De novo assembly and analysis of crow lungs transcriptome
Bibliographic record
Abstract
The jungle crow (Corvus macrorhynchos) belongs to the order Passeriformes of bird species and is important for avian ecological and evolutionary genetics studies. However, there is limited information on the transcriptome data of this species. In the present study, we report the characterization of the lung transcriptome of the jungle crow using GS FLX Titanium XLR70. Altogether, 1,510,303 high-quality sequence reads with 581,198,230 bases was de novo assembled into 22,169 isotigs (isotig represents an individual transcript) and 784,009 singletons. Using these isotigs and 581,681 length-filtered (greater than 300 bp) singletons, 20,010 unique protein-coding genes were identified by BLASTx comparison against a nonredundant (nr) protein sequence database. Comparative analysis revealed that 46,604 (70.29%) and 51,642 (72.48%) of the assembled transcripts have significant similarity to zebra finch and chicken RefSeq proteins, respectively. As determined by GO annotation and KEGG pathway mapping, functional annotation of the unigenes recovered diverse biological functions and processes. Transcripts putatively involved in the immune response were identified. Furthermore, 20,599 single nucleotide polymorphisms (SNPs) and 7525 simple sequence repeats (SSRs) were retrieved from the assembled transcript database. This resource should lay an important base for future ecological, evolutionary, and conservation genetic studies on this species and in other related species.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".