High-resolution analysis of DNA regulatory elements by synthetic saturation mutagenesis
By Rupali Patwardhan, Choli Lee, O. Litvin, David L. Young, Dana Pe'er, Jay Shendure. 2009
371 citations recorded by the source corpus.
We present a method that harnesses massively parallel DNA synthesis and sequencing for the high-throughput functional analysis of regulatory sequences at single-nucleotide resolution. As a proof of concept, we quantitatively assayed the effects of all possible single-nucleotide mutations for three bacteriophage promoters and three mammalian core promoters in a single experiment per promoter. The method may also serve as a rapid screening tool for regulatory element engineering in synthetic biology.
Subfields: Genetics, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Genetics
- Jay Shendure
- Martin Kircher
- Douglas M. Fowler
- Daniela M. Witten
- Gregory M. Cooper
- Carlos L. Araya
- Brian J. O'Roak
- Eran Segal
In Molecular Biology
- Jay Shendure
- Douglas M. Fowler
- Martin Kircher
- Daniela M. Witten
- Brian J. O'Roak
- Carlos L. Araya
- David R. Kelley
- Gregory M. Cooper
Citations in the corpus
- CADD: predicting the deleteriousness of variants throughout the human genome (2018, neutral): “Examples include the task of discriminating ClinVar pathogenic (7) versus common human genetic variants (8); correlation with experimentally measured functional effects in regulatory elements (9–12); and gene-wide frequencies of somatic mutations in cancer genes (13).”
- A general framework for estimating the relative pathogenicity of human genetic variants (2014, methodology): “Further, we obtained the HBB promoter data of Patwardhan et al. 29 .”
- Gene regulatory network inference in the era of single-cell multi-omics (2023, neutral): “The massively parallel reporter assay 120 is a technique that allows to test whether candidate genomic regions can induce gene expression in episomal vectors.”
- Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks (2015, positive): “Saturation mutagenesis experiments, in which every mutation to a sequence is tested, are a powerful tool for dissecting the exact nucleotides driving a functional activity (Patwardhan et al. 2009; Melnikov et al. 2012).”
- Learning the sequence determinants of alternative splicing from millions of random sequences. (2015, methodology): “…next-generation sequencing with extensive variation have been applied to study transcription (Melnikov et al., 2012; Patwardhan et al., 2012; Patwardhan et al., 2009; Sharon et al., 2012; Smith et al., 2013; White et al., 2013), translation (Noderer et al., 2014), mRNA stability (Oikonomou…”
- Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters (2012, methodology): “Briefly, we obtained a mixed barcoded oligonucleotide pool synthesized on Agilent programmable microarrays 11,14,15 that represents our promoter library and inserted it into a low-copy plasmid such that it was inserted upstream of a ~100-bp TATA-containing core promoter followed by a yellow…”
- Needles in stacks of needles: finding disease-causal variants in a wealth of genomic data (2011, neutral): “Second, several groups are developing strategies whereby variants in regulatory sequence”
- Massively parallel functional dissection of mammalian enhancers in vivo (2012, methodology): “We previously reported a method called 'synthetic saturation mutagenesis”
- Deep mutational scanning: assessing protein function on a massive scale (2011, background): “An effective solution to sequencing error requires that each library member bear a unique barcode sequence [26, 40].”
- Large-scale de novo DNA synthesis: technologies and applications (2014, methodology): “76 used an oligo pool of 18,492 synthetic promoter mutants for bacteriophage and mammalian Pol II core promoters with corresponding barcodes for NGS readout to quantitate expression differences and map important bases for core activity in these promoters.”