ArticleNature communications2025
Image-based DNA sequencing encoding for detecting low-mosaicism somatic mobile element insertions.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Active mobile elements in the human genome can create novel mobile element insertions (MEIs) in somatic tissues. Detection of somatic MEIs, particularly those with low mosaicism, remains a significant challenge due to sequencing artifacts and alignment errors. Existing methods lack sensitivity or require biased manual inspection. Here we present RetroNet, a deep learning algorithm that encodes sequencing reads into images to identify somatic MEIs with as few as two reads. Trained on diverse datasets, RetroNet outperforms previous methods and eliminates the need for manual examinations. RetroNet achieves high precision (0.885) and recall (0.579) on a cancer cell line, detecting insertions in just 1.79% of cells. RetroNet is also effective for degraded DNA, like circulating tumor DNA. This tool is applicable to the rapidly generated short-read sequencing data and has the potential to provide further insights into the functional and pathological implications of somatic retrotranspositions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.