SNPLift: Fast and accurate conversion of genetic variant coordinates across genome assemblies
Bibliographic record
Abstract
Abstract Motivation The advent of high-throughput sequencing technologies and the availability of reference genomes have provided an unprecedented opportunity to discover and genotype millions of genetic variants in hundreds or even thousands of samples. Variant calling, the identification of genetic variants from raw sequencing data, is both time-consuming and computationally demanding. Currently, reference genomes are evolving very rapidly and new assembly versions come out more and more frequently. To take advantage of new or improved reference genomes, raw reads alignments, genotype calling, and filtration must typically all be redone. This is a costly and time consuming operation that is not always viable when projects are under time constraints. Results Here, we introduce SNPLift, a bioinformatic pipeline that can quickly transfer the coordinate of nucleotide variants (SNPs and Indels) between different versions of reference genomes. We tested SNPLift on nine SNP datasets in VCF format from different species ( Homo sapiens, Arabidopsis thaliana, Coregonus clupeaformis, Medicato truncatula, Oriza sativa, Salvelinus namaycush, Solanum lycopersicum, Zea mays, and Glycine max ). Depending on the species, we achieved accurate lifting of variants ranging from 92.92% to 99.69%. Importantly, SNPLift significantly reduces the computational resources and time required for variant analysis compared to performing a complete re-analysis using a new reference genome. SNPLift offers a fast and efficient solution to leverage the benefits of updated or improved reference genomes. Availability and implementation SNPLift is available at https://github.com/enormandeau/snplift with its documentation. It contains a script that runs an automated test on a small dataset, composed of 190,443 SNPs in chromosome 1 of Medicago truncatula . SNPLift uses only common tools that are easy to install and works under Linux and MacOS.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".