Identification of molecular markers in Labeo rohita towards better carbohydrate utilization
Bibliographic record
Abstract
The contribution of aquaculture products in providing nutritional and food security to human is increasing expeditiously with the increase in animal protein demand.Feed cost contributes more than 60% of the cost of aquaculture production.Henceforth, formulation of cheap fish feed is one of the greatest challenges in aquaculture industry.Carbohydrates are the cheap source of dietary energy.So their level of utilization in fish is an exciting area in research for decreasing the fish feed cost.Molecular markers such as microsatellite and single nucleotide polymorphism (SNP) are used for genetic mapping, quantitative trait loci identification and genome-wide association studies in several aquaculture species.In this experiment, SNPs and microsatellite markers linked to carbohydrate utilization in Labeo rohita were identified.Liver tissue samples of Labeo rohita and Labeo bata were collected from individuals fed with a customized diet with 40% carbohydrate for a period of 21 days.RNA was extracted and cDNA library was prepared and sequenced on Illumina NextSeq 500 platform.7.5 GB of data was generated from each species.Assembly of rohu data resulted in 70, 225 contigs, out of which 6284 microsatellite markers were identified.Among which, 3838, 1817, 488, 132 and 9 were di-, tri-, tetra-, penta-and hexa-repeats, respectively.Primer modelling was successful for 4190 sequences.Similarly, 2, 14, 071 SNPs were identified using CLC bio v7.0.4 and utilizing Illumina reads obtained from Labeo bata.This study can be helpful in efficient use of carbohydrate in Labeo species for decreasing feed cost globally.
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 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".