CressInt : A user-friendly web resource for genome-scale exploration of gene regulation in Arabidopsis thaliana

Abstract

The thale cress Arabidopsis thaliana is a powerful model organism for studying a wide variety of biological processes. Recent advances in sequencing technology have resulted in a wealth of information describing numerous aspects of A. thaliana genome function. However, there is a relative paucity of computational systems for efficiently and effectively using these data to create testable hypotheses. We present CressInt, a user-friendly web resource for exploring gene regulatory mechanisms in A. thaliana on a genomic scale. The CressInt system incorporates a variety of genome-wide data types relevant to gene regulation, including transcription factor (TF) binding site models, ChIP-seq, DNase-seq, eQTLs, and GWAS. We demonstrate the utility of CressInt by showing how the system can be used to (1) Identify TFs binding to the promoter of a gene of interest; (2) identify genetic variants that are likely to impact TF binding based on a ChIP-seq dataset; and (3) identify specific TFs whose binding might be impacted by phenotype-associated variants. CressInt is freely available at http://cressint.cchmc.org.

Learning from Co-expression Networks: Possibilities and Challenges

“…One of the common strategies for all co-expression network studies is the integration of disparate data sources for the biological interpretation of networks. As a result, the development of integrative web interfaces such as CressInt (Chen et al, 2015 ) are needed to facilitate the integration of available genomics data. Furthermore, the development of computational tools, such as machine learning based algorithms, although computationally intense, will support the optimal integration and exploitation of prioritization strategies (Radivojac et al, 2013 ).…”

TF2Network: predicting transcription factor regulators and gene regulatory networks in Arabidopsis using publicly available binding site information

“…PlantPAN extends this straightforward promoter analysis by performing gene group analysis and predicts the co-occurrence of TFBSs in the promoter ( 22 ). CressInt is a web resource which integrates a wide range of gene regulation datasets, including TFBSs, genome-wide histone modifications and open chromatin information ( 23 ). As such it does not only identify TFBSs for a promoter of interest but is also able to predict which genetic variants impact the binding ability of a specific TF.…”

TF2Network: predicting transcription factor regulators and gene regulatory networks in Arabidopsis using publicly available binding site information

“…histone modifications and open chromatin information (23). As such it does not only identify TFBSs for a promoter of interest but is also able to predict which genetic variants impact the binding ability of a specific TF.…”

“…Given the limitations associated to simply mapping TFBS to promoters, more advanced filtering schemes have been implemented which increase the specificity to computationally map functional TFBS and construct GRNs. Examples of such filtering steps comprise exploiting conservation of non-coding DNA or the integrating coregulatory gene information with TFBSs (23,(25)(26)(27)(28)(29)(30)(31). These filtering approaches, however, do not fully resolve the problem of false positives and might also suffer from false negatives, where functional binding events remain undetected.…”