A Phased <i>Canis lupus familiaris</i> Labrador Retriever Reference Genome Utilizing High Molecular Weight DNA Extraction Methods and High Resolution Sequencing Technologies
Bibliographic record
Abstract
ABSTRACT Reference genome fidelity is critically important for genome wide association studies (GWAS), yet many are incomplete or too dissimilar from the study population. A typical whole genome sequencing approach implies short-read technologies resulting in fragmented assemblies with regions of ambiguity low complexity. Further information is lost by economic necessity when genotyping populations, as lower resolution technologies such as genotyping arrays are commonly utilized. Here we present a phased reference genome for Canis lupus familiaris utilizing high molecular weight sequencing technologies. We tested wet lab and bioinformatic approaches to demonstrate a minimum workflow to generate the 2.4 gigabase genome for a Labrador Retriever. The resulting de novo assembly required eight Oxford Nanopore R9.4 flowcells (~23X depth) and running a 10X Genomics library on the equivalent of one lane of an Illumina NovaSeq S1 flowcell (~88X depth), bringing the cost of generating a nearly complete reference genome to less than $10K. Mapping of publicly available short-read data from ten Labrador Retrievers against this breed-specific reference resulted in an average of approximately 1% more aligned reads compared to mapping against the current gold standard reference (CanFam3.1, p<0.001), indicating a more complete breed-specific reference. An average 15% reduction of variant calls was observed from the same mapped data, which increases the chance of identifying low effect size variants in a GWAS. We believe that by incorporating the cost to produce a full genome assembly into any large-scale canine genotyping study, an investigator can make an informed cost/benefit analysis regarding genotyping technology.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".