Identification and characterization of long intergenic noncoding RNAs in bovine mammary glands
Bibliographic record
Abstract
BACKGROUND: Mammary glands of dairy cattle produce milk for the newborn offspring and for human consumption. Long intergenic noncoding RNAs (lincRNAs) play various functions in eukaryotic cells. However, types and roles of lincRNAs in bovine mammary glands are still poorly understood. RESULTS: Using computational methods, 886 unknown intergenic transcripts (UITs) were identified from five RNA-seq datasets from bovine mammary glands. Their non-coding potentials were predicted by using the combination of four software programs (CPAT, CNCI, CPC and hmmscan), with 184 lincRNAs identified. By comparison to the NONCODE2016 database and a domestic-animal long noncoding RNA database (ALDB), 112 novel lincRNAs were revealed in bovine mammary glands. Many lincRNAs were found to be located in quantitative trait loci (QTL). In particular, 36 lincRNAs were found in 172 milk related QTLs, whereas one lincRNA was within clinical mastitis QTL region. In addition, targeted genes for 10 lincRNAs with the highest fragments per kilobase of transcript per million fragments mapped (FPKM) were predicted by LncTar for forecasting potential biological functions of these lincRNAs. Further analyses indicate involvement of lincRNAs in several biological functions and different pathways. CONCLUSION: Our study has provided a panoramic view of lincRNAs in bovine mammary glands and suggested their involvement in many biological functions including susceptibility to clinical mastitis as well as milk quality and production. This integrative annotation of mammary gland lincRNAs broadens and deepens our understanding of bovine mammary gland biology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".