Analysis of the Pythium ultimum transcriptome using Sanger and Pyrosequencing approaches
Notice bibliographique
Résumé
BACKGROUND: Pythium species are an agriculturally important genus of plant pathogens, yet are not understood well at the molecular, genetic, or genomic level. They are closely related to other oomycete plant pathogens such as Phytophthora species and are ubiquitous in their geographic distribution and host rage. To gain a better understanding of its gene complement, we generated Expressed Sequence Tags (ESTs) from the transcriptome of Pythium ultimum DAOM BR144 (= ATCC 200006 = CBS 805.95) using two high throughput sequencing methods, Sanger-based chain termination sequencing and pyrosequencing-based sequencing-by-synthesis. RESULTS: A single half-plate pyrosequencing (454 FLX) run on adapter-ligated cDNA from a normalized cDNA population generated 90,664 reads with an average read length of 190 nucleotides following cleaning and removal of sequences shorter than 100 base pairs. After clustering and assembly, a total of 35,507 unique sequences were generated. In parallel, 9,578 reads were generated from a library constructed from the same normalized cDNA population using dideoxy chain termination Sanger sequencing, which upon clustering and assembly generated 4,689 unique sequences. A hybrid assembly of both Sanger- and pyrosequencing-derived ESTs resulted in 34,495 unique sequences with 1,110 sequences (3.2%) that were solely derived from Sanger sequencing alone. A high degree of similarity was seen between P. ultimum sequences and other sequenced plant pathogenic oomycetes with 91% of the hybrid assembly derived sequences > 500 bp having similarity to sequences from plant pathogenic Phytophthora species. An analysis of Gene Ontology assignments revealed a similar representation of molecular function ontologies in the hybrid assembly in comparison to the predicted proteomes of three Phytophthora species, suggesting a broad representation of the P. ultimum transcriptome was present in the normalized cDNA population. P. ultimum sequences with similarity to oomycete RXLR and Crinkler effectors, Kazal-like and cystatin-like protease inhibitors, and elicitins were identified. Sequences with similarity to thiamine biosynthesis enzymes that are lacking in the genome sequences of three Phytophthora species and one downy mildew were identified and could serve as useful phylogenetic markers. Furthermore, we identified 179 candidate simple sequence repeats that can be used for genotyping strains of P. ultimum. CONCLUSION: Through these two technologies, we were able to generate a robust set (approximately 10 Mb) of transcribed sequences for P. ultimum. We were able to identify known sequences present in oomycetes as well as identify novel sequences. An ample number of candidate polymorphic markers were identified in the dataset providing resources for phylogenetic and diagnostic marker development for this species. On a technical level, in spite of the depth possible with 454 FLX platform, the Sanger and pyro-based sequencing methodologies were complementary as each method generated sequences unique to each platform.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».