Kelpie: generating full-length ‘amplicons’ from whole-metagenome datasets

Abstract

IntroductionWhole-metagenome sequencing can be a rich source of information about the structure and function of entire metagenomic communities, but getting accurate and reliable results from these datasets can be challenging. Analysis of these datasets is founded on the mapping of sequencing reads onto known genomic regions from known organisms, but short reads will often map equally well to multiple regions, and to multiple reference organisms. Assembling metagenomic datasets prior to mapping can generate much longer and more precisely mappable sequences but the presence of closely related organisms and highly conserved regions makes metagenomic assembly challenging, and some regions of particular interest can assemble poorly. One solution to these problems is to use specialised tools, such as Kelpie, that can accurately extract and assemble full-length sequences for defined genomic regions from whole-metagenome datasets.MethodsKelpie is a kMer-based tool that generates full-length amplicon-like sequences from whole-metagenome datasets. It takes a pair of primer sequences and a set of metagenomic reads, and uses a combination of kMer filtering, error correction and assembly techniques to construct sets of full-length inter-primer sequences.ResultsThe effectiveness of Kelpie is demonstrated here through the extraction and assembly of full-length ribosomal marker gene regions, as this allows comparisons with conventional amplicon sequencing and published metagenomic benchmarks. The results show that the Kelpie-generated sequences and community profiles closely match those produced by amplicon sequencing, down to low abundance levels, and running Kelpie on the synthetic CAMI metagenomic benchmarking datasets shows similar high levels of both precision and recall.ConclusionsKelpie can be thought of as being somewhat like an in-silico PCR tool, taking a primer pair and producing the resulting ‘amplicons’ from a whole-metagenome dataset. Marker regions from the 16S rRNA gene were used here as an example because this allowed the overall accuracy of Kelpie to be evaluated through comparisons with other datasets, approaches and benchmarks. Kelpie is not limited to this application though, and can be used to extract and assemble any genomic region present in a whole metagenome dataset, as long as it is bound by a pairs of highly conserved primer sequences.

Desulfuromonas sp. 'CSMB_57’, isolation and genomic insights from the most abundant bacterial taxon in eastern Australian coals

“…A full-length 16S rRNA sequence was reconstructed using Kelpie v2.0.11 [ 27 ]. blast n searches of the full-length sequence revealed 15 16S rRNA sequences with greater than 99% identity to Desulfuromonas sp.…”

“…The genome assembly was manually curated by examining contig breaks, determining the cause and, where possible, gaps between the contig breaks were bridged. The resulting contigs were annotated using Prokka v1.14.5 [ 26 ] and a full length 16S rRNA sequence was reconstructed using Kelpie v2.0.11 [ 27 ]. In order to directly compare genes from the taxa described here and ‘ Candidatus Desulfuromonas subbituminosa’ this taxon was downloaded from IMG/M [ 28 ] (IMG genome ID: 2603880216) and was also subjected to gene calling using Prokka v.1.14.5.…”

Methanogenic archaea in subsurface coal seams are biogeographically distinct: an analysis of metagenomically‐derived mcrA sequences

“…mcrA gene sequences were extracted from the metagenomic datasets using Kelpie (Greenfield et al ., 2019 ). Kelpie takes a pair of primer sequences and extracts the corresponding between‐primer regions from a metagenomic dataset, working much like an in silico PCR tool.…”

“…The aims of the present study were thus to identify mcrA genes within existing, publicly available metagenomic datasets using a tool called ‘Kelpie’ (Greenfield et al ., 2019 ), and then to (i) explore the biogeographic distribution of methanogens using the presence of mcrA genes, (ii) infer dominant methanogenic pathways in each basin based on close relatives of the mcrA ‐containing taxa, (iii) determine whether 16S rRNA gene amplicons from the same metagenomes capture this methanogen diversity, and (iv) compare results against some of the previously identified primer pairs used for mcrA amplification.…”

Draft Genome Sequence of Desulfovibrio sp. Strain CSMB_222, Isolated from Coal Seam Formation Water

“…The resultant DNA library was sequenced on a HiSeq 2500 system, producing 150-bp paired-end reads (Macrogen, South Korea). The genome sequence data produced 5,291,357 raw reads, which were error corrected using Blue v2.1.4 ( 7 ) and assembled using SPAdes v3.13.2 ( 8 ), and full-length 16S rRNA was extracted using Kelpie v2.0.1 ( 9 ) (Genomic Information Desulfovibrio sp. CSMB_222; https://www.doi.org/10.25919/m6yy-fc50 ).…”