Search

RNA-Seq examines global gene expression to provide insights into cellular processes, and it can be particularly informative when comparing contrasting physiological states or strains. Although relatively routine in many laboratories, there are many steps involved in performing a transcriptomics experiment to ensure representative and high-quality results are generated for analysis. In this chapter, we present the application of widely used bioinformatic methodologies to assess, trim, and filter RNA-seq reads for quality using FastQC and Trim Galore, respectively. High-quality reads are mapped using Bowtie2 and differentially expressed genes across different groups were estimated using the DEseq2 R-Bioconductor package. In addition, we describe the various steps to perform the sample-wise data quality assessment by generating exploratory plots through the DESeq2 package. Simple steps to calculate the significant differentially expressed genes, up- and down-regulated genes, and exporting the data and images are also included. A Venn diagram is a useful method to compare the differentially expressed genes across various comparisons and steps to generate the Venn diagram from DESeq2 results are provided. Finally, the output from DESeq2 is compared to published results from EdgeR. The Clostridium autoethanogenum data are published and publicly available.

RNA-Seq examines global gene expression to provide insights into cellular processes, and it can be particularly informative when comparing contrasting physiological states or strains. Although relatively routine in many laboratories, there are many steps involved in performing a transcriptomics experiment to ensure representative and high-quality results are generated for analysis. In this chapter, we present the application of widely used bioinformatic methodologies to assess, trim, and filter RNA-seq reads for quality using FastQC and Trim Galore, respectively. High-quality reads are mapped using Bowtie2 and differentially expressed genes across different groups were estimated using the DEseq2 R-Bioconductor package. In addition, we describe the various steps to perform the sample-wise data quality assessment by generating exploratory plots through the DESeq2 package. Simple steps to calculate the significant differentially expressed genes, up- and down-regulated genes, and exporting the data and images are also included. A Venn diagram is a useful method to compare the differentially expressed genes across various comparisons and steps to generate the Venn diagram from DESeq2 results are provided. Finally, the output from DESeq2 is compared to published results from EdgeR. The Clostridium autoethanogenum data are published and publicly available.

Background: Clostridium autoethanogenum strain JA1-1 (DSM 10061) is an acetogen capable of fermenting CO, CO2 and H2 (e.g. from syngas or waste gases) into biofuel ethanol and commodity chemicals such as 2,3-butanediol. A draft genome sequence consisting of 100 contigs has been published. Results: A closed, high-quality genome sequence for C. autoethanogenum DSM10061 was generated using only the latest single-molecule DNA sequencing technology and without the need for manual finishing. It is assigned to the most complex genome classification based upon genome features such as repeats, prophage, nine copies of the rRNA gene operons. It has a low G + C content of 31.1%. Illumina, 454, Illumina/454 hybrid assemblies were generated and then compared to the draft and PacBio assemblies using summary statistics, CGAL, QUAST and REAPR bioinformatics tools and comparative genomic approaches. Assemblies based upon shorter read DNA technologies were confounded by the large number repeats and their size, which in the case of the rRNA gene operons were ~5 kb. CRISPR (Clustered Regularly Interspaced Short Paloindromic Repeats) systems among biotechnologically relevant Clostridia were classified and related to plasmid content and prophages. Potential associations between plasmid content and CRISPR systems may have implications for historical industrial scale Acetone-Butanol-Ethanol (ABE) fermentation failures and future large scale bacterial fermentations. While C. autoethanogenum contains an active CRISPR system, no such system is present in the closely related Clostridium ljungdahlii DSM 13528. A common prophage inserted into the Arg-tRNA shared between the strains suggests a common ancestor. However, C. ljungdahlii contains several additional putative prophages and it has more than double the amount of prophage DNA compared to C. autoethanogenum. Other differences include important metabolic genes for central metabolism (as an additional hydrogenase and the absence of a phophoenolpyruvate synthase) and substrate utilization pathway (mannose and aromatics utilization) that might explain phenotypic differences between C. autoethanogenum and C. ljungdahlii. Conclusions: Single molecule sequencing will be increasingly used to produce finished microbial genomes. The complete genome will facilitate comparative genomics and functional genomics and support future comparisons between Clostridia and studies that examine the evolution of plasmids, bacteriophage and CRISPR systems.