In one paragraphArticle in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
35 authors.
Jinhao WangGilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.ORCID 0009-0008-7747-1851 Jessica A SwitzenbergGilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.ORCID 0000-0003-4569-3720 Steven J LoshGilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.ORCID 0009-0000-5286-4714 Ziying WangCollege of Informatics, Huazhong Agricultural University, Hongshan District, Wuhan, China.
Torrin L McDonaldGilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.ORCID 0000-0001-5670-9375 Camille MummGilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.ORCID 0000-0002-7585-9851 Aidan BurnDepartment of Pathology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.ORCID 0000-0001-8162-3389 Alexander E UrbanDepartment of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.ORCID 0000-0001-9772-933X Kathleen H BurnsDepartment of Pathology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.ORCID 0000-0003-1620-3761 SMaHT MEI Working Group
Alan P BoyleGilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.ORCID 0000-0002-2081-1105 Ryan E MillsGilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.ORCID 0000-0003-3425-6998 Weichen ZhouGilbert S. Omenn Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA.ORCID 0000-0003-4755-1072 Funding
WashU-VAI Somatic Mosaicism across Human Tissues (SMaHT) Program Genome Characterization CenterUM1DA058219 · NIDA · WASHINGTON UNIVERSITY · PI Robert Scott Fulton, Hui Shen · 2023 to 2026
$29.1MData Analysis Center for Somatic Mosaicism Across Human Tissues NetworkUM1DA058230 · NIDA · HARVARD MEDICAL SCHOOL · PI Peter J Park · 2023 to 2026
$6.3MMolecular and Computational Tools for Identifying Somatic Mosaicism in Human TissuesUH3NS132084 · NINDS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Alan P Boyle, RYAN E MILLS · 2025 to 2026
$1.3MEstablishing and benchmarking advanced methods to comprehensively characterize somatic genome variation in single human cellsUH3NS132146 · NINDS · STANFORD UNIVERSITY · PI Alexander Eckehart Urban · 2025 to 2026
$1.2MEstablishing and benchmarking advanced methods to comprehensively characterize somatic genome variation in single human cellsUG3NS132146 · NINDS · STANFORD UNIVERSITY · PI URBAN, ALEXANDER ECKEHART, VACCARINO, FLORA M · 2023 to 2024
$922kSingle Molecule Detection of L1 Insertions and IntermediatesUG3NS132127 · NINDS · DANA-FARBER CANCER INST · PI BERNSTEIN, BRADLEY EVAN, BURNS, KATHLEEN H · 2023 to 2024
$879kMolecular and Computational Tools for Identifying Somatic Mosaicism in Human TissuesUG3NS132084 · NINDS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BOYLE, ALAN P, MILLS, RYAN E · 2023 to 2024
$789kNIDA NIH HHS UM1 DA058219NIDA NIH HHS UM1 DA058230NINDS NIH HHS UG3 NS132084NINDS NIH HHS UG3 NS132127NINDS NIH HHS UG3 NS132146NINDS NIH HHS UH3 NS132084NINDS NIH HHS UH3 NS132146
6 · The paper itselfAbstract
Mobile element insertions (MEI) shape the human genome in both germline and somatic tissues. While inherited MEIs are well characterized, mapping somatic MEIs (sMEI) in non-cancer tissues remains challenging due to their low allelic fraction and repetitive nature. We established an integrative framework for sMEI analysis leveraging modern sequencing technologies and analytical innovations. We first benchmarked sMEI detection and demonstrated advantages of long-read and MEI-targeted sequencing for ultra-low-frequency events using a mixture of well-established cell lines. We then showed that haplotype phasing and donor-specific assemblies refine sMEI detection, effectively distinguishing from germline and false signals in
Identifiers
PMID41278868
PMCPMC12632373
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