Transposable element annotation of the rice genome
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».