Evidence map›Paper›PMID 39989619›Full record

ArticleComputational and structural biotechnology journal2025

Genome biology of long non-coding RNAs in humans: A virtual karyotype.

Alessandro Palma, Giulia Buonaiuto, Monica Ballarino, Pietro Laneve

Abstract read
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Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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

4 authors.

Alessandro PalmaDepartment of Biology and Biotechnologies "Charles Darwin", Sapienza University of Rome, Piazzale Aldo Moro 5, Rome 00185, Italy.
Giulia BuonaiutoDepartment of Biology and Biotechnologies "Charles Darwin", Sapienza University of Rome, Piazzale Aldo Moro 5, Rome 00185, Italy.
Monica BallarinoDepartment of Biology and Biotechnologies "Charles Darwin", Sapienza University of Rome, Piazzale Aldo Moro 5, Rome 00185, Italy.
Pietro LaneveDepartment of Biology and Biotechnologies "Charles Darwin", Sapienza University of Rome, Piazzale Aldo Moro 5, Rome 00185, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long non-coding RNAs (lncRNAs) represent a groundbreaking class of RNA molecules that exert regulatory functions with remarkable tissue and cellular specificity. Although the identification of functionally significant lncRNAs is increasing, a comprehensive profiling of their genomic features remains elusive. Here, we present a detailed overview of the distribution of lncRNA genes across human chromosomes and describe key RNA features-what we refer to as a "virtual lncRNA karyotype"-that provide insights into their biosynthesis and function. To achieve this, we leveraged existing human annotation files to construct a statistical genomic portrait of lncRNAs in comparison with protein-coding genes (PCGs). We found that lncRNAs are unevenly distributed across chromosomes and identified regions of high lncRNA density on chromosomes 18, 13, and X, which overlap with PCG-rich regions. Additionally, we observed that lncRNAs generally exhibit shorter gene lengths and fewer splicing variants compared to protein-coding transcripts, with a subset displaying pronounced clustering patterns that may indicate functional relevance. Finally, we identified several clinically associated and experimentally validated SNPs impacting lncRNA genes (lncGs). Overall, this study provides a foundational reference for exploring the non-coding genome, offering new insights into the genomic characteristics of lncRNAs. These findings may enhance our understanding of their biological significance and potential roles in disease.

Indexed as

ChromosomesFunctional genomicsGenomicsLong non-coding RNASNPs

Identifiers

PMID39989619
PMCPMC11847481

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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.