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 can be found on here.
Current there is no proper pip package for GRADitude available - but it's work in progress. :)
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 [email protected]: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 .
usage: graditude [-h] [--version]
{create,min_row_sum_ercc,min_row_sum,drop_column,robust_regression,normalize,scaling,correlation_all_against_all,selecting_specific_features,heatmap,plot_kinetics,clustering,clustering_elbow,silhouette_analysis,pca,t_sne,umap,correlation_rnas_protein,correlation_distribution_graph,plot_network_graph,clustering_proteins,dimension_reduction_proteins,correlation_specific_gene,interactive_plots,correlation_replicates,find_complexes}
...
positional arguments:
{create,min_row_sum_ercc,min_row_sum,drop_column,robust_regression,normalize,scaling,correlation_all_against_all,selecting_specific_features,heatmap,plot_kinetics,clustering,clustering_elbow,silhouette_analysis,pca,t_sne,umap,correlation_rnas_protein,correlation_distribution_graph,plot_network_graph,clustering_proteins,dimension_reduction_proteins,correlation_specific_gene,interactive_plots,correlation_replicates,find_complexes}
commands
min_row_sum_ercc Filter the ERCC table based on the min row sum. It
calculates the sum row_wise and discard the rows with
a sum below the threshold specified
min_row_sum Filter the gene quantification table based on the min
row sum. It calculates the sum row_wise and discard
the rows with a sum below the threshold specified
drop_column 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
robust_regression 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
correlation_all_against_all
This subcommand calculate the correlation coefficients
all against all
selecting_specific_features
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
plot_kinetics 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
clustering_elbow This subcommands plot the elbow graph in order to
choose the ideal number of clusters necessary for the
k-means and the hierarchical clustering
silhouette_analysis
This subcommand can be used to interpret the distance
between clusters
pca This subcommand performs the PCA-principal component
dimension reduction
t_sne This subcommand performs the t-sne dimension reduction
umap This subcommand performs the umap dimension reduction
correlation_rnas_protein
This subcommand performs the Spearman or Pearson
correlation coefficients of two tables.
correlation_distribution_graph
This subcommand plots the distribution of the
correlation coefficients as histogram
plot_network_graph This subcommand plots the network plot. It can be used
to plot for example sequencing data vs protein data or
ncRNAs vs proteins etc.
clustering_proteins
This subcommand performs the unsupervised clustering
of protein data
dimension_reduction_proteins
t-sne analysis of Mass spectrometry data
correlation_specific_gene
This subcommand calculate the Spearman or Pearson
correlation of a specific gene or protein against all
interactive_plots This subcommand is useful to visualize interactive a
plot after a dimension reduction algorithm has been
applied.
correlation_replicates
This subcommand allows to see the distribution of the
correlation coefficient between two biological
replicates
find_complexes 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