Evidence map›Paper›PMID 25017872›Full record

ArticleBMC genomics2014

Identification of tissue-enriched novel transcripts and novel exons in mice.

Seong-Eui Hong, Hong Ki Song, Do Han Kim

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.8field-weighted citation impact, top 29% of its field
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

8 citing papers in PubMed, 15 citations in OpenAlex.

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  8. What to compare and how: Comparative transcriptomics for Evo-Devo.Journal of experimental zoology. Part B, Molecular and developmental evolution · 2015
    Review
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 at 1 institution in 1 country.

Seong-Eui Hong
Hong Ki Song
Do Han KimSchool of Life Sciences and Systems Biology Research Center, Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro (Oryong-dong), Buk-gu, Gwangju 500-712, Korea. dhkim@gist.ac.kr.
Gwangju Institute of Science and Technology · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRNA sequencing (RNA-seq) has revolutionized the detection of transcriptomic signatures due to its high-throughput sequencing ability. Therefore, genomic annotations on different animal species have been rapidly updated using information from tissue-enriched novel transcripts and novel exons.

results34 putative novel transcripts and 236 putative tissue-enriched exons were identified using RNA-Seq datasets representing six tissues available in mouse databases. RT-PCR results indicated that expression of 21 and 2 novel transcripts were enriched in testes and liver, respectively, while 31 of the 39 selected novel exons were detected in the testes or heart. The novel isoforms containing the identified novel exons exhibited more dominant expression than the known isoforms in heart and testes. We also identified an example of pathology-associated exclusion of heart-enriched novel exons such as Sorbs1 and Cluh during pressure-overload cardiac hypertrophy.

conclusionThe present study depicted tissue-enriched novel transcripts, a tissue-specific isoform switch, and pathology-associated alternative splicing in a mouse model, suggesting tissue-specific genomic diversity and plasticity.

Indexed as

Alternative SplicingAnimalsCardiomegalyExonsHigh-Throughput Nucleotide SequencingLiverMaleMiceMicrofilament ProteinsMyocardiumProtein IsoformsRNASequence Analysis, RNATestisTranscriptomeMicrofilament ProteinsponsinProtein IsoformsRNA

Identifiers

PMID25017872
PMCPMC4111849
OpenAlexW2045787380

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.