Sitemap Organising the Website & Expanding Contents

FRONTPAGE

Landing frontpage of the Pi web, with faceted search returning diseases, genes (linked page illustrated) and structures (3D view illustrated)

GATEWAY

Starting point to explore immune diseases supported in Pi

DISEASE-SPECIFIC PAGES

Illustrating 1) the disease-specific page on target genes; and 2) the gene-centric page specific to the disease

Disease-specific page on target genes


Gene-centric page specific to the disease

COMPARE

Enabling comparisons involving multiple diseases

pi_database_202108.sql.gz containing 7 tables designed to store database contents.

pi_bucket

Containing info about target tractability (organised as buckets)
mysql> DESC pi_bucket;
+-------------+----------------------+------+-----+---------+-------+
| Field       | Type                 | Null | Key | Default | Extra |
+-------------+----------------------+------+-----+---------+-------+
| gene        | varchar(20)          | NO   | PRI | NULL    |       |
| modality    | enum('AB','PR','SM') | NO   | PRI | NULL    |       |
| bucket      | varchar(2)           | NO   | PRI |         |       |
| name        | varchar(50)          | NO   |     | NULL    |       |
| description | varchar(200)         | NO   |     | NULL    |       |
+-------------+----------------------+------+-----+---------+-------+
    
  • The gene column is the gene symbol
  • The modality column can be one of three drug modalities including antibody (AB), small molecule (SM) and PROTAC (PR)
  • The bucket column is the bucket identifier starting with the letter 'B' followed by an integer
  • The name column is the bucket name
  • The description column is the description of buckets detailing the tractability evidence

pi_category

Containing info about gene druggable categories sourced from DGIdb
mysql> DESC pi_category;
+----------+-------------+------+-----+---------+-------+
| Field    | Type        | Null | Key | Default | Extra |
+----------+-------------+------+-----+---------+-------+
| gene     | varchar(20) | NO   | PRI | NULL    |       |
| category | varchar(50) | NO   | PRI |         |       |
+----------+-------------+------+-----+---------+-------+
    
  • The gene column is the gene symbol
  • The category column shows gene druggable categories

pi_disease

Containing info about immune-mediated diseases
mysql> DESC pi_disease;
+------------+---------------+------+-----+---------+-------+
| Field      | Type          | Null | Key | Default | Extra |
+------------+---------------+------+-----+---------+-------+
| disease    | varchar(3)    | NO   | PRI | NULL    |       |
| name       | varchar(30)   | NO   |     | NULL    |       |
| descriptor | varchar(1000) | NO   |     | NULL    |       |
+------------+---------------+------+-----+---------+-------+
    
  • The disease column is the 2- or 3-letter disease code
  • The name column is the disease name
  • The descriptor column is the short descriptor for disease

pi_drug

Containing info about drug therapeutics sourced from ChEMBL
mysql> DESC pi_drug;
+---------------------+--------------+------+-----+---------+-------+
| Field               | Type         | Null | Key | Default | Extra |
+---------------------+--------------+------+-----+---------+-------+
| disease             | varchar(5)   | NO   | PRI |         |       |
| gene                | varchar(20)  | NO   | PRI | NULL    |       |
| max_phase           | int unsigned | NO   | PRI | 0       |       |
| drug                | varchar(255) | NO   | PRI |         |       |
| mechanism_of_action | varchar(250) | NO   | PRI |         |       |
| action_type         | varchar(50)  | YES  |     | NULL    |       |
| source              | varchar(500) | NO   |     | NULL    |       |
+---------------------+--------------+------+-----+---------+-------+
    
  • The disease column is the 2- or 3-letter disease code
  • The gene column is the gene symbol
  • The max_phase column is the maximum phase of drug development reached
  • The drug column is the drug name
  • The mechanism_of_action column is the mechanism of action for drug targeting
  • The action_type column is the action type for drug targeting
  • The source column shows the primary sources for drug targeting

pi_genomic

Containing info about genomic evidence
mysql> DESC pi_genomic;
+-----------+-----------------+------+-----+---------+-------+
| Field     | Type            | Null | Key | Default | Extra |
+-----------+-----------------+------+-----+---------+-------+
| disease   | varchar(5)      | NO   | PRI |         |       |
| gene      | varchar(20)     | NO   | PRI | NULL    |       |
| gene_type | varchar(5)      | NO   |     | NULL    |       |
| cell_type | varchar(100)    | NO   | PRI | NULL    |       |
| snp       | varchar(15)     | NO   | PRI | NULL    |       |
| pvalue    | double unsigned | YES  |     | NULL    |       |
+-----------+-----------------+------+-----+---------+-------+
    
