Evidence map›Paper›PMID 26838501›Full record

ArticleGenome biology2016

Evolutionary analysis across mammals reveals distinct classes of long non-coding RNAs.

Jenny Chen, Alexander A Shishkin, Xiaopeng Zhu, Sabah Kadri, Itay Maza, Mitchell Guttman, Jacob H Hanna, Aviv Regev, Manuel Garber

Abstract read
In one paragraph

Article in Genome biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 115 papers.

0numbers the graph read from it
0cells of the map it votes in
115citing 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

115 citing papers in PubMed.

  1. LncRNAEpigenetics · 2026
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  13. Computational Resources for lncRNA Functions and Targetome.Methods in molecular biology (Clifton, N.J.) · 2025
    Review
  14. Article
  15. Article
  16. Review
  17. Article
  18. Review
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  20. Article

55 more citing papers are in PubMed but not listed here.

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

9 authors.

Jenny ChenBroad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
Alexander A ShishkinDivision of Biology and Biological Engineering, California Institute of Technology, Cambridge, MA, 02140, USA.
Xiaopeng ZhuProgram in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, 01655, USA.
Sabah KadriBroad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
Itay MazaDepartment of Molecular Genetics, Weizmann Institute of Science, Rehovot, 76100, Israel.
Mitchell GuttmanDivision of Biology and Biological Engineering, California Institute of Technology, Cambridge, MA, 02140, USA.
Jacob H HannaDepartment of Molecular Genetics, Weizmann Institute of Science, Rehovot, 76100, Israel.
Aviv RegevBroad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
Manuel GarberProgram in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, 01655, USA. Manuel.Garber@umassmed.edu.

Funding

University of Massachusetts Center for Clinical Science and Translational SupplementUL1TR001453 · NCATS · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI LUZURIAGA, KATHERINE F · 2015 to 2024
$39.0M
Training Program in Bioinformatics and Integrative GenomicsT32HG002295 · NHGRI · MASSACHUSETTS INSTITUTE OF TECHNOLOGY · PI Peter J Park · 2001 to 2026
$15.8M
Center for Cell CircuitsP50HG006193 · NHGRI · BROAD INSTITUTE, INC. · PI REGEV, AVIV · 2011 to 2015
$15.6M
Trinity: Transcriptome assembly for genetic and functional analysis of cancerU24CA180922 · NCI · BROAD INSTITUTE, INC. · PI HAAS, BRIAN · 2013 to 2022
$7.5M
Rules of gene expression modeled on human dendritic cell response to pathogensU01HG007910 · NHGRI · UNIV OF MASSACHUSETTS MED SCH WORCESTER · PI GARBER, MANUEL, LUBAN, JEREMY · 2015 to 2017
$6.1M
Howard Hughes Medical InstituteNCATS NIH HHS UL1 TR001453NCI NIH HHS U24 CA180922NHGRI NIH HHS P50 HG006193NHGRI NIH HHS T32 HG002295NHGRI NIH HHS U01 HG007910
6 · The paper itself

Abstract

backgroundRecent advances in transcriptome sequencing have enabled the discovery of thousands of long non-coding RNAs (lncRNAs) across many species. Though several lncRNAs have been shown to play important roles in diverse biological processes, the functions and mechanisms of most lncRNAs remain unknown. Two significant obstacles lie between transcriptome sequencing and functional characterization of lncRNAs: identifying truly non-coding genes from de novo reconstructed transcriptomes, and prioritizing the hundreds of resulting putative lncRNAs for downstream experimental interrogation.

resultsWe present slncky, a lncRNA discovery tool that produces a high-quality set of lncRNAs from RNA-sequencing data and further uses evolutionary constraint to prioritize lncRNAs that are likely to be functionally important. Our automated filtering pipeline is comparable to manual curation efforts and more sensitive than previously published computational approaches. Furthermore, we developed a sensitive alignment pipeline for aligning lncRNA loci and propose new evolutionary metrics relevant for analyzing sequence and transcript evolution. Our analysis reveals that evolutionary selection acts in several distinct patterns, and uncovers two notable classes of intergenic lncRNAs: one showing strong purifying selection on RNA sequence and another where constraint is restricted to the regulation but not the sequence of the transcript.

conclusionOur results highlight that lncRNAs are not a homogenous class of molecules but rather a mixture of multiple functional classes with distinct biological mechanism and/or roles. Our novel comparative methods for lncRNAs reveals 233 constrained lncRNAs out of tens of thousands of currently annotated transcripts, which we make available through the slncky Evolution Browser.

Indexed as

Evolution, MolecularAnimalsMammalsMolecular Sequence AnnotationRNA, Long NoncodingSequence AlignmentSequence Analysis, RNASoftwareTranscriptomeRNA, Long Noncoding

Identifiers

PMID26838501
PMCPMC4739325

What OpenQuestion holds

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LicenceCC BY
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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.