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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