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Correcting signal biases and detecting regulatory elements in STARR-seq data
- Source :
- Genome Res
- Publication Year :
- 2021
- Publisher :
- Cold Spring Harbor Laboratory, 2021.
-
Abstract
- High-throughput reporter assays such as self-transcribing active regulatory region sequencing (STARR-seq) have made it possible to measure regulatory element activity across the entire human genome at once. The resulting data, however, present substantial analytical challenges. Here, we identify technical biases that explain most of the variance in STARR-seq data. We then develop a statistical model to correct those biases and to improve detection of regulatory elements. This approach substantially improves precision and recall over current methods, improves detection of both activating and repressive regulatory elements, and controls for false discoveries despite strong local correlations in signal.
- Subjects :
- 0303 health sciences
Genome, Human
SIGNAL (programming language)
High-Throughput Nucleotide Sequencing
Method
Statistical model
Computational biology
Variance (accounting)
Biology
Regulatory region
03 medical and health sciences
Enhancer Elements, Genetic
0302 clinical medicine
STARR-seq
Bias
Genetics
Humans
Human genome
Precision and recall
030217 neurology & neurosurgery
Genetics (clinical)
030304 developmental biology
Subjects
Details
- ISSN :
- 15495469 and 10889051
- Volume :
- 31
- Database :
- OpenAIRE
- Journal :
- Genome Research
- Accession number :
- edsair.doi.dedup.....057a6434446bafb764a94bdbc51e9856