Evidence map›Paper›PMID 42646360›Full record

ArticleNon-coding RNA2026

Fraction-Seq: An Integrated Experimental and Computational Workflow for Determining the Localization and Abundance of Small Non-Coding RNAs in Subcellular Compartments.

Siddhartha Shah, Tess Cherlin, Yi Jing, Stepan Nersisyan, Benjamin Leiby, Isidore Rigoutsos

Abstract read
In one paragraph

Article in Non-coding RNA, 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

6 authors.

Siddhartha ShahComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA 19017, USA.ORCID 0009-0005-8719-4972
Tess CherlinComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA 19017, USA.ORCID 0000-0001-7495-7313
Yi JingComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA 19017, USA.
Stepan NersisyanComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA 19017, USA.ORCID 0000-0002-8830-4679
Benjamin LeibyDivision of Biostatistics and Bioinformatics, Thomas Jefferson University, Philadelphia, PA 19017, USA.ORCID 0000-0003-0761-8383
Isidore RigoutsosComputational Medicine Center, Thomas Jefferson University, Philadelphia, PA 19017, USA.ORCID 0000-0003-1529-8631

Funding

Specialized Tools and Auto-updatable Scalable Interactive Databases to Study isomiRs, tRFs and rRFs in Human and MouseR01HG012784 · NHGRI · THOMAS JEFFERSON UNIVERSITY · PI Isidore Rigoutsos · 2023 to 2026
$2.2M
NHGRI NIH HHS R01 HG012784NIH HHS 1R01HG127841-01A1
6 · The paper itself

Abstract

Small non-coding RNAs (sncRNAs) have garnered considerable attention in recent years, following accumulating evidence of their critical roles in many cellular processes. Among sncRNAs, microRNAs (miRNAs) and their isoforms (isomiRs), tRNA-derived fragments (tRFs), rRNA-derived fragments (rRFs), and Y RNA-derived fragments (yRFs) account for more than 95% of all sncRNAs found in cells. Despite their critical regulatory roles, most sncRNAs remain uncharacterized because their functionalization is a lengthy and challenging undertaking. Knowing an sncRNA's abundance helps prioritize among the various choices, while knowing where it localizes in the cell greatly limits the number and identity of its potential targets and helps understand its function. Most studies to date have assumed that the subcellular localization of the various sncRNA classes is understood and remains unchanged across cell types. However, as we have recently demonstrated, the subcellular distribution of sncRNAs follows complex patterns that depend on the sequence of the sncRNA, the presence or absence of post-transcriptionally added non-templated nucleotides, and the cell type. To determine the subcellular localization and abundance of sncRNAs and aid the design of targeted experimental studies of sncRNA function, we developed "Fraction-seq." The method combines an experimental and an analytical component to remove cross-fraction contamination and reconstruct the true abundance of sncRNAs in each considered fraction. Fraction-seq can be applied to any adherent cells from any organism without modifications and can reconstruct the abundance of all categories of sncRNAs. Accompanying this detailed protocol is a newly developed port of the original SAS codes to the widely used R programming language. The codes and an example are freely available through our center's GitHub page.

Indexed as

Fraction-seqmicroRNAs (miRNAs)miRNA isoforms (isomiRs)rRNA-derived fragments (rRFs)small non-coding RNAs (sncRNAs)tRNA-derived fragments (tRFs)Y RNA-derived fragments (yRFs)

Identifiers

PMID42646360
PMCPMC13516553

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.