Evidence map›Paper›PMID 23450794›Full record

ArticleG3 (Bethesda, Md.)2013

Accurate identification and analysis of human mRNA isoforms using deep long read sequencing.

Hagen Tilgner, Debasish Raha, Lukas Habegger, Mohammed Mohiuddin, Mark Gerstein, Michael Snyder

Abstract read
In one paragraph

Article in G3 (Bethesda, Md.), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing 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

38 citing papers in PubMed.

  1. Article
  2. Long-read Sequencing of Nascent RNA from Budding and Fission Yeasts.bioRxiv : the preprint server for biology · 2025
    Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Realizing the potential of full-length transcriptome sequencing.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2019
    Review
  19. Article
  20. Article
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.

Hagen TilgnerDepartment of Genetics, Stanford University, Stanford, California 94305, USA.
Debasish Raha
Lukas Habegger
Mohammed Mohiuddin
Mark Gerstein
Michael Snyder

Funding

Production Center for Global Mapping of Regulatory ElementsU54HG004558 · NHGRI · YALE UNIVERSITY · PI SNYDER, MICHAEL P. · 2007 to 2011
$15.4M
Production Center for Mapping Regulatory Regions of the Human GenomeU54HG006996 · NHGRI · STANFORD UNIVERSITY · PI SNYDER, MICHAEL P. · 2012 to 2016
$14.6M
iPSC Derived EC as Surrogates Using Pulmonary Hypertension as a Prototype DiseaseU01HL107393 · NHLBI · STANFORD UNIVERSITY · PI RABINOVITCH, MARLENE, SNYDER, MICHAEL P. · 2011 to 2015
$8.7M
NHGRI NIH HHS 1U54HG006996NHGRI NIH HHS 3U54HG004558NHGRI NIH HHS U54 HG004558NHGRI NIH HHS U54 HG006996NHLBI NIH HHS U01 HL107393
6 · The paper itself

Abstract

Precise identification of RNA-coding regions and transcriptomes of eukaryotes is a significant problem in biology. Currently, eukaryote transcriptomes are analyzed using deep short-read sequencing experiments of complementary DNAs. The resulting short-reads are then aligned against a genome and annotated junctions to infer biological meaning. Here we use long-read complementary DNA datasets for the analysis of a eukaryotic transcriptome and generate two large datasets in the human K562 and HeLa S3 cell lines. Both data sets comprised at least 4 million reads and had median read lengths greater than 500 bp. We show that annotation-independent alignments of these reads provide partial gene structures that are very much in-line with annotated gene structures, 15% of which have not been obtained in a previous de novo analysis of short reads. For long-noncoding RNAs (i.e., lncRNA) genes, however, we find an increased fraction of novel gene structures among our alignments. Other important aspects of transcriptome analysis, such as the description of cell type-specific splicing, can be performed in an accurate, reliable and completely annotation-free manner, making it ideal for the analysis of transcriptomes of newly sequenced genomes. Furthermore, we demonstrate that long read sequence can be assembled into full-length transcripts with considerable success. Our method is applicable to all long read sequencing technologies.

Indexed as

Alternative SplicingChromosome MappingDNA, ComplementaryExonsGene Expression ProfilingGene LibraryGenome, HumanHeLa CellsHumansIntronsK562 CellsMolecular Sequence AnnotationReproducibility of ResultsRibosomesRNA IsoformsRNA, Long NoncodingDNA, ComplementaryRNA IsoformsRNA, Long NoncodinghumanRNARoche sequencingsplicingtranscriptome

Identifiers

PMID23450794
PMCPMC3583448

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.