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Chapter 2.4.5 : Generation and Analysis of Microbial Metatranscriptomes

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Generation and Analysis of Microbial Metatranscriptomes, Page 1 of 2

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Abstract:

Analysis of the collective RNA pool from a microbial community - the metatranscriptome - yields valuable information on microbial gene expression patterns and biogeochemical processes in natural environments. Molecular and analytical tools for analyzing metatranscriptomes using high-throughput sequencing have advanced rapidly in recent years and continue to evolve and expand. The technique is increasingly available to individual research projects, even those with a modest budget or lacking an extensive bioinformatics toolkit. A core set of metatranscriptomic practices can now be identified, with key steps including RNA extraction, messenger RNA (mRNA) enrichment, synthesis of complementary DNA (cDNA), shotgun sequencing of cDNA, and bioinformatic analysis of sequence data. This chapter explores key questions that researchers should consider before beginning a metatranscriptomic study and then describes in detail the major steps of a sequencing-based metatranscriptomic analysis, from RNA isolation to functional and taxonomic analysis of sequence data. The questions and methods described here provide an introductory framework for environmental microbiologists interested in using metatranscriptome sequencing to explore microbial community gene expression.

Citation: Sarode N, Parris D, Ganesh S, Seston S, Stewart F. 2016. Generation and Analysis of Microbial Metatranscriptomes, p 2.4.5-1-2.4.5-19. In Yates M, Nakatsu C, Miller R, Pillai S (ed), Manual of Environmental Microbiology, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818821.ch2.4.5
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Raw reads are the input for the workflow. The analysis can be divided into five subsections (enclosed within dotted boxes): pre-processing, assembly (optional), annotation, statistical analysis, and analysis of biological patterns. Some of the most commonly used tools and a rough estimate of the time required per step are listed. The time estimates will vary considerably depending on the computational resources available and the size of the input data set. The estimates shown here are based on a 64-bit Linux machine with 12 CPUs and 50 Gb RAM, and a data set of ∼1 million 300 × 300 paired-end Illumina reads (MiSeq). It is important to stress that the final step, the conversion of transcriptome patterns into meaningful inferences about microbial physiology, is the most labor-intensive. This process may require months of manual exploration and validation of the sequence data, often using task-specific scripts beyond the scope of the standard analysis pipeline. Multiple steps in this workflow can be achieved using a single resource (see Table 3 and text), although such platforms offer limited flexibility for adding custom modules to the pipeline. doi:10.1128/9781555818821.ch2.4.5.f1

Citation: Sarode N, Parris D, Ganesh S, Seston S, Stewart F. 2016. Generation and Analysis of Microbial Metatranscriptomes, p 2.4.5-1-2.4.5-19. In Yates M, Nakatsu C, Miller R, Pillai S (ed), Manual of Environmental Microbiology, Fourth Edition. ASM Press, Washington, DC. doi: 10.1128/9781555818821.ch2.4.5
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