The assembly of the mink blood transcriptoms generated by the next generation sequencing using the dog genome
Bibliographic record
Abstract
The next generation sequencing techniques provide a great opportunity for deciphering various aspects of the mink biology. Alignment and mapping of the massive number of short reads produced by these techniques is computationally challenging, specifically for species such as the mink with little genome sequence information. The objectives of this study was to evaluate the merit of the dog genome, the closest species to the mink with complete genome sequence data, as a reference for the assembly of short reads generated by the next generation sequencing. Blood samples from 12 anaesthetized mink were collected by heart puncture into the PAXgene Blood RNA tubes. Libraries were prepared from total RNA using the Illumina TruSeq™ RNA sample preparation kit, and were sequenced using HiScanSQ, producing 75 bp reads from each end. A total of 794,241,780 paired-end reads (59.5×109 bp) were generated (ranging from 7.5 million to 182 million reads per sample). The reads were aligned to the dog genome using different stringencies (0 to 8 mismatches/75 bp). Only 5.7% of the reads were aligned within exons of approximately 11,200 known genes on the dog genome with varying coverage depth. Some reads were mapped outside annotated dog transcripts (4.3%), in intronic regions (0.62%), at the exon boundaries (1.6%) or to rRNA and snRNA (0.02%). The results suggested that genomes of other species, such as the cat and ferret, should be explored to indentify the remaining 87.7% of the unmapped mink transcripts. This sequence database is useful for the assembly and annotation of the mink genome.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".