Transposable element annotation of the rice genome
Bibliographic record
Abstract
MOTIVATION: The high content of repetitive sequences in the genomes of many higher eukaryotes renders the task of annotating them computationally intensive. Presently, the only widely accepted method of searching and annotating transposable elements (TEs) in large genomic sequences is the use of the RepeatMasker program, which identifies new copies of TEs by pairwise sequence comparisons with a library of known TEs. Profile hidden Markov models (HMMs) have been used successfully in discovering distant homologs of known proteins in large protein databases, but this approach has only rarely been applied to known model TE families in genomic DNA. RESULTS: We used a combination of computational approaches to annotate the TEs in the finished genome of Oryza sativa ssp. japonica. In this paper, we discuss the strengths and the weaknesses of the annotation methods used. These approaches included: the default configuration of RepeatMasker using cross_match, an implementation of the Smith-Waterman-Gotoh algorithm; RepeatMasker using WU-BLAST for similarity searching; and the HMMER package, used to search for TEs with profile HMMs. All the results were converted into GFF format and post-processed using a set of Perl scripts. RepeatMasker was used in the case of most TE families. The WU-BLAST implementation of RepeatMasker was found to be manifold faster than cross_match with only a slight loss in sensitivity and was thus used to obtain the final set of data. HMMER was used in the annotation of the Mutator-like element (MULE) superfamily and the miniature inverted-repeat transposable element (MITE) polyphyletic group of families, for which large libraries of elements were available and which could be divided into well-defined families. The HMMER search algorithm was extremely slow for models over 1000 bp in length, so MULE families with members over 1000 bp long were processed with RepeatMasker instead. The main disadvantage of HMMER in this application is that, since it was developed with protein sequences in mind, it does not search the negative DNA strand. With the exception of TE families with essentially palindromic sequences, reverse complement models had to be created and run to compensate for this shortcoming. We conclude that a modification of RepeatMasker to incorporate libraries of profile HMMs in searches could improve the ability to detect degenerated copies of TEs. AVAILABILITY: The Perl scripts and TE sequences used in construction of the RepeatMasker library and the profile HMMs are available upon request.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".