Understanding how cis -regulatory function is encoded in DNA sequence using massively parallel reporter assays and designed sequences

ABSTRACTMotivationThe majority of the human genome is composed of non-coding regions containing regulatory elements such as enhancers, which are crucial for controlling gene expression. Many variants associated with complex traits are in these regions, and may disrupt gene regulatory sequences. Consequently, it is important to not only identify true enhancers but also to test if a variant within an enhancer affects gene regulation. Recently, allele-specific analysis in high-throughput reporter assays, such as massively parallel reporter assays (MPRA), have been used to functionally validate non-coding variants. However, we are still missing high-quality and robust data analysis tools for these datasets.ResultsWe have further developed our method for allele-specific analysis QuASAR (quantitative allele-specific analysis of reads) to analyze allele-specific signals in barcoded read counts data from MPRA. Using this approach, we can take into account the uncertainty on the original plasmid proportions, over-dispersion, and sequencing errors. The provided allelic skew estimate and its standard error also simplifies meta-analysis of replicate experiments. Additionally, we show that a beta-binomial distribution better models the variability present in the allelic imbalance of these synthetic reporters and results in a test that is statistically well calibrated under the null. Applying this approach to the MPRA data by Tewheyet al.(2016), we found 602 SNPs with significant (FDR 10%) allele-specific regulatory function in LCLs. We also show that we can combine MPRA with QuASAR estimates to validate existing experimental and computational annotations of regulatory variants. Our study shows that with appropriate data analysis tools, we can improve the power to detect allelic effects in high throughput reporter assays.Availabilityhttp://github.com/piquelab/QuASAR/tree/master/[email protected];[email protected]

Download Full-text

Deciphering regulatory DNA sequences and noncoding genetic variants using neural network models of massively parallel reporter assays

PLoS ONE ◽

10.1371/journal.pone.0218073 ◽

2019 ◽

Vol 14 (6) ◽

pp. e0218073 ◽

Cited By ~ 17

Author(s):

Rajiv Movva ◽

Peyton Greenside ◽

Georgi K. Marinov ◽

Surag Nair ◽

Avanti Shrikumar ◽

...

Keyword(s):

Neural Network ◽

Dna Sequences ◽

Genetic Variants ◽

Network Models ◽

Massively Parallel ◽

Neural Network Models ◽

Regulatory Dna Sequences ◽

Reporter Assays ◽

Regulatory Dna

Download Full-text

Statistical considerations for the analysis of massively parallel reporter assays data

Genetic Epidemiology ◽

10.1002/gepi.22337 ◽

2020 ◽

Vol 44 (7) ◽

pp. 785-794

Author(s):

Dandi Qiao ◽

Corwin M. Zigler ◽

Michael H. Cho ◽

Edwin K. Silverman ◽

Xiaobo Zhou ◽

...

Keyword(s):

Massively Parallel ◽

Reporter Assays

Download Full-text

P2-143: USING MASSIVELY PARALLEL REPORTER ASSAYS TO IDENTIFY PROTECTIVE FUNCTIONAL VARIANTS IN THE APOE REGION

Alzheimer s & Dementia ◽

10.1016/j.jalz.2019.06.2550 ◽

2019 ◽

Vol 15 ◽

pp. P628-P628

Author(s):

Karen Nuytemans ◽

Derek J. van Booven ◽

Natalia K. Hofmann ◽

Farid Rajabli ◽

Anthony J. Griswold ◽

...

Keyword(s):

Massively Parallel ◽

Functional Variants ◽

Reporter Assays

Download Full-text

Deciphering regulatory DNA sequences and noncoding genetic variants using neural network models of massively parallel reporter assays

10.1101/393926 ◽

2018 ◽

Cited By ~ 3

Author(s):

Rajiv Movva ◽

Peyton Greenside ◽

Georgi K. Marinov ◽

Surag Nair ◽

Avanti Shrikumar ◽

...

Keyword(s):

