Multiplexed Spliced-Leader Sequencing: A high-throughput, selective method for RNA-seq in Trypanosomatids
Bibliographic record
Abstract
High throughput sequencing techniques are poorly adapted for in vivo studies of parasites, which require prior in vitro culturing and purification. Trypanosomatids, a group of kinetoplastid protozoans, possess a distinctive feature in their transcriptional mechanism whereby a specific Spliced Leader (SL) sequence is added to the 5'end of each mRNA by trans-splicing. This allows to discriminate Trypansomatid RNA from mammalian RNA and forms the basis of our new multiplexed protocol for high-throughput, selective RNA-sequencing called SL-seq. We provided a proof-of-concept of SL-seq in Leishmania donovani, the main causative agent of visceral leishmaniasis in humans, and successfully applied the method to sequence Leishmania mRNA directly from infected macrophages and from highly diluted mixes with human RNA. mRNA profiles obtained with SL-seq corresponded largely to those obtained from conventional poly-A tail purification methods, indicating both enumerate the same mRNA pool. However, SL-seq offers additional advantages, including lower sequencing depth requirements, fast and simple library prep and high resolution splice site detection. SL-seq is therefore ideal for fast and massive parallel sequencing of parasite transcriptomes directly from host tissues. Since SLs are also present in Nematodes, Cnidaria and primitive chordates, this method could also have high potential for transcriptomics studies in other organisms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".