Sequence data for Clostridium autoethanogenum using three generations of sequencing technologies

Abstract

During the past decade, DNA sequencing output has been mostly dominated by the second generation sequencing platforms which are characterized by low cost, high throughput and shorter read lengths for example, Illumina. The emergence and development of so called third generation sequencing platforms such as PacBio has permitted exceptionally long reads (over 20 kb) to be generated. Due to read length increases, algorithm improvements and hybrid assembly approaches, the concept of one chromosome, one contig and automated finishing of microbial genomes is now a realistic and achievable task for many microbial laboratories. In this paper, we describe high quality sequence datasets which span three generations of sequencing technologies, containing six types of data from four NGS platforms and originating from a single microorganism, Clostridium autoethanogenum. The dataset reported here will be useful for the scientific community to evaluate upcoming NGS platforms, enabling comparison of existing and novel bioinformatics approaches and will encourage interest in the development of innovative experimental and computational methods for NGS data.

Energy Conservation Associated with Ethanol Formation from H 2 and CO 2 in Clostridium autoethanogenum Involving Electron Bifurcation

“…The 16S RNA sequences between the three strains are 99% similar; also, the gene arrangement and sequence in the Wood-Ljungdahl operon are almost identical (6). For C. autoethanogenum (7,8) and C. ljungdahlii (9), complete genome sequences are available, and a comparative genomic analysis suggested some key differences (7). C. autoethanogenum can grow on CO as the sole carbon and energy source, forming mainly ethanol and acetic acid but also 2,3-butanediol, lactic acid, and some H 2 as fermentation products (6,10).…”

Inferring Heterozygosity from Ancient and Low Coverage Genomes

“…, Utturkar et al 2015). Sequences generated with higher quality will positively affect estimation accuracy.…”

Metabolic engineering of Clostridium autoethanogenum for selective alcohol production

“…Insight into the metabolic capabilities of C. autoethanogenum has been gleaned from the determination of its genome sequence (Brown et al, 2014, Humphreys et al, 2015, Utturkar et al, 2015) and ‘omics’ data (Köpke et al, 2011, Marcellin et al, 2016, Mock et al, 2015) under different growth conditions (fructose, CO and H 2 +CO 2 ). Moreover, some understanding of how the acetogen conserves energy while generating reduced products ethanol and 2,3-butanediol autotrophically was derived through the determination of the specific activities and cofactor specificities of all relevant oxidoreductases from cell extracts (Mock et al, 2015, Wang et al, 2013).…”