An automated sample allocation system for multiplexed Next-Generation Sequencing
Bibliographic record
Abstract
The Illumina Next-Generation Sequencing (NGS) technology enables us to sequence several samples together. This is made possible by adding unique barcode to each sample sequenced. The multiplexing of the samples should be done in such a way that there is not barcode clash and samples loaded into the flowcell should provide high sequencing throughput. In addition, when there is a pool of libraries, the samples with higher priority should be given preference for selection. Currently, there is no program available that can prepare an optimal run plan for the Illumina sequencing runs. The sequencing team spends several manual hours to generate a run plan. Some of the complicated run plans can take up to 24 hours to prepare. Despite spending several hours to prepare run plan it can still have errors and be non-optimal. In this study, we present an automated system to generate run plan for a given sequencing run. Our system utilizes constraint optimization framework to make optimal run plan. We use MiniZinc software to develop the constraint optimization framework. Our method takes input library barcode sequence, data amount needed, priority and sample identifier to generate the optimal run plan. The method is programmed to maximize the inclusion of samples per lane and give priority to sample with high priority. The model provides with an optimal plan for 80 libraries within 18 seconds. In addition to several advantages we observed that this method provides better utilization of flow-cell to provide maximum throughput. This system was widely validated on previous runs. The system is available as web-based tool and we are in process to integrate it with our Laboratory Information Management System (LIMS).
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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 | 0.002 | 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) | 0.015 | 0.004 |
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".