Evaluating tools for transcription factor binding site prediction

Jayaram, Narayan; Usvyat, Daniel; Martin, Andrew C.R.

doi:10.1186/s12859-016-1298-9

Cited by 91 publications

(69 citation statements)

References 94 publications

(142 reference statements)

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“…The performance by which TF binding models are able to distinguish their binding regions from random genomic regions has been well characterized 8,9 . To assess how well these predictors perform at 2/36 identifying the impact of variants, known regulatory variants are often employed, which include variants from the Human Genome Mutation Database (HGMD), genome-wide association studies (GWAS), and quantitative trait loci (QTL) studies 10-12 .…”

Section: Introductionmentioning

confidence: 99%

Allele-specific transcription factor binding as a benchmark for assessing variant impact predictors

Wagih

Merico

Delong

et al. 2018

Preprint

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Genetic variation has long been known to alter transcription factor binding sites, resulting in sometimes major phenotypic consequences. While the performance for current binding site predictors is well characterised, little is known on how these models perform at predicting the impact of variants. We collected and curated over 132,000 potential allele-specific binding (ASB) ChIP-seq variants across 101 transcription factors (TFs). We then assessed the accuracy of TF binding models from five different methods on these high-confidence measurements, finding that deep learning methods were the best performing yet still have room for improvement. Importantly, machine learning methods were consistently better than the venerable position weight matrix (PWM). Finally, predictions for certain TFs were consistently poor, and our investigation supports efforts to use features beyond sequence, such as methylation, DNA shape, and post-translational modifications. We submit that ASB data is a valuable benchmark for variant impact on TF binding. 23/3624/36

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Section: Introductionmentioning

confidence: 99%

Allele-specific transcription factor binding as a benchmark for assessing variant impact predictors

Wagih

Merico

Delong

et al. 2018

Preprint

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“…The complexity of regulator-target relationship applies to transcription regulation as well; a transcription factor usually regulates multiple genes, and a gene is always regulated by multiple transcription factors. And, in both cases, the consensus binding sites are short, leading to low signal-to-noise ratio in computational site predictions (40). Evolutionary conservation helped, in both cases, to alleviate this technical difficulty (41).…”

Section: Discussionmentioning

confidence: 99%

Intensive and Specific Feedback Self-control of the Argonautes and MicroRNA Targeting Activity

Wang

Gill

Zhang

2018

Preprint

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The miRNA pathway consists of three segments -biogenesis, targeting and downstream regulatory effectors. How the cells control their activities remains incompletely understood. This study explored the intrinsically complex miRNA-mRNA targeting relationships, and suggested differential mechanistic control of the three segments. We first analyzed evolutionarily conserved sites for conserved miRNAs in the human transcriptome. Strikingly, AGO1, AGO2 and AGO3 are all among the top 14 mRNAs with highest numbers of unique conserved miRNA sites, and so is ANKRD52, the phosphatase regulatory subunit of the recently identified AGO phosphorylation cycle (AGOs, CSNK1A1, ANKRD52 and PPP6C). The mRNAs for TNRC6, which acts together with loaded AGO to channel miRNA-mediated regulation actions onto specific mRNAs, are also heavily miRNA-targeted. Moreover, mRNAs of the AGO phosphorylation cycle share much more than expected miRNA binding sites. In contrast, upstream miRNA biogenesis mRNAs do not display these characteristics, and neither do the downstream regulatory effector mRNAs. In a word, miRNAs heavily and directly feedbackregulate their targeting machinery mRNAs, but neither upstream biogenesis nor downstream regulatory effector mRNAs. The observation was then confirmed with experimentally determined miRNA-mRNA target relationships. In summary, our exploration of the miRNA-mRNA target relationship uncovers intensive, and specific, feedback auto-regulation of miRNA targeting activity directly by miRNAs themselves, i.e., segment-specific feedback autoregulation of miRNA pathway. Our results also suggest that the complexity of miRNA-mRNA targeting relationship -a defining feature of miRNA biology -should be a rich source for further functional exploration.

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“…Numerous motif analysis tools have been published in the past decade in order to prioritize important TFs for future validation (Jayaram et al, 2016;Boeva, 2016). One major category of tools identify enriched motifs that appear more frequently at given regions of interest than random genomic regions (Heinz et al, 2010;Machanick and Bailey, 2011;Siebert and Söding, 2016).…”

Section: Introductionmentioning

confidence: 99%

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

Shen

Hoeksema

Ouyang

et al. 2020

Preprint

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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 TFs are prone to be affected by a given variant. 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. Here, we present MAGGIE, a novel method for identifying motifs mediating TF binding and function. By leveraging measurements from diverse genotypes, MAGGIE uses a statistical approach to link mutation 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 to the state-of-the-art motif analysis approaches. We use MAGGIE to reveal insights into the divergent functions of distinct NF-κB factors in the pro-inflammatory macrophages, showing its promise in discovering novel functions of TFs. The Python package for MAGGIE is freely available at https://github.com/zeyang-shen/maggie.

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Evaluating tools for transcription factor binding site prediction

Cited by 91 publications

References 94 publications

Allele-specific transcription factor binding as a benchmark for assessing variant impact predictors

Allele-specific transcription factor binding as a benchmark for assessing variant impact predictors

Intensive and Specific Feedback Self-control of the Argonautes and MicroRNA Targeting Activity

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

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