Evidence map›Paper›PMID 26818975›Full record

ArticleBMC genomics2016

Comprehensive identification of sexually dimorphic genes in diverse cattle tissues using RNA-seq.

Minseok Seo, Kelsey Caetano-Anolles, Sandra Rodriguez-Zas, Sojeong Ka, Jin Young Jeong, Sungkwon Park, Min Ji Kim, Whan-Gook Nho, Seoae Cho, Heebal Kim and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed
4.5field-weighted citation impact, top 6% 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

23 citing papers in PubMed, 27 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Paternal impact on the developmental programming of sexual dimorphism.Frontiers in cell and developmental biology · 2024
    Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  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

11 authors at 4 institutions in 2 countries.

Minseok SeoInterdisciplinary Program in Bioinformatics, Seoul National University, Kwan-ak St. 599, Kwan-ak Gu, Seoul, South Korea, 151-741, Republic of Korea. nijorral@gmail.com.
Kelsey Caetano-AnollesDepartment of Animal Sciences, University of Illinois, Urbana, IL, 61801, USA. caetano1@illinois.edu.
Sandra Rodriguez-ZasDepartment of Animal Sciences, University of Illinois, Urbana, IL, 61801, USA. rodrgzzs@illinois.edu.
Sojeong KaDepartment of Agricultural Biotechnology, Animal Biotechnology Major, and Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul, 151-921, Republic of Korea. skasnu@snu.ac.kr.
Jin Young JeongDivision of Animal Products R&D, National Institute of Animal science, #1500 Kongjwipatjwi-ro, Wansan-gu, Jeonju-si, Jeollabuk-do, 565-851, Republic of Korea. jeongjinyoung73@gmail.com.
Sungkwon ParkDepartment of food science and technology, Sejong University, 98 Gun-Ja-Dong, Seoul, 143-747, Republic of Korea. sungkwonpark@hotmail.com.
Min Ji KimDepartment of food science and technology, Sejong University, 98 Gun-Ja-Dong, Seoul, 143-747, Republic of Korea. mjkim00@korea.kr.
Whan-Gook NhoDepartment of Swine & Poultry Science, National College of Agriculture and Fisheries, #1515 Kongjwipatjwi-ro, Wansan-gu, Jeonju-si, Jeollabuk-do, 560-500, Republic of Korea. nhowgook@korea.kr.
Seoae ChoCHO&KIM genomics, Main Bldg. #514, SNU Research Park, Seoul National University Mt.4-2, NakSeoungDae, Gwanakgu, Seoul, 151-919, Republic of Korea. seoae@cnkgenomics.com.
Heebal KimInterdisciplinary Program in Bioinformatics, Seoul National University, Kwan-ak St. 599, Kwan-ak Gu, Seoul, South Korea, 151-741, Republic of Korea. heebal@snu.ac.kr.
Hyun-Jeong LeeInterdisciplinary Program in Bioinformatics, Seoul National University, Kwan-ak St. 599, Kwan-ak Gu, Seoul, South Korea, 151-741, Republic of Korea. hyunj68@korea.kr.
Seoul National University · KRNational Institute of Animal Science · KRSejong University · KRUniversity of Illinois Urbana-Champaign · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMolecular mechanisms associated with sexual dimorphism in cattle have not been well elucidated. Furthermore, as recent studies have implied that gene expression patterns are highly tissue specific, it is essential to investigate gene expression in a variety of tissues using RNA-seq. Here, we employed and compared two statistical methods, a simple two group test and Analysis of deviance (ANODEV), in order to investigate bovine sexually dimorphic genes in 40 RNA-seq samples distributed across two factors: sex and tissue.

resultsAs a result, we detected 752 sexually dimorphic genes across tissues from two statistical approaches and identified strong tissue-specific patterns of gene expression. Additionally, significantly detected sex-related genes shared between two mammal species (cattle and rat) were identified using qRT-PCR.

conclusionsResults of our analyses reveal that sexual dimorphism of metabolic tissues and pituitary gland in cattle involves various biological processes. Several differentially expressed genes between sexes in cattle and rat species are shared, but show tissue-specific patterns. Finally, we concluded that two distinct statistical approaches have their advantages and disadvantages in RNA-seq studies investigating multiple tissues.

Indexed as

AnimalsCattleFemaleGene Expression RegulationMaleOligonucleotide Array Sequence AnalysisRatsRNASex CharacteristicsRNA

Identifiers

PMID26818975
PMCPMC4728830
OpenAlexW2290100795

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.