Unlocking the secrets of the genome

Celniker, S; Dillon, Laura A. L.; Gerstein, Mark; Gunsalus, Kristin C.; Henikoff, Steven; Karpen, Gary H.; Kellis, M; Lai, Eric C.; Lieb, Jason D.; MacAlpine, David M.; Micklem, Gos; Piano, Fabio; Snyder, M; Stein, Lincoln; White, Kevin P.; Waterston, Robert H.

doi:10.1038/459927a

Cited by 768 publications

(904 citation statements)

References 35 publications

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“…Based on the genomes of well-characterized model systems, multicellular organisms dedicate a significant portion of their protein coding genes (6-8%) to the expression of between 1000-2500 DNA-binding transcription factors 3 . With the exception of data from ENCODE and modENCODE projects [4][5][6][7] , in vivo genome-wide TF location data is available for relatively few TFs. To expand this analysis for a wider range of organisms, scalable methods are needed for the low-cost and high-throughput examination of thousands of TFs.…”

Section: Introductionmentioning

confidence: 99%

Mapping genome-wide transcription-factor binding sites using DAP-seq

Bartlett¹,

O’Malley²,

Huang³

et al. 2017

Nat Protoc

441

285

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In order to enable low-cost, high-throughput generation of cistrome and epicistrome maps for any organism, we developed DNA affinity purification sequencing (DAP-seq), a transcription factor (TF) binding site discovery assay that couples affinity-purified TFs with next-generation sequencing of a genomic DNA library. The method is fast, inexpensive, and more easily scaled than ChIP-seq. DNA libraries are constructed using native genomic DNA from any source of interest, preserving cell-and tissue-specific chemical modifications that are known to impact TF binding (such as DNA methylation) and providing increased specificity compared to in silico motif prediction methods such as protein binding microarrays (PBM) and systematic evolution of ligands by exponential enrichment (SELEX). The resulting DNA library is incubated with an affinity-tagged in vitro expressed TF, and TF-DNA complexes are purified using magnetic separation of the affinity tag. Bound genomic DNA is eluted from the TF and sequenced using next-generation sequencing. Sequence reads are mapped to a reference genome, identifying genome-wide binding locations for each TF assayed, from which sequence motifs can then be

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Section: Introductionmentioning

confidence: 99%

Mapping genome-wide transcription-factor binding sites using DAP-seq

Bartlett¹,

O’Malley²,

Huang³

et al. 2017

Nat Protoc

441

285

View full text Add to dashboard Cite

show abstract

“…ChIP-seq is widely used, with the majority of data provided by the ENCODE (The ENCODE Project Consortium 2012) and modENCODE (Celniker et al 2009) projects produced with this technology. After mapping the NGS reads, the main part of the quantitative analysis is to infer the genomic sites where the protein of interest binds by finding regions with an enrichment of mapped reads.…”

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confidence: 99%

Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-seq data

Teng

Irizarry

2017

Genome Res.

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The main application of ChIP-seq technology is the detection of genomic regions that bind to a protein of interest. A large part of functional genomics' public catalogs is based on ChIP-seq data. These catalogs rely on peak calling algorithms that infer protein-binding sites by detecting genomic regions associated with more mapped reads (coverage) than expected by chance, as a result of the experimental protocol's lack of perfect specificity. We find that GC-content bias accounts for substantial variability in the observed coverage for ChIP-seq experiments and that this variability leads to false-positive peak calls. More concerning is that the GC effect varies across experiments, with the effect strong enough to result in a substantial number of peaks called differently when different laboratories perform experiments on the same cell line. However, accounting for GC content bias in ChIP-seq is challenging because the binding sites of interest tend to be more common in high GC-content regions, which confounds real biological signals with unwanted variability. To account for this challenge, we introduce a statistical approach that accounts for GC effects on both nonspecific noise and signal induced by the binding site. The method can be used to account for this bias in binding quantification as well to improve existing peak calling algorithms. We use this approach to show a reduction in false-positive peaks as well as improved consistency across laboratories.

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“…As part of the modENCODE project (Celniker et al 2009), we used two independent methods, CAGE and 59 RLM-RACE, to map and validate TSS distributions within promoter regions of long capped transcripts expressed at significant levels, either maternally or zygotically, in the developing D. melanogaster embryo. These methods are complementary in two ways.…”

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confidence: 99%

Genome-wide analysis of promoter architecture in Drosophila melanogaster

Hoskins

Landolin²,

Brown³

et al. 2010

Genome Res.

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226

344

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Core promoters are critical regions for gene regulation in higher eukaryotes. However, the boundaries of promoter regions, the relative rates of initiation at the transcription start sites (TSSs) distributed within them, and the functional significance of promoter architecture remain poorly understood. We produced a high-resolution map of promoters active in the Drosophila melanogaster embryo by integrating data from three independent and complementary methods: 21 million cap analysis of gene expression (CAGE) tags, 1.2 million RNA ligase mediated rapid amplification of cDNA ends (RLM-RACE) reads, and 50,000 cap-trapped expressed sequence tags (ESTs). We defined 12,454 promoters of 8037 genes. Our analysis indicates that, due to non-promoter-associated RNA background signal, previous studies have likely overestimated the number of promoter-associated CAGE clusters by fivefold. We show that TSS distributions form a complex continuum of shapes, and that promoters active in the embryo and adult have highly similar shapes in 95% of cases. This suggests that these distributions are generally determined by static elements such as local DNA sequence and are not modulated by dynamic signals such as histone modifications. Transcription factor binding motifs are differentially enriched as a function of promoter shape, and peaked promoter shape is correlated with both temporal and spatial regulation of gene expression. Our results contribute to the emerging view that core promoters are functionally diverse and control patterning of gene expression in Drosophila and mammals.

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Unlocking the secrets of the genome

Abstract: Despite the successes of genomics, little is known about how genetic information produces complex organisms. A look at the crucial functional elements of fly and worm genomes could change that.

Cited by 768 publications

References 35 publications

Mapping genome-wide transcription-factor binding sites using DAP-seq

Mapping genome-wide transcription-factor binding sites using DAP-seq

Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-seq data

Genome-wide analysis of promoter architecture in Drosophila melanogaster

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