GEDSpy - python library

GEDSpy is the python library for gene list enrichment with genes ontology, pathways and potential drugs

Author: Jakub Kubiś

Institute of Bioorganic Chemistry
Polish Academy of Sciences
Department of Molecular Neurobiology

Description

GEDSpy is the python library for biological data analysis uses. It is helpful for RNAseq, single-cell RNAseq, proteomics, and other OMIC high-throughput biological analysis where are obtained lots of differentials expressed genes or proteins. GEDSpy is based on Gene Ontology [GO], PANTHER, KEGG and Reactome information. For potential drugs searching was used ZINC platform.

Used data bases:

• Gene Ontology [http://geneontology.org/]
• PANTHER [http://www.pantherdb.org/]
• KEGG [https://www.genome.jp/kegg/]
• Reactome [https://reactome.org/]
• ZINC [https://zinc.docking.org/]

Installation

In command line write:

pip install GEDSpy

Usage

Example list of genes:

gene_list = ['CACNA1I','CALD1','CAMK1G','CAMK2N1','CAMSAP1','CCL15','CCL16','CCNL2','CCT8P1','CD46','CDC14A','CDK18','CDK19','CES3','CHEK2',
			 'CHID1','COL6A3','CPVL','CYP3A43','CYP3A5','DBNL','DUSP10','DUSP9','ECHS1','EGFR','EGR2','ELL2','ERMP1','ESR1','F7','FAM171A1',
			 'FAM20C','FGFR2','FH','FLAD1','FUT3','GAA','GBA2','GGCX','GJB1','GLRX5','GNAI2','GNB2','GNB3','GPNMB','GRB10','GRHPR','HMGCS2',
			 'HSD17B4','HSP90AB1','IER3IP1','IGF2R','IL1R1','INF2','IRAK1','ITGA1','ITGA7','ITIH1','ITIH3','ITIH4','ITPR1','ITSN1','JAK1',
			 'KALRN','KCNQ2','KCNQ4','KDM3A','KIAA0090','KIAA1161','KMO','KRAS','KSR1','LAMA5','LAMB2','LCN2','MAP2K7','MAP4K2','MAP4K3',
			 'MAPK13','MARCO','MAST2','MAT1A','MATR3','MCM8','MFSD10','MGAT5','MTMR10','MUSK','MYO9B','NBAS','NCOA6','NCSTN','NDUFA4','NEK4',
			 'NPR2','NUDT2','NUP210','ORC3L','PAOX','PEMT','PEX14','PFKL','PHKA2','PIM1','PLXND1','PMM1','PON3','POR','PPARG','PPARGC1B',
			 'PPP2R1A','PRKCE','PTK2B','PTP4A1','PTPN23','PTPRF','PTPRK','RARA','RNF10','RNF14','RNF165','ROCK2','RRBP1','RREB1','SCN1A','SDC1',
			 'SEPHS1','SERPINA1','SERPINA10','SFXN5','SHROOM1','SIL1','SIRPA','SLC12A7','SLC13A3','SLC16A2','SLC17A7','SLC22A23','SLC22A9',
			 'SLC23A2','SLC25A11','SLC25A25','SLC38A3','SLC45A3','SLC4A5','SLC5A1','SLC7A2','SLC8A3','SLC9A6','SLCO1A2','SLCO1B3','SMARCA2',
			 'SNRK','SNX4','SORBS1','SPEN','SPR','SRF','STAB1','STAT1','SUCLG2','SULT1B1','SULT1E1','TBC1D2B','TCHP','TGFBI','TGOLN2','THPO',
			 'TIE1','TIMM13','TLK2','TMEM62','TNFSF14','TNK2','TNS1','TPI1','TRIB3','TRMT11','TTYH3']

1. Import library

import GEDSpy

2. Download gene ontology and pathways information

res1 = GEDSpy.GOPa.gopa_enrichment(gene_list, species = 'hs')

• gene_list - list of analyzed genes
• species - ['ms'] mouse | ['hs'] homo sapiens. Default: 'hs'

Out: Data frame with gene ontology and pathways information

3. Statistic for gene infomration

res2 = GEDSpy.GOPa.gopa_stat(res1, p_val = 0.05, adj = 'BF', dir = 'results', name = 'GOPa')

• p_val - lower threshold for significant p-value. Default: 0.05
• adj - ['BF'] Bonferroni adjusted of p-value or ['None'] lack of adjusting. Default: 'None'
• dir - graph saveing place. Default: CWD/results
• name - graph name prefix. Default: 'GOPa'

Out: Data frame with dtatistic for gene ontology and pathways information

Figure 1 Significant pathways graph based on input gene list

4. Searcheing interactions among genes based on mutual pathways and ontology

res3 = GEDSpy.GOPa.gene_network(res2, p_val = 0.05, adj = 'BF',  dir = 'results' , name = 'gene_relation')

• p_val - lower threshold for significant p-value. Default: 0.05
• adj - ['BF'] Bonferroni adjusted of p-value or ['None'] lack of adjusting. Default: 'None'
• dir - graph saveing place. Default: CWD/results
• name - graph name. Default: 'gene_relation'

Out: Data frame with gene interactions

Figure 2 Gene relation graph

5. Searcheing interactions among pathways and ontology based on mutual genes

res4 = GEDSpy.GOPa.gopa_network(res2, p_val = 0.05, adj = 'BF', dir = 'results', name = 'GOPa_network')

• p_val - lower threshold for significant p-value. Default: 0.05
• adj - ['BF'] Bonferroni adjusted of p-value or ['None'] lack of adjusting. Default: 'None'
• dir - graph saveing place. Default: CWD/results
• name - graph name. Default: 'GOPa_network'

Out: Data frame with gene interactions

Figure 3 Ontology and pathways relation graph

Connecting genes list significant involved in ontology and pathways from previous results:

zinc_gene_list = list(res3['Gen1']) + list(res3['Gen2'])

• It is example of potetntial gene list. There can be use any set of genes

6. Searcheing potential drugs

res5 = GEDSpy.zinc.zinc_drug(zinc_gene_list, zinc_type = 'all', species = 'all')

• zinc_gene_list - list of analyzed genes
• zinc_type - type of substances from ZINC db: ['all'] | ['observations'] | ['substances'] | ['purchasable']. Default: 'all'
• species - ['ms'] mouse | ['hs'] homo sapiens | ['all'] both species. Default: 'all'

Out: Data frame with drugs information

6. Statistic analysis for potential drugs

res6 = GEDSpy.zinc.zinc_plot(res5, p_val = 0.05, adj = 'None',  dir = 'results', name = 'drugs')

• p_val - lower threshold for significant p-value. Default: 0.05
• adj - ['BF'] Bonferroni adjusted of p-value or ['None'] lack of adjusting. Default: 'None'
• dir - graph saveing place. Default: CWD/results
• name - graph name. Default: 'drugs'

Out: Data frame with drugs statistic

Figure 4 Significant drugs graph based on input gene list