  • The disease column is the 2- or 3-letter disease code
  • The gene column is the gene symbol
  • The gene_type column is the type of genomic seed genes
  • The cell_type column lists the cell types
  • The snp column is the dbSNP identifier
  • The pvalue column is the p-values detected by GWAS

pi_pdb

Containing info about druggable pockets predicted based on known PDB structures
mysql> DESC pi_pdb;
+-----------+---------------+------+-----+---------+-------+
| Field     | Type          | Null | Key | Default | Extra |
+-----------+---------------+------+-----+---------+-------+
| gene      | varchar(20)   | NO   | PRI | NULL    |       |
| uniprot   | varchar(10)   | NO   | PRI | NULL    |       |
| pdb_chain | varchar(15)   | NO   | PRI | NULL    |       |
| pdb       | varchar(5)    | NO   |     | NULL    |       |
| chain     | varchar(10)   | NO   |     | NULL    |       |
| pocket    | enum('Y','N') | NO   |     | NULL    |       |
+-----------+---------------+------+-----+---------+-------+
    
  • The gene column is the gene symbol
  • The uniprot column is the UniProt identifier
  • The pdb_chain column shows the PDB chain in the structure
  • The pdb column is the PDB code
  • The chain column shows which PDB chain
  • The pocket column specifies whether or not the PDB chain contains the druggable pockets

pi_priority

Containing info about target priority
mysql> DESC pi_priority;
+--------------------+--------------------+------+-----+---------+-------+
| Field              | Type               | Null | Key | Default | Extra |
+--------------------+--------------------+------+-----+---------+-------+
| disease            | varchar(5)         | NO   | PRI |         |       |
| gene               | varchar(20)        | NO   | PRI | NULL    |       |
| rank               | int unsigned       | YES  |     | NULL    |       |
| rating             | double             | YES  |     | NULL    |       |
| nGene              | mediumint unsigned | NO   |     | 0       |       |
| cGene              | mediumint unsigned | NO   |     | 0       |       |
| eGene              | mediumint unsigned | NO   |     | 0       |       |
| seed               | enum('Y','N')      | NO   |     | NULL    |       |
| dGene              | mediumint unsigned | NO   |     | 0       |       |
| pGene              | mediumint unsigned | NO   |     | 0       |       |
| fGene              | mediumint unsigned | NO   |     | 0       |       |
| crosstalk          | enum('Y','N')      | NO   |     | NULL    |       |
| phased             | varchar(2)         | YES  |     | NULL    |       |
| druggable_category | mediumint unsigned | NO   |     | 0       |       |
| pdb_pocket         | mediumint unsigned | NO   |     | 0       |       |
| magnitude          | double             | YES  |     | NULL    |       |
| direction          | enum('I','D','-')  | NO   |     | NULL    |       |
| description        | varchar(1000)      | NO   |     | NULL    |       |
+--------------------+--------------------+------+-----+---------+-------+
    
  • The disease column is the 2- or 3-letter disease code
  • The gene column is the gene symbol
  • The rank column shows priority rank
  • The rating column shows priority rating (scored 0-5)
  • The nGene column specifies whether or not the nearby gene
  • The cGene column shows how many conformation genes (cell types) are identified
  • The eGene column shows how many eQTL genes (cell types) are identified
  • The seed column specifies whether or not it is a genomic seed gene (nGene, cGene or eGene)
  • The dGene column specifies whether or not the disease gene
  • The pGene column specifies whether or not the phenotype gene
  • The fGene column specifies whether or not the function gene
  • The crosstalk column specifies whether or not the pathway crosstalk gene
  • The phased column shows the drug development phase information targeting the gene, namely, preclinical phases 0-3 (P0-P3), phase 4 (P4; approved), otherwise '-'
  • The druggable_category column shows the number of gene druggable categories
  • The pdb_pocket column shows the number of druggable pockets based on PDB structures
  • The magnitude column specifies the estimates of effect size (magnitude) on disease
  • The direction column specifies the estimates of effect direction on disease
  • The description column is the gene description

USAGE

Restoring the database
mysql -u root -p -e "create database pi;"
gunzip < pi_database_202108.sql.gz | mysql -u root -p pi
    

Showing tables
mysql> USE pi;
mysql> SHOW tables;
+-------------+
| pi_bucket   |
| pi_category |
| pi_disease  |
| pi_drug     |
| pi_genomic  |
| pi_pdb      |
| pi_priority |
+-------------+
    

Extracting pathways crosstalk genes in Multiple Sclerosis (MS)
mysql> SELECT gene,rating,description FROM pi_priority WHERE disease='MS' and crosstalk='Y';
+----------+------------------+------------------------------------------------------------+
| gene     | rating           | description                                                |
+----------+------------------+------------------------------------------------------------+
| CD3D     | 3.35080030156153 | CD3d molecule                                              |
| CD3E     | 3.42984078279532 | CD3e molecule                                              |
| CD3G     | 3.36283894499324 | CD3g molecule                                              |
| CD4      | 3.84211138613646 | CD4 molecule                                               |
| CD40     | 4.09069218691624 | CD40 molecule                                              |
| CD80     | 3.51309447797322 | CD80 molecule                                              |
| CD86     | 3.86358361572689 | CD86 molecule                                              |
| CD8A     |  3.7713984905519 | CD8a molecule                                              |
| CDK4     | 3.36314860797353 | cyclin dependent kinase 4                                  |
| GH1      | 3.28003935980419 | growth hormone 1                                           |
| IFNG     | 3.35178195680181 | interferon gamma                                           |
| IFNGR1   | 3.68129957906975 | interferon gamma receptor 1                                |
| IL10     | 3.39301077350483 | interleukin 10                                             |
| IL12A    | 3.44597274350518 | interleukin 12A                                            |
| IL12B    | 3.45064200047236 | interleukin 12B                                            |
| IL2      | 3.26387267975032 | interleukin 2                                              |
| IL2RA    | 3.77737122651625 | interleukin 2 receptor subunit alpha                       |
| IL7R     | 3.89020505777676 | interleukin 7 receptor                                     |
| IRF1     | 3.55134023208536 | interferon regulatory factor 1                             |
| IRF3     | 3.45328702733898 | interferon regulatory factor 3                             |
| IRF5     | 3.52267944150431 | interferon regulatory factor 5                             |
| IRF9     | 3.55064925773335 | interferon regulatory factor 9                             |
| LCK      | 3.18952838424379 | LCK proto-oncogene, Src family tyrosine kinase             |
| MAPK1    | 4.25349978965674 | mitogen-activated protein kinase 1                         |
| MYC      | 3.38620467690507 | MYC proto-oncogene, bHLH transcription factor              |
| NFKB1    | 3.73865859299998 | nuclear factor kappa B subunit 1                           |
| NFKBIA   | 3.47571622561559 | NFKB inhibitor alpha                                       |
| SOCS1    | 3.70599921146337 | suppressor of cytokine signaling 1                         |
| STAT3    | 3.40946436915169 | signal transducer and activator of transcription 3         |
| TNFRSF1A | 3.62110604866511 | TNF receptor superfamily member 1A                         |
| TRAF2    | 2.95384907354841 | TNF receptor associated factor 2                           |
| TRAF3    |  3.2668223879653 | TNF receptor associated factor 3                           |
| TYK2     |  3.5565594737874 | tyrosine kinase 2                                          |
| VCAM1    | 3.46078655932035 | vascular cell adhesion molecule 1                          |
| ZAP70    | 3.15408662162023 | zeta chain of T cell receptor associated protein kinase 70 |
+----------+------------------+------------------------------------------------------------+
    

MAIN PAPERS

Main papers (the approach, the review and the infrastructures)
    Priority index: database of genetic targets in immune-mediated disease

    Nucleic Acids Research 2021

    DOI: 10.1093/nar/gkab994
    From genome-wide association studies to rational drug target prioritisation in inflammatory arthritis

    Lancet Rheumatology 2020

    DOI: 10.1016/S2665-9913(19)30134-1
    A genetics-led approach defines the drug target landscape of 30 immune-related traits

    Nature Genetics 2019

    DOI: 10.1038/s41588-019-0456-1 Access the recommendation on F1000Prime

EDITORIALS

Editorials (comments, news and views)

SELECTED CITATIONS

Selected papers using or citing Pi
    SNP-to-gene linking strategies reveal contributions of enhancer-related and candidate master-regulator genes to autoimmune disease

    Cell Genomics 2022

    DOI: 10.1016/j.xgen.2022.100145

    Epigenomic analysis reveals a dynamic and context-specific macrophage enhancer landscape associated with innate immune activation and tolerance

    Genome Biology 2022

    DOI: 10.1186/s13059-022-02702-1

    A molecular map of T cell activation gives insights into immune disease

    Nature Genetics 2022

    DOI: 10.1038/s41588-022-01067-2

    Comparative genetic analysis of psoriatic arthritis and psoriasis for the discovery of genetic risk factors and risk prediction modelling

    Arthritis & Rheumatology 2022

    DOI: 10.1002/art.42154

    Partitioning gene-level contributions to complex-trait heritability by allele frequency identifies disease-relevant genes

    American Journal of Human Genetics 2022

    DOI: 10.1016/j.ajhg.2022.02.012

    A blood atlas of COVID-19 defines hallmarks of disease severity and specificity

    Cell 2022

    DOI: 10.1016/j.cell.2022.01.012

    Identification of genetic risk loci and prioritization of genes and pathways for myasthenia gravis: a genome-wide association study

