ptm-pose 0.4.0

PTM-POSE: PTM Projection onto Splice Events

PTM-POSE (PTM Projection Onto Splice Events)

Full documentation

PTM-POSE is an easily implementable tool to project PTM sites onto splice event data generated from RNA sequencing data and is compatible with any splice event quantification tool that outputs genomic coordinates of different splice events (MATS, SpliceSeq, etc.). PTM-POSE harnesses PTMs that have been mapped to their genomic location by a sister package, ExonPTMapper. It also contains functions for annotating these PTMs with information from various databases, like PhosphoSitePlus and ELM.

Install PTM-POSE

You can install PTM-POSE either through pip or conda. The base version will install all packages needed to project PTMs onto your data and annotate them with functional information, but if you would like to use the analysis modules for downstream analysis, you can also install additional dependencies needed by adding the optional dependendencies full_analysis. This includes packages like gseapy, kinase-library, networkx, and nease.

pip install:

#base packages for projection and annotation
pip install ptm-pose

#include all packages that are needed for additional analysis
pip install ptm-pose[full_analysis]

conda:

#only option is to install base package installs and install other dependencies separately
conda install -c conda-forge ptm-pose

Running PTM-POSE

To run PTM-POSE, you first need to process your data such that each row corresponds to a unique splice event with the genomic location of that splice event (chromosome, strand, and the bounds of the spliced region). Strand can be indicated using either '+'/'-' or 1/-1. If desired, you can also provide a delta PSI and significance value which will be included in the final PTM dataframe. Any additional columns will be kept. At a minimum, the dataframe should look something like this (optional but recommended parameters indicated):

event_id (optional)	Gene name (recommend)	chromosome	strand	region_start	region_end	dPSI (optional)	significance (optional)
first_event	CSTN1	1	-	9797555	9797612	0.362	0.032

Once the data is in the correct format, simply run the project_ptms_onto_splice_events() function. By default, PTM-POSE assumes the provided coordinates are in hg38 coordinates, but you can use older coordinate systems with the coordinate_type parameter.

from ptm-pose import project

my_splice_data_annotated, spliced_ptms = project.project_ptms_onto_splice_events(my_splice_data, 
                                                                                  chromosome_col = 'chromosome',
                                                                                  strand_col = 'strand',
                                                                                  region_start_col = 'region_start',
                                                                                  region_end_col =  'region_end',
                                                                                  event_id_col = 'event_id',
                                                                                  gene_col = 'Gene name',
                                                                                  dPSI_col='dPSI',
                                                                                  coordinate_type = 'hg19')

Altered Flanking Sequences

In addition to the previously mentioned columns, we will need to know the location of the flanking exonic regions next to the spliced region. Make sure your dataframe contains the following information prior to running flanking sequence analysis:

event_id (optional)	Gene name (recommended)	chromosome	strand	region_start	region_end	first_flank_start	first_flank_end	second_flank_start	second_flank_end	dPSI (optional)	significance (optional)
first_event	CSTN1	1	-	9797555	9797612	9687655	9688446	9811223	9811745	0.362	0.032

Then, as with differentially included PTMs, you only need to run get_flanking_changes_from_splice_data() function:

from ptm-pose import project

altered_flanks = project.get_flanking_changes_from_splice_data(my_splice_data,
                                                                                  chromosome_col = 'chromosome',
                                                                                  strand_col = 'strand',
                                                                                  region_start_col = 'region_start',
                                                                                  region_end_col =  'region_end',
                                                                                  first_flank_start_col = 'first_flank_start',
                                                                                  first_flank_end_col = 'first_flank_end',
                                                                                  second_flank_start_col = 'second_flank_start',
                                                                                  second_flank_end_col = 'second_flank_start',
                                                                                  event_id_col = 'event_id',
                                                                                  gene_col = 'Gene name',
                                                                                  dPSI_col='dPSI',
                                                                                  coordinate_type = 'hg19')

Downstream Analysis

PTM-POSE also provides functions in the annotate module for annotating the above outputs with functional information from various databases: PhosphoSitePlus, RegPhos, PTMcode, PTMInt, ELM, DEPOD, OmniPath, and PTMsigDB. You can then identify PTMs with specific functions, interaction, etc. with the analyze module. For more information and examples of analysis, see the full documentation.

Have questions?

Please reach out to Sam Crowl ([e-mail]) if you have questions or suggestions about new analysis functions that you would like to see implemented. We hope to continue to expand the analysis that can be easily performed with this package as time goes on, and welcome any feedback.