowed)
This tab displays a heatmap of coverage per readlength at a specific region (like start site of coding sequences) over all genes selected.
Study and gene select works same as for browser specified above. In addition to have the option to specify all genes (default).
Here additional options are shown:
5’ extension (extend viewed window upstream from point, default 30)
3’ extension (extend viewed window downstreamfrom point, default 30)
Extension works like this, first extend to transcript coordinates.
After gene end extend in genomic coordinates
If chromosome boundary is reached, remove those genes from the full set.
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.
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)
Display all samples for a specific organism over selected gene.
Organism, experiment and gene explained above - Group on: the metadata column to order plot by - K-means clusters: How many k-means clusters to use, if > 1, Group will be sorted within the clusters, but K-means have priority.
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.
This tab displays a QC of pshifted coverage per readlength (like start site of coding sequences) over all genes selected.
The display panel shows what can be specified to display, the possible select boxes are same as for heatmap above:
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
Bottom plot: Fourier transform (3nt periodicity quality, clean peak means good periodicity)
This tab displays the fastq QC output from fastp, as a html page.
The display panel shows what can be specified to display, you can select from organism, study and library.
Displays the html page.
Metadata tab displays information about studies.
Here you input a study accession number in the form of either:
On top the abstract of the study is displayed, and on bottom a table of all metadata found from the study is displayed.
All files are packed into ORFik experiments for easy access through the ORFik API:
File formats used internally in experiments are: - Annotation (gtf + TxDb for random access) - Fasta genome (.fasta, + index for random access) - Sequencing libraries (all duplicated reads are collapsed) - random access (only for collapsed read lengths): bigwig - Full genome coverage (only for collapsed read lengths): covRLE - Full genome coverage (split by read lengths): covRLElist - count Tables (Summarized experiments, r data serialized .rds) - Library size list (Integer vector, .rds) - Precomputed gene coverages per organism: fst (used for metabrowser)
For our webpage the processing pipeline used is massiveNGSpipe which wraps over multiple tools:
If you’re not familiar with terms like “p-shifting” or “p-site offset”, it’s best to walk through ORFikOverview vignette, especially chapter 6 “RiboSeq footprints automatic shift detection and shifting”
This app is created as a collaboration with:
Main authors and contact: