ModEx

A text mining system for extracting mode of regulation of Transcription Factor-gene regulatory interaction

Overview

Deciphering the network of TF-target interactions with information on mode of regulation (activation vs. repression) is an important step toward understanding the regulatory pathways that underlie complex traits. There are many experimental, computational, and manually curated databases of TF-gene interactions. In particular, high-throughput ChIP-seq datasets provide a large-scale map or transcriptional regulatory interactions.

However, these interactions are not annotated with information on context and mode of regulation. Such information is crucial to gain a global picture of gene regulatory mechanisms and can aid in developing machine learning models for applications such as biomarker discovery, prediction of response to therapy, and precision medicine.

we introduce a text-mining system, to annotate ChIP-seq derived interaction with such meta data through mining PubMed articles.

Dependencies and Installation

ModEx can be installed using the GitHub repository. All of the dependencies will be installed via setup.py script.

git clone https://github.com/samanfrm/ModEx
cd gbnet
python3 setup.py install --user
cd ..

Import libraries

We need to import required libraries into the script:

import pandas as pd
import functions as fn
import os

Run the system for a sample query interaction

First, the paths to necessary files and dictionaries must be defined based on relative path from script directory:

input_directory=os.path.realpath('../Data')

Positive=[]
[Positive.append(line.strip().upper()) for line in open(input_directory+"/Positive.txt")]
Negative=[]
[Negative.append(line.strip().upper()) for line in open(input_directory+"/Negative.txt")]

genes_ents=input_directory + "/ALL_Human_Genes_Info.csv"
genes=pd.read_csv(genes_ents,sep=',',header=(0))
genes.fillna('', inplace=True)

lookup_ids=pd.read_csv(input_directory+"/ncbi_id_lookup.csv",sep='\t',header=(0))

Then, we need to create the query variables and assign them with the transcription factor and target genes entrez IDs respectively:

# [TF_ID, Target_ID]
query_id=[26574,4609]

Next, we need to set the binding port to Stanford CoreNLP as the parser:

parser_port="8000"

Also, optional values for the MeSH term and email address should be defined:

mesh='humans'
email='[email protected]'

Finally, we can run the test mining system to annotate the query interaction as well as associated evidence and citations:

res=fn.modex(query_id,parser_port,Positive,Negative,lookup_ids,genes,mesh,email)

The result is a dataframe including mode of regulation and all of the associated citations and evidence sentences for the annotation:

src_entrez	trg_entrez	srcname	trgname	mode	score	evi_pmid	evi_sent
26574	4609	AATF	MYC	positive	4	20924650;2054...	[20924650]WE HAVE UNAMB...

Citation

Saman Farahmand, Todd Riley, Kourosh Zarringhalam, "ModEx: A text mining system for extracting mode of regulation of Transcription Factor-gene regulatory interaction", BioRxiv, 2019