De novo assembly of RNA-Seq data allows us to study transcriptomes without the need for a genome sequence, such as in non-model organisms of ecological and evolutionary importance, cancer samples, or the microbiome. In this protocol, we describe the use of the Trinity platform for de novo transcriptome assembly from RNA-Seq data in non-model organisms. We also present Trinity鈥檚 supported companion utilities for downstream applications, including RSEM for transcript abundance estimation, R/Bioconductor packages for identifying differentially expressed transcripts across samples, and approaches to identify protein coding genes. In an included tutorial we provide a workflow for genome-independent transcriptome analysis leveraging the Trinity platform. The software, documentation and demonstrations are freely available from http://trinityrnaseq.sf.net.
Gene silencing through RNA interference (RNAi) has revolutionized the study of gene 98 function, particularly in non-model insects. However, in Lepidoptera (moths and butterflies) 99 RNAi has many times proven to be difficult to achieve. Most of the negative results have been 100 anecdotal and the positive experiments have not been collected in such a way that they are 101 possible to analyze. In this review, we have collected detailed data from more than 150 102 experiments including all to date published and many unpublished experiments. Despite a 103 large variation in the data, trends that are found are that RNAi is particularly successful in the 104 family Saturniidae and in genes involved in immunity. On the contrary, gene expression in 105 epidermal tissues seems to be most difficult to silence. In addition, gene silencing by feeding 106 dsRNA requires high concentrations for success. Possible causes for the variability of success 107 in RNAi experiments in Lepidoptera are discussed. The review also points to a need to further 108 investigate the mechanism of RNAi in lepidopteran insects and its possible connection to the 109 innate immune response. Our general understanding of RNAi in Lepidoptera will be further 110 aided in the future as our public database at http://insectacentral.org/RNAi will continue to 111 gather information on RNAi experiments.
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