Comparison of Commercially Available Target Enrichment Methods for Next Generation Sequencing with Illumina Platform
Bibliographic record
Abstract
r5a-1 Over the last four years, we witnessed the tremendous advances in Next Generation Sequencing (NGS) that have dramatically decreased the cost of whole genome sequencing. However, the cost of sequencing larger genomes is still significant. In addition and depending on the goal of study, whole genome sequencing creates a large amount of additional/auxiliary data that complicates data analysis. Recently, a number of new commercial methods were introduced for isolating subsets of genomes that greatly enhance the efficiency of NGS by allowing researchers to focus on their regions of interest. For the 2009/10 DSRG study, we compared three of the most popular new enrichment methods in the market; Agilent SureSelect in-solution capture method, Febit and NimbleGen array capture methods. All three companies obtained the same genomic DNA stock and performed DNA capture on the same specified regions. Following capture, the Illumina Genome Analyzer II system was used, in two different laboratories, to generate the sequence data. We present our data comparing the three approaches in terms of cost, quality, reproducibility and most importantly completeness and depth of coverage.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.004 | 0.003 |
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".