Analysis of the transcriptome of the protozoan Theileria parva using MPSS reveals that the majority of genes are transcriptionally active in the schizont stage
Bibliographic record
Abstract
Massively parallel signature sequencing (MPSS) was used to analyze the transcriptome of the intracellular protozoan Theileria parva. In total 1,095,000, 20 bp sequences representing 4371 different signatures were generated from T.parva schizonts. Reproducible signatures were identified within 73% of potentially detectable predicted genes and 83% had signatures in at least one MPSS cycle. A predicted leader peptide was detected on 405 expressed genes. The quantitative range of signatures was 4-52,256 transcripts per million (t.p.m.). Rare transcripts (<50 t.p.m.) were detected from 36% of genes. Sequence signatures approximated a lognormal distribution, as in microarray. Transcripts were widely distributed throughout the genome, although only 47% of 138 telomere-associated open reading frames exhibited signatures. Antisense signatures comprised 13.8% of the total, comparable with Plasmodium. Eighty five predicted genes with antisense signatures lacked a sense signature. Antisense transcripts were independently amplified from schizont cDNA and verified by sequencing. The MPSS transcripts per million for seven genes encoding schizont antigens recognized by bovine CD8 T cells varied 1000-fold. There was concordance between transcription and protein expression for heat shock proteins that were very highly expressed according to MPSS and proteomics. The data suggests a low level of baseline transcription from the majority of protein-coding genes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".