Correcting signal biases and detecting regulatory elements in STARR-seq data

Kim, Young-Sook; Johnson, Graham D.; Seo, Jungkyun; Barrera, Alejandro; Cowart, Thomas N.; Majoros, William H.; Ochoa, Alejandro; Allen, Andrew S.; Reddy, Timothy E.

doi:10.1101/gr.269209.120

Cited by 12 publications

(25 citation statements)

References 46 publications

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“…In this study, we presented a program of Fast-NR, in which both read counts and shape similarity are considered, for the detection of NREs using STARR-seq datasets and compare its performance with other four programs of csaw (Lun and Smyth, 2016), MEDIPS (Lienhard et al, 2014), PePr (Zhang et al, 2014), and CRADLE (Kim et al, 2021). Among them, MEDIPS, designed for DNA methylation analysis, shows worst compatibility with silencer identification on either simulated or experimentally generated datasets.…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, only 56% silencers identified by CRADLE overlapped with the reported silencers (Figure 3F). These results suggest that many CRADLE-specific silencers were identified because of the heavy correction procedures that are integral to CRADLE (Kim et al, 2021). These CRADLE-specific silencers seemed to be "falsepositives" in terms of the reduction rate in the reporter cDNA reads.…”

Section: Performance Of Fast-nr and Other Potential Nre Identificatio...mentioning

confidence: 97%

“…We simulated a pair of STARR-seq datasets to test the NRE identification powers of Fast-NR and other methods including csaw (Lun and Smyth, 2016), MEDIPS (Lienhard et al, 2014), PePr (Zhang et al, 2014), and CRADLE (Kim et al, 2021) (see Supplementary Table S1) using default parameters. We used the input insert reads, which are acquired by sequencing plasmids recovered from the transfected cells, and mapped them to chromosome 22 in the enhancer-screening STARR-seq experiment in A549 cells (GSE114063) as the simulation basis.…”

Section: Performance Of Fast-nr and Other Potential Nre Identificatio...mentioning

confidence: 99%

“…However, the specificity, robustness, accuracy, and resolution of these programs have not been evaluated for silencer identification. CRADLE, a recently published method, is designed for enhancer identification (Kim et al, 2021). Theoretically, CRADLE can detect silencers as well.…”

Section: Introductionmentioning

confidence: 99%

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Integration of Count Difference and Curve Similarity in Negative Regulatory Element Detection

Wang

Fang

et al. 2022

Front. Genet.

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Negative regulatory elements (NREs) down-regulate gene expression by inhibiting the activities of promoters or enhancers. The repressing activity of NREs can be measured globally by massively parallel reporter assays (MPRAs). However, most existing algorithms are designed for the statistical detection of positively enriched signals in MPRA datasets. To identify reduced signals in MPRA experiments, we designed a NRE identification program, fast-NR, by integrating the count and graphic features of sequenced reads to detect NREs using datasets generated by experiments of self-transcribing active regulatory region sequencing (STARR-seq). Fast-NR identified hundreds of silencers in human K562 cells that can be validated by independent methods.

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Section: Discussionmentioning

confidence: 99%

Section: Performance Of Fast-nr and Other Potential Nre Identificatio...mentioning

confidence: 97%

Section: Performance Of Fast-nr and Other Potential Nre Identificatio...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Integration of Count Difference and Curve Similarity in Negative Regulatory Element Detection

Wang

Fang

et al. 2022

Front. Genet.

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“…More recently, with the availability of fully sequenced genomes and next-generation sequencing methods, high-throughput methods have been developed to functionally assay putative CRMs on a genomic scale (e.g., [ 13 , 14 , 15 ]). STARR-seq, which elegantly converts CRMs into their own reporters by cloning them downstream of a core promoter and sequencing the output, is one increasingly popular method [ 14 , 16 , 17 , 18 , 19 , 20 ]. Although reporter-based methods have long been viewed as a gold standard, due to the fact that they provide a direct functional readout of regulatory activity, there is growing recognition that these methods can lead to both false-positive and false-negative results [ 7 , 8 , 21 ].…”

Section: Empirical Approaches To Crm Discoverymentioning

confidence: 99%

Annotating the Insect Regulatory Genome

Asma

Halfon

2021

Insects

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An ever-growing number of insect genomes is being sequenced across the evolutionary spectrum. Comprehensive annotation of not only genes but also regulatory regions is critical for reaping the full benefits of this sequencing. Driven by developments in sequencing technologies and in both empirical and computational discovery strategies, the past few decades have witnessed dramatic progress in our ability to identify cis-regulatory modules (CRMs), sequences such as enhancers that play a major role in regulating transcription. Nevertheless, providing a timely and comprehensive regulatory annotation of newly sequenced insect genomes is an ongoing challenge. We review here the methods being used to identify CRMs in both model and non-model insect species, and focus on two tools that we have developed, REDfly and SCRMshaw. These resources can be paired together in a powerful combination to facilitate insect regulatory annotation over a broad range of species, with an accuracy equal to or better than that of other state-of-the-art methods.

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Identifying transcription factor-bound activators and silencers in the chromatin accessible human genome using ATAC-STARR-seq

Hodges

Hansen

2022

Preprint

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Massively parallel reporter assays test the capacity of putative gene regulatory elements to drive transcription on a genome-wide scale. Most gene regulatory activity occurs within accessible chromatin, and recently described methods have combined assays that capture these regions — such as assay for transposase-accessible chromatin using sequencing (ATAC-seq) — with self-transcribing active regulatory region sequencing (STARR-seq) to selectively assay the regulatory potential of accessible DNA (ATAC-STARR-seq). Here, we report a multi-omic approach that quantifies regulatory activity, chromatin accessibility, and transcription factor (TF) occupancy with one assay using ATAC-STARR-seq. Our strategy, including important updates to the ATAC-STARR-seq assay design and workflow, enabled high-resolution testing of ~50 million unique DNA fragments tiling ~101,000 accessible chromatin regions in human lymphoblastoid cells. We discovered that 30% of all accessible regions contain an activator, a silencer or both. We demonstrate that activators and silencers represent distinct functional groups that are enriched for unique sets of TF motifs and are marked by specific combinations of histone modifications. Using Tn5 cut-sites retained by the ATAC-STARR library, we performed TF footprinting and stratified these groups by the presence of specific TF footprints that are supported by chromatin immunoprecipitation data. We found that activators and silencers clustered by distinct TF footprint combinations are enriched for distinct gene regulatory pathways, and thus, represent distinct gene regulatory networks of human lymphoblastoid cell function. Altogether, these data highlight the multi-facted capabilities of ATAC-STARR-seq to comprehensively investigate the regulatory landscape of the human genome all from a single DNA fragment source.

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Correcting signal biases and detecting regulatory elements in STARR-seq data

Cited by 12 publications

References 46 publications

Integration of Count Difference and Curve Similarity in Negative Regulatory Element Detection

Integration of Count Difference and Curve Similarity in Negative Regulatory Element Detection

Annotating the Insect Regulatory Genome

Identifying transcription factor-bound activators and silencers in the chromatin accessible human genome using ATAC-STARR-seq

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