Methodologies for Transcript Profiling Using Long-Read Technologies
Bibliographic record
Abstract
RNA sequencing using next generation sequencing technologies (NGS) is currently the standard approach for gene expression profiling, particularly for large scale high-throughput studies. NGS technologies comprise of high throughput, cost efficient short-read RNA-Seq while emerging single molecule, long-read RNA-Seq technologies have enabled new approaches to study the transcriptome and its function. The emerging single molecule, long-read technologies are currently commercially available by Pacific Bioscience (PacBio) and Oxford Nanopore Technologies (ONT), while new methodologies based on short-read sequencing approaches are also being developed in order to provide long range single molecule level information, for example the ones represented by the 10X Genomics linked read methodology. The shift towards long-read sequencing technologies for transcriptome characterization is based on current increases in throughput and decreases in cost, making these attractive for de novo transcriptome assembly, isoform expression quantification and in-depth RNA species analysis. These types of analyses were challenging with standard short sequencing approaches due to the complex nature of the transcriptome which consists of variable lengths of transcripts and multiple alternatively spliced isoforms for most genes as well as the high sequence similarity of highly abundant species of RNA, such as rRNAs. Here we aim to focus on single molecule level sequencing technologies and single cell technologies which, combined with perturbation tools, allow the analysis of complete RNA species, whether short or long, at high resolution. In parallel these tools have opened new ways in understanding gene functions at the tissue, network and pathway level, as well as their detailed functional characterisation. Analysis of the epi-transcriptome, including RNA methylation and modification and the effects of such modifications on biological systems is now enabled through direct RNA sequencing instead of classical indirect approaches. However, many difficulties and challenges remain, such as methodologies to generate full length RNA or cDNA libraries from all different species of RNAs, not only poly-A containing transcripts, the identification of allele specific transcripts due to current error rates of single molecule technologies, while the bioinformatics analysis on long read data for accurate identification of 5’ and 3’UTRs is still in development.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.012 |
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".