Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data

Piqué-Regi, Roger; Degner, Jacob F.; Pai, Athma A.; Gaffney, Daniel J.; Gilad, Yoav; Pritchard, Jonathan K.

doi:10.1101/gr.112623.110

Cited by 514 publications

(627 citation statements)

References 44 publications

(49 reference statements)

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“…4). The high GC content in these states is consistent with high gene density (32), and their open chromatin architecture enables high mutagenesis as well as coding function and associated regulation (21,44). The deletion/substitution-warm and cold autosomal states show trends opposite to those of hot and insertion-warm states, and the microsatellite state is depleted in genes and functional marks (Fig.…”

Section: Resultsmentioning

confidence: 87%

Segmenting the human genome based on states of neutral genetic divergence

Don¹,

Ananda

Chiaromonte³

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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Many studies have demonstrated that divergence levels generated by different mutation types vary and covary across the human genome. To improve our still-incomplete understanding of the mechanistic basis of this phenomenon, we analyze several mutation types simultaneously, anchoring their variation to specific regions of the genome. Using hidden Markov models on insertion, deletion, nucleotide substitution, and microsatellite divergence estimates inferred from human-orangutan alignments of neutrally evolving genomic sequences, we segment the human genome into regions corresponding to different divergence states-each uniquely characterized by specific combinations of divergence levels. We then parsed the mutagenic contributions of various biochemical processes associating divergence states with a broad range of genomic landscape features. We find that high divergence states inhabit guanine-and cytosine (GC)-rich, highly recombining subtelomeric regions; low divergence states cover inner parts of autosomes; chromosome X forms its own state with lowest divergence; and a state of elevated microsatellite mutability is interspersed across the genome. These general trends are mirrored in human diversity data from the 1000 Genomes Project, and departures from them highlight the evolutionary history of primate chromosomes. We also find that genes and noncoding functional marks [annotations from the Encyclopedia of DNA Elements (ENCODE)] are concentrated in high divergence states. Our results provide a powerful tool for biomedical data analysis: segmentations can be used to screen personal genome variants-including those associated with cancer and other diseases-and to improve computational predictions of noncoding functional elements. W hole-genome sequencing studies have demonstrated that divergence estimates for several mutation types (e.g., nucleotide substitutions, insertions, and deletions) vary substantially across the human genome. This phenomenon has been studied at various genomic scales and evolutionary distances (reviewed in ref. 1), and-whereas initially of interest solely to evolutionary biologists-is now entering the purview of main biomedical research. Specifically, human population (e.g., ref.2) and cancer (3, 4) genome resequencing projects have revealed that incidences of single nucleotide polymorphisms (SNPs), insertions and deletions (indels), and copy number variants (CNVs) vary across the genome. Divergence estimates for different mutation types also covary across the genome (5, 6)-e.g., substitution rates increase in regions with high indel rates (7)-suggesting that regional variation is an important and general characteristic of mutations.Variation in divergence is often linked to genomic landscape features such as base composition, replication timing, and recombination rates (1). For instance, nucleotide substitution rates are elevated in late-replicating regions because of an accumulation of single-stranded DNA susceptible to endogenous damage (8) and are affected by chromatin structure (9) and ...

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Section: Resultsmentioning

confidence: 87%

Segmenting the human genome based on states of neutral genetic divergence

Don¹,

Ananda

Chiaromonte³

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

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“…Therefore, dPCA analysis of multiple surrogate datasets (e.g., HM ChIP-seq) provides a solution to unsupervised characterization of gene regulation dynamics, and it allows one to infer differential binding of many TFs simultaneously using the same set of experiments. Unlike several recent studies that use surrogates to predict TF binding in one condition (19,20), dPCA allows one to predict dynamic changes of TF binding across conditions.…”

Section: Example I: Analysis Of Differential Chromatin Patterns At Tfmentioning

confidence: 99%

Differential principal component analysis of ChIP-seq

Wang

Yang

2013

Proc. Natl. Acad. Sci. U.S.A.

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We propose differential principal component analysis (dPCA) for analyzing multiple ChIP- sequencing datasets to identify differential protein–DNA interactions between two biological conditions. dPCA integrates unsupervised pattern discovery, dimension reduction, and statistical inference into a single framework. It uses a small number of principal components to summarize concisely the major multiprotein synergistic differential patterns between the two conditions. For each pattern, it detects and prioritizes differential genomic loci by comparing the between-condition differences with the within-condition variation among replicate samples. dPCA provides a unique tool for efficiently analyzing large amounts of ChIP-sequencing data to study dynamic changes of gene regulation across different biological conditions. We demonstrate this approach through analyses of differential chromatin patterns at transcription factor binding sites and promoters as well as allele-specific protein–DNA interactions.

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“…Many studies have noted the difficulty of accurately predicting transcription factor binding from binding site models alone, with few differences in the number or strength of binding sites between regions that are weakly or strongly bound Pique-Regi et al 2011). For each of the 14 factors with welldefined binding sites, we computed the total number of recognition motifs for these factors within 500 bp of each ChIP peak summit as a measure of its naïve binding potential.…”

Section: Tagteam Motifs Demarcate Highly Bound Clusters Of Transcriptmentioning

confidence: 99%

The TAGteam motif facilitates binding of 21 sequence-specific transcription factors in the Drosophila embryo

Satija¹,

Bradley²

2012

Genome Res.

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Highly overlapping patterns of genome-wide binding of many distinct transcription factors have been observed in worms, insects, and mammals, but the origins and consequences of this overlapping binding remain unclear. While analyzing chromatin immunoprecipitation data sets from 21 sequence-specific transcription factors active in the Drosophila embryo, we found that binding of all factors exhibits a dose-dependent relationship with ''TAGteam'' sequence motifs bound by the zinc finger protein Vielfaltig, also known as Zelda, a recently discovered activator of the zygotic genome. TAGteam motifs are present and well conserved in highly bound regions, and are associated with transcription factor binding even in the absence of canonical recognition motifs for these factors. Furthermore, levels of binding in promoters and enhancers of zygotically transcribed genes are correlated with RNA polymerase II occupancy and gene expression levels. Our results suggest that Vielfaltig acts as a master regulator of early development by facilitating the genome-wide establishment of overlapping patterns of binding of diverse transcription factors that drive global gene expression.[Supplemental material is available for this article.]Genome-wide chromatin immunoprecipitation (ChIP) experiments in diverse metazoans have shown that many transcription factors bind thousands of highly overlapping loci in undifferentiated cells (Boyer et al.

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Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data

Cited by 514 publications

References 44 publications

Segmenting the human genome based on states of neutral genetic divergence

Segmenting the human genome based on states of neutral genetic divergence

Differential principal component analysis of ChIP-seq

The TAGteam motif facilitates binding of 21 sequence-specific transcription factors in the Drosophila embryo

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