About GRADitude

GRADitude - The GRAD-seq data analysis tool

Grad-seq is a high-throughput profiling approach for the organism-wide detection of RNA-RNA and RNA-protein interactions in which molecular complexes are separated in a gradient by shape and size (Smirnov et al. 2016 PNAS). It offers new means to study the role of different RNA and protein components in various macromolecular assemblies by analyzing fractions of a glycerol gradient by a high- throughput sequencing approaches combined with mass spectrometry. The Grad-seq approach offers a way to study the distribution of all RNA involvement in various macromolecular assemblies.

GRADitude is a computational tool for the analysis of Grad-seq in-gradient profiling.

This open source tool performs all required steps to translate sequencing data of a Grad-seq experiment into a list of potential molecular complexes.

Documentation

Documentation can be found on here.

Installation

Current there is no proper pip package for GRADitude available - but it's work in progress. :)

Github

All the source code of GRADitude can be retrieve from our Git repository. Using the following commands can clone the source code easily.

$ git clone https://github.com/foerstner-lab/GRADitude.git

or

$ git clone git@github.com:foerstner-lab/GRADitude.git

In order to make GRADitude runnable, we have to create a soft link of graditudelib in bin.

$ cd GRADitude/bin

$ ln -s ../graditudelib .

Arguments

```bash usage: graditude [-h] [--version] {create,minrowsumercc,minrowsum,dropcolumn,robustregression,normalize,scaling,correlationallagainstall,selectingspecificfeatures,heatmap,plotkinetics,clustering,clusteringelbow,silhouetteanalysis,pca,tsne,umap,correlationrnasprotein,correlationdistributiongraph,plotnetworkgraph,clusteringproteins,dimensionreductionproteins,correlationspecificgene,interactiveplots,correlationreplicates,findcomplexes} ...

positional arguments: {create,minrowsumercc,minrowsum,dropcolumn,robustregression,normalize,scaling,correlationallagainstall,selectingspecificfeatures,heatmap,plotkinetics,clustering,clusteringelbow,silhouetteanalysis,pca,tsne,umap,correlationrnasprotein,correlationdistributiongraph,plotnetworkgraph,clusteringproteins,dimensionreductionproteins,correlationspecificgene,interactiveplots,correlationreplicates,findcomplexes} commands minrowsumercc Filter the ERCC table based on the min row sum. It calculates the sum rowwise and discard the rows with a sum below the threshold specified minrowsum Filter the gene quantification table based on the min row sum. It calculates the sum rowwise and discard the rows with a sum below the threshold specified dropcolumn It filters a table dropping a specific column.It is usually used to drop the lysate column that is not required for the downstream analysis robustregression It compares the ERCC concentration in mix with the ERCC reads and take it out the outliers normalize This subcommand calculates the ERCC size factor and normalize the gene quantification table based on that scaling This subcommand scales tables using different scaling methods correlationallagainstall This subcommand calculate the correlation coefficients all against all selectingspecificfeatures This subcommand allows to select specific features in a normalized table (ncRNAs, CDS, etc.) heatmap This subcommand is useful to visualize the in-gradient behavior of a larger group of transcripts or proteins plotkinetics This subcommand plot the kinetics of a specific transcript or protein to better visualize their behavior within the gradient clustering This subcommand performs unsupervised clustering using different algorithm clusteringelbow This subcommands plot the elbow graph in order to choose the ideal number of clusters necessary for the k-means and the hierarchical clustering silhouetteanalysis This subcommand can be used to interpret the distance between clusters pca This subcommand performs the PCA-principal component dimension reduction tsne This subcommand performs the t-sne dimension reduction umap This subcommand performs the umap dimension reduction correlationrnasprotein This subcommand performs the Spearman or Pearson correlation coefficients of two tables. correlationdistributiongraph This subcommand plots the distribution of the correlation coefficients as histogram plotnetworkgraph This subcommand plots the network plot. It can be used to plot for example sequencing data vs protein data or ncRNAs vs proteins etc. clusteringproteins This subcommand performs the unsupervised clustering of protein data dimensionreductionproteins t-sne analysis of Mass spectrometry data correlationspecificgene This subcommand calculate the Spearman or Pearson correlation of a specific gene or protein against all interactiveplots This subcommand is useful to visualize interactive a plot after a dimension reduction algorithm has been applied. correlationreplicates This subcommand allows to see the distribution of the correlation coefficient between two biological replicates findcomplexes With this subcommand we look at how many of the know proteincomplexes are actually present in our specific data sets.It finds if all the subunit of that specific complexes are present and calculate the correlation version Print version

optional arguments: -h, --help show this help message and exit ```