Dataset Information

MAGGIE: leveraging genetic variation to identify DNA sequence motifs mediating transcription factor binding and function.

ABSTRACT: MOTIVATION:Genetic variation in regulatory elements can alter transcription factor (TF) binding by mutating a TF binding motif, which in turn may affect the activity of the regulatory elements. However, it is unclear which motifs are prone to impact transcriptional regulation if mutated. Current motif analysis tools either prioritize TFs based on motif enrichment without linking to a function or are limited in their applications due to the assumption of linearity between motifs and their functional effects. RESULTS:We present MAGGIE (Motif Alteration Genome-wide to Globally Investigate Elements), a novel method for identifying motifs mediating TF binding and function. By leveraging measurements from diverse genotypes, MAGGIE uses a statistical approach to link mutations of a motif to changes of an epigenomic feature without assuming a linear relationship. We benchmark MAGGIE across various applications using both simulated and biological datasets and demonstrate its improvement in sensitivity and specificity compared with the state-of-the-art motif analysis approaches. We use MAGGIE to gain novel insights into the divergent functions of distinct NF-?B factors in pro-inflammatory macrophages, revealing the association of p65-p50 co-binding with transcriptional activation and the association of p50 binding lacking p65 with transcriptional repression. AVAILABILITY AND IMPLEMENTATION:The Python package for MAGGIE is freely available at https://github.com/zeyang-shen/maggie. The accession number for the NF-?B ChIP-seq data generated for this study is Gene Expression Omnibus: GSE144070. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

SUBMITTER: Shen Z

PROVIDER: S-EPMC7355228 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

MAGGIE: leveraging genetic variation to identify DNA sequence motifs mediating transcription factor binding and function.

Shen Zeyang Z Hoeksema Marten A MA Ouyang Zhengyu Z Benner Christopher C Glass Christopher K CK

Bioinformatics (Oxford, England) 20200701 Suppl_1

<h4>Motivation</h4>Genetic variation in regulatory elements can alter transcription factor (TF) binding by mutating a TF binding motif, which in turn may affect the activity of the regulatory elements. However, it is unclear which motifs are prone to impact transcriptional regulation if mutated. Current motif analysis tools either prioritize TFs based on motif enrichment without linking to a function or are limited in their applications due to the assumption of linearity between motifs and their ...[more]

PMID: 32657363

Similar Datasets

Project description:BackgroundClassically, models of DNA-transcription factor binding sites (TFBSs) have been based on relatively few known instances and have treated them as sites of fixed length using position weight matrices (PWMs). Various extensions to this model have been proposed, most of which take account of dependencies between the bases in the binding sites. However, some transcription factors are known to exhibit some flexibility and bind to DNA in more than one possible physical configuration. In some cases this variation is known to affect the function of binding sites. With the increasing volume of ChIP-seq data available it is now possible to investigate models that incorporate this flexibility. Previous work on variable length models has been constrained by: a focus on specific zinc finger proteins in yeast using restrictive models; a reliance on hand-crafted models for just one transcription factor at a time; and a lack of evaluation on realistically sized data sets.ResultsWe re-analysed binding sites from the TRANSFAC database and found motivating examples where our new variable length model provides a better fit. We analysed several ChIP-seq data sets with a novel motif search algorithm and compared the results to one of the best standard PWM finders and a recently developed alternative method for finding motifs of variable structure. All the methods performed comparably in held-out cross validation tests. Known motifs of variable structure were recovered for p53, Stat5a and Stat5b. In addition our method recovered a novel generalised version of an existing PWM for Sp1 that allows for variable length binding. This motif improved classification performance.ConclusionsWe have presented a new gapped PWM model for variable length DNA binding sites that is not too restrictive nor over-parameterised. Our comparison with existing tools shows that on average it does not have better predictive accuracy than existing methods. However, it does provide more interpretable models of motifs of variable structure that are suitable for follow-up structural studies. To our knowledge, we are the first to apply variable length motif models to eukaryotic ChIP-seq data sets and consequently the first to show their value in this domain. The results include a novel motif for the ubiquitous transcription factor Sp1.

Project description:A number of previous studies have predicted transcription factor binding sites (TFBSs) by exploiting the position of genomic landmarks like the transcriptional start site (TSS). The studies' methods are generally too computationally intensive for genome-scale investigation, so the full potential of 'positional regulomics' to discover TFBSs and determine their function remains unknown. Because databases often annotate the genomic landmarks in DNA sequences, the methodical exploitation of positional regulomics has become increasingly urgent. Accordingly, we examined a set of 7914 human putative promoter regions (PPRs) with a known TSS. Our methods identified 1226 eight-letter DNA words with significant positional preferences with respect to the TSS, of which only 608 of the 1226 words matched known TFBSs. Many groups of genes whose PPRs contained a common word displayed similar expression profiles and related biological functions, however. Most interestingly, our results included 78 words, each of which clustered significantly in two or three different positions relative to the TSS. Often, the gene groups corresponding to different positional clusters of the same word corresponded to diverse functions, e.g. activation or repression in different tissues. Thus, different clusters of the same word likely reflect the phenomenon of 'positional regulation', i.e. a word's regulatory function can vary with its position relative to a genomic landmark, a conclusion inaccessible to methods based purely on sequence. Further integrative analysis of words co-occurring in PPRs also yielded 24 different groups of genes, likely identifying cis-regulatory modules de novo. Whereas comparative genomics requires precise sequence alignments, positional regulomics exploits genomic landmarks to provide a 'poor man's alignment'. By exploiting the phenomenon of positional regulation, it uses position to differentiate the biological functions of subsets of TFBSs sharing a common sequence motif.

Dataset Information

MAGGIE: leveraging genetic variation to identify DNA sequence motifs mediating transcription factor binding and function.

Publications

MAGGIE: leveraging genetic variation to identify DNA sequence motifs mediating transcription factor binding and function.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets