Profiling the HeLa S3 Transcriptome using Randomly Primed cDNA and Massively Parallel Short-Read Sequencing
Bibliographic record
Abstract
Sequence-based methods for transcriptome characterization have typically relied on generation of either serial analysis of gene expression tags or expressed sequence tags. Although such approaches have the potential to enumerate transcripts by counting sequence tags derived from them, they typically do not robustly survey the majority of transcripts along their entire length. Here we show that massively parallel sequencing of randomly primed cDNAs, using a next-generation sequencing-by-synthesis technology, offers the potential to generate relative measures of mRNA and individual exon abundance while simultaneously profiling the prevalence of both annotated and novel exons and exon-splicing events. This technique identifies known single nucleotide polymorphisms (SNPs) as well as novel single-base variants. Analysis of these variants, and previously unannotated splicing events in the HeLa S3 cell line, reveals an overrepresentation of gene categories including those previously implicated in cancer.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".