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-κB 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-κB ChIP-seq data generated for this study is Gene Expression Omnibus: GSE144070. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Mounting evidence suggests several diseases and biological processes target transcription termination to misregulate gene expression. Disruption of transcription termination leads to readthrough transcription past the 3' end of genes, which can result in novel transcripts, changes in epigenetic states and altered 3D genome structure. RESULTS: We developed Automatic Readthrough Transcription Detection (ARTDeco), a tool to detect and analyze multiple features of readthrough transcription from RNA-seq and other next-generation sequencing (NGS) assays that profile transcriptional activity. ARTDeco robustly quantifies the global severity of readthrough phenotypes, and reliably identifies individual genes that fail to terminate (readthrough genes), are aberrantly transcribed due to upstream termination failure (read-in genes), and novel transcripts created as a result of readthrough (downstream of gene or DoG transcripts). We used ARTDeco to characterize readthrough transcription observed during influenza A virus (IAV) infection, validating its specificity and sensitivity by comparing its performance in samples infected with a mutant virus that fails to block transcription termination. We verify ARTDeco's ability to detect readthrough as well as identify read-in genes from different experimental assays across multiple experimental systems with known defects in transcriptional termination, and show how these results can be leveraged to improve the interpretation of gene expression and downstream analysis. Applying ARTDeco to a gene expression data set from IAV-infected monocytes from different donors, we find strong evidence that read-in gene-associated expression quantitative trait loci (eQTLs) likely regulate genes upstream of read-in genes. This indicates that taking readthrough transcription into account is important for the interpretation of eQTLs in systems where transcription termination is blocked. CONCLUSIONS: ARTDeco aids researchers investigating readthrough transcription in a variety of systems and contexts.

BACKGROUND: Motif enrichment analysis (MEA) identifies over-represented transcription factor binding (TF) motifs in the DNA sequence of regulatory regions, enabling researchers to infer which transcription factors can regulate transcriptional response to a stimulus, or identify sequence features found near a target protein in a ChIP-seq experiment. Score-based MEA determines motifs enriched in regions exhibiting extreme differences in regulatory activity, but existing methods do not control for biases in GC content or dinucleotide composition. This lack of control for sequence bias, such as those often found in CpG islands, can obscure the enrichment of biologically relevant motifs. RESULTS: We developed Motif Enrichment In Ranked Lists of Peaks (MEIRLOP), a novel MEA method that determines enrichment of TF binding motifs in a list of scored regulatory regions, while controlling for sequence bias. In this study, we compare MEIRLOP against other MEA methods in identifying binding motifs found enriched in differentially active regulatory regions after interferon-beta stimulus, finding that using logistic regression and covariates improves the ability to call enrichment of ISGF3 binding motifs from differential acetylation ChIP-seq data compared to other methods. Our method achieves similar or better performance compared to other methods when quantifying the enrichment of TF binding motifs from ENCODE TF ChIP-seq datasets. We also demonstrate how MEIRLOP is broadly applicable to the analysis of numerous types of NGS assays and experimental designs. CONCLUSIONS: Our results demonstrate the importance of controlling for sequence bias when accurately identifying enriched DNA sequence motifs using score-based MEA. MEIRLOP is available for download from https://github.com/npdeloss/meirlop under the MIT license.