Effective gene collection from the metatranscriptome of marine microorganisms
Metagenomic studies, accelerated by the evolution of sequencing technologies and the rapid development of genomic analysis methods, can reveal genetic diversity and biodiversity in various samples including those of uncultured or unknown species. This approach, however, cannot be used to identify active functional genes under actual environmental conditions. Metatranscriptomics, which is similar in approach to metagenomics except that it utilizes RNA samples, is a powerful tool for the
... tomic study of environmental samples. Unlike metagenomic studies, metatranscriptomic studies have not been popular to date due to problems with reliability, repeatability, redundancy and cost performance. Here, we propose a normalized metatranscriptomic method that is suitable for the collection of genes from samples as a platform for comparative transcriptomics. Results: We constructed two libraries, one non-normalized and the other normalized library, from samples of marine microorganisms taken during daylight hours from Hiroshima bay in Japan. We sequenced 0.6M reads for each sample on a Roche GS FLX, and obtained 0.2M genes after quality control and assembly. A comparison of the two libraries showed that the number of unique genes was larger in the normalized library than in the nonnormalized library. Functional analysis of genes revealed that a small number of gene groups, ribosomal RNA genes and chloroplast genes, were dominant in both libraries. Taxonomic distribution analysis of the libraries suggests that Stramenopiles form a major taxon that includes diatoms. The normalization technique thus increases unique genes, functional categories of genes, and taxonomic richness. Conclusions: Normalization of the marine metatranscriptome could be useful in increasing the number of genes collected, and in reducing redundancies among highly expressed genes. Gene collection through the normalization method was effective in providing a foundation for comparative transcriptomic analysis.