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Cis-regulatory Sequences

Cis-regulatory Sequences

Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...

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Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

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Published on: September 16, 2019

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

Young-Sook Kim^1,2,3,4,5, Graham D Johnson^1,2,3,4, Jungkyun Seo^1,2,3,4,5

¹Department of Biostatistics and Bioinformatics, Division of Integrative Genomics, Duke University Medical School, Durham, North Carolina 27710, USA.

Genome Research

|March 16, 2021

Summary

We identified technical biases in self-transcribing active regulatory region sequencing (STARR-seq) data and developed a statistical model to correct them. This improves the detection of regulatory elements across the genome.

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Last Updated: Nov 12, 2025

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Published on: September 16, 2019

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

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High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

Published on: October 5, 2018

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

High-throughput reporter assays like self-transcribing active regulatory region sequencing (STARR-seq) enable genome-wide measurement of regulatory element activity.
STARR-seq data present significant analytical challenges due to inherent technical biases.
Accurate identification of regulatory elements is crucial for understanding gene regulation.

Purpose of the Study:

To identify and characterize technical biases in STARR-seq data.
To develop a statistical model for correcting these biases.
To improve the precision and recall of regulatory element detection using STARR-seq.

Main Methods:

Analysis of STARR-seq data to identify sources of technical variance.
Development and application of a novel statistical model for bias correction.
Comparison of the new method against existing approaches for regulatory element detection.

Main Results:

Technical biases were identified as the primary source of variance in STARR-seq data.
The developed statistical model effectively corrects for these biases.
The improved method demonstrates enhanced precision and recall for detecting both activating and repressive regulatory elements.
The approach successfully controls for false discoveries, even with strong local signal correlations.

Conclusions:

A robust statistical method has been established to address technical biases in STARR-seq data.
This approach significantly enhances the accuracy and reliability of regulatory element identification.
The findings facilitate more precise genome-wide analysis of gene regulatory elements.