Evidence map›Paper›PMID 23759593›Full record

SynthesisCell cycle (Georgetown, Tex.)2013

A meta-analysis of the genomic and transcriptomic composition of complex life.

Ganqiang Liu, John S Mattick, Ryan J Taft

Abstract readMeta-Analysis
In one paragraph

Synthesis in Cell cycle (Georgetown, Tex.), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers.

0numbers the graph read from it
0cells of the map it votes in
92citing 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

92 citing papers in PubMed.

  1. Review
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  16. Organismal complexity strongly correlates with the number of protein families and domains.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
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  19. Review
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32 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

3 authors.

Ganqiang LiuInstitute for Molecular Bioscience, The University of Queensland, Brisbane, QLD Australia.
John S Mattick
Ryan J Taft

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is now clear that animal genomes are predominantly non-protein-coding, and that these sequences encode a wide array of RNA transcripts and other regulatory elements that are fundamental to the development of complex life. We have previously argued that the proportion of an animal genome that is non-protein-coding DNA (ncDNA) correlates well with its apparent biological complexity. Here we extend on that work and, using data from a total of 1,627 prokaryotic and 153 eukaryotic complete and annotated genomes, show that the proportion of ncDNA per haploid genome is significantly positively correlated with a previously published proxy of biological complexity, the number of distinct cell types. This is in contrast to the amount of the genome that encodes proteins, which we show is essentially unchanged across Metazoa. Furthermore, using a total of 179 RNA-seq data sets from nematode (47), fruit fly (72), zebrafish (20) and human (42), we show, consistent with other recent reports, that the vast majority of ncDNA in animals is transcribed. This includes more than 60 human loci previously considered "gene deserts," many of which are expressed tissue-specifically and associated with previously reported GWAS SNPs. These results suggest that ncDNA, and the ncRNAs encoded within it, may be intimately involved in the evolution, maintenance and development of complex life.

Indexed as

AnimalsDNA, IntergenicEvolution, MolecularGenomeHumansIntronsOpen Reading FramesTranscription, GeneticTranscriptomeDNA, IntergeniccomplexityevolutionlincRNAnon-coding DNAnoncoding RNAsmall RNA

Identifiers

PMID23759593
PMCPMC3737309

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.