Evidence map›Paper›PMID 40750633›Full record

ArticleNature communications2025

PAL-AI reveals genetic determinants that control poly(A)-tail length during oocyte maturation, with relevance to human fertility.

Kehui Xiang, David P Bartel

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Kehui XiangHoward Hughes Medical Institute, Cambridge, MA, USA. kxiang@wi.mit.edu.ORCID http://orcid.org/0000-0002-3770-1367
David P BartelHoward Hughes Medical Institute, Cambridge, MA, USA. dbartel@wi.mit.edu.ORCID http://orcid.org/0000-0002-3872-2856

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In oocytes of mammals and other animals, gene regulation is mediated primarily through changes in poly(A)-tail length. Here, we introduce PAL-AI, an integrated neural network machine-learning model that accurately predicts tail-length changes in maturing oocytes of frogs and mammals. We show that PAL-AI learned known and previously unknown sequence elements and their contextual features that control poly(A)-tail length, enabling it to predict tail-length changes resulting from 3'-untranslated region single-nucleotide substitutions. It also predicted tail-length-mediated translational changes, allowing us to nominate genes important for oocyte maturation. When comparing predicted tail-length changes in human oocytes with genomic datasets of the All of Us Research Program and gnomAD, we found that genetic variants predicted to disrupt tail lengthening have been under negative selection in the human population, thereby linking mRNA tail lengthening to human female fertility.

Indexed as

FertilityOocytesOogenesisPoly A3' Untranslated RegionsAnimalsFemaleHumansMachine LearningRNA, Messenger3' Untranslated RegionsPoly ARNA, Messenger

Identifiers

PMID40750633
PMCPMC12316995

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.