Evidence map›Paper›PMID 42523507›Full record

ArticlebioRxiv : the preprint server for biology2026

OTTR-CLASH: improved biochemical and bioinformatic identification of Argonaute 2-mediated microRNA-target RNA interactions.

Paul D Kaufman, Haibo Liu, Kai Hu, Lucas Ferguson, Kathleen Collins, Lihua Julie Zhu, Thoru Pederson

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. 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
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Paul D KaufmanDepartment of Molecular, Cell and Cancer Biology, University of Massachusetts Medical School, Worcester, MA USA.ORCID 0000-0003-3089-313X
Haibo LiuDepartment of Molecular, Cell and Cancer Biology, University of Massachusetts Medical School, Worcester, MA USA.
Kai HuDepartment of Molecular, Cell and Cancer Biology, University of Massachusetts Medical School, Worcester, MA USA.
Lucas FergusonDepartment of Molecular and Cell Biology, University of California, Berkeley, CA USA.
Kathleen CollinsDepartment of Molecular and Cell Biology, University of California, Berkeley, CA USA.
Lihua Julie ZhuDepartment of Molecular, Cell and Cancer Biology, University of Massachusetts Medical School, Worcester, MA USA.
Thoru PedersonDepartment of Biochemistry and Molecular Biotechnology, University of Massachusetts Medical School, Worcester, MA USA.

Funding

Cell Biology of Mammalian NucleiR35GM152201 · NIGMS · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI PAUL D. KAUFMAN · 2024 to 2026
$1.3M
NIGMS NIH HHS R35 GM152201
6 · The paper itself

Abstract

Various methods have detected miRNA-target interactions via immunoprecipitation of UV-crosslinked Argonaute ribonucleoprotein complexes, followed by intermolecular ligation of bound miRNAs to target strands, forming chimeric RNAs. To date, these methods have relied on conventional viral reverse transcriptases (RTs) to generate cDNAs for sequencing. However, crosslinked RNAs often retain adducts after purification, which can make them poor templates for viral RTs. Here, we adapted OTTR (Ordered Two-Template Relay) techniques to generate cDNAs from Ago2-bound RNAs. OTTR makes use of a modified retroelement-encoded RT, which is strongly processive even on templates with modifications or adducts. We show that this "OTTR-CLASH" method increases the frequency of generating chimeric RNAs compared to previous methods. We also developed an improved bioinformatic pipeline for analysis of these data, and we use this to catalog miRNA-target interactions not previously described in the literature.

Identifiers

PMID42523507
PMCPMC13405391

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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.