Enhancing the throughput and multiplexing capabilities of next generation sequencing for efficient implementation of pooled shRNA and CRISPR screens
Bibliographic record
Abstract
Next generation sequencing is becoming the method of choice for functional genomic studies that use pooled shRNA or CRISPR libraries. A key challenge in sequencing these mixed-oligo libraries is that they are highly susceptible to hairpin and/or heteroduplex formation. This results in polyclonal, low quality, and incomplete reads and reduces sequencing throughput. Unfortunately, this challenge is significantly magnified in low-to-medium throughput bench-top sequencers as failed reads significantly perturb the maximization of sequence coverage and multiplexing capabilities. Here, we report a methodology that can be adapted to maximize the coverage on a bench-top, Ion PGM System for smaller shRNA libraries with high efficiency. This ligation-based, half-shRNA sequencing strategy minimizes failed sequences and is also equally amenable to high-throughput sequencers for increased multiplexing. Towards this, we also demonstrate that our strategy to reduce heteroduplex formation improves multiplexing capabilities of pooled CRISPR screens using Illumina NextSeq 500. Overall, our method will facilitate sequencing of pooled shRNA or CRISPR libraries from genomic DNA and maximize sequence 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 | 0.004 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".