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

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.

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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

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Discovering epistatic feature interactions from neural network models of regulatory DNA sequences

Bioinformatics ◽

10.1093/bioinformatics/bty575 ◽

2018 ◽

Vol 34 (17) ◽

pp. i629-i637 ◽

Cited By ~ 23

Author(s):

Peyton Greenside ◽

Tyler Shimko ◽

Polly Fordyce ◽

Anshul Kundaje

Keyword(s):

Neural Network ◽

Dna Sequences ◽

Network Models ◽

Neural Network Models ◽

Feature Interactions ◽

Regulatory Dna Sequences ◽

Regulatory Dna

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Analysis of Regulatory DNA Sequences by Dual-Luciferase Reporter Assays in Ewing Sarcoma

Methods in Molecular Biology - Ewing Sarcoma ◽

10.1007/978-1-0716-1020-6_10 ◽

2020 ◽

pp. 139-149

Author(s):

Tilman L. B. Hölting ◽

Maximilian M. L. Knott

Keyword(s):

Dna Sequences ◽

Ewing Sarcoma ◽

Luciferase Reporter ◽

Regulatory Dna Sequences ◽

Reporter Assays ◽

Regulatory Dna

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A comprehensive fitness landscape model reveals the evolutionary history and future evolvability of eukaryotic cis-regulatory DNA sequences

10.1101/2021.02.17.430503 ◽

2021 ◽

Author(s):

Eeshit Dhaval Vaishnav ◽

Carl G. de Boer ◽

Moran Yassour ◽

Jennifer Molinet ◽

Lin Fan ◽

...

Keyword(s):

Gene Expression ◽

Dna Sequences ◽

Two Dimensions ◽

Fitness Landscapes ◽

Regulatory Sequence ◽

Stabilizing Selection ◽

Regulatory Sequences ◽

Mutational Robustness ◽

Regulatory Dna Sequences ◽

Regulatory Dna

Mutations in non-coding cis-regulatory DNA sequences can alter gene expression, organismal phenotype, and fitness. Fitness landscapes, which map DNA sequence to organismal fitness, are a long-standing goal in biology, but have remained elusive because it is challenging to generalize accurately to the vast space of possible sequences using models built on measurements from a limited number of endogenous regulatory sequences. Here, we construct a sequence-to-expression model for such a landscape and use it to decipher principles of cis-regulatory evolution. Using tens of millions of randomly sampled promoter DNA sequences and their measured expression levels in the yeast Sacccharomyces cerevisiae, we construct a deep transformer neural network model that generalizes with exceptional accuracy, and enables sequence design for gene expression engineering. Using our model, we predict and experimentally validate expression divergence under random genetic drift and strong selection weak mutation regimes, show that conflicting expression objectives in different environments constrain expression adaptation, and find that stabilizing selection on gene expression leads to the moderation of regulatory complexity. We present an approach for detecting selective constraint on gene expression using our model and natural sequence variation, and validate it using observed cis-regulatory diversity across 1,011 yeast strains, cross-species RNA-seq from three different clades, and measured expression-to-fitness curves. Finally, we develop a characterization of regulatory evolvability, use it to visualize fitness landscapes in two dimensions, discover evolvability archetypes, quantify the mutational robustness of individual sequences and highlight the mutational robustness of extant natural regulatory sequence populations. Our work provides a general framework that addresses key questions in the evolution of cis-regulatory sequences.

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MPRAnalyze - A statistical framework for Massively Parallel Reporter Assays

10.1101/527887 ◽

2019 ◽

Author(s):

Tal Ashuach ◽

David Sebastian Fischer ◽

Anat Kreimer ◽

Nadav Ahituv ◽

Fabian Theis ◽

...

Keyword(s):

Dna Sequences ◽

Linear Models ◽

Simulated Data ◽

R Package ◽

Massively Parallel ◽

Published Data ◽

Regulatory Sequence ◽

Regulatory Sequences ◽

Statistical Framework ◽

Reporter Assays

AbstractMassively parallel reporter assays (MPRAs) are a technique that enables testing thousands of regulatory DNA sequences and their variants in a single, quantitative experiment. Despite growing popularity, there is lack of statistical methods that account for the different sources of uncertainty inherent to these assays, thus effectively leveraging their promise. Development of such methods could help enhance our ability to identify regulatory sequences in the genome, understand their function under various setting, and ultimately gain a better understanding of how the regulatory code and its alteration lead to phenotypic consequence.Here we present MPRAnalyze: a statistical framework dedicated to analyzing MPRA count data. MPRAnalyze addresses the major questions that are posed in the context of MPRA experiments: estimating the magnitude of the effect of a regulatory sequence in a single condition setting, and comparing differential activity of regulatory sequences across multiple conditions. The framework uses a nested construction of generalized linear models to account for uncertainty in both DNA and RNA observations, controls for various sources of unwanted variation, and incorporates negative controls for robust hypothesis testing, thereby providing clear quantitative answers in complex experimental settings.We demonstrate the robustness, accuracy and applicability of MPR-Analyze on simulated data and published data sets and compare it against the existing analysis methodologies. MPRAnalyze is implemented as an R package and is publicly available through Bioconductor [1].

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