Neural Network ◽

Dna Sequences ◽

Genetic Variants ◽

Massively Parallel ◽

Regulatory Sequence ◽

Regulatory Activity ◽

Neural Network Models ◽

Regulatory Dna Sequences ◽

Reporter Assays ◽

Regulatory Dna

AbstractThe relationship between noncoding DNA sequence and gene expression is not well-understood. Massively parallel reporter assays (MPRAs), which quantify the regulatory activity of large libraries of DNA sequences in parallel, are a powerful approach to characterize this relationship. We present MPRA-DragoNN, a convolutional neural network (CNN)-based framework to predict and interpret the regulatory activity of DNA sequences as measured by MPRAs. While our method is generally applicable to a variety of MPRA designs, here we trained our model on the Sharpr-MPRA dataset that measures the activity of ~500,000 constructs tiling 15,720 regulatory regions in human K562 and HepG2 cell lines. MPRA-DragoNN predictions were moderately correlated (Spearman ρ = 0.28) with measured activity and were within range of replicate concordance of the assay. State-of-the-art model interpretation methods revealed high-resolution predictive regulatory sequence features that overlapped transcription factor (TF) binding motifs. We used the model to investigate the cell type and chromatin state preferences of predictive TF motifs. We explored the ability of our model to predict the allelic effects of regulatory variants in an independent MPRA experiment and fine map putative functional SNPs in loci associated with lipid traits. Our results suggest that interpretable deep learning models trained on MPRA data have the potential to reveal meaningful patterns in regulatory DNA sequences and prioritize regulatory genetic variants, especially as larger, higher-quality datasets are produced.

Download Full-text

An open-source platform to distribute and interpret data from multiplexed assays of variant effect

10.1101/555797 ◽

2019 ◽

Cited By ~ 3

Author(s):

Daniel Esposito ◽

Jochen Weile ◽

Jay Shendure ◽

Lea M Starita ◽

Anthony T Papenfuss ◽

...

Keyword(s):

Clinical Research ◽

Large Scale ◽

Massively Parallel ◽

Sequence Variant ◽

Sequence Variants ◽

Public Repository ◽

Single Experiment ◽

Multiplex Assays ◽

Reporter Assays ◽

Variant Effect

AbstractMultiplex Assays of Variant Effect (MAVEs), such as deep mutational scans and massively parallel reporter assays, test thousands of sequence variants in a single experiment. Despite the importance of MAVE data for basic and clinical research, there is no standard resource for their discovery and distribution. Here we present MaveDB, a public repository for large-scale measurements of sequence variant impact, designed for interoperability with applications to interpret these datasets. We also describe the first of these applications, MaveVis, which retrieves, visualizes, and contextualizes variant effect maps. Together, the database and applications will empower the community to mine these powerful datasets.

Download Full-text

Massively parallel reporter perturbation assay uncovers temporal regulatory architecture during neural differentiation

10.1101/2021.03.12.435197 ◽

2021 ◽

Author(s):

Anat Kreimer ◽

Tal Ashuach ◽

Fumitaka Inoue ◽

Alex Khodaverdian ◽

Nir Yosef ◽

...

Keyword(s):

Neural Differentiation ◽

Cellular Differentiation ◽

Early Time ◽

Regulatory Elements ◽

Fine Tuning ◽

Massively Parallel ◽

Regulatory Architecture ◽

Cellular Environment ◽

Sequence Components ◽

Reporter Assays

AbstractGene regulatory elements play a key role in orchestrating gene expression during cellular differentiation, but what determines their function over time remains largely unknown. Here, we performed perturbation-based massively parallel reporter assays at seven early time points of neural differentiation to systematically characterize how regulatory elements and motifs within them guide cellular differentiation. By perturbing over 2,000 putative DNA binding motifs in active regulatory regions, we delineated four categories of functional elements, and observed that activity direction is mostly determined by the sequence itself, while the magnitude of effect depends on the cellular environment. We also find that fine-tuning transcription rates is often achieved by a combined activity of adjacent activating and repressing elements. Our work provides a blueprint for the sequence components needed to induce different transcriptional patterns in general and specifically during neural differentiation.

Download Full-text

Predicting Transcriptional Regulatory Activities with Deep Convolutional Networks

10.1101/099879 ◽

2017 ◽

Cited By ~ 4

Author(s):

Joe Paggi ◽

Andrew Lamb ◽

Kevin Tian ◽

Irving Hsu ◽

Pierre-Louis Cedoz ◽

...

Keyword(s):

Neural Network ◽

Network Architecture ◽

Transcriptional Activity ◽

Cell Types ◽

Massively Parallel ◽

Neural Network Architecture ◽

Regulatory Regions ◽

Convolutional Networks ◽

Transcriptional Regulatory ◽

Reporter Assays

AbstractMassively parallel reporter assays (MPRAs) are a method to probe the effects of short sequences on transcriptional regulation activity. In a MPRA, short sequences are extracted from suspected regulatory regions, inserted into reporter plasmids, transfected into cell-types of interest, and the transcriptional activity of each reporter is assayed. Recently, Ernst et al. presented MPRA data covering 15750 putative regulatory regions. We trained a multitask convolutional neural network architecture using these sequence expression readouts which predicts as output the expression level outputs across four combinations of cell types and promoters. The model allows for the assigning of importance scores to each base through in silico mutagenesis, and the resulting importance scores correlated well with regions enriched for conservation and transcription factor binding.

Download Full-text