RESUMO
Maritime pine (Pinus pinasterAit.) is a widely distributed conifer species in Southwestern Europe and one of the most advanced models for conifer research. In the current work, comprehensive characterization of the maritime pine transcriptome was performed using a combination of two different next-generation sequencing platforms, 454 and Illumina. De novo assembly of the transcriptome provided a catalogue of 26 020 unique transcripts in maritime pine trees and a collection of 9641 full-length cDNAs. Quality of the transcriptome assembly was validated by RT-PCR amplification of selected transcripts for structural and regulatory genes. Transcription factors and enzyme-encoding transcripts were annotated. Furthermore, the available sequencing data permitted the identification of polymorphisms and the establishment of robust single nucleotide polymorphism (SNP) and simple-sequence repeat (SSR) databases for genotyping applications and integration of translational genomics in maritime pine breeding programmes. All our data are freely available at SustainpineDB, the P. pinaster expressional database. Results reported here on the maritime pine transcriptome represent a valuable resource for future basic and applied studies on this ecological and economically important pine species.
Assuntos
Biotecnologia , Genoma de Planta/genética , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Pinus/genética , Polimorfismo de Nucleotídeo Único , Transcriptoma , Cruzamento , DNA Complementar/genética , Bases de Dados Genéticas , Tamanho do Genoma , Genótipo , Repetições de Microssatélites/genética , Anotação de Sequência Molecular , Família Multigênica , RNA de Plantas/genética , Análise de Sequência de DNA , Fatores de Transcrição/genética , ÁrvoresRESUMO
Genetic algorithms are widely used in the estimation of expression profiles from microarrays data. However, these techniques are unable to produce stable and robust solutions suitable to use in clinical and biomedical studies. This paper presents a novel two-stage evolutionary strategy for gene feature selection combining the genetic algorithm with biological information extracted from the KEGG database. A comparative study is carried out over public data from three different types of cancer (leukemia, lung cancer and prostate cancer). Even though the analyses only use features having KEGG information, the results demonstrate that this two-stage evolutionary strategy increased the consistency, robustness and accuracy of a blind discrimination among relapsed and healthy individuals. Therefore, this approach could facilitate the definition of gene signatures for the clinical prognosis and diagnostic of cancer diseases in a near future. Additionally, it could also be used for biological knowledge discovery about the studied disease.