pyiptmnet 0.1.8

Python client for iPTMNet REST API - https://research.bioinformatics.udel.edu/iptmnet/

A simple and lightweight client library for iPTMnet.

What is it

pyiPTMnet is a thin wrapper around the rest api of iPTMnet database. It makes it very easy to query iPTMnet database and integrate the results into any existing bioinformatics pipeline.

Installation

Please not this package is only available for python 3.0+

Install from PyPI

pip install pyiptmnet

Install from Github

pip install git+https://github.com/udel-cbcb/pyiptmnet.git#egg=pyiptmnet

Quick start

The API consists of functions that mirror the functionality of the iPTMNet rest api. Below you can find a few examples of the functions provided by the API.

Info

Retriving information for an entry with an iPTMnet ID - Q15796

#imports
import pyiptmnet.api as api

# get the information for Q15796 
api.get_info("Q15796")

Result

Type : dictionary

{
  "uniprot_ac": "Q15796",
  "uniprot_id": "SMAD2_HUMAN",
  "protein_name": "Mothers against decapentaplegic homolog 2;",
  "gene_name": "SMAD2",
  "synonyms": [
    "MADH2",
    "MADR2"
  ],
  "organism": {
    "taxon_code": 9606,
    "species": "Homo sapiens",
    "common_name": "Human"
  },
  "pro": {
    "id": "PR:Q15796",
    "name": "mothers against decapentaplegic homolog 2 (human)",
    "definition": "A smad2 that is encoded in the genome of human.",
    "short_label": "hSMAD2",
    "category": "organism-gene"
  }
}

Search

To search the iPTMnet database for entries related to the gene smad2, you can use the search function as follows.

#imports
import pyiptmnet.api as api
from pyiptmnet.enums import *

# search the database
api.search("smad2", Termtype.ProteinGeneName, Role.EnzymeOrSubstrate)

Result

Type : dataframe

iptm_id	protein_name	gene_name	synonyms	organism_taxon_code	organism_species	organism_common_name	substrate_role	substrate_num	enzyme_role	enzyme_num	ptm_dependent_ppi_role	ptm_dep_ppi_num	sites	isoforms
O70436	Mothers against decapentaplegic homolog 2;	Smad2	Madh2	10116	Rattus norvegicus	Rat	True	0	False	0	False	0	6	0
Q1W668	Mothers against decapentaplegic homolog 2;	SMAD2		9913	Bos taurus	Bovine	True	0	False	0	False	0	4	0

Bulk

To perform a bulk query for ptm enzymes on the database with a list of PTM sites in a csv file - sites.csv you can use get_ptm_enzymes_from_file function.

#imports
import pyiptmnet.api as api
from pyiptmnet.enums import *

# search the database
api.get_ptm_enzymes_from_file("sites.csv")

Result

Type : dataframe

ptm_type	site	site_position	score	source	pmid	enz_name	enz_id	sub_name	sub_id
Phosphorylation	S2	2	2	HPRD	8898866,20068231	PRKCB	P05771	ANXA2	P07355
Phosphorylation	S7	7	4	HPRD,neXtPro	20166139,12773393,20089855,17924679,11438671	RPS6KA5	O75582	HMGN1	P05114
Phosphorylation	T60	60	4	neXtProt,PSP	21355052,16081417	SGK1	O00141	WNK1	Q9H4A3

Running tests

python -m unittest test.tests

Citation

If you like our work and it helps you in your research, please cite us using the following citation.

@article{10.1093/database/baz157,
    author = {Gavali, Sachin and Cowart, Julie and Chen, Chuming and Ross, Karen E and Arighi, Cecilia and Wu, Cathy H},
    title = "{RESTful API for iPTMnet: a resource for protein post-translational modification network discovery}",
    journal = {Database},
    volume = {2020},
    year = {2020},
    month = {05},
    abstract = "{iPTMnet is a bioinformatics resource that integrates protein post-translational modification (PTM) data from text mining and curated databases and ontologies to aid in knowledge discovery and scientific study. The current iPTMnet website can be used for querying and browsing rich PTM information but does not support automated iPTMnet data integration with other tools. Hence, we have developed a RESTful API utilizing the latest developments in cloud technologies to facilitate the integration of iPTMnet into existing tools and pipelines. We have packaged iPTMnet API software in Docker containers and published it on DockerHub for easy redistribution. We have also developed Python and R packages that allow users to integrate iPTMnet for scientific discovery, as demonstrated in a use case that connects PTM sites to kinase signaling pathways.}",
    issn = {1758-0463},
    doi = {10.1093/database/baz157},
    url = {https://doi.org/10.1093/database/baz157},
    note = {baz157},
    eprint = {https://academic.oup.com/database/article-pdf/doi/10.1093/database/baz157/33205374/baz157.pdf},
}

