Advertisement ยท 728 ร— 90

Posts by Sambina Islam Aninta

Post image

Overall, the library design choices made when building an MPRA can directly affect which variant effects are measured and how large they appear. Functionally important variants may be underestimated or missed depending on where they are placed in the construct. 6/6

1 month ago 2 0 0 0
Post image Post image

We also find that ~1% of variants overlap Pol III promoters in Alu elements, where both the A and B box motifs must be present in the probe for the variant effect to be captured correctly. 5/6

1 month ago 2 0 1 0
Post image

In a subset of cases, cooperative interactions between TFs contribute to position-specific variant effects. 4/6

1 month ago 2 0 1 0
Post image

The main driver of this positional bias is position-dependent TF activity. TFs don't contribute equally to expression from all locations in a sequence, so variants that perturb TF binding inherit this dependence. Closer to the TSS tends to show larger effects, but this is TF-specific. 3/6

1 month ago 2 0 1 0
Post image

MPRAs typically test variants in the center of ~200bp constructs. We shifted variant positions across the probe and found that while the direction of effects is usually preserved, the magnitude can vary substantially depending on placement. 2/6

1 month ago 2 0 1 0
Position-dependent variant effects reveal importance of context in genomic regulation Gene expression is governed by the DNA sequence, which is read out through complex interactions between transcription factors (TFs), co-activators, and chromatin. Massively Parallel Reporter Assays (MPRAs) provide a high-throughput framework for functionally characterizing how regulatory DNA sequences impact the expression of a model gene. MPRAs have also proven to be useful for measuring the effects of genetic variation, where each allele is typically tested in the center of ~200 bp of genomic context cloned into the MPRA, but the impact of variant position and local context remains largely unexplored. In this study, we systematically investigate how shifting the position of a variant within an MPRA probe influences its regulatory activity using models that predict expression in MPRAs from DNA sequence. We find that while the direction of variant effects is usually preserved across positions, the magnitude of expression changes can vary substantially depending on where the variant is placed within the construct. This positional bias appears to be largely explained by the strong position-dependent activity of TFs whose binding the variants perturb. In a subset of cases, interactions consistent with cooperativity between TFs also contribute to position-specific effects. ~1% of variants appear to disrupt RNA polymerase III (Pol III) promoters within Alu elements, resulting in position-specificity because both A and B boxes are required for function and exclusion of either motif due to window shifts disrupts the variants' effects. However, we saw little evidence to support the hypothesis that the positional dependence of variant effects resulted from the redundancy of motifs. Overall, our study demonstrates the complexity of cis-regulatory grammar and how it can confound the interpretation of regulatory variants. ### Competing Interest Statement R.T. has filed intellectual property related to MPRA and MPRA models. The other authors declare no competing interests.

MPRAs are the gold-standard tool for measuring how DNA sequences drive gene expression and prioritizing variant effects.
In this preprint we asked: does it matter WHERE you place a variant in an MPRA?
Spoiler: yes, and it might lead you to miss disease-causing variants. 1/6
doi.org/10.64898/202...

1 month ago 13 7 1 2

SOOOO MANY GENOMICS MODELSSSS! ๐Ÿ˜ฑ Often unclear which is best since they benchmark differently! In this preprint, we introduce GAME, a new framework that utilizes APIs to enable sustainable, uniform model evaluation so we can see which is actually best for each task. doi.org/10.1101/2025...

9 months ago 20 9 1 1
Advertisement
Preview
GAME: Genomic API for Model Evaluation The rapid expansion of genomics datasets and the application of machine learning has produced sequence-to-activity genomics models with ever-expanding capabilities. However, benchmarking these models ...

Thanks for reading! Please let us know what you think, and support GAME by contributing modules!
Preprint: doi.org/10.1101/2025...
GitHub: github.com/de-Boer-Lab/...

9 months ago 0 1 1 0