A high-quality, long-read genome assembly of the endangered ring-tailed lemur (<i>Lemur catta</i>)
Bibliographic record
Abstract
BACKGROUND: The ring-tailed lemur (Lemur catta) is a charismatic strepsirrhine primate endemic to Madagascar. These lemurs are of particular interest, given their status as a flagship species and widespread publicity in the popular media. Unfortunately, a recent population decline has resulted in the census population decreasing to <2,500 individuals in the wild, and the species's classification as an endangered species by the IUCN. As is the case for most strepsirrhine primates, only a limited amount of genomic research has been conducted on L. catta, in part owing to the lack of genomic resources. RESULTS: We generated a new high-quality reference genome assembly for L. catta (mLemCat1) that conforms to the standards of the Vertebrate Genomes Project. This new long-read assembly is composed of Pacific Biosciences continuous long reads (CLR data), Optical Mapping Bionano reads, Arima HiC data, and 10X linked reads. The contiguity and completeness of the assembly are extremely high, with scaffold and contig N50 values of 90.982 and 10.570 Mb, respectively. Additionally, when compared to other high-quality primate assemblies, L. catta has the lowest reported number of Alu elements, which results predominantly from a lack of AluS and AluY elements. CONCLUSIONS: mLemCat1 is an excellent genomic resource not only for the ring-tailed lemur community, but also for other members of the Lemuridae family, and is the first very long read assembly for a strepsirrhine.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".