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Record W2220041087 · doi:10.5713/ajas.15.0525

Characterizing Milk Production Related Genes in Holstein Using RNA-seq

2015· article· en· W2220041087 on OpenAlexaff
Minseok Seo, Hyun Jeong Lee, Kwondo Kim, Kelsey Caetano-Anollés, Jin Young Jeong, Sungkwon Park, Young Kyun Oh, Seoae Cho, Heebal Kim

Bibliographic record

VenueAsian-Australasian Journal of Animal Sciences · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsBiotechnology Research Institute
FundersRural Development Administration
KeywordsRNA-SeqGeneProduction (economics)BiologyComputational biologyRNAGeneticsTranscriptomeGene expression

Abstract

fetched live from OpenAlex

Although the chemical, physical, and nutritional properties of bovine milk have been extensively studied, only a few studies have attempted to characterize milk-synthesizing genes using RNA-seq data. RNA-seq data was collected from 21 Holstein samples, along with group information about milk production ability; milk yield; and protein, fat, and solid contents. Meta-analysis was employed in order to generally characterize genes related to milk production. In addition, we attempted to investigate the relationship between milk related traits, parity, and lactation period. We observed that milk fat is highly correlated with lactation period; this result indicates that this effect should be considered in the model in order to accurately detect milk production related genes. By employing our developed model, 271 genes were significantly (false discovery rate [FDR] adjusted p-value<0.1) detected as milk production related differentially expressed genes. Of these genes, five (albumin, nitric oxide synthase 3, RNA-binding region (RNP1, RRM) containing 3, secreted and transmembrane 1, and serine palmitoyltransferase, small subunit B) were technically validated using quantitative real-time polymerase chain reaction (qRT-PCR) in order to check the accuracy of RNA-seq analysis. Finally, 83 gene ontology biological processes including several blood vessel and mammary gland development related terms, were significantly detected using DAVID gene-set enrichment analysis. From these results, we observed that detected milk production related genes are highly enriched in the circulation system process and mammary gland related biological functions. In addition, we observed that detected genes including caveolin 1, mammary serum amyloid A3.2, lingual antimicrobial peptide, cathelicidin 4 (CATHL4), cathelicidin 6 (CATHL6) have been reported in other species as milk production related gene. For this reason, we concluded that our detected 271 genes would be strong candidates for determining milk production.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.293
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations57
Published2015
Admission routes1
Has abstractyes

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