Evidence map›Paper›PMID 40973498›Full record

ArticleGenome research2025

Deep structural clustering reveals hidden systematic biases in RNA sequencing data.

Qiang Su, Yi Long, Deming Gou, Junmin Quan, Xiaoming Zhou, Qizhou Lian

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genome research, 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
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.

Qiang Su *Faculty of Synthetic Biology, Shenzhen University of Advanced Technology, Shenzhen 518107, China; su@chemie.uni-siegen.de zhouxm@scnu.edu.cn qz.lian@siat.ac.cn.ORCID 0000-0001-7922-8996
Yi Long *Institute of Chemical Biology, Shenzhen Bay Laboratory, Shenzhen 518132, China.
Deming GouShenzhen Key Laboratory of Microbial Genetic Engineering, Vascular Disease Research Center, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen 518060, China.
Junmin QuanState Key Laboratory of Chemical Oncogenomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen 518055, China.
Xiaoming ZhouSchool of Life Sciences, MOE Key Laboratory of Laser Life Science and Guangdong Provincial Key Laboratory of Laser Life Science, College of Biophotonics, South China Normal University, Guangzhou 510631, China; su@chemie.uni-siegen.de zhouxm@scnu.edu.cn qz.lian@siat.ac.cn.
Qizhou LianFaculty of Synthetic Biology, Shenzhen University of Advanced Technology, Shenzhen 518107, China; su@chemie.uni-siegen.de zhouxm@scnu.edu.cn qz.lian@siat.ac.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

RNA sequencing (RNA-seq) is a pivotal tool for transcriptomic analysis, providing comprehensive exploration of gene expression across diverse biological contexts. However, RNA-seq data are susceptible to various biases that can significantly compromise the accuracy and reliability of transcript quantification. This study investigates the influence of high-dimensional RNA structures on local sequencing efficiency using an innovative unsupervised variational autoencoder-Gaussian mixture model (VAE-GMM). The VAE-GMM effectively captures intricate high-dimensional

Indexed as

RNARNA-SeqSequence Analysis, RNACluster AnalysisGene Expression ProfilingHumansMachine LearningPrincipal Component AnalysisTranscriptomeRNA

Identifiers

PMID40973498
PMCPMC12581952

What OpenQuestion holds

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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.