Description

Massively Parallel Reporter Assays (MPRAs) are high-throughput methods that measure the regulatory activity of thousands of candidate DNA sequences in parallel. Each fragment is cloned next to a reporter gene and tagged with a unique barcode; sequencing the resulting reporter RNA quantifies how strongly each fragment drives expression. Most assays place the candidate fragment upstream of the reporter to measure transcriptional activation; some place it in the 3' untranslated region instead, where the readout reflects post- transcriptional effects on mRNA stability, decay, or translation. When matched reference and mutated versions of a sequence are tested side-by-side, the effect of a genetic variant on regulatory activity can be measured directly.

This track collection brings together results from two MPRA databases, one for the complete sequence fragments and one for the impact of variants in selected fragments:

• MPRA Base — 41,275 experimentally tested cis-regulatory elements curated from the MPRA Base database, which integrates MPRA, STARR-seq, and related reporter assay experiments across many cell types and conditions (Zhao et al., 2023).
• MPRAVarDB — 239,028 variants mapped to hg38 (of 242,818 total) from 18 MPRA studies, tested for effects on transcriptional or post-transcriptional regulatory activity across over 30 cell lines and 30 human diseases and traits (Jin et al., 2024).

Note on cell lines: The cell line shown for each element or variant is the reporter cell line in which the sequence was assayed. Most rows test human DNA in human cells. Several studies used mouse cell lines (Neuro-2a, N2A, NIH/3T3, MIN6) as reporter systems for human regulatory sequences. One MPRA Base study (Mattioli et al., 2020) tested mouse orthologous sequences in mouse embryonic stem cells (mESC); those items retain hg38 coordinates, derived from the orthologous human position by liftOver.

Data Access

See the individual subtrack documentation pages linked above for detailed information on how to download and intersect the annotations.

Credits

Thanks to Tao Wang and colleagues at the University of Florida for MPRAVarDB, and to Varda Singhal and the Ahituv Lab at the University of California San Francisco for MPRA Base.

References

Jin W, Xia Y, Nizomov J, Liu Y, Li Z, Lu Q, Chen L. MPRAVarDB: an online database and web server for exploring regulatory effects of genetic variants. Bioinformatics. 2024 Oct 1;40(10). PMID: 39325859; PMC: PMC11464417

Zhao J, Baltoumas FA, Konnaris MA, Mouratidis I, Liu Z, Sims J, Agarwal V, Pavlopoulos GA, Georgakopoulos-Soares I, Ahituv N. MPRAbase: A Massively Parallel Reporter Assay Database. bioRxiv. 2023 Nov 22;. PMID: 38045264; PMC: PMC10690217