ABSTRACT
MOTIVATION: Aberrant DNA methylation in transcription factor binding sites has been shown to lead to anomalous gene regulation that is strongly associated with human disease. However, the majority of methylation-sensitive positions within transcription factor binding sites remain unknown. Here we introduce SEMplMe, a computational tool to generate predictions of the effect of methylation on transcription factor binding strength in every position within a transcription factor's motif. RESULTS: SEMplMe uses ChIP-seq and whole genome bisulfite sequencing to predict effects of methylation within binding sites. SEMplMe validates known methylation sensitive and insensitive positions within a binding motif, identifies cell type specific transcription factor binding driven by methylation, and outperforms SELEX-based predictions for CTCF. These predictions can be used to identify aberrant sites of DNA methylation contributing to human disease. AVAILABILITY AND IMPLEMENTATION: SEMplMe is available from https://github.com/Boyle-Lab/SEMplMe .
Subject(s)
DNA Methylation , Transcription Factors , Binding Sites , Gene Expression Regulation , Humans , Protein Binding , Transcription Factors/metabolismABSTRACT
BACKGROUND: Zebrafish are a foundational model organism for studying the spatio-temporal activity of genes and their regulatory sequences. A variety of approaches are currently available for editing genes and modifying gene expression in zebrafish, including RNAi, Cre/lox, and CRISPR-Cas9. However, the lac operator-repressor system, an E. coli lac operon component which has been adapted for use in many other species and is a valuable, flexible tool for inducible modulation of gene expression studies, has not been previously tested in zebrafish. RESULTS: Here we demonstrate that the lac operator-repressor system robustly decreases expression of firefly luciferase in cultured zebrafish fibroblast cells. Our work establishes the lac operator-repressor system as a promising tool for the manipulation of gene expression in whole zebrafish. CONCLUSION: Our results lay the groundwork for the development of lac-based reporter assays in zebrafish, and adds to the tools available for investigating dynamic gene expression in embryogenesis. We believe this work will catalyze the development of new reporter assay systems to investigate uncharacterized regulatory elements and their cell-type specific activities.
ABSTRACT
MOTIVATION: Genome-wide association studies have revealed that 88% of disease-associated single-nucleotide polymorphisms (SNPs) reside in noncoding regions. However, noncoding SNPs remain understudied, partly because they are challenging to prioritize for experimental validation. To address this deficiency, we developed the SNP effect matrix pipeline (SEMpl). RESULTS: SEMpl estimates transcription factor-binding affinity by observing differences in chromatin immunoprecipitation followed by deep sequencing signal intensity for SNPs within functional transcription factor-binding sites (TFBSs) genome-wide. By cataloging the effects of every possible mutation within the TFBS motif, SEMpl can predict the consequences of SNPs to transcription factor binding. This knowledge can be used to identify potential disease-causing regulatory loci. AVAILABILITY AND IMPLEMENTATION: SEMpl is available from https://github.com/Boyle-Lab/SEM_CPP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Subject(s)
Genome-Wide Association Study , Polymorphism, Single Nucleotide , Binding Sites , Chromatin Immunoprecipitation , Protein Binding , Transcription FactorsABSTRACT
One of the formative goals of genetics research is to understand how genetic variation leads to phenotypic differences and human disease. Genome-wide association studies (GWASs) bring us closer to this goal by linking variation with disease faster than ever before. Despite this, GWASs alone are unable to pinpoint disease-causing single nucleotide polymorphisms (SNPs). Noncoding SNPs, which represent the majority of GWAS SNPs, present a particular challenge. To address this challenge, an array of computational tools designed to prioritize and predict the function of noncoding GWAS SNPs have been developed. However, fewer than 40% of GWAS publications from 2015 utilized these tools. We discuss several leading methods for annotating noncoding variants and how they can be integrated into research pipelines in hopes that they will be broadly applied in future GWAS analyses.
