OrgConv: detection of gene conversion using consensus sequences and its application in plant mitochondrial and chloroplast homologs
Bibliographic record
Abstract
BACKGROUND: The ancestry of mitochondria and chloroplasts traces back to separate endosymbioses of once free-living bacteria. The highly reduced genomes of these two organelles therefore contain very distant homologs that only recently have been shown to recombine inside the mitochondrial genome. Detection of gene conversion between mitochondrial and chloroplast homologs was previously impossible due to the lack of suitable computer programs. Recently, I developed a novel method and have, for the first time, discovered recurrent gene conversion between chloroplast mitochondrial genes. The method will further our understanding of plant organellar genome evolution and help identify and remove gene regions with incongruent phylogenetic signals for several genes widely used in plant systematics. Here, I implement such a method that is available in a user friendly web interface. RESULTS: OrgConv (Organellar Conversion) is a computer package developed for detection of gene conversion between mitochondrial and chloroplast homologous genes. OrgConv is available in two forms; source code can be installed and run on a Linux platform and a web interface is available on multiple operating systems. The input files of the feature program are two multiple sequence alignments from different organellar compartments in FASTA format. The program compares every examined sequence against the consensus sequence of each sequence alignment rather than exhaustively examining every possible combination. Making use of consensus sequences significantly reduces the number of comparisons and therefore reduces overall computational time, which allows for analysis of very large datasets. Most importantly, with the significantly reduced number of comparisons, the statistical power remains high in the face of correction for multiple tests. CONCLUSIONS: Both the source code and the web interface of OrgConv are available for free from the OrgConv website http://www.indiana.edu/~orgconv. Although OrgConv has been developed with main focus on detection of gene conversion between mitochondrial and chloroplast genes, it may also be used for detection of gene conversion between any two distinct groups of homologous sequences.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".