AVITI as an alternative to Illumina for low-cost genome-wide genotyping
Bibliographic record
Abstract
Advancements in sequencing technologies have dramatically transformed genomics research by enabling the analysis of genetic information with unprecedented scale and efficiency. Next-generation sequencing, renowned for its high-throughput capabilities, has significantly reduced costs and expanded the scope of sequencing applications. Among these, sequencing by synthesis on Illumina systems is predominant, favored for its accuracy and cost-effectiveness. However, emerging technologies like Element Biosciences’ sequencing by Avidity (AVITI) are beginning to challenge this dominance. In this study, we sequenced and genotyped a library of 40 Cannabis samples using both the AVITI and Illumina NovaSeq systems. After filtering out low-quality variants, both technologies showed an 81.2% overlap with 98.9% concordance in genotype calls. AVITI stands out for its flexibility and reduced per-base costs, presenting a viable option particularly for mid-sized laboratories. As the scientific community continues to seek ways to lower genotyping expenses, the combination of the AVITI system with NanoGBS library preparation offers a cost-effective solution adaptable to a wide range of project sizes.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".