Recovering complete mitochondrial genome sequences from RNA-Seq: A case study of Polytomella non-photosynthetic green algae
Bibliographic record
Abstract
Thousands of mitochondrial genomes have been sequenced, but there are comparatively few available mitochondrial transcriptomes. This might soon be changing. High-throughput RNA sequencing (RNA-Seq) techniques have made it fast and cheap to generate massive amounts of mitochondrial transcriptomic data. Here, we explore the utility of RNA-Seq for assembling mitochondrial genomes and studying their expression patterns. Specifically, we investigate the mitochondrial transcriptomes from Polytomella non-photosynthetic green algae, which have among the smallest, most reduced mitochondrial genomes from the Archaeplastida as well as fragmented rRNA-coding regions, palindromic genes, and linear chromosomes with telomeres. Isolation of whole genomic RNA from the four known Polytomella species followed by Illumina paired-end sequencing generated enough mitochondrial-derived reads to easily recover almost-entire mitochondrial genome sequences. Read-mapping and coverage statistics also gave insights into Polytomella mitochondrial transcriptional architecture, revealing polycistronic transcripts and the expression of telomeres and palindromic genes. Ultimately, RNA-Seq is a promising, cost-effective technique for studying mitochondrial genetics, but it does have drawbacks, which are discussed. One of its greatest potentials, as shown here, is that it can be used to generate near-complete mitochondrial genome sequences, which could be particularly useful in situations where there is a lack of available mtDNA data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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".