A pipeline for targeted metagenomics of environmental bacteria.

BackgroundMetagenomics and single cell genomics provide a window into the genetic repertoire of yet uncultivated microorganisms, but both methods are usually taxonomically untargeted. The combination of fluorescence in situ hybridization (FISH) and fluorescence activated cell sorting (FACS) has the potential to enrich taxonomically well-defined clades for genomic analyses.MethodsCells hybridized with a taxon-specific FISH probe are enriched based on their fluorescence signal via flow cytometric cell sorting. A recently developed FISH procedure, the hybridization chain reaction (HCR)-FISH, provides the high signal intensities required for flow cytometric sorting while maintaining the integrity of the cellular DNA for subsequent genome sequencing. Sorted cells are subjected to shotgun sequencing, resulting in targeted metagenomes of low diversity.ResultsPure cultures of different taxonomic groups were used to (1) adapt and optimize the HCR-FISH protocol and (2) assess the effects of various cell fixation methods on both the signal intensity for cell sorting and the quality of subsequent genome amplification and sequencing. Best results were obtained for ethanol-fixed cells in terms of both HCR-FISH signal intensity and genome assembly quality. Our newly developed pipeline was successfully applied to a marine plankton sample from the North Sea yielding good quality metagenome assembled genomes from a yet uncultivated flavobacterial clade.ConclusionsWith the developed pipeline, targeted metagenomes at various taxonomic levels can be efficiently retrieved from environmental samples. The resulting metagenome assembled genomes allow for the description of yet uncharacterized microbial clades. Video abstract

Local Binary Patterns as a Feature Descriptor in Alignment-free Visualisation of Metagenomic Data

Shotgun sequencing has facilitated the analysis of complex microbial communities. However, clustering and visualising these communities without prior taxonomic information is a major challenge. Feature descriptor methods can be utilised to extract these taxonomic relations from the data. Here, we present a novel approach consisting of local binary patterns (LBP) coupled with randomised singular value decomposition (RSVD) and Barnes-Hut t-stochastic neighbor embedding (BH-tSNE) to highlight the underlying taxonomic structure of the metagenomic data. The effectiveness of our approach is demonstrated using several simulated and a real metagenomic datasets

Deconvolute individual genomes from metagenome sequences through short read clustering.

Metagenome assembly from short next-generation sequencing data is a challenging process due to its large scale and computational complexity. Clustering short reads by species before assembly offers a unique opportunity for parallel downstream assembly of genomes with individualized optimization. However, current read clustering methods suffer either false negative (under-clustering) or false positive (over-clustering) problems. Here we extended our previous read clustering software, SpaRC, by exploiting statistics derived from multiple samples in a dataset to reduce the under-clustering problem. Using synthetic and real-world datasets we demonstrated that this method has the potential to cluster almost all of the short reads from genomes with sufficient sequencing coverage. The improved read clustering in turn leads to improved downstream genome assembly quality