Microbial interactions have a key role in global geochemical cycles. Although we possess significant knowledge about the general biochemical processes occurring in microbial communities, we are often unable to decipher key functions of individual microorganisms within the environment in part owing to the inability to cultivate or study them in isolation. Here, we circumvent this shortcoming through the use of single-cell genome sequencing and a novel low-input metatranscriptomics protocol to reveal the intricate metabolic capabilities and microbial interactions of an alkane-degrading methanogenic community. This methanogenic consortium oxidizes saturated hydrocarbons under anoxic conditions through a thus-far-uncharacterized biochemical process. The genome sequence of a dominant bacterial member of this community, belonging to the genus Smithella, was sequenced and served as the basis for subsequent analysis through metabolic reconstruction. Metatranscriptomic data generated from less than 500 pg of mRNA highlighted metabolically active genes during anaerobic alkane oxidation in comparison with growth on fatty acids. These data sets suggest that Smithella is not activating hexadecane by fumarate addition. Differential expression assisted in the identification of hypothetical proteins with no known homology that may be involved in hexadecane activation. Additionally, the combination of 16S rDNA sequence and metatranscriptomic data enabled the study of other prevalent organisms within the consortium and their interactions with Smithella, thus yielding a comprehensive characterization of individual constituents at the genome scale during methanogenic alkane oxidation.
Squeezambler and datasets are available at http://compbio.cs.wayne.edu/software/squeezambler/.
MotivationIntimately tied to assembly quality is the complexity of the de Bruijn graph built by the assembler. Thus, there have been many paradigms developed to decrease the complexity of the de Bruijn graph. One obvious combinatorial paradigm for this is to allow the value of k to vary; having a larger value of k where the graph is more complex and a smaller value of k where the graph would likely contain fewer spurious edges and vertices. One open problem that affects the practicality of this method is how to predict the value of k prior to building the de Bruijn graph. We show that optimal values of k can be predicted prior to assembly by using the information contained in a phylogenetically-close genome and therefore, help make the use of multiple values of k practical for genome assembly.ResultsWe present HyDA-Vista, which is a genome assembler that uses homology information to choose a value of k for each read prior to the de Bruijn graph construction. The chosen k is optimal if there are no sequencing errors and the coverage is sufficient. Fundamental to our method is the construction of the maximal sequence landscape, which is a data structure that stores for each position in the input string, the largest repeated substring containing that position. In particular, we show the maximal sequence landscape can be constructed in O(n + n log n)-time and O(n)-space. HyDA-Vista first constructs the maximal sequence landscape for a homologous genome. The reads are then aligned to this reference genome, and values of k are assigned to each read using the maximal sequence landscape and the alignments. Eventually, all the reads are assembled by an iterative de Bruijn graph construction method. Our results and comparison to other assemblers demonstrate that HyDA-Vista achieves the best assembly of E. coli before repeat resolution or scaffolding.AvailabilityHyDA-Vista is freely available [1]. The code for constructing the maximal sequence landscape and choosing the optimal value of k for each read is also separately available on the website and could be incorporated into any genome assembler.
As the vast majority of all microbes are unculturable, single-cell sequencing has become a significant method to gain insight into microbial physiology. Single-cell sequencing methods, currently powered by multiple displacement genome amplification (MDA), have passed important milestones such as finishing and closing the genome of a prokaryote. However, the quality and reliability of genome assemblies from single cells are still unsatisfactory due to uneven coverage depth and the absence of scattered chunks of the genome in the final collection of reads caused by MDA bias. In this work, our new algorithm Hybrid De novo Assembler (HyDA) demonstrates the power of co-assembly of multiple single-cell genomic data sets through significant improvement of the assembly quality in terms of predicted functional elements and length statistics. Co-assemblies contain significantly more base pairs and protein coding genes, cover more subsystems, and consist of longer contigs compared to individual assemblies by the same algorithm as well as state-of-the-art single-cell assemblers SPAdes and IDBA-UD. Hybrid De novo Assembler (HyDA) is also able to avoid chimeric assemblies by detecting and separating shared and exclusive pieces of sequence for input data sets. By replacing one deep single-cell sequencing experiment with a few single-cell sequencing experiments of lower depth, the co-assembly method can hedge against the risk of failure and loss of the sample, without significantly increasing sequencing cost. Application of the single-cell coassembler HyDA to the study of three uncultured members of an alkane-degrading methanogenic community validated the usefulness of the co-assembly concept.
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