Running PTM-POSE#

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](NaegleLab/ExonPTMapper). It also contains functions for annotating these PTMs with information from various databases, like PhosphoSitePlus and ELM.

Formatting Data#

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 (recommended)	chromosome	strand	region start	region end	dPSI (optional)	significance (optional)
first_event	CSTN1	1	-1	9797555	9797612	0.362	0.032

PTM-POSE allows you to assess two potential impacts of splicing on PTMs:

Differential inclusion: lost or gained from the isoform as a result of a splice event
Altered flanking sequences: the PTM site is present in both isoforms, but the adjacent residues around a PTM are changed in one isoform such that its linear motif that drives many protein interactions is unique

Identifying differentially included PTMs#

Once the data is in the correct format, simply run the project_ptms_onto_splice_events() function, indicating the column names corresponding each data element. By default, PTM-POSE assumes the provided coordinates are in hg38 coordinates, but you can use older coordinate systems with the coordinate_type parameter. If you have saved ptm_coordinates locally, you can set this parameter to None.

from ptm-pose import project

my_splice_data_annotated, spliced_ptms = project.project_ptms_onto_splice_events(my_splice_data,
        ptm_coordinates,
        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)

first event

Gene name (recommended)

CSTN1

chromosome

1

strand

-1

region start

9797555

region end

9797612

first flank start

9687655

first flank end

9688446

second flank start

9811223

second flank end

9811745

dPSI (recommended)

0.362

significance (recommended)

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,
        ptm_coordinates,
        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')

Combining outputs#

In some cases you may wish to work with a combined file that indicates both differential inclusion and altered flanking sequence events. This can be done quickly by running:

from ptm_pose import analyze
combined_output = analyze.combine_outputs(spliced_ptms, altered_flanks)

Annotating PTMs with Functional Information#

Beyond projecting PTMs onto your data, we have also provided additional functions for appending information on the function, relationships, and interactions of each post-translational modification that have been recorded in various databases. These annotations include information from:

Database	Annotation types	PTM-POSE function
PhosphoSitePlus	Function Biological Process interactions	annotate.add_PSP_regulatory_site_data(spliced_ptms, file = "/path/to/file/Regulatory_sites.gz")
PhosphoSitePlus	Kinase substrates	annotate.add_PSP_kinase_substrate_data(spliced_ptms, file = "/path/to/file/Kinase_Substrate_Dataset.gz"
DEPOD	Phosphatase substrates	annotate.add_DEPOD_data(spliced_ptms, file = "/path/to/file/")
RegPhos	Kinase substrates	annotate.add_RegPhos_data(spliced_ptms, file = "/path/to/file/")
ELM	Interactions	annotate.add_PTMcode_interprotein(spliced_ptms, file = "/path/to/file/")
ELM	Linear motifs	annotate.add_PTMcode_intraprotein(spliced_ptms, file = "/path/to/file/")
PTMcode	Interactions	annotate.add_PTMcode_interprotein(spliced_ptms, file = "/path/to/file/")
PTMcode	Intraprotein contacts	annotate.add_PTMcode_intraprotein(spliced_ptms, file = "/path/to/file/")

Rather than running each function individually, you can also use the master function annotate_ptms() to annotate with all desired information at once.

We are continuing to work on adding functions to append more contextual information for individual PTMs. If you have suggestions for what information you would like to be added, please let us know!

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. You can then identify PTMs with specific functions, interaction, etc. with the analyze module. See an example on a real dataset [here](Examples/ESRP1_knockdown).

Running PTM-POSE

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