Dataset Information

Predicting gene expression in massively parallel reporter assays: A comparative study.

ABSTRACT: In many human diseases, associated genetic changes tend to occur within noncoding regions, whose effect might be related to transcriptional control. A central goal in human genetics is to understand the function of such noncoding regions: given a region that is statistically associated with changes in gene expression (expression quantitative trait locus [eQTL]), does it in fact play a regulatory role? And if so, how is this role "coded" in its sequence? These questions were the subject of the Critical Assessment of Genome Interpretation eQTL challenge. Participants were given a set of sequences that flank eQTLs in humans and were asked to predict whether these are capable of regulating transcription (as evaluated by massively parallel reporter assays), and whether this capability changes between alternative alleles. Here, we report lessons learned from this community effort. By inspecting predictive properties in isolation, and conducting meta-analysis over the competing methods, we find that using chromatin accessibility and transcription factor binding as features in an ensemble of classifiers or regression models leads to the most accurate results. We then characterize the loci that are harder to predict, putting the spotlight on areas of weakness, which we expect to be the subject of future studies.

SUBMITTER: Kreimer A

PROVIDER: S-EPMC5560998 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature

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Publications

Predicting gene expression in massively parallel reporter assays: A comparative study.

Kreimer Anat A Zeng Haoyang H Edwards Matthew D MD Guo Yuchun Y Tian Kevin K Shin Sunyoung S Welch Rene R Wainberg Michael M Mohan Rahul R Sinnott-Armstrong Nicholas A NA Li Yue Y Eraslan Gökcen G Amin Talal Bin TB Tewhey Ryan R Sabeti Pardis C PC Goke Jonathan J Mueller Nikola S NS Kellis Manolis M Kundaje Anshul A Beer Michael A MA Keles Sunduz S Gifford David K DK Yosef Nir N

Human mutation 20170309 9

In many human diseases, associated genetic changes tend to occur within noncoding regions, whose effect might be related to transcriptional control. A central goal in human genetics is to understand the function of such noncoding regions: given a region that is statistically associated with changes in gene expression (expression quantitative trait locus [eQTL]), does it in fact play a regulatory role? And if so, how is this role "coded" in its sequence? These questions were the subject of the Cr ...[more]

PMID: 28220625

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Project description:Motivation:The 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 (MPRAs), have been used to functionally validate non-coding variants. However, we are still missing high-quality and robust data analysis tools for these datasets. Results:We 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, we found 602 SNPs with significant (false discovery rate 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. Availability and implementation:http://github.com/piquelab/QuASAR/tree/master/mpra. Contact:fluca@wayne.edu or rpique@wayne.edu. Supplementary information:Supplementary data are available online at Bioinformatics.

Dataset Information

Predicting gene expression in massively parallel reporter assays: A comparative study.

Publications

Predicting gene expression in massively parallel reporter assays: A comparative study.

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