Citations of the underlying Sources

#	Name	Publication	PubMed
1	HPRD	Prasad, T. S. K. et al. (2009) Human Protein Reference Database - 2009 Update. Nucleic Acids Research. 37, D767-72.	18988627
2	phospho.ELM	Dinkel H, Chica C, Via A, Gould CM, Jensen LJ, Gibson TJ, Diella F. Nucleic Acids Res. 2011 Jan;39(Database issue) 261-7. doi: 10.1093/nar/gkq1104.	21062810
3	p3DB	Yao Q, Ge H, Wu S, Zhang N, Chen W, Xu C, Gao J, Thelen JJ, Xu D. (2013) P3DB 3.0: From plant phosphorylation sites to protein networks. Nucleic Acids Res 2013. 42(Database issue):D1206-D1213	24243849
4	PhosphoGrid	Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M. Biogrid: A General Repository for Interaction Datasets. Nucleic Acids Res. 2006; 34:D535-9.	16381927
5	PomBase	Wood V, Harris MA, McDowall MD, Rutherford K, Vaughan BW, Staines DM, Aslett M, Lock A, Bähler J, Kersey PJ, Oliver SG. PomBase: a comprehensive online resource for fission yeast. Nucleic Acids Res. 2012;40(Database issue):D695-9. Epub 2011	22039153
6	neXtProt	Gaudet P, Michel PA, Zahn-Zabal M, Britan A, Cusin I, Domagalski M, Duek PD, Gateau A, Gleizes A, Hinard V, Rech de Laval V, Lin JJ, Nikitin F, Schaeffer M, Teixeira D, Lane L, Bairoch A. The neXtProt knowledgebase on human proteins: 2017 update. Nucleic Acids Res. 2017; 45(D1):D177-D182 doi:10.1093/nar/gkw1062	27899619
7	Signor	Perfetto L, Briganti L, Calderone A, Cerquone Perpetuini A, Iannuccelli M, Langone F, Licata L, Marinkovic M, Mattioni A, Pavlidou T, Peluso D, Petrilli LL, Pirrò S, Posca D, Santonico E, Silvestri A, Spada F, Castagnoli L, Cesareni G. SIGNOR: a database of causal relationships between biological entities. Nucleic Acids Res. 2016;44(D1):D548-54. doi: 10.1093/nar/gkv1048	26467481
8	dbSNO	Chen YJ, Lu CT, Su MG, Huang KY, Ching WC, Yang HH, Liao YC, Chen YJ, Lee TY. dbSNO 2.0: a resource for exploring structural environment, functional and disease association and regulatory network of protein S-nitrosylation. Nucleic Acids Res. 2015;43(Database issue):D503-11. doi: 10.1093/nar/gku1176.	25399423
9	PhosphoSitePlus	Hornbeck PV, Zhang B, Murray B, Kornhauser JM, Latham V, Skrzypek E. PhosphoSitePlus, 2014: mutations, PTMs and recalibrations. Nucleic Acids Res. 2015;43(Database issue):D512-20. doi: 10.1093/nar/gku1267.	25514926
10	PhosPhAt	Durek P, Schmidt R, Heazlewood JL, Jones A, Maclean D, Nagel A, Kersten B, Schulze WX. PhosPhAt: the Arabidopsis thaliana phosphorylation site database. An update. Nucleic Acids Res. 38: D828-D834 (2010)	17984086
11	UniProt	The UniProt Consortium. UniProt: the universal protein knowledgebase Nucleic Acids Res. 45: D158-D169 (2017)	27899622
12	PRO	Natale DA, Arighi CN, Blake JA, Bona J, Chen C, Chen SC, Christie KR, Cowart J, D'Eustachio P, Diehl AD, Drabkin HJ, Duncan WD, Huang H, Ren J, Ross K,Ruttenberg A, Shamovsky V, Smith B, Wang Q, Zhang J, El-Sayed A, Wu CH. Protein Ontology (PRO): enhancing and scaling up the representation of protein entities. Nucleic Acids Res.2017;45(D1):D339-D346. doi: 10.1093/nar/gkw1075.	27899649
13	RLIMS-P	Torii M, Li G, Li Z, Oughtred R, Diella F, Celen I, Arighi CN, Huang H, Vijay-Shanker K, Wu CH. RLIMS-P: an online text-mining tool for literature-based extraction of protein phosphorylation information. Database (Oxford).2014. pii: bau081. doi: 10.1093/database/bau081.	25122463
14	eFIP	Wang Q, Ross KE, Huang H, Ren J, Li G, Vijay-Shanker K, Wu CH, Arighi CN. Analysis of Protein Phosphorylation and Its Functional Impact on Protein-Protein Interactions via Text Mining of the Scientific Literature. Methods Mol Biol. 2017;1558:213-232. doi: 10.1007/978-1-4939-6783-4_10.	28150240
15	SGD	Paskov KM, Wong ED, Karra K, Engel SR, Cherry JM. Curated protein information in the Saccharomyces genome database. Database (Oxford). 2017 Jan 1;2017(1). doi: 10.1093/database/bax011	28365727
16	Biomuta	Dingerdissen HM, Torcivia-Rodriguez J, Hu Y, Chang TC, Mazumder R, Kahsay R. BioMuta and BioXpress: mutation and expression knowledgebases for cancerbiomarker discovery. Nucleic Acids Res. 2018 Jan 4;46(D1):D1128-D1136. doi:10.1093/nar/gkx907	30053270