    PNAS 2022

    DOI: 10.1073/pnas.2108672119

    Drug repositioning trends in rare and intractable diseases

    Drug Discovery Today 2022

    DOI: 10.1016/j.drudis.2022.01.013

    Fine mapping with epigenetic information and 3D structure

    Seminars in Immunopathology 2022

    DOI: 10.1007/s00281-021-00906-4

    Advancing the use of genome-wide association studies for drug repurposing

    Nature Reviews Genetics 2021

    DOI: 10.1038/s41576-021-00387-z

    Endophenotype-based in silico network medicine discovery combined with insurance record data mining identifies sildenafil as a candidate drug for Alzheimer’s disease

    Nature Aging 2021

    DOI: 10.1038/s43587-021-00138-z

    An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci

    Nature Genetics 2021

    DOI: 10.1038/s41588-021-00945-5

    Recent advances in understanding the genetic basis of systemic lupus erythematosus

    Seminars in Immunopathology 2021

    DOI: 10.1007/s00281-021-00900-w

    Genetic overlap between type 1 diabetes and other autoimmune diseases

    Seminars in Immunopathology 2021

    DOI: 10.1007/s00281-021-00885-6

    Immune checkpoint blockade sensitivity and progression-free survival associates with baseline CD8 + T cell clone size and cytotoxicity

    Science Immunology 2021

    DOI: 10.1126/sciimmunol.abj8825

    Genomic insights into myasthenia gravis identify distinct immunological mechanisms in early and late onset disease

    Annals of Neurology 2021

    DOI: 10.1002/ana.26169

    Identifying therapeutic drug targets using bidirectional effect genes

    Nature Communications 2021

    DOI: 10.1038/s41467-021-21843-8

    Fine-mapping, trans-ancestral and genomic analyses identify causal variants, cells, genes and drug targets for type 1 diabetes

    Nature Genetics 2021

    DOI: 10.1038/s41588-021-00880-5

    Genome-wide meta-analysis, fine-mapping and integrative prioritization implicate new Alzheimer’s disease risk genes

    Nature Genetics 2021

    DOI: 10.1038/s41588-020-00776-w

    Facing the urgency of therapies for progressive MS — a Progressive MS Alliance proposal

    Nature Reviews Neurology 2021

    DOI: 10.1093/nar/gkaa1027

    A catalog of GWAS fine-mapping efforts in autoimmune disease

    American Journal of Human Genetics 2021

    DOI: 10.1016/j.ajhg.2021.03.009

    Open Targets Platform: supporting systematic drug–target identification and prioritisation

    Nucleic Acids Research 2021

    DOI: 10.1093/nar/gkaa1027

    Host genetics and infectious disease: new tools, insights and translational opportunities

    Nature Reviews Genetics 2020

    DOI: 10.1038/s41576-020-00297-6

    The support of genetic evidence for cardiovascular risk induced by antineoplastic drugs

    Science Advances 2020

    DOI: 10.1126/sciadv.abb8543

    RIPK1 gene variants associate with obesity in humans and can be therapeutically silenced to reduce obesity in mice

    Nature Metabolism 2020

    DOI: 10.1038/s42255-020-00279-2

    Complex genetic signatures in immune cells underlie autoimmunity and inform therapy

    Nature Genetics 2020

    DOI: 10.1038/s41588-020-0684-4

    Tissue-specific genetic features inform prediction of drug side effects in clinical trials

    Science Advances 2020

    DOI: 10.1126/sciadv.abb6242

    Identifying collagen VI as a target of fibrotic diseases regulated by CREBBP/EP300

    Proceedings of the National Academy of Sciences of the United States of America 2020

    DOI: 10.1073/pnas.2004281117

    Peripheral CD8+ T cell characteristics associated with durable responses to immune checkpoint blockade in patients with metastatic melanoma

    Nature Medicine 2020

    DOI: 10.1038/s41591-019-0734-6

    Drug repurposing to improve treatment of rheumatic autoimmune inflammatory diseases

    Nature Reviews Rheumatology 2019

    DOI: 10.1038/s41584-019-0337-0

    Unique transcriptome signatures and GM-CSF expression in lymphocytes from patients with spondyloarthritis

    Nature Communications 2017

    DOI: 10.1038/s41467-017-01771-2


2022 © Priority index | Accelerating drug target discovery leveraging human genetics - in all major immune-mediated diseases (individually and combinatorially) - at the gene and pathway crosstalk level - under the discovery and supervised mode | Developed and Maintained by Profs. Hai Fang  and Julian C